阅读说明：本技术 用于治疗癌症的方法和组合物 (Methods and compositions for treating cancer ) 是由 詹尼弗·A·沃戈 万切斯瓦兰·戈帕拉克里希南 迈尔斯·C·安德鲁斯 劳伦斯·齐特福格尔 瓦莱 于 2019-11-21 设计创作，主要内容包括：本文描述了用于治疗癌症和用于预测对象对检查点抑制剂联合疗法的应答的方法和组合物。本公开的方面涉及在对象中治疗癌症和/或降低针对疗法的毒性的方法,其包括向对象施用包含至少一种属于以下的属中的一者或多于一者的经分离或经纯化的细菌群体的组合物：解黄酮菌属、Dielma、阿克曼氏菌属、别样杆菌属、拟杆菌属、丁酸单胞菌属、吸血弧菌属、泰泽氏菌属、狄氏副拟杆菌、Fournierella、Fournierella massiliensis、Eisenbergiella tayi、蒂西耶氏菌目、Hungateiclostridium thermocellum、长链多尔氏菌、柯氏喜热菌、Muricomes、地生孢杆菌、沼泽普雷沃氏菌、嗜黑麦乳杆菌、芬氏拟杆菌、约氏乳杆菌、堆肥副土地杆菌和Anaerotignum lactatifermentans,并且其中所述方法还包括用(i)PD-1、PDL1或PDL2抑制剂和(ii)CTLA-4、B7-1或B7-2抑制剂的组合治疗对象。(Described herein are methods and compositions for treating cancer and for predicting a subject's response to checkpoint inhibitor combination therapy. Aspects of the present disclosure relate to methods of treating cancer and/or reducing toxicity to therapy in a subject comprising administering to the subject a composition comprising at least one isolated or purified bacterial population belonging to one or more than one of the genera: flavonolactobacillus, Dielma, akkermansia, xenorhabdus, bacteroides, butanomonas, haemophilus, terezuelis, parabacteroides dieselii, Fournierella maliensis, Eisenbergiella tayi, dicelia, hungatricelliforme themacellum, dolichopus longus, coxiella carolinae, Muricomes, geobacillus cereus, prevotella marylaniculata, lactobacillus secalophilus, bacteroides finnii, lactobacillus johnsonii, agrobacterium composted paramenia, and antiaerotica gntab transfer, and wherein the method further comprises treating the subject with a combination of (i) a PD-1, PDL1, or 2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor.)

1. A method of treating cancer in a subject, the method comprising:

(a) administering to the subject a composition comprising at least one isolated or purified bacterial population belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, bloodsucker, Tazerlella, Deuterobacter, Fournierella maliensis, Eisenbergiella tayi, Discoderiales, Hungatricidiales themocellum, Dolerella melleus, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium compostum, and Anaeroticum montanatis residues; and

(b) treating the subject with a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor.

2. The method of claim 1, wherein the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

3. The method of claim 2, wherein the composition comprises at least one isolated or purified population of bacteria belonging to the genus akkermansia.

4. The method of claim 1, wherein the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: bacteroides fragilis, Vibrio, Taziella, long chain bacteria, Coxietz, Muricomes intestini, Geobacillus, Anerargignum lactatimenteans.

5. The method of any one of claims 1 to 4, wherein the cancer is skin cancer.

6. The method of any one of claims 1 to 4, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protruberans, merkel cell carcinoma, kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland cancer, cancer of the microcapsular adnexa, paget's disease of the breast, atypical fibroma, leiomyosarcoma, or angiosarcoma.

7. The method of any one of claims 1 to 4, wherein the cancer is melanoma.

8. The method of claim 7, wherein the melanoma is metastatic melanoma, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, lentigo acralis melanoma, cutaneous melanoma, or desmoplastic melanoma.

9. The method of claim 8, wherein the melanoma comprises cutaneous melanoma.

10. The method of any one of claims 1 to 9, wherein the method further comprises administering at least one additional anti-cancer therapy.

11. The method of claim 10, wherein the at least one additional anti-cancer therapy is a surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biologic therapy.

12. The method of any one of claims 1 to 11, wherein the inhibitor of (i), the inhibitor of (ii), and/or at least one additional anti-cancer therapy is administered intratumorally, intraarterially, intravenously, intravascularly, intrapleurally, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, transdermally, subcutaneously, regionally, directionally, orally, or by direct injection or infusion.

13. The method of any one of claims 1 to 12, wherein the method is defined as a method of treating cancer in a subject diagnosed with a cancerous tumor.

14. The method of any one of claims 1 to 13, wherein the method comprises or further comprises reducing or preventing one or more adverse events.

15. The method of any one of claims 1 to 14, wherein the method comprises or further comprises reducing or preventing one or more severe adverse events.

16. The method of claim 14 or 15, wherein the adverse event or severe adverse event is further classified as an immune-related adverse event.

17. The method of any one of claims 1 to 16, wherein the subject has been determined to have an unfavorable microbial profile in the gut microbiome.

18. The method of claim 17, wherein the unfavorable profile comprises a bacterial population comprising bacteria belonging to one or more of the genera bacteroides, alisteria, Coprobacter, Intestinibacter, and paracasei.

19. The method of claim 18, wherein the unfavorable profile comprises a bacterial population comprising one or more of bacteroides faecalis, bacteroides enterobacter, Coprobacter, Intestinibacter bartletti, parasutella secinda, and alisteria propionicum.

20. The method of claim 18 or 19, wherein the adverse profile is further defined as a toxicity-related profile.

21. The method of claim 17, wherein the unfavorable profile comprises a population of bacteria comprising bacteria belonging to one or more than one of the genera: lactobacillus, Bacteroides, Prevotella, Citrobacter, Clostridium, Hungateicosidium, Eubacterium, Hafniaceae, Enterobacter, Hafnia, Rosebury, Weissella, Bacillus, Lactobacillus, and Klebsiella.

22. The method of claim 21, wherein the unfavorable profile comprises a population of bacteria comprising one or more than one of: lactobacillus reuteri, Bacteroides fragilis, Prevotella faecalis, Prevotella serrata, Clostridium marinum, Hungatricidium aldrich, Citrobacter murinus, Eubacterium borgpoensis, Citrobacter freundii, Eubacterium hophilum, Enterobacter cloacae, Hafnia alvei, Rosebergeri hominis, Weissella mesenteroides and Klebsiella aerogenes.

23. The method of claim 21 or 22, wherein said adverse profile is further defined as a non-responder profile.

24. The method of any one of claims 17 to 23, wherein the subject is determined to comprise an adverse microbial profile by analyzing microbiome in a sample from the subject.

25. The method of claim 24, wherein the sample is a stool sample.

26. The method of claim 24 or 25, wherein the analyzing comprises performing 16S ribosomal sequencing and/or metagenomic whole genome sequencing.

27. The method of any one of claims 1 to 26, wherein the subject has previously received treatment for cancer.

28. The method of claim 27, wherein the subject has been determined to be a non-responder to a previous treatment.

29. The method of claim 27 or 28, wherein the patient has been determined to have a toxic response to a previous treatment.

30. The method of any one of claims 27-29, wherein the prior treatment comprises immune checkpoint blockade monotherapy or combination therapy.

31. The method of claim 30, wherein the prior treatment comprises an immune checkpoint blockade monotherapy comprising only one of PD-1, PDL1, PDL2, CTLA-4, B7-1, or B7-2 inhibitors.

32. The method of any one of claims 1 to 31, wherein the cancer is recurrent.

33. The method of any one of claims 1 to 32, wherein the inhibitor of (i) is an anti-PD-1 monoclonal antibody and/or the inhibitor of (ii) is an anti-CTLA-4 monoclonal antibody.

34. The method of claim 33, wherein (i) comprises nivolumab, palbociclumab, or pidilizumab.

35. The method of claim 33 or 34, wherein (ii) comprises ipilimumab or tremelimumab.

36. The method of any one of claims 1 to 35, wherein the subject is treated with the isolated bacterial population prior to or concurrently with the treatment in (i) and (ii).

37. The method of any one of claims 1-36, wherein the purified population of bacteria comprises bacteria from at least two genera or species, and wherein the ratio of the two bacteria is 1: 1.

38. The method of any one of claims 1 to 37, wherein the composition comprises at least 2 different bacterial species or genera, or bacterial genera.

39. The method of any one of claims 1 to 38, wherein the composition provides an alpha diversity of at least 5 upon administration to the subject.

40. The method of any one of claims 1 to 39, wherein the subject has been diagnosed with stage III or stage IV cancer.

41. The method of any one of claims 1 to 40, wherein the cancer comprises stage III or stage IV cancer.

42. The method of any one of claims 1 to 41, wherein the method further comprises administering an antibiotic.

43. The method of claim 42, wherein the antibiotic is administered prior to or concurrently with the administration of the composition comprising at least one isolated or purified bacterial population.

44. A method of treating cancer in a subject, the method comprising administering to a subject determined to have a favorable microbial profile in the gut microbiome a combination of (i) a PD-1, PDL1 or PDL2 inhibitor and (ii) a CTLA-4, B7-1 or B7-2 inhibitor.

45. The method of claim 44, wherein the cancer is a skin cancer.

46. The method of claim 44, wherein the cancer is basal cell skin carcinoma, squamous cell skin carcinoma, melanoma, dermatofibrosarcoma protruberans, Mercker cell carcinoma, Kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcapsular appendage carcinoma, Paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

47. The method of claim 44, wherein the cancer is melanoma.

48. The method of claim 47, wherein the melanoma is metastatic melanoma, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentigo melanoma, cutaneous melanoma, or desmoplastic melanoma.

49. The method of claim 48, wherein the melanoma comprises cutaneous melanoma.

50. The method of any one of claims 44 to 49, wherein the method further comprises administering at least one additional anti-cancer therapy.

51. The method of claim 50, wherein the at least one additional anti-cancer therapy is a surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biologic therapy.

52. The method of any one of claims 44 to 51, wherein the inhibitor of (i), the inhibitor of (ii), and/or at least one additional anti-cancer therapy is administered intratumorally, intraarterially, intravenously, intravascularly, intrapleurally, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, transdermally, subcutaneously, regionally, directionally, orally, or by direct injection or infusion.

53. The method of any one of claims 44-52, wherein the method is defined as a method of treating cancer in a subject having a cancerous tumor.

54. The method of any one of claims 44-53, wherein treating cancer comprises reducing or preventing one or more adverse events.

55. The method of any one of claims 44-53, wherein treating cancer comprises reducing or preventing one or more severe adverse events.

56. The method of claim 54 or 55, wherein the adverse event or severe adverse event is further classified as an immune-related adverse event.

57. The method of any one of claims 44 to 56, wherein said favorable profile comprises a population of bacteria comprising bacteria belonging to one or more than one of the genera or species of: bacteroides fragilis, Vibrio, Taziella, long chain bacteria, Coxietz, Muricomes intestini, Geobacillus, Anerargignum lactatimenteans.

58. The method of claim 57, wherein the favorable profile comprises a population of bacteria comprising one or more than one of Bacteroides fragilis, Vibrio and Tyzilla.

59. The method of claim 57 or 58, wherein said favorable profile is further defined as a non-toxic profile.

60. The method of any one of claims 44 to 56, wherein said favorable profile comprises a population of bacteria comprising bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

61. The method of claim 60, wherein the favorable profile comprises a population of bacteria comprising one or more than one of Bacteroides faecalis, Butyrimonas faecalis, Flavonoidea Percoll, Dielma fosidiosa, Arthrobacter heterophyllus, and Ackermanella mucophila.

62. The method of claim 60 or 61, wherein said favorable profile is further defined as an effective profile.

63. The method of any one of claims 44 to 62, wherein the subject comprises a favorable microbial profile as determined by analyzing microbiome in a sample from the subject.

64. The method of claim 63, wherein the sample is a stool sample or an oral cavity sample.

65. The method of claim 63 or 64, wherein analyzing comprises performing 16S ribosomal sequencing and/or metagenomic whole genome sequencing.

66. The method of any one of claims 44-65, wherein the subject has previously received treatment for cancer.

67. The method of claim 66, wherein said subject has been determined to be a non-responder to a previous treatment.

68. The method of claim 66 or 67, wherein the patient has been determined to have a toxic response to a previous treatment.

69. The method of any one of claims 66-68, wherein prior treatment comprises immune checkpoint blockade monotherapy or combination therapy.

70. The method of claim 69, wherein the prior treatment comprises an immune checkpoint blockade monotherapy comprising only one of PD-1, PDL1, PDL2, CTLA-4, B7-1, or B7-2 inhibitors.

71. The method of any one of claims 44-70, wherein the cancer is recurrent.

72. The method of any one of claims 44 to 71, wherein the inhibitor of (i) and/or (ii) is an anti-PD-1 monoclonal antibody or an anti-CTLA-4 monoclonal antibody.

73. The method of claim 72, wherein (i) comprises nivolumab, palbociclumab, or pidilizumab.

74. The method of claim 72 or 73, wherein (ii) comprises ipilimumab or tremelimumab.

75. The method of any one of claims 44-62, wherein the subject has not previously been treated with an immune checkpoint blockade monotherapy or a combination therapy.

76. The method of any one of claims 44-75, wherein the subject has been diagnosed with stage III or stage IV cancer.

77. The method of any one of claims 44-76, wherein the cancer comprises stage III or stage IV cancer.

78. A method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising:

(a) detecting a microbial profile in a sample obtained from the subject;

(b) predicting a toxic response to the therapy when bacteria of one or more of Bacteroides, Brewster, Coprobacter, Intestibacter, and ParaSaxat are detected in a sample from the subject; or

(c) Predicting a non-toxic response to the therapy when one or more bacteria of the genus or species bacteroides fragilis, vibrio fluvialis, terezia, dolichia longum, calophyllum chrysosporium, Muricomes intestini, geobacillus underground, antiaerotigum lactatification are detected in a sample from the subject.

79. The method of claim 78, wherein a toxic response is predicted when one or more of Bacteroides faecalis, Bacteroides enterobacter, Coprobacter, Intestibacter bartlett ti, Parastutella secunda, and Brevibacterium proprionate is detected in a sample from the subject.

80. The method of claim 78, wherein a non-toxic response is predicted when one or more of Bacteroides fragilis, Vibrio fluvialis, and Tyzilla is detected in a sample from the subject.

81. A method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising:

detecting a microbial profile in a sample obtained from the subject;

predicting an effective response to the therapy when one or more bacteria of the genera or species bacteroides faecalis, butyromonas, flavonolyticus, Dielma, alistipes viscovorans, Fournierella massilis, bacteroides coprophilus, Eisenbergiella tayi, seidella and hungatricellium thermocellum are detected in a sample from the subject; or

Predicting an ineffective response to the therapy when one or more bacteria of the genera or species of lactobacillus, bacteroides fragilis, prevotella, citrobacter, clostridium hirsutum, hunteichii, citrobacter murinus, eubacterium borreligiosum, harderiaceae, citrobacter freundii, eubacterium hophilum, enterobacter cloacae, harderiella alveorum, harderiella, human rosporus, weissella mesenteroides, enterobacter, lactobacillus reuteri, bacillus, lactobacillus, klebsiella aerogenes, and klebsiella are detected in a sample from the subject.

82. The method of claim 81, wherein an effective response is predicted when one or more of Bacteroides faecalis, butyricomonas faecalis, Flavonoides Percoll, Dielma rustidias, Arkermansia allowae, and Ackermansia muciniphila is detected in a sample from the subject.

83. The method of claim 81, wherein an ineffective response is predicted when one or more of Lactobacillus reuteri, Bacteroides fragilis, Prevotella faecalis, and Prevotella saxatilis is detected.

84. A method comprising detecting in a subject one or more than one of: bacteroides faecalis, Bacteroides intestinalis, Listeria fragilis, Vibrio fluvialis, Tyr zeylanicum, Flavonoides pernici, Dielma rustidiosa, Butyrimonas farichis, Heterobacter, Ackermanella viscophila, Lactobacillus reuteri, Prevotella faecalis, Prevotella saxifragi, Citrobacter, Clostridium hirsutum, Hugateicatrididium aldrich, Citrobacter murinum, Eubacterium crenatum, Hafniaceae, Citrobacter freundii, Eubacterium hopanii, Enterobacter cloacae, Hafnia alvei, Hafnia, Rosematerium human Roseburia, Weissella mesenteroides, Enterobacter, Bacteroides, Lactobacillus, Klebsiella aerogenes, Klebsiella, Diprobacter Coprobacter, Intestibacter intestinitella, Paraselis, Klebsiella pneumoniae, Forniella faecalis, Forniella, Klebsiella, Enterobacter, Klebsiella, Enterobacter strain, Klebsiella, Enterobacter, Klebsiella, Enterobacter strain, Klebsiella, or Klebsiella, Enterobacter strain, Klebsiella, or Enterobacter strain, etc, Yersinia diconii, Hungateicylium thermocellum, Dolerella mellonella, Pyrococcus coxsaceus, Muricomes intestini, Geobacillus underground and Anaerotignum lactatiferenans.

85. The method of claim 84, wherein the subject has been diagnosed with cancer.

86. The method of any one of claims 78-83 or 85, wherein the cancer is skin cancer.

87. The method of claim 86, wherein the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protruberans, merkel cell carcinoma, kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, microcapsular appendage carcinoma, paget's disease of the breast, atypical fibroxanthoma, leiomyosarcoma, or angiosarcoma.

88. The method of any one of claims 78-83 or 85, wherein the cancer is melanoma.

89. The method of claim 88, wherein the melanoma is metastatic melanoma, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentigo melanoma, cutaneous melanoma, or desmoplastic melanoma.

90. The method of claim 89, wherein the melanoma comprises cutaneous melanoma.

91. The method of any one of claims 84-90, wherein the subject has been diagnosed with stage III or stage IV cancer.

92. The method of any one of claims 78-83 or 86-91, wherein the method further comprises treating a subject predicted to have a non-toxic or effective response with an immune checkpoint blockade combination therapy.

93. The method of any one of claims 78 to 83 or 86 to 92, wherein the method further comprises treating a subject predicted to have a toxic or ineffective response with a composition comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens.

94. The method of claim 93, wherein the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

95. The method of claim 93 or 94, wherein the method further comprises treating the subject with a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor.

96. The method of any one of claims 78 to 95, wherein said method comprises administering at least one additional anti-cancer therapy.

97. The method of claim 96, wherein the at least one additional anti-cancer therapy is surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biologic therapy.

98. The method of claim 95 or 96, wherein the inhibitor of (i), the inhibitor of (ii), and/or at least one additional anti-cancer therapy is administered intratumorally, intraarterially, intravenously, intravascularly, intrapleurally, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, transdermally, subcutaneously, regionally, directionally, orally, or by direct injection or infusion.

99. The method of any one of claims 78 to 98, wherein the subject has previously received treatment for cancer.

100. The method of claim 99, wherein said subject has been determined to be a non-responder to a previous treatment.

101. The method of claim 99 or 100, wherein the patient has been determined to have a toxic response to a previous treatment.

102. The method of any one of claims 78 to 101, wherein prior treatment comprises immune checkpoint blockade monotherapy or combination therapy.

103. The method of claim 102, wherein the prior treatment comprises an immune checkpoint blockade monotherapy comprising only one of PD-1, PDL1, PDL2, CTLA-4, B7-1, or B7-2 inhibitors.

104. The method of any one of claims 78 to 103, wherein the cancer is recurrent.

105. The method of any one of claims 95 to 104, wherein the inhibitor of (i) and/or (ii) is an anti-PD-1 monoclonal antibody or an anti-CTLA-4 monoclonal antibody.

106. The method of claim 105, wherein (i) comprises nivolumab, palbociclumab, or pidilizumab.

107. The method of claim 105 or 106, wherein (ii) comprises ipilimumab or tremelimumab.

108. The method of any one of claims 78 to 102, wherein the subject has not previously been treated with an immune checkpoint blockade monotherapy or a combination therapy.

109. The method of any one of claims 78 to 108, wherein the sample is a stool sample or an oral cavity sample.

110. The method of any one of claims 78 to 109, wherein the detecting comprises performing 16S ribosomal sequencing and/or metagenomic whole genome sequencing.

111. A composition comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens.

112. A composition comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

113. A composition comprising at least two isolated or purified populations of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens.

114. A composition comprising at least two isolated or purified populations of bacteria belonging to two or more than two of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

115. The composition of any one of claims 111 to 114, wherein the concentration of each bacterial population in the composition is at least 1 x 10³CFU。

116. The composition of claim 113 or 115, wherein the composition is a live bacterial product.

117. The composition of any one of claims 111-116, wherein the bacteria are lyophilized, freeze-dried, or frozen.

118. The composition of any one of claims 111-117, wherein the composition is formulated for oral delivery.

119. The composition of claim 118, wherein the composition formulated for oral delivery is a tablet or capsule.

120. The composition of claim 119, wherein the tablet or capsule comprises an acid resistant enteric coating.

121. The composition of any one of claims 111-116, wherein the composition comprising at least one isolated or purified bacterial population or at least two isolated or purified bacterial populations is formulated for rectal administration, administration via colonoscopy, administration via sigmoidoscope through nasogastric tube, or administration by enema.

122. The composition of any one of claims 111-116, wherein the composition is capable of being reformulated as comprising a liquid, a suspension, a gel, a geltab, a semi-solid, a tablet, a sachet, a lozenge, a capsule, or as an enteral formulation for final delivery.

123. The composition of any one of claims 111-122, wherein the composition is formulated for multiple administrations.

124. The composition of any one of claims 111-123, wherein the composition further comprises a pharmaceutically acceptable excipient.

125. The composition of any one of claims 111-124, wherein the purified population of bacteria comprises bacteria from at least two genera or species, and wherein the ratio of the two bacteria is 1: 1.

126. The composition of any one of claims 111 to 125, wherein the composition comprises at least 2 different bacterial species or genera.

127. The composition of any one of claims 111 to 126, wherein the composition provides an alpha diversity of at least 5 upon administration to the subject.

Technical Field

The present invention relates to the fields of molecular biology and medicine.

Background

Cancer treatment has made great progress over the past decade through the use of targeted therapies and immunotherapy. Checkpoint blockade immunotherapy can alleviate the primary inhibitory signals of T lymphocytes by blocking immunosuppressive ligand-receptor interactions involving CTLA-4 and PD-1, thereby enhancing potential T cell-mediated antitumor immune activity. However, general reduction of systemic inhibitory signals also activates T lymphocytes reactive to self-antigens, resulting in loss of self-tolerance and immune-related adverse events. Patients who develop high toxicity often require temporary or permanent cessation of treatment and may require extensive immunosuppression for extended periods of time to counter their toxicity. In various treatment regimens for immunotherapy, anti-CTLA-4 and anti-PD-1 combination therapy provides superior response rates compared to administration of the same agent as monotherapy, however, this is offset by the higher risk of severe toxicity occurring. The high frequency of serious to life-threatening toxicities associated with anti-CTLA-4 and anti-PD-1 therapy has been the limiting factor in the development of this form of therapy by clinicians.

While several factors have been identified that are relevant to patient response to immune checkpoint inhibitor therapy, there remains a need in the art for the prediction of toxicity due to immune checkpoint blockade therapy and the prediction of responders to immune checkpoint blockade combination therapy. Classifying patients as likely and unlikely to experience toxicity and/or responsiveness to checkpoint blockade therapy based on one or more biomarkers will provide patients with more effective and therapeutic treatment as patients may be provided with the most effective treatment before further spread of the disease.

Disclosure of Invention

Described herein are methods and compositions for treating cancer and for predicting a subject's response to checkpoint inhibitor combination therapy. In one aspect, the present disclosure relates to a method of treating cancer and/or reducing the toxicity of a therapy in a subject comprising administering to the subject a composition comprising at least one isolated or purified bacterial population belonging to one or more than one of the genera or species of: flavonoids (Flavonfractor), Dielma, Akkermansia (Akkermansia), Allibacterium (Alisipes), Bacteroides (Bacteroides), butyromonas (Butyrimonas), Haemophilus (Vampirovibrio), Tyjzerella (Tyzzerella), Parabacter DIformis (Parabacteriaceae), Fournierella maliensis, Eisenbergia tayi, Yersinia comycolatoides (Tissiella), Hungaticotridium thermophilum, Doremella (Dorema formicarinicans), Thermomyces chrysanthemi (Caloramorrhasis), Murries, Geobacillus (Geosporaceae), Watermobacterium (Prevotella), Lactobacillus paracasei (Lampsorobacter), Lactobacillus paracasei (Lactobacteri), or Lactobacillus paracasei (Lactobacterium), or Lactobacillus paracasei (Lactobacterium, and wherein the method further comprises treating the subject with a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor. In some embodiments, the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis (Bacteroides stercoris), Parabacteroides dirichiana, Fournierella massilis, Bacteroides coprophilus (Bacteroides coprophilus), Eisenbergella tayi, Bacteroides, Hungatricelliformes themocellum.

In another aspect, the disclosure relates to a method of treating cancer and/or reducing the toxicity of a therapy in a subject comprising administering to the subject a composition comprising stool from a healthy patient, from a patient determined to be responsive to an immune checkpoint blockade monotherapy or combination therapy, or from a patient determined to have a non-toxic response to an immune checkpoint blockade monotherapy or combination therapy, wherein the method further comprises treating the subject with a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor. In some embodiments, the stool is transferred into the colon or rectum of the subject.

In another aspect, the present disclosure relates to a method of reducing or preventing adverse events associated with combination checkpoint blockade therapy comprising the step of administering to a subject a composition comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, bloodsucker, Tazerlella, Parabacteroides diecutanensis, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidia thermocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntab illustration, or the bacterial species disclosed in FIG. 28C.

In another aspect, the disclosure relates to a method of treating cancer and/or reducing the toxicity of a therapy in a subject comprising administering to a subject determined to have a favorable microbial profile in the gut microbiome a combination of (i) a PD-1, PDL1 or PDL2 inhibitor and (ii) a CTLA-4, B7-1 or B7-2 inhibitor.

In another aspect, the disclosure relates to a method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising: detecting a microbial profile in a sample obtained from a subject; predicting a toxic response to therapy when bacteria of one or more of the genera bacteroides, alisteris (Dialister), Coprobacter, Intestinibacter and paracasei (parasutella) are detected in a sample from the subject; or predicting a non-toxic response to the therapy when one or more bacteria of the genus or species Bacteroides fragilis (Bacteroides fragilis), vibrio fluvialis, tereza, dolichia longa, calophyllum chrysosporium, Muricomes intestini, geobacillus subterraneus, antiaerobacterium lactiferians are detected in a sample from the subject.

In another aspect, the disclosure relates to a method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising: detecting a microbial profile in a sample obtained from a subject; predicting a toxic response to therapy when a favorable microbial profile is detected in a sample from the subject; or to predict a non-toxic response to therapy when an adverse microbial profile is detected in a sample from a subject.

In some embodiments, the toxic response comprises one or more than one irAE. In some embodiments, the toxic response comprises an adverse event at or above grade 3. In some embodiments, the toxic response comprises one or more of interstitial pneumonia, colitis, hypothyroidism, liver dysfunction, skin rash, vitiligo, hypophysitis, type 1 diabetes, kidney dysfunction, myasthenia gravis, neuropathy, myositis, and uveitis. In some embodiments, the toxic response does not include one or more of interstitial pneumonia, colitis, hypothyroidism, liver dysfunction, skin rash, vitiligo, hypophysitis, type 1 diabetes, kidney dysfunction, myasthenia gravis, neuropathy, myositis, and does not include uveitis. In some embodiments, an irAE comprises one or more of interstitial pneumonia, colitis, hypothyroidism, liver dysfunction, skin rash, vitiligo, hypophysis, type 1 diabetes, kidney dysfunction, myasthenia gravis, neuropathy, myositis, and uveitis. In some embodiments, one or more of interstitial pneumonia, colitis, hypothyroidism, liver dysfunction, skin rash, vitiligo, hypophysitis, type 1 diabetes, renal dysfunction, myasthenia gravis, neuropathy, myositis, and uveitis are excluded. In some embodiments, the methods do not include treatment of colitis and/or do not include diagnosing a patient with or having colitis.

In some aspects, the subject is predicted to be a non-responder to a CICB when one or more of Robertkochia marina, aldehydia equoiensis (allerrdeutzia equilificiens), Lawsonia intracellularis (Lawsonia intracellularis), or Lactobacillus ruber (Lactobacillus satsumensis) is detected in a biological sample from the subject.

In another aspect, the present disclosure relates to a method of treating cancer in a subject comprising (1) first administering to the subject a composition comprising an isolated or purified bacterial population comprising at least one bacterium belonging to a genus or species selected from the group consisting of: flavonolactobacillus, Dielma, akkermansia, xenorhabdus, bacteroides, butyromonas, haemophilus, terezeri, parabacteroides diesella, Fournierella, fourniella maliensis, Eisenbergiella tayi, diceliformes, hungatricella thermocellum, dolichia longata, klebsiella, Muricomes, geobacillus cereus, prevotella palustris, lactobacillus secalophilus, bacteroides ferdii, lactobacillus johnsonii, parabacteroides merensis, and antiaeroticaum gntab transfer, and (2) subsequently administering to the subject a combination immunotherapy consisting essentially of (a) a PD-1, PDL1, or PDL2 inhibitor and (2) a CTLA-4, B7-1, or B7-2 inhibitor. In some embodiments, the isolated or purified population of bacteria comprises at least one bacterium belonging to a genus or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

In another aspect, the disclosure relates to a method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising: detecting a microbial profile in a sample obtained from a subject; predicting an effective response to the therapy when one or more bacteria of the genera or species bacteroides faecalis, butyromonas, flavonolyticus, Dielma, alistipes and Akkermansia muciniphila (Akkermansia muciniphila) are detected in a sample from the subject; or predicting an ineffective response to therapy when bacteria of one or more of the genera or species of Lactobacillus (Lactobacillus), bacteroides fragilis and Prevotella (Prevotella) are detected in a sample from the subject.

In another aspect, the disclosure relates to a method of predicting response to immune checkpoint inhibitor combination therapy in a subject having cancer, the method comprising: detecting a microbial profile in a sample obtained from a subject; predicting an effective response to therapy when a favorable profile is detected in a sample from the subject; or predicting an ineffective response to therapy when an adverse profile is detected in a sample from the subject.

In another aspect, the present disclosure is directed to a method comprising detecting one or more of the following in a subject: bacteroides faecalis, Bacteroides faecalis (Bacteroides caccae), Bacteroides enterica (Bacteroides intestinalis), Brevibacterium, Bacteroides fragilis, Vibrio fluvialis, Tazerlella, Bacteroides faecalis, Flavonivoror planutiii, Butyrimonas faecalis, Alisipes industrins, Dielma rustidia, Achimania mucolytica, Lactobacillus reuteri (Lactobacillius), Bacteroides fragilis, Prevotella faecalis (Prevotella copri) and Prevotella shahii (Prevotella shahii).

In another aspect, the present disclosure is directed to a method comprising detecting one or more of the following in a subject: bacteroides faecalis, Bacteroides intestinalis, Aliskis, Bacteroides fragilis, Haemophilus, Taziella, Flavonoides Percoll, Dielma rustidiosa, Butyrimonas farichis, Heterobacter, Ackermanella mucronophila, Lactobacillus reuteri (Clostridia), Prevotella faecalis, Prevotella sakesii, Citrobacter, Clostridium marinum, Huntingeicotridium aldichii, Citrobacter (Citrobacter rodentium), Eubacterium gourmentii (Eubacterium sulci), Hafniaceae (Hafniacae), Citrobacter freudenreichii (Citrobacter freundii), Eubacterium halenii (Eubacterium halilicii), Enterobacter cloacae (Enterobacter weicicola), Lactobacillus plantarum (Lactobacillus), Lactobacillus paracasei (Lactobacillus paracasei), Lactobacillus paracasei (Lactobacillus membrane, Lactobacillus (human), Lactobacillus paracasei (Lactobacillus), Lactobacillus membrane (Lactobacillus), Lactobacillus paracasei (Lactobacillus), Lactobacillus membrane (Rosteri), Lactobacillus (Rosteri), and Bacillus, Klebsiella aerogenes, Klebsiella pneumoniae, Coprobacter, Intestibacter bartlett, Intestibacter, Parasterella secunda, Aristobacter propionicum (Dialisifer propionicifaciens), Paradelbrueckii, Fournierella, Fourniciens massiensis, Bacteroides copromorpha, Eisenbergiella tayi, Discorea, Huntingeicotridium thermocellum, Doctoria longum, Thermus chrysanthemi, Muricomes, Muricomeles intestini, Geobacillus, and Anaerococcus taiticum strains disclosed in FIG. 28C.

In another aspect, the present disclosure is directed to a method comprising detecting one or more of the following in a subject: bacteroides faecalis, Bacteroides intestinalis, Allierella terrestris, Bacteroides fragilis, Vibrio fluvialis, Taziella terezii, Bacteroides faecalis, Flavonoidea perniciae, Dielma rostidiosa, Achimedella viscophila, Lactobacillus reuteris, Prevotella faecalis, Prevotella sarmentosa, Mastigomycota pachyta, Clostridiaceae, Aliskips indentis, Bacteroides sterosorosis, Clostridium lactitifaciens, Acysivirginia alkaniphila, Acetastereospecific, Vibrio acetobacter ethanologens, Acidobacterium bovis, Achromobacter delbrueckii, Acidovorax equorum, Acidobacterium gynecolophilum, Ocimum viscoselinum, Acidobacterium acidophilum, Acidobacterium acidiprista, Acidobacterium acidipritium, Acidobacterium sp, Acidobacterium acidipritium, Acidobacterium acidiprodiella, Acidobacterium acidiprodiella, Acidobacterium acidiprodium, Acidobacterium acidiprodium, Acidobacterium acidiprodium, Acidobacterium acidiprodium, Acidobacterium acidiprodium, Acidobacterium, Acido, Anaerotaenia tora, human colonic anaerobic clavuligerum, Anaerotruncus rubinfarnis, deodouring anaerobic bulimia, Bacteroides acidogenesis cacumenis, Bacteroides faecinciselae, Bacteroides farinoshiclae, Bacteroides sterirosaris, bacteriodes xylanisolvens, Enterobacter enterica, Beduini massilisensis, Bifidobacterium pseudolongum, Bulawsteria sulutissi, Breznaki blaticola, Breznakia pacinia papyridae, Coccidioides elegans, Vibrio pentosaceus, Catobacter hongkongensis, Christensella malansenii, Christennensis, Clostridium sporogenes, Clostridium difficile, Clostridium sporogenes, Clostridium butyricum, Clostridium perfoliatum, Clostridium difficile, Clostridium sporogenes, Clostridium difficile, Clostridium butyricum, Clostridium bifidum, Clostridium perfoliatum, Clostridium sporogenes, Clostridium perfoliatum, Clostridium butyricum, Clostridium sporogenes, Clostridium stercorallium, Clostridium perfoliatum, Clostridium sterculum, Clostridium stercoralliforme, Clostridium sterculum, Clostridium perfoliatum, Clostridium sterculum kohlia, Clostridium sterculum kohlia, Clostridium sterculum kohlia, Clostridium sterculum kohliae, Clostridium sterculum kohlia, Clostridium sterculum, clostridium lyticum, Clostridium straminilyticum, Clostridium viridans, Clostridium delignificanum, Coprobobacter secunduns, enterococcus acutus, Curvularia maliensis, Delluvatalea saccharophila, Gomphagi gondii, Desulfobacter metallothionein, Campylobacter orientalis, Desulfovibrio sulphureus, Vibrio simplex, Vibrio formis, Eisenbergiella massilis, Emergenecia timonensis, enterococcus enterobacter enterocolitica, Enterobacterium enterobacter, Enterobacter musculus, Erysipelotricum ramosus, Erysipelothrix lutescens, Escherichia coli, sterol-producing Bacillus faecalis, Eubacterium longipes, Eubacterium inertium, Eubacterium multivorans, Lactobacillus ventricusflexus, Lactobacillus paracasei, Lactobacillus casei, Flaviviparus, Lactobacillus sanotis, Lactobacillus sanfrancisella, Lactobacillus casei, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanwicia, Lactobacillus sanwicelitis, Lactobacillus sanillustrating strain, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanwicelitis, Lactobacillus sanobacter, Lactobacillus sanillustrating strain, Lactobacillus sanobacter strain, Lactobacillus sanillustrating strain, Lactobacillus sanobacter, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, strain, Lactobacillus sanillustrating strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, human milk bacillus, intestinal lactobacillus, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus taiwanensis, Lawsonia intracellularis, Longibacurum muris, Marvinbryantia formuliens, Millilonella maliensis, Serratia subsira, Muribactulum intestinale, Murimonas intestin, Natranavira pectinora, Neglecta timelensis, Microfetida, Olsenerella profunda, rodent Tre. mutans, Dermatophagi, Pectinathus praecox, Paracoccus paracasei, human Paracoccus, Paravibacter cacicola, Pectiococcus nigra, Phocae maliensis, Porphyromonas Porphyromonas (Porphyromonas monocatae), Volvisia oralis, Volvisis furcellularis, Pectinopileria, Pectinathus fascicularia, Pecticola, Pectinatum, Pecticola, Pectinatum, Pecticola, Pectinospora, Pecticola, Pectium, Pecticola, Pseudomonas, Pecticola, Pseudomonas, Pecticola, ruminococcus flavus, Ruminococcus actively, Ruthenium lactatiformans, Sphingomonas kyeonggiensis, Spiroplasma tachyphylum, Termite bacillus, Stomobaculum longum, Acidococcus oligocens, Streptococcus danieliae, Cotrophomonas wowensis, Thermomomonas taiwanensis, Tydinella neolytica, Tysinalella neoxil, Vallitalia pronyensis and Chlorella haemophila.

In another aspect, the present disclosure relates to compositions comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens.

In some embodiments, the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

In another aspect, the present disclosure relates to compositions comprising at least two isolated or purified populations of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens. In some embodiments, the composition comprises at least two isolated or purified populations of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

In another aspect, the present disclosure relates to compositions comprising at least one, at least two, or 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 (or any derivable range thereof) isolated or purified populations of bacteria of the following: parabacteroides destructor, Fournierella maliensis, Eisenbergiella tayi, Yersinia dicentri, Hungateiclysis tribulus, Thermoascus longchain, Pyrococcus coxielli, Muricomes, Geobacillus, Martensivorax, Lactobacillus acidophilus, Bacteroides finesse, Lactobacillus johnsonii, Dermatopterium, Flavobacterium sp, Bacteroides sp, Dielma, Akermania, Arthrobacter, Anaerobacter, Anaerognum lactacidentinens, Bacteroides faecalis, Enterobacter, Aristobacter, Pseudosciaenopsis, Pseudobacter fragilis, Haemophilus, Tyrosomus, Clonopsis faecalis, Dielma fascicularia, Acidophilus, Lactobacillicium virescens, Lactobacillus acidophilus, Fragilsonii, Fragilis fragilis, Oxycolatopsis, Oxycotinas, Alcaligenes, Alcalifornia, Clostridium, Alcaligenes, Clostridium sticklandii, Clostridium sporogenes, Clostridium, Alcaligenes, and Alcaligenes, Acetastasis muris, cellulolytic vibrio acetate, ethanologenic vibrio acetate, bovine acholemia, Acidovorax radialis, Esomeriobacter equorum, Acidovorax radialis, Achimalobacter mucronatum, Achimales incarnatum, Alisipes indistinctus, Alisipes obliesi, Exiguobacterium putrescentium, Exiguobacterium senii, Acetobacter xylinum, saccharomycete, Alkalibacillus bacchia, Exiguobacterium caninum, Anaerobacter charis chartisense, Anaerocola cellulicaulifloytica, Anaerobiospora mobilis, Anaerobiosssita, Anerobacter coli, Anaerocerinus rubicinensis, Anaerosporus Acidovorax, Anaeroticus acidovorans, Salmonella abortus, Pseudoperonospora chrysospermannica, Clostridium butyricum, Clostridium paraguariensis, Clostridium butyricum, Clostridium paragonium, Clostridium butyricum, Clostridium paragonium, Clostridium butyricum, Clostridium sporogenes, Clostridium paragonium, Clostridium sporogenes, Clostridium butyricum, Clostridium paragonium, Clostridium sporogenes, Clostridium butyricum, Clostridium paragonium, Clostridium butyricum, Clostridium paragonium, Clostridium butyricum, Clostridium sp, Clostridium paragonium, Clostridium sp, Clostridium butyricum, Clostridium paragonium, Clostridium sp, Clostridium butyricum, Clostridium sp, Bacillus cereum, Clostridium sp, Clostridium alcaligenes, Clostridium asparaguens, Clostridium fastidiosa, Clostridium cellulolyticum, Clostridium Bright yellow, Clostridium Spirosoma, Clostridium quail, Clostridium heulans, Clostridium indole, Clostridium jizhou island, Clostridium fermentum, Clostridium laval, Clostridium methylpentosicum, Clostridium orotate, Clostridium oryzae, Clostridium maceraldii, Clostridium polysaccharolyticum, Clostridium populus, Clostridium saccharolyticum, Clostridium sardeiense, Clostridium schizolysis, Clostridium strychnisolens, Clostridium viridans, Clostridium xylanolyticum, Copropbacter cundus, Clostridia faecalis, Culturomyca massilisensis, Defluviella saccharophila, Bacillus entosus, Clostridium metallothiofida, Bacillus reductium thiofida, curvularia orientalis, desulfurization, Vibrio simplex desulfurization, Vibrio perforatum, Escherichia coli, Enterobacter, Salmonella coli, Salmonella enterica, Salmonella, Salmon, Escherichia coli, faecalsterol-producing Eubacterium, Eubacterium elongatum, Eubacterium rodentium, Eubacterium inert, Eubacterium polytrichum, Eubacterium ventriosum, faecal coprinus, Flavivirida pacifica, Flanibacter pusillus, Flantibacter palmerii, Gordonibacter faecionis, Cellulobacter halodurans, Harryflintis acetispora, Maryland's, anaerobacter saccharivorans, Ihubacter masseliensis, Intestimunitons burriciduens, Irregularacter murinus, Lachnocostris pacense, Lactobacillus anidermatis, Lactobacillus faecalis, Lactobacillus gasseri, Lactobacillus casei, Lactobacillus reuteri, Lactobacillus paracasei, human enterobacterias, Lactobacillus paracasei, human enterobacterias, human enterobacteriales, human enterobacterias, human enterobacteriales, human enterobacterii, human enterobacterias, human enterobacteriales, human enterobacteria, human enterobacterii, parvibacterium caecicola, Pediococcus niger, Phocae massiensis, Porphyromonas catorii, Prevotella oralis, Prevotella faecalis, Prevotella proteolifera, Vibrio ruminobutyrate, Pseudoflavobacterium hirsutum, Pseudoflavobacterium pseudomajus, Pseudoflavobacterium fuliginosum, Raoultibacter timenensis, Rhizobium straminnorzae, Raosbeckia faecalis, Raosbeckia hominis, Raosbeckia enterobacter, Ruminostroiditium thermocellulosum, Ruminococcus chamomillae, Ruminococcus coprinus, Ruminococcus flavus, Ruminococcus livenopoccus, Runibacterium lactotiformans, Sphingomongium keygygnas, Sphingomongium, Proteobromobacterium, Termite, Salmonella zeae, Thermobacteroides avicularis, Thermobacteroides eutroplasma, Thermobacteroides, Thermoascus, or Thermoascus urensis.

In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more than one of the following species: flavonoids, Bacteroides faecalis, Butyrimonas faecalis, Dielma, Akkermansia and Alisipes insistintus. In some embodiments, the composition does not comprise bacteroides cacteus. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more of Dielma and akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more of the genera alistipes, dielmas and akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to the genus akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified akkermansia muciniphila population. In some embodiments, the composition comprises or further comprises a population of bacteria comprising akkermansia muciniphila and one or more of Dielma rustidiosa and Alistipes indigentinostus. In some embodiments, the bacteria of the genus flavonolactobacillus include flavonolactobacillus pernici. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: bacteroides fragilis, Vibrio, Taziella, long chain bacteria, Coxietz, Muricomes intestini, Geobacillus, Anerargignum lactatimenteans. In some embodiments, the composition comprises or further comprises at least one isolated or purified bacteroides enteric flora. In some embodiments, the composition comprises or further comprises at least one isolated or purified bacterial population belonging to the phylum firmicutes, order clostridiales and family ruminococcaceae. In some embodiments, the composition comprises or further comprises flavonolactobacillus perniciae and/or Dielma rustidia. In some embodiments, the composition comprises or further comprises bacteroides faecalis, butyricomonas faecalimine, flavonolyticus perniciae, Dielma fascidiosa, Alistipes indestinctus, and akkermansia muciniphila.

In some embodiments, the composition comprises less than 1 x 10⁵、1×10⁴、1×10³Or 1X 10²Individual CFUs or cells (or any derivable range therein) classified as bacteria of the firmicutes, clostridiales and ruminococcaceae families. In some embodiments, the composition comprises less than 1 x 10⁵、1×10⁴、1×10³Or 1X 10²Individual CFUs or cells (or any derivable range thereof) of bacteria belonging to the families ruminococcaceae, clostridiaceae, pilospiraceae, micrococcidae and/or veillonellaceae.

In some embodiments, the cancer is a skin cancer. In some embodiments, the cancer is basal cell skin cancer, squamous cell skin cancer, melanoma, dermatofibrosarcoma protruberans, merkel cell carcinoma, kaposi's sarcoma, keratoacanthoma, spindle cell tumor, sebaceous gland carcinoma, cancer of the microcapsular adnexa, paget's disease of the breast, atypical fibromatosis, leiomyosarcomas, or angiosarcoma. In some embodiments, the cancer is melanoma. In some embodiments, the melanoma is metastatic melanoma, lentigo maligna melanoma, superficial spreading melanoma, nodular melanoma, acral lentigo melanoma, cutaneous melanoma, or desmoplastic melanoma. In some embodiments, the cancer comprises melanoma of the skin.

In some embodiments, the cancer comprises recurrent cancer. In some embodiments, the cancer comprises a recurrent metastatic cancer. In some embodiments, the cancer comprises a recurrence of the cancer in the region of the primary tumor. In some embodiments, the cancer comprises metastatic cancer. In some embodiments, the cancer comprises stage III or stage IV cancer. In some embodiments, the cancer comprises stage I or stage II cancer. In some embodiments, the cancer does not include stage I or stage II cancer.

In some embodiments, the method further comprises administering at least one additional anti-cancer therapy. In some embodiments, the at least one additional anti-cancer therapy is a surgical therapy, chemotherapy, radiation therapy, hormonal therapy, immunotherapy, small molecule therapy, receptor kinase inhibitor therapy, anti-angiogenic therapy, cytokine therapy, cryotherapy, or biologic therapy. In some embodiments, the additional anti-cancer therapy comprises a cancer therapy described herein.

In some embodiments, (i) a PD-1, PDL1, or PDL2 inhibitor, (ii) a CTLA-4, B7-1, or B7-2 inhibitor, and/or at least one additional anti-cancer therapy is administered intratumorally, intraarterially, intravenously, intravascularly, intrapleurally, intraperitoneally, intratracheally, intrathecally, intramuscularly, endoscopically, intralesionally, transdermally, subcutaneously, regionally, directionally, orally, or by direct injection or infusion. In some embodiments, the route of administration is a route described herein.

In some embodiments, a method is defined as a method of treating cancer in a subject diagnosed with cancer. In some embodiments, the methods comprise or further comprise reducing or preventing one or more adverse events. In some embodiments, the methods comprise or further comprise reducing or preventing one or more severe adverse events. In some embodiments, treating cancer comprises reducing or preventing one or more severe adverse events. In some embodiments, the methods are used to reduce the toxicity of immunotherapy, such as immune checkpoint blockade combination therapy. In some embodiments, reducing toxicity comprises reducing adverse events. In some embodiments, an adverse event or severe adverse event is also classified as an immune-related adverse event. In some embodiments, the method comprises preventing or reducing immune-related adverse events. In some embodiments, the adverse event is classified as a severe adverse event. In some embodiments, adverse events at or above grade 3 are prevented. The grade of adverse event is assessed by methods known in the art, for example, according to the NCI universal terminology criteria (CTCAE) for adverse events.

In some embodiments, the subject has been determined to have an unfavorable microbial profile in the gut microbiome. In some embodiments, the unfavorable profile includes a bacterial population comprising bacteria belonging to one or more than one of the genera bacteroides, alisteria, Coprobacter, enterobacter, and paracasei. In some embodiments, the unfavorable profile comprises a population of bacteria comprising one or more than one of bacteroides faecalis, bacteroides enterobacter, Coprobacter, Intestinibacter bartletti, paranutella secunda, and alisteria propionicum. In some embodiments, the unfavorable profile comprises erysipelas trilium ramosum. In some embodiments, the unfavorable profile comprises a population of bacteria comprising bacteria belonging to one or more than one of the genera: lactobacillus, Bacteroides, Prevotella, Citrobacter, Clostridium, Hungateicosidium, Eubacterium, Hafniaceae, Enterobacter, Hafnia, Roseburia, Weissella, Bacillus, Lactobacillus, and Klebsiella. In some embodiments, the unfavorable profile includes a bacterial population comprising one or more than one of: lactobacillus reuteri, Bacteroides fragilis, Prevotella faecalis, and Prevotella sakei. In some embodiments, the unfavorable profile includes one or more than one of: prevotella faecalis, Prevotella scherzerlich, Vibrio butyricum, Brucella hydrogenotrophica, Bacteroides fragilis, Vibrio scillatus, Lactobacillus family, Lactobacillus reuteri, Clostridium saccharophilus, and Megasphaera massiliensis. In some embodiments, the unfavorable profile includes at least, up to, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 (or any derivable range thereof) of: citrobacter, Clostridium hirsutum, Hungateic aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophilum, Enterobacter cloacae, Hafnia alvei, Hafnia, Rabyella humanus, Weissella mesenteroides, Enterobacter, Bacillus, Lactolerida, Klebsiella aerogenes, Klebsiella, Bacteroides intestinalis, Coprobacter, Intestibacter barvetti, Intestibacter, Parastuttera secunda secnda, Alieria propionicum, Prevotella faecalis, Prevotella salmonellae, Vibrio, Brucella hydrogenfrangible, Bacteroides, Pseudobulbus butyricum, Vibrio lactis, Lactobacillus, Lactobacillaceae, Lactobacillus reuteri, Clostridium saccharophilus, and Messalissocallensis. In some embodiments, the unfavorable profile is also classified as a non-responsive profile or an ineffective profile. A non-responsive profile refers to the profile of microorganisms in a subject, particularly in the gut of a subject, which is present in a subject that is non-responsive to immune checkpoint blockade combination therapy. In some embodiments, the non-response profile comprises at least, up to, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 (or any derivable range thereof) of: citrobacter, Clostridium hirsutum, Hungateic aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophathei, Enterobacter cloacae, Hafnia alvei, Hafnia, Rabyella humanus, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillus, Klebsiella aerogenes, Klebsiella, Prevotella coproagulae, Prevotella serrata, Vibrio butyricum, Brucella hydrogenotrophica, Bacteroides fragilis, Vibrio pseudosciaeformis, Lactobacillus, Lactobacilliaceae, Lactobacillus reuteri, Clostridium saccharophilus, and Megasphaera massica. In some embodiments, the unfavorable profile includes one or more than one of: bacteroides faecalis, negativicites, bacteroides enterocolitica, clostridium species, clostridium clostridia and listeria. In some embodiments, the unfavorable profile includes at least, up to, or exactly 1, 2, 3, 4, 5, 6, or 7 (or any derivable range thereof) of: coprobacter, intestinobacter bartletti, intestinobacter, paranutteriella secunda, alisteria propionicum, bacteroides faecalis, bacteroides coprocola, negativicites, bacteroides entericus, clostridia species, clostridium and alisteria. In some embodiments, the adverse profile is also classified as a toxicity-related profile. Toxicity-related profiles refer to the microbial profile in a subject, particularly in the gut of a subject, that is present in a subject that has experienced toxicity as a result of immune checkpoint blockade combination therapy. In some embodiments, the non-response profile comprises at least, up to or exactly 1, 2, 3, 4, 5, 6 or 7 (or any derivable range thereof) of: bacteroides faecalis, negativicites, bacteroides enterocolitica, clostridium species, clostridium clostridia and listeria.

In some embodiments, the relative abundance of bacteria belonging to the genus or species of at least 10% is determined: citrobacter, Clostridium hirsutum, Hungateic aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophathei, Enterobacter cloacae, Hafnia alvei, Hafnia, human Ralstonia, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillales, Klebsiella aerogenes, Klebsiella, Bacteroides intestinalis, Coprobacter, Intestinibacter bartlett, Intestinibacter, Parasterella, Serratia propionibacteria, Bacteroides, Talaromyces, Lactobacillus, and/or Prevotella. The term relative abundance is the percentage composition of a particular species of organism relative to the total number of organisms in a region, such as a sample from a subject. In some embodiments, bacteria belonging to the genus or species of the following are determined to be present in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein): citrobacter, Clostridium hirsutum, Hungateic aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophathei, Enterobacter cloacae, Hafnia alvei, Hafnia, human Ralstonia, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillales, Klebsiella aerogenes, Klebsiella, Bacteroides intestinalis, Coprobacter, Intestinibacter bartlett, Intestinibacter, Parasterella, Aralisnera propionicum, Talaromyces, Lactobacillus, Pseudopterobacter and/or Prevotella. In some embodiments, the combined relative abundance of bacteria classified as one or more of the following is determined as at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein): citrobacter, Clostridium hirsutum, Hungateic aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophathei, Enterobacter cloacae, Hafnia alvei, Hafnia, human Ralstonia, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillales, Klebsiella aerogenes, Klebsiella, Bacteroides intestinalis, Coprobacter, Intestinibacter bartlett, Intestinibacter, Parasterella, Aralisnera propionicum, Talaromyces, Lactobacillus, Pseudopterobacter and/or Prevotella. In some embodiments, the relative abundance of bacteria belonging to the genus bacteroides is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of a bacterium belonging to the genus brewsteria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus lactobacillus is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus prevotella is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein).

In some embodiments, the method further comprises comparing the measured microbial profile in the sample from the patient to a control sample. The control may be a microbial profile derived from a sample taken from a patient who is non-responder, or has experienced toxicity or has not experienced toxicity to the immune checkpoint blockade combination therapy.

In some embodiments, the subject comprises an adverse microorganism profile as determined by analyzing the microbiome in a sample from the subject. In some embodiments, the sample is a stool sample or an oral sample. In some embodiments, analyzing comprises performing 16S ribosomal sequencing and/or metagenomic whole genome sequencing.

In some embodiments, the subject has been previously treated for cancer. In some embodiments, the determined subject is a non-responder to a previous treatment. In some embodiments, the patient has been determined to have a toxic response to a previous treatment. In some embodiments, the prior treatment comprises an immune checkpoint blockade monotherapy or an immune checkpoint blockade combination therapy. In some embodiments, the prior treatment comprises an immune checkpoint blockade monotherapy comprising only one of the PD-1, PDL1, PDL2, CTLA-4, B7-1, or B7-2 inhibitors. In some embodiments, the immune checkpoint blockade combination therapy comprises a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor. In some embodiments, (i) is a PD-1 antibody and/or inhibitor, and (ii) is a CTLA-4 inhibitor. In some embodiments, (i) is an anti-PD-1 monoclonal antibody and/or (ii) is an anti-CTLA-4 monoclonal antibody. In some embodiments, (i) comprises nivolumab, palivizumab, or pidilizumab. In some embodiments, (ii) comprises ipilimumab or tremelimumab. In some embodiments, the subject has previously been treated with an immune checkpoint blockade monotherapy or an immune checkpoint blockade combination therapy.

In some embodiments, the subject is treated with the isolated bacterial population prior to or concurrently with the treatments in (i) and (ii). In some embodiments, the subject is treated with the isolated bacterial population after the treatment in (i) and (ii). In some embodiments, treatment with the microbial composition is performed at least or at most 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, or 24 hours or 1 day, 2 days, 3 days, 4 days, 5 days, or 6 days or 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, or 6 weeks (or any derivable range therein) before or after treatment with the inhibitor of (i) and (ii). In some embodiments, treatment with the microbial composition is performed at least or up to 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, or 24 hours or 1 day, 2 days, 3 days, 4 days, 5 days, or 6 days or 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, or 6 weeks (or any derivable range therein) of treatment with the inhibitor of (i) and (ii).

In some embodiments, the purified population of bacteria comprises bacteria from at least two genera or species, and wherein the ratio of the two bacteria is 1: 1. In some embodiments, the purified bacterial population comprises bacteria from at least, up to, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 30, 40, or 50 (or any derivable range therein) different bacterial families, genera, or species. In some embodiments, the ratio of bacteria of one family, genus, or species to bacteria of another family, genus, or species present in the composition is at least, up to, or at exactly 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, 1:10, 1:20, 1:25, 1:30, 1:35, 1:40, 1:45, 1:50, 1:55, 1:60, 1:65, 1:70, 1:75, 1:80, 1:85, 1:90, 1:95, 1:100, 1:150, 1:200, 1:250, 1:300, 1:350, 1:400, 1:450, 1:500, 1:600, 1:700, 1:800, 1:900, 1:1000, 1:1500, 1:2000, 1:3500, 1:3000, 5000: 1:500, 651: 600, 1:1550, 1:2000, 7000, 1:1, 200, 1:2000, 1:1, 200, 1:1, 200, 1:1, 1:1, 1:1, 1, 1:7500, 1:8000, 1:8500, 1:9000, 1:9500, 1:10000, 1:1200, 1:14000, 1:16000, 1:18000, 1:20000, 1:30000, 1:40000, 1:50000, 1:60000, 1:70000, 1:80000, 1:90000, or 1:100000 (or any derivable range therein).

In some embodiments, the composition provides at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 a diversity. Methods for calculating alpha diversity are known in the art. For example, the inverse simpson index can be used to estimate the taxonomic a diversity of the samples, which is described in example 1. In some embodiments, the composition is administered in an effective amount. In some embodiments, an effective amount comprises an amount that provides at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 (or any derivable range therein) of a diversity in a subject.

In some embodiments, the bacteria belong to the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides diecutanensis, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidia themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmorata, Lactobacillus seclothilus, Bacteroides farinosus, Lactobacillus johnsonii, Dermatopterium composte and Anaeroticum gntatatisfaciens in at least, at most, or exactly 1X 10 ³1, 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Or 1 x 10¹⁶Individual cells or CFU (or any of them)Derivative range) is applied. In some embodiments, the bacteria belong to the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacters, Fournierella massilisis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor, Hungatricles themocellum in at least, at most or exactly 1 × 10³1, 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Or 1 x 10¹⁶The amount of individual cells or CFU (or any derivable range therein). In some embodiments, the total amount of bacteria administered is at least, at most, or exactly 1 x 10³1, 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10 ¹⁴1, 1 × 10¹⁵Or 1 x 10¹⁶Individual cells or CFUs (or any derivable range therein). In some embodiments, a specific amount of bacteria, such as a particular species of bacteria, can be at least, at most, or exactly 1 x 10³1, 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Or 1 x 10¹⁶Individual cells or CFUs (or any derivable range therein). In some embodiments, the composition may contain at least, up to or just 1 x 10³1, 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Or 1 x 10¹⁶Cells or CFUs from a phylum, family, genus or species of the bacteria described herein (or any derivable range therein). In some embodiments, the composition may contain less than at least, up to, or exactly 1 x 10⁶1, 1 × 10⁵1, 1 × 10⁴1, 1 × 10³Or 1 x 10²Cells or CFUs from a phylum, family, genus or species of the bacteria described herein (or any derivable range therein).

In some embodiments, the method further comprises administering an antibiotic. In some embodiments, the antibiotic can be a broad spectrum antibiotic. In some embodiments, a mixture of at least 1, 2, 3, 4, or 5 antibiotics is administered. In some embodiments, the antibiotic comprises ampicillin, streptomycin, and colistin, and combinations thereof. In some embodiments, the antibiotic is administered prior to the composition comprising the at least one isolated or purified bacterial population. In some embodiments, the antibiotic is administered concurrently with the composition comprising at least one isolated or purified bacterial population. In some embodiments, the antibiotic is administered at least or at most 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 12 hours, or 24 hours or 1 day, 2 days, 3 days, 4 days, 5 days, or 6 days or 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, or 6 weeks (or any derivable range therein) before or after the microbial composition.

In some embodiments, a favorable profile includes a population of bacteria comprising bacteria belonging to one or more than one of the genera bacteroides, haemophilus, and tazerlington. In some embodiments, favorable profiles include populations of bacteria comprising one or more than one of the following: bacteroides fragilis, Vibrio, Taziella, long chain bacteria, Coxietz, Muricomes intestini, Geobacillus, Anerargignum lactatimenteans. In some embodiments, a favorable profile comprises bacteria in a relative abundance of less than 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% of one or more of firmicutes, clostridiales, and ruminococcaceae. In some embodiments, favorable profiles include populations of bacteria comprising one or more than one of the following: paralicheniella, Fournierella maliensis, Bacteroides copromorphus, Eisenbergia tayi, Yersinia, Huntingeichidium thermocellulosum, Dolerella longata, Thermus coreana, Muricomes intestini, Geobacillus underground, Anaerognatum lactifera, Bacteroides fragilis, Vibrio trematodinus, Taziella, Achromobacter mucedoides, Achromobacter faecalis, Dielicidia, Mycoplasma pachynomenum, Clostridiales, ruminococcaceae, flavobacterium albopictus, Alisiperidis, Acidophilia sterlisoides, Acidophilis acidophilus, Acidophilia, Acidophilium, Acidicola, Acidophilium, Acidides, or its, Alkalibacillus bacchia, Corynebacterium caninum, Anaerobacterium chartosolvense, Anaerocolmna cellulicularia, Anaerobiospora mobilis, Anaerotalaria tora, human colonic anaerobacter, Anaerotruncus rubinflantis, Deolfactory anaerobacter, Bacteroides acidimeris, Bacteroides cacumenis, Bacteroides caecilomyces caecimikuri, Bacteroides farinosus, Bacteroides faecincisella, Bacteroides sterirorides, Pseudoxylosomnicoides, Enterobacter intestinalis, Beduiniensis malensis, Bifidobacterium pseudobifidum, Blauteria mucida, Breznaikia blains, Breznaiola, Breznaidea panodae, Vibrio gallinarciparum, Clostridium butyricum, Clostridium calcoaceticus, Clostridium lactis, Clostridium sporotrichioides, Clostridium sporogenes, Clostridium butyricum, Clostridium bifidum, Clostridium perfringens, Clostridium bifidum, Clostridium perfringens, Clostridium gordonii, Clostridium berculum, Clostridium gordonii, Clostridium gordon, Clostridium oryzae, Clostridium macerans, Clostridium polysaccharolyticum, Clostridium populus, Clostridium saccharolyticum, Clostridium sardiense, Clostridium lytic, Clostridium stromanisolvens, Clostridium viridans, Clostridium xylanolyticum, Copropbacter secundus, enterococcus faecalis, Culturomyces macensis, Defluvittalea saccharophila, Bacillus gibsonii, Acidithiobacillus metallothionein, Curvularia orientalis, Vibrio desulfuridus, Vibrio simplex desulfuridomyces, Vibrio formicoaceticus, Eisengibella maliensis, Emergerica timonensis, enterococcus hirae, Enterobacter mucosae, Enterobacter enterobacter, Enterobacter mulus, Erysipellicitococcus ramosus, Rhodococcus larvae, Escherichia coli, Salmonella faecalis, Salmonella holderii, Salmonella faecalis, Salmonella enterica, Hippobacter halobacter, Hippobacter, Bacillus halobacter, Hippobacter, Hippo, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter strain, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter, Salmonella, and Salmonella, ihubacter massiviensis, intestinomonas butyricproducens, Irregularibacter muralis, Lachnocristidium pacaines, Lactobacillus animalis, Lactobacillus faecalis, Lactobacillus gasseri, Lactobacillus casei, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus paracasei, Longium muriseri, Marvinbryantia formuliensis, Millilonella massiviensis, Microspirillum scherzeylanicum, Murebaudi intestinale, Murimonas intestinii, Natranovirga petiolivora, Neglecta, Microspirillus visteri, Olseneferi profusis, Vibrio tre trelligenes, Valeribacter valerianicolae, Pacific acid bacterium, Paraconia, Parasitis, Pseudoperonospora paranois, Phanerochaea, Phanerochaete, Phaseolus, Phase, Ralstonia enterocolitica, Ruminicystidium thermocellum, Ruminococcus championellis, Ruminococcus faecalis, Ruminococcus flavus, Ruminococcus actively, Ruthenium lactaeformans, Sphingomonas kyeongiensis, Spiroplasma stenotropha, Termite, Stomobaculum longum, Streptococcus oligosaccharea, Streptococcus danieliae, Cotrophomonas vorax, Thermomonomonas taiwanensis, Tetranya californica, Tyndazilla lake, Hemicella, Urinuria, Turimonas muricas, Tyzzerella nexilis, Vallitalia proyensis, and Vibrio shikovia.

In some embodiments, favorable profiles include bacterial populations that exclude one or more than one of the following: paralicheniella, Fournierella maliensis, Bacteroides copromorphus, Eisenbergia tayi, Yersinia, Huntingeichidium thermocellulosum, Dolerella longata, Thermus coreana, Muricomes intestini, Geobacillus underground, Anaerognatum lactifera, Bacteroides fragilis, Vibrio trematodinus, Taziella, Achromobacter mucedoides, Achromobacter faecalis, Dielicidia, Mycoplasma pachynomenum, Clostridiales, ruminococcaceae, flavobacterium albopictus, Alisiperidis, Acidophilia sterlisoides, Acidophilis acidophilus, Acidophilia, Acidophilium, Acidicola, Acidophilium, Acidides, or its, Alkalibacillus bacchi, Corynebacterium caninum, Anaerobacterium chartosolvense, Anaerocolmna cellulicularia, Anaerobiospora mobilis, Anaerotalaria tora, human colonic anaerobacter, Anaerotruncus rubinflantis, Deolfactory anaerobacter, Bacteroides acidificans, Bacteroides cacumenis, Bacteroides faecalis, Brevibacterium massiliensis, Brevibacterium pseudomoniliformis, Brevibacterium faecalis, Beduiniense, Bifidobacterium pseudobifidum, Brevibacterium crenatum, Breznaiella blakea blica, Breznavirus, Clostridium clavatum, Clostridium calcoaceticus, Clostridium sporogenes, Clostridium butyricum, Clostridium bifidum, Clostridium butyricum, Clostridium bifidum, Clostridium butyricum, Clostridium bifidum, Clostridium perfringens, Clostridium butyricum, Clostridium acidum, Clostridium butyricum, Clostridium acidum, Clostridium butyricum, Clostridium acidum, Clostridium berberiberberidactylum, Clostridium acidum, Clostridium berberiberberidactylum, Clostridium berberidactylum, Clostridium acidum, Clostridium butyricum, Clostridium acidum, clostridium oryzae, Clostridium macerans, Clostridium polysaccharolyticum, Clostridium populus, Clostridium saccharolyticum, Clostridium sardiense, Clostridium lytic, Clostridium stromanisolvens, Clostridium viridans, Clostridium xylanolyticum, Copropbacter secundus, enterococcus faecalis, Culturomyces macensis, Defluvittalea saccharophila, Bacillus gibsonii, Acidithiobacillus metallothionein, Curvularia orientalis, Vibrio desulfuridus, Vibrio simplex desulfuridomyces, Vibrio formicoaceticus, Eisengibella maliensis, Emergerica timonensis, enterococcus hirae, Enterobacter mucosae, Enterobacter enterobacter, Enterobacter mulus, Erysipellicitococcus ramosus, Rhodococcus larvae, Escherichia coli, Salmonella faecalis, Salmonella holderii, Salmonella faecalis, Salmonella enterica, Hippobacter halobacter, Hippobacter, Bacillus halobacter, Hippobacter, Hippo, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter strain, Bacillus halobacter, Bacillus halobacter, Bacillus halobacter, Salmonella, and Salmonella, ihubacter massiviensis, intestinomonas butyricproducens, Irregularibacter muralis, Lachnocristidium pacaines, Lactobacillus animalis, Lactobacillus faecalis, Lactobacillus gasseri, Lactobacillus casei, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus paracasei, Longium murisii, Marvinbryantia formuliensis, Millilonella massiviensis, Microspirillum scherzeylanicum, Murebaudi intestinale, Murimonas intestinii, Natranovirga petiolivora, Neglecta, Microspirillus visteri, Olseneferi profusis, Vibrio trefinely tremorgin, Valeribacter valerianicum, Pacific acid bacterium, Paraconiella, Paraconica, Pseudoperonospora, Porphytes, Phaseolus purpurea, Phanerochaeta, Phaseolus, Phaseolu, Ralstonia enterocolitica, Ruminicystidium thermocellum, Ruminococcus championellis, Ruminococcus faecalis, Ruminococcus flavus, Ruminococcus actively, Ruthenium lactaeformans, Sphingomonas kyeongiensis, Spiroplasma stenotropha, Termite, Stomobaculum longum, Streptococcus oligosaccharea, Streptococcus danieliae, Cotrophomonas vorax, Thermomonomonas taiwanensis, Tetranya californica, Tyndazilla lake, Hemicella, Urinuria, Turimonas muricas, Tyzzerella nexilis, Vallitalia proyensis, and Vibrio shikovia.

In some embodiments, an advantageous profile comprises a population of bacteria comprising 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the following (or any derivable range therein): parabacteroides dickinsonii, Fournierella maliensis, Bacteroides copromorphus, Eisenbergiella tayi, Tisserellas, Hunateicosidium thermocellum, Donerobacterium formate, Thermus corniferus, Muricomes intestini, Geobacillus, Aneraceae, Lactitutellaria, Bacteroides fragilis, Haemophilus, Tyroseus, Achromobacter mucronatum, Bacteroides faecalis, Dielicidia, Mycoplasma pachynomenii, Myxococcaceae, Aminococcaceae, Flavonoides albus, Alisiperiella, Acidophilippinensis, Acidophiliella, Acidophilippinensis, Acidophilium, Acidicola, Acidophilium, Acidicola, Acidophilium, Acidicola, Acidophilium, Acidicola, Acidides, Acidicola, Acidides, or Eisenia, Acidides, or its, or, Corynebacterium cibotium, Anaerobacter chartisolvense, Anaerococcus cellulolytica, Anaerococcus mobilis, Anaerotaenia torta, human anaerobic Corynebacterium colons, Anaerotruncus rubinflantis, Deodorantinovorax, Bacteroides acidiproducens, Bacteroides caecimiis, Bacteroides spinosus, Bacteroides faecidium, Bacteroides farichiella, Bacteroides rothecinella, Bacteroides rothecaristicus, Bacteroides sterrosoisonoris, Bacteroides xylanisolvens, Enterobacter intestinalis, Beduinialis massissens, Bifidobacterium pseudolongum, Brezuelella mucronatum, Bretzia blattaria, Bretzia pacinia, Clostridium butyricum, Clostridium stercoralloides, Clostridium sterculia, Clostridium stercoralloides, Clostridium sterculis, Clostridium stercorallicum, Clostridium sterculosis, Clostridium bifidum, Clostridium sterculis, Clostridium bifidum-ova, Clostridium bifidum, Clostridium perfringens, Clostridium gordonii, Clostridium berculosis, Clostridium berculicium, Clostridium berculicinum, Clostridium berculicinolyticum, Clostridium bifidum, Clostridium berculum carotovorum, Clostridium berculum kojikukojicamorhigeri, Clostridium berculum kojikukojikukojikukojii, Clostridium berculum kojicamorhigerum, Clostridium berculum kojikukojii, Clostridium berculum kojii, Clostridium berculum kojivum, Clostridium berculum kojikukojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojikukojivum, Clostridium berculum kojivum, Clostridium berculum, Clostridium berculi, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum, Clostridium bercul, Clostridium lyticum, Clostridium polysaccharolyticum, Clostridium populi, Clostridium saccharolyticum, Clostridium sardeiense, Clostridium lysis, Clostridium stromanisolvens, Clostridium viridans, Clostridium xylanolyticum, Copropbacter secundus, enterococcus faecalis, Curtius macelensis, Defluvila sacchara, Defluviatilis degthiobacillus, reduced metal desulphatobacillus, Curvularia orientalis, Desulfovibrio sulphureus, Vibrio simplex, Dockers formigenes, Eisenbergia maliensis, Emergericia timonensis, enterococcus hirae, Enterobacter intestinalis, Enterobacter bacteriophagus, Enterobacter muridus, Pseudolyticus, Isiperidium ramosus, Erysipelothrix, Exigus larvae, Escherichia coli, Enterobacter acidophilus, Aciditis longissimus, Mycobacterium halodurans, Escherichia coli, Bacillus stearobacteria, Bacillus stearothermophilus, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus, intestimonas butyricolinis, Irregularis, Lachnoclaris, Lachnocristerium pacaines, Lactobacillus animalis, Lactobacillus faecalis, Lactobacillus gasseri, human milk bacillus, Lactobacillus enterobacter, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus paracasei, Lactobacillus intracellularis, Longianulus mucidum, Marvinbryantia formuliensis, Milliconella maliensis, Microspirillus senilis, Murbianulus intestinale, Murimonas intestinalis, Naterae virginica, Neglicta timonensis, Microbacterium putida, Olsla profundus, Bacillus tremorgin, Dermatopteridium parvus, Paracoccus fasciatus, Paracoccus human, Paracoccus, Phanerochaelis, Phanerochaete paragallica, Phanerochaete-la-purpurea, Phanerochaete-ia, Phanerochaete-la, Phanerochaete-ia, Phaneralis, Phanerochaete-la-brussella, Phanerochaete-la, Phaneralis, Phanerochaete-berella-berk, Phaneralis, Phanervonivea, Phanervona, Phanervonivea, Phanervosum-berrocia, Phanervosum, Phanerochaemorus, Phanervosum-bereukawarfarinodes-bereukayae, Phanervosum, Phanerus, Phanervosum, ruminic LOSTRIDIUM THERMOCELLUM, Ruminococcus championellis, Ruminococcus faecalis, Ruminococcus flavus, Ruminococcus acicularis, Ruthenium lactatiformans, Sphingomonas kyeongiensis, Spiroplasma stenotrophomonas tachyphylum, Trichosporon termitarium, Stomobaculum longum, Streptococcus olicus, Streptococcus danieliae, Cotrophomonas vorax, Thermomomonas taiwanensis, Tydinium californicum, Tysium lake Tyndae, Hemicella, Turcissus, Turicius muris, Tyzzerella nexilis, Vallitalea propyensis, and Vibrio shikobuxiensis.

In some embodiments, favorable profiles include bacterial populations comprising one or more than one of the following in an amount of relative abundance of at least, at most, or exactly 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein): parabacteroides dickinsonii, Fournierella maliensis, Bacteroides copromorphus, Eisenbergiella tayi, Tisserelles, Huntingateicosidium themocellum, Dorkshires formate, Pyrococcus curviensis, Muricomes intestini, Geobacillus, Anaerothecium, Lactitutescens, Bacteroides fragilis, Haemophilus, Tyroseus, Achimedermatophilus, Bacteroides faecalis, Dielicididiosis, Mycoplasma pachynomenii, Mycomycetalens, ruminococcaceae, flavobacterium, Alisiperidis, Acidithiaceae, Acidophilus alberidis, Acidophilus viscophilus, Acidophilus stercorissoris, Acidophilus stercoralis, Acidophilus flavus, Acidophilus faecalis, Acidophilus acidipridus, Acidophilus acidipridus, Acidophilus acidipridus, Acidophilus, Acidobacterium, Acidophilus acidipridus, Acidobacterium, Corynebacterium cibotium, Anaerobacter chartisolvense, Anaerococcus cellulolytica, Anaerococcus mobilis, Anaerotaenia torta, human anaerobic Corynebacterium colons, Anaerotruncus rubinflantis, Deodorantinovorax, Bacteroides acidiproducens, Bacteroides caecimiis, Bacteroides spinosus, Bacteroides faecidium, Bacteroides farichiella, Bacteroides rothecinella, Bacteroides rothecaristicus, Bacteroides sterrosoisonoris, Bacteroides xylanisolvens, Enterobacter intestinalis, Beduinialis massissens, Bifidobacterium pseudolongum, Brezuelella mucronatum, Bretzia blattaria, Bretzia pacinia, Clostridium butyricum, Clostridium stercoralloides, Clostridium sterculia, Clostridium stercoralloides, Clostridium sterculis, Clostridium stercorallicum, Clostridium sterculosis, Clostridium bifidum, Clostridium sterculis, Clostridium bifidum-ova, Clostridium bifidum, Clostridium perfringens, Clostridium gordonii, Clostridium berculosis, Clostridium berculicium, Clostridium berculicinum, Clostridium berculicinolyticum, Clostridium bifidum, Clostridium berculum carotovorum, Clostridium berculum kojikukojicamorhigeri, Clostridium berculum kojikukojikukojikukojii, Clostridium berculum kojicamorhigerum, Clostridium berculum kojikukojii, Clostridium berculum kojii, Clostridium berculum kojivum, Clostridium berculum kojikukojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum kojikukojivum, Clostridium berculum kojivum, Clostridium berculum, Clostridium berculi, Clostridium berculum kojivum, Clostridium berculum kojivum, Clostridium berculum, Clostridium bercul, Clostridium lyticum, Clostridium polysaccharolyticum, Clostridium populi, Clostridium saccharolyticum, Clostridium sardeiense, Clostridium lysis, Clostridium stromanisolvens, Clostridium viridans, Clostridium xylanolyticum, Copropbacter secundus, enterococcus faecalis, Curtius macelensis, Defluvila sacchara, Defluviatilis degthiobacillus, reduced metal desulphatobacillus, Curvularia orientalis, Desulfovibrio sulphureus, Vibrio simplex, Dockers formigenes, Eisenbergia maliensis, Emergericia timonensis, enterococcus hirae, Enterobacter intestinalis, Enterobacter bacteriophagus, Enterobacter muridus, Pseudolyticus, Isiperidium ramosus, Erysipelothrix, Exigus larvae, Escherichia coli, Enterobacter acidophilus, Aciditis longissimus, Mycobacterium halodurans, Escherichia coli, Bacillus stearobacteria, Bacillus stearothermophilus, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus faecalis, Bacillus, intestimonas butyricolinis, Irregularibacter muralis, Lachnocristerium pacaines, Lactobacillus casei, Lactobacillus gasseri, Lactobacillus casei, Lactobacillus enterobacter, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus paracasei, Lactobacillus casei, Lactobacillus intracellularis, Longiensis murinum, Marvinbryantia formuliensis, Milliconella massilis, Microspirillum senii, Murbianulus intestinale, Murimonas intestinalis, Naterae virginica, Neglicta timonensis, Microbacterium visfatidum, Olsla profundus, Bacillus tremorgin, Tremella pulegeri, Pectinatus paragua, human Paracoccus, Phaseolus aureus, Phenoea, Phaseolus lactis, Phaseolus paranois, Streptococcus faecalis, Porphyromonas sp, Porphyromonas lactis sp, Porphyromonas sp, Por, Ruminic LOSTRIDIUM THERMOCELLUM, Ruminococcus championellis, Ruminococcus faecalis, Ruminococcus flavus, Ruminococcus acicularis, Ruthenium lactatiformans, Sphingomonas kyeongiensis, Spiroplasma stenotrophomonas tachyphylum, Termite spore bacillus, Stomobaculum longum, Streptococcus oliticus, Streptococcus danieliae, Cotrophomonas wowensis, Thermomyces taishanensis, Tydinada californica, Tyshike lake Tyndalla, Hemicturiae, Turicius muensis, Tyzzerella nexilis, Vallitalia proyensis, and Chlorella trematophaga.

In some embodiments, favorable profiles are also classified as non-toxic association profiles. A non-toxic association profile refers to the profile of microorganisms in a subject, particularly in the gut of a subject, that is present in a subject that does not experience toxicity, experiences low levels of toxicity, or experiences less than grade 3 immune-related side effects as a result of immune checkpoint blockade combination therapy. In some embodiments, favorable profiles include populations of bacteria comprising bacteria belonging to one or more than one of the genera: dorea, Thermus, Muricomes, Geobacillus, Anaerotigum, Bacteroides, butyric acid monad, flavonolactopsis, Dielma, Ottelia and Akkermansia. In some embodiments, favorable profiles include a population of bacteria comprising one or more than one of the following: long-chain Dolicheniella, thermophilic bacteria, Muricomes intestini, Geobacillus, Aneraoticum lactatifaciens, Bacteroides faecalis, Butyrimonas faecalis, Dielma rustidiosa, Alisipes insistinus and Ackermanella viscosus. In some embodiments, a favorable profile is also defined as an effective profile. An effective profile refers to the profile of microorganisms in a subject, in particular in the gut of a subject, which is present in a subject that is responsive to immune checkpoint blockade combination therapy. In some embodiments, an effective response comprises an increase in CD8+ cells in a tumor sample or infiltrate. In some embodiments, an effective response comprises an increase in the number and/or density of T cells or an increase in the entropy of tumor T cell infiltrates. Entropy can be determined by methods known in the art and described herein. For example, Shannon entropy (Shannon entropy) and rui entropy (Renyi entropy) can be used to compare TCR diversity between different humans or between different T-cell phenotypes.

In some embodiments, the bacteria belonging to the genus or species of: the relative abundance of heterobacter, bacteroides, butyromonas, parabacteroides diesei, Fournierella maliensis, Eisenbergiella tayi, yersinia, hungatricotridium thermocellum, dolichella, calophyllum chrysogenum, Muricomes, geobacillus, prevotella marolalis, lactobacillus cerealophilus, bacteroides finesse, lactobacillus johnsonii, agrobacterium composti, agrobacterium paratertii, antiae, bacteroides fragilis, vibrio fluvialis, takaurella, flavonolyticus, Dielma, or akmannheimia is at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, bacteria belonging to the following are identified: the invention also relates to a method of treating a cancer in a subject suffering from cancer, comprising administering to the subject an effective amount of a compound of the invention selected from the group consisting of heterobacter, bacteroides, butyromonas, parabacteroides dirichiana, Fournierella maliensis, Eisenbergiella tayi, yersinia, hungatricidium thermocellum, dolichella, chrysosporium, muricides, geotrichum, prevotella marjorana, lactobacillus cerevisiae, bacteroides johnsonii, agrobacterium composteda, agrobacterium paraterticola, bacteroides fragilis, vibrio, takawakakii, flavonolactophila, Dielma or akmansia in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of a combination of xenobacter, bacteroides, butyromonas, parabacteroides diels, Fournierella massilisensis, Eisenbergiella tayi, yersinia, hungatricotridium thermocellum, dolichella, klebsiella, chrysosporium, Muricomes, geobacillus, prevotella, lactobacillus cerevisiae, bacteroides fragilis, lactobacillus johnsonii, lactobacillus paracasei, paramylobacter paradiguensis, lactobacillus plantarum lactamica, bacteroides fragilis, vibrio, takawakawakamii, flavonolyticus, dielmama, or akmansia is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides fragilis is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus vibrio is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus tazieria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus flavonolum is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to Dielma is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus akkermansia is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the toxic response is predicted when one or more of bacteroides faecalis, bacteroides enterobacter, Coprobacter, intestinobacter bartletti, parasutella secinda, and alisteria propionicum is detected in a sample from the subject. In some embodiments, a toxic response is predicted when the relative abundance of one or more of bacteroides faecalis, bacteroides enterobacter, Coprobacter, Intestinibacter bartletti, parautellaria secunda, and alisteria propionicum is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, bacteria belonging to bacteroides faecalis, bacteroides enterobacter, Coprobacter, Intestinibacter bartletti, parasutella secunda, and alisteria propionicum are determined to be present in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of the combination of bacteroides faecalis, bacteroides enterobacter, Coprobacter, Intestinibacter bartletti, parasiteella secunda, and alisteria propionicum is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides cacteae is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides coprocola is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides enterobacteriaceae is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus brewsteria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein).

In some embodiments, a non-toxic response is predicted when one or more of bacteroides fragilis, vibrio, terezia, dolichia, calophilus coxsaceus, Muricomes intestini, geobacillus underground, antiaertignum lacetifaciens is detected. In some embodiments, a non-toxic response is predicted when the relative abundance of one or more of bacteroides fragilis, vibrio fluvialis, terezia tazeriana, dolichia longata, caloriteria chrysanthemi, Muricomes intestini, geobacillus underground, antiaertignum lacetifaciens is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the bacteria belonging to bacteroides fragilis, vibrio, terezia, dolichia, caloriteria chrysosporium, Muricomes intestini, geobacillus underground, antiaerotigum lacetifaciens are determined to be present in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of a combination of bacteroides fragilis, vibrio, terezia, dolichia longata, caloriteria chrysanthemi, Muricomes intestini, geobacillus underground, antiatherignum lactiferous bacteria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides fragilis is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus vibrio is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to the genus tazieria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein).

In some embodiments, an effective response is predicted when one or more of parabacteroides dymanii, Fournierella maliensis, bacteroides coprophilus, Eisenbergiella tayi, seixideriales, hungatricotridium thermocellum, bacteroides faecalis, flavobacterium perniciae, Dielma hustibia, and akkermansia muciniphila is detected in a sample from the subject. In some embodiments, a valid response is predicted when one or more of paramacteroides, Fournierella, Eisenbergiella, seyeriales, hungateicotridium, bacteroides, butyromonas, flavonolyticus, Dielma, allobacter, and akkermansia is detected in a sample from the subject. In some embodiments, an effective response is predicted when one or more of bacteroides faecalis, butyricomonas faecalis, flavonolyticus perniciae, Dielma fascidiosa, Alistipes indestinctus, and akkermansia muciniphila is detected in a sample from the subject. In some embodiments, an effective response is predicted when the relative abundance of one or more of parabacteroides merdae, Fournierella maliensis, bacteroides coprophilus, Eisenbergiella tayi, yerziliana, hungatricotridium thermocellum, bacteroides faecalis, butycycimonas faeciominis, flavonolactogenes pellucida, Dielma fascidiosis, Alistipes indectantus, and akkermansia muciniphilae is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the bacteria belonging to parabacteroides merdae, Fournierella maliensis, bacteroides coprophilus, Eisenbergiella tayi, yersinia, hungatriclostrium themocellum, bacteroides faecalis, butyricmonas faecionis, flavobacterium pernici, Dielma fascicularis, Alistipes insistinatus, and akkermanella mucosae are determined to be present in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of a combination of parabacteroides merdae, Fournierella maliensis, bacteroides coprophilus, Eisenbergiella tayi, yersinia, hungatricotridium thermocellum, bacteroides faecalis, butycycimonas faecionis, flavobacterium pernicicola, Dielma fascicularis, Alistipes indentinostus, and akkermansia muciniphila is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides cacteae is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to flavonolyticus perniciae is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to Dielma husidiosa is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of a bacterium belonging to akkermansia muciniphila is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to butyricomonas faecaliomins is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to Alistipes indestinctus is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein).

In some embodiments, a null response is predicted when one or more of lactobacillus reuteri, bacteroides fragilis, prevotella faecalis, prevotella sarmentosa, clostridium heuchii, huntateicolitrium aldrich, citrobacter murinus, eubacterium borgpoensis, citrobacter freundii, eubacterium hophilus, enterobacter cloacae, hafnia alvei, human rosporium, weissella mesenteroides, and klebsiella aerogenes is detected. In some embodiments, an ineffective response is predicted when one or more than one of lactobacillus reuteri, bacteroides fragilis, prevotella faecalis, prevotella serrulata, clostridium hirsutum, clostridium helveticus, huntateicolitrium aldrich, citrobacter murinus, eubacterium borgpoensis, citrobacter freundii, eubacterium halloysii, enterobacter cloacae, hafnia alvei, human raspberella, weissella mesenteroides, and klebsiella aerogenes is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein) in relative abundance. In some embodiments, the bacteria belonging to lactobacillus reuteri, bacteroides fragilis, prevotella faecalis, prevotella sarmentosa, clostridium hirsutum, huntateicolithium aldrich, citrobacter murinus, eubacterium borreligiosum, citrobacter freundii, eubacterium hophilus, enterobacter cloacae, hafnia alvei, rawhislera mancebaceri, weissella mesenteroides, and klebsiella aerogenes are determined to be present in a relative abundance of at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of the combination of lactobacillus reuteri, bacteroides fragilis, prevotella faecalis, prevotella sarmentosa, clostridium hirsutum, huntateicolostidium aldrich, citrobacter murinus, eubacterium borgpoensis, citrobacter freundii, eubacterium hophilus, enterobacter cloacae, hafnia alvei, rawhichia mancescens, weissella mesenteroides, and klebsiella aerogenes is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to lactobacillus reuteri is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to bacteroides fragilis is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to prevotella faecalis is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85% or 90% (or any derivable range therein). In some embodiments, the relative abundance of bacteria belonging to prevotella sarmentosa is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein).

In some embodiments, a favorable profile, a non-toxic associated profile, and/or an effective profile excludes bacteria from one or more of the families ruminococcaceae, clostridiaceae, lachnospiraceae, micrococcidae, and/or veillonellaceae or includes bacteria from one or more of the families ruminococcaceae, clostridiaceae, lachnospiraceae, micrococcidae, and/or veillonellaceae in a relative abundance of less than 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% (or any derivable range therein).

In some embodiments, an advantageous profile, a non-toxic associated profile, and/or an effective profile excludes bacteria from one or more of the following or includes bacteria from one or more of the following in a relative abundance of less than 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% (or any derivable range therein): bacteroides faecalis, Parabacteroides destructor, Fournierella massilisis, Bacteroides copromoides, Eisenbergiella tayi, Tisserellas, Hungedeicotridium thermocellum, Dolerella formigenes, Coxiella carolina, Muricomes intestini, Geobacillus underground, Anaerothecium lactacidans, Bacteroides coagulans, Clostridium alternun, Clostridium acremonium, Clostridium alcellobellum, Clostridium amygdalinum, Clostridium asparagus, Clostridium cellulosum, Clostridium tridahlia, Clostridium torum, Clostridium difficile, Clostridium bifidum, Clostridium butyricum, Clostridium difficile, Clostridium bifidum, Clostridium natum, Clostridium hirsutake, Clostridium stereum, Clostridium marinum 15053, Clostridium sinense, Clostridium methylpentoxidum, Clostridium butyricum, Clostridium ramosum, Clostridium berchemium pernicicola, Clostridium berculum, Clostridium berchemium, Clostridium berculosum, Clostridium berculum terellens, Clostridium berculum terellens, Clostridium berculum tereum, Clostridium berculum kayae, Clostridium berculum kayagarlicum, Clostridium berculum kayae, Clostridium berculum, Clostridium bercul, Clostridium xylanolyticum, Enterobacter devulcani, Eubacterium procumbens ATCC 33656, Eubacterium longipes, Eubacterium collectins ATCC 27750, Eubacterium hophyticus, Eubacterium amethystoides, Eubacterium indolens, Eubacterium gracilli, Ruminococcus strabilis, Acetactivorans, Vibrio cellulolyticus acetate, Vibrio ethanologens, Brucella brucei 0502, Brucella invertebrata, Acinetobacter johnsonii, Actinomyces baumannii, Actinomyces dentata, Actinomyces carinatus, Acutabacterium, Aerococcus shallot, Microbacterium fastidiosa, Ardisia funiculorum, Alisipes obliqus, Acistipes sp, Ardisia oncomelae, Ardisia sargasseri WAL 8301, Ardisia coprinus JC136, Alkaliphyllinus bacilli, Alkaliphileria meterians, Alkaliphilex farinaceus, Alkalira paludnerla, Acellae strain, Anaerobacterium chartisolvens, Anaerobiospirillum Tomentosum, Anaerobium acetobacter, Anaerobiospirillum NCTC 9810, Anaerococcus proveniensis, Anaerococcus vaginalis ATCC 51170, Anaerococcus jejuensis, Acerobiospirillum gracilis, Anerobiospheromone, Anerococcus geminicola (Anaerobiospirillum), Anaerobiosilacticola senegalensis, Anolobacter anaerobacter, Acerobacter wisonii, Anaerobiosporus, Anaerostis butyratus, Anaerobiospirillus, Anaerorhabdus anaerobacter, Anaerorhabdus hardus, Anaerobiospirillum aurantiacum, Anaerobiospirillum pseudomerium, Anaerolyticum halobacter, Aquaticum nomarum, Arcobacter bacterioides, Arthrospirillum protileus, Anaerobacter acidipropionici, Anaerobacter asiaticus, Anaerobacter xylinus, Anaerobacter paragonicus, Microcolibacillus acidipropioideus, Microides, Microbacterium acidipropioideus, Microbacterium acidiprodiella, Microbacterium acidiprodiella, Microbacterium, Microbacterium acidiprodium, Microbacterium, Microbacterium, Microbacterium, Microbacterium Micro, Bacteroides thetaiotaomicron, Bacteroides monorphis, Bacteroides xylolyticus XB1A, Bacteroides xylanolyticus, Barnesia enterica, Beduini masseliensis, Bifidobacterium bifidum, Bifidobacterium odontoid, Bifidobacterium longum subspecies infantis, Blautia caecimuris, Blautia coprobulina, Blautia glauca DSM 20583, Brauera hydrogenotrophus, Brauera silt, DSM 14534, Brautumia weckera DSM 19850, Brauda synechoides, Kluyveromyces gallinarum, Butyrimonas paravialis, Vibrio sciformis, Thermus gasseri, Thermomyces celer, Caloraminax quintovorans, Campylobacter xylinus, Clostridium cellulosae strain DSM 03, Clostridium torula, Clostridium tricornutum, Campylobacter xylinus, Campylobacter xylaria, and Streptococcus mutans, Campylorophus strain, Christensella minuta, Christensella timenensis, Christensella taklimakanensis, Citrobacter freundii, Closibacter sphaericus pormorum, Clostridium difficile ATCC 9689 ═ DSM 1296, Clostridium amyloliquefaciens, Clostridium baumannii, Clostridium butyricum, Clostridium cadaveris, Clostridium canicola, Clostridium aerogenes, Clostridium bradycardia DSM 5427, Clostridium marinum, Clostridium oryzicola, Clostridium paraputrescentium, Clostridium hernanum, Clostridium perfringens, Clostridium quinquefortii, Clostridium saccharolyticum, Clostridium sporogenes, Clostridium subvermicum, Corynebacterium aerogenes, Corynebacterium glutamicum, Corynebacterium paradoxorum, Comamonas testosteroni, Coprobacter fascicularis B1, Desmococcus faecalis, Corynebacterium diphterianum, Corynebacterium firmus, Corynebacterium Corynebacterium paraguaricum, Corynebacterium parvum, Corynebacterium parvurica, ATCC thiolyticum, Streptococcus faecalis, Corynebacterium, Streptococcus faecalis, Clostridium, Corynebacterium, Streptococcus faecalis, Corynebacterium, Streptococcus faecalis, Corynebacterium, Streptococcus faecalis, Corynebacterium, Streptococcus faecalis, Corynebacterium, Streptococcus, Edison deordvibrio aidalbergii, Derdomovibrio seashore, Desulfurvibrio lazulii, Derdomovibrio simplex, Desulfovibrio zogenes zostera marina, Desulfovibrio alcalophilus AHT 1, Acidithiobacillus aminovorans, Aliskis turbidicola, Disinfection acidiprodia, Dietzia alimentaria 72, Doldol longum, zymomonas gardneri ATCC BAA-286, Zymomonas morganii, Eighuria lenta, Eisenbergiella tayi, Emergecium vulgare, Enorma maliense phI, enterococcus, Entermorhabdus muris, Haerbia yun-3, Eubacterium faecalis, Eusterol, Eubacterium acidiprovale, Eubacterium oxydisulfides, Vibrio paraguariensis ATCC 35585, Eubacterium monoides, Acidobacter ventricusum, Exigus mulicaris, Faliaceae, Falleriella villosa, Eubacterium faecalis, Eubacterium strain, Vibrio, Eubacterium acidiferous, Eubacterium strain, Eubacterium acide, Eubacterium, Flavonoids perniciella, Flinibacter butyrricus, Frisingicoccus caecimuris, Fuscophyllus fucoidanus, Fusicatenicola saccharovorans, Fusobacterium nucleatum, Fusobacterium similis, Fusobacterium variabilis, Fusobacterium denitrificans, Salmonella haemolyticum, Blastomyces formate, Gordobacterium urolyticum, Klebsiella halodurans JW/YJL-S1, Pediococcus nivorans, Rhizophilus bovis, Haemophilus haemolyticus, Helicobacterium caeruleus, Pesticeus solani (Hespertissoides), Hollandella biformis, Holland AP2, Halanobacter anii, Hunganella effulvii, Hutiaceae halophilus, Lactobacillus casei, Lactobacillus brevis 19542, Lactobacillus paracasei, lactobacillus pentosus, Lactobacillus reuteri, lactococcus garvieae, lactococcus ovale, Acidocellus oralis, Firmiella hubner, Firmiella hongkongensis, Firmiella vachelli, Leuconostoc benemiae, Levy yella massiensis, Loriellosis calverticola, Cladosporium thermophilum, Salmonella chromophila JCM 21150, Marvinbryantia formaldehigens, Fluoroxylum caudatum, Methylobacter smithii ATCC 35061, Methylobacterium methylobacterium metformis Luminyensis B6332, Methylobacterium torvum, Hiragana, Mobilalea sibirica, Mobilella mobilis, Corynebacterium parvum, Leptobacterium lentigo, Microbacterium laevigatum, Microbacterium puleum, Moraxerum, Moraxella nonilifaciens, Neisseria oralis, Morganella multocida, Moraxensis, Ochrobacter xylella jejunipes, Ochrobacter xylinus, Ochrobactrum grillus, Ochrobactrum grillungiella auricula, Ochrobactrum, Ocimum, Ochrobactrum, Ocimum, Oc, Bacteroides rodensis GH1, Dermatopterium valerate, Acetobacter pratense, Pantoea agglomerans, Paecilomyces angustifolia, Paecilomyces faecalis, Parabacteroides cubensis, Parabacteroides gordonii, Parabacteroides coprinus (Parabacteroides merdae), Parasporum oligovorans, human ParaSauterella paracasei, Paracutella secunda, Micromonas dimiella minutissima, Peptococcum niger, Peptophilus duerdenii ATCC BAA-1640, Peptophilus grissonsis ph5, Peptophilus koenoseniae, Peptophilus senensis JC140, Streptococcus stomatitis, Phascotococculus cccusinatutes, Phocae maliensis, Poibacterium, Porphyromobacter benthamii, Porphyromonas pulposus ATCC 3383, Porphyromonas furiosaemorbus sp 3606, Porphyra, Porphyromonas cinerea, Klebsiella, Porphyromonas brueckii, Moraxella sp III, Porphyromonas sp 3, Porphyromonas brussella furiosa, Porphyromonas sp 3, Porphyromonas, and Porphyromonas sp 3, Porphyromonas cinerea, Porphyromonas sp 3, Porphyromonas 3, Porphyromonas, and Klebsiella, Porphyromonas, and Morinas, Porphyromonas, and Klebsiella, and C3, and Klebsiella, Porphyromonas, and Klebsiella, and P.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp, Prevotella proteorum, Propionispira arcuata, Propiromyces mirabilis, providencia retzii, Seudobacteria cellulosolens ATCC 35603 DSM 2933, Vibrio ruminalis, Pseudoflavobacterium hirsutum ATCC 29799, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mandolina mandellii, Pseudomonas nitroreducens, Pseudomonas putida, Raoultella ornithinolytica, Raoultella planticola, Raoultella histolytica, Raoulbacter massilensis, Robinsonia peiorensis, Rombouttia timeninensis, Ralsbergia harzieri, Raosbeckia A2-183, Rosusbyakashigella enterica, Gluconopsis sacchari 16841, Rossinia ATCC 17361754, Ruminococcus fascicularis, Morchella subcoccus, Salmonella berculus, Salmonella viridiflavis ATCC 6757, Ruidomyces flavus, Ruidococcus xanthus, Brucella viridae ATCC 29176, Brucella furnacles, Brucella flavus edulococcus 361782, Ruinococcus rhodococcus flavus, Brucella acidea 3680, Brucella furciferrica, Brucella acidea 36178, Brucella furaeoticus, Brucella farinaceus, Brucella acidia officinalis, Brucella acidilalis, Brucella acidum 361718, Brucella acidum viridis, Brucella acidum 36178, Brucella acidum, Brucella acidum 36178, Brucella acidum, Brucella acidum, p. sputum-producing strain ATCC 35185, S.lentus ATCC 700122, Slackia piriformis YIT 12062, Agrobacterium canadensis, P.moji, Sphingomonas aquaticus, Spiroplam allegnense, Spiroplam china, Tabanus maculans, mosquito-dwelling Spiroplam, Fluorophaga fuliginosum, Termite spore bacillus, Staphylococcus aureus, maltophilia, Stomatoccum longum, Streptococcus agalactiae ATCC 32, Streptococcus crisis, Streptococcus equi, Streptococcus gordonii, Streptococcus mutans, Streptococcus paracoccus, Streptococcus paramastis, Pediococcus mutans, Vibrio lyticus succinate, Salmonella ciboticus, stenosala, Streptococcus mutans, Synbiotic 13813, Tersoporomythicus, Vibrio albus, Lethrobacter xyli, Streptococcus gordonii, Streptococcus valla, Streptococcus gordonii, Streptococcus valla, Ledonii, Streptococcus valla, Leontariella, Leptorum, Leptophyceae, Leptophysalsa, Leptophysalla, Leptophysalmonea, Leptophysalla, Salmonella, Leptophysalla, Vanilla, Leptophysalla, Leptophyceae, Vanilla, Mylabra, Myxola, veillonella destructor, Veillonella dispar, Veillonella parvula, Varda vallisa, Geobacillus volcanii and Weissella confusa.

In some embodiments, the adverse profile, toxicity-associated profile, and/or ineffective profile does not include one or more than one of the following: citrobacter, Clostridium hirsutum, Hungateichium aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophagi, Enterobacter cloacae, Hafnia alvei, Hafnia, human Raschia, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillales, Klebsiella aerogenes, Klebsiella, Enterobacter enterobacter, Coprobacter, Intestibacter bartlett, Intestifer, Parastutterella, Parasterella, Alisma propionicum, human Klebsiella anobacter coli, Klebsiella variovorans, Bacteroides thetaiotaomicron, Paraprevorella clara, Oenomelia gynecox, Clostridia pulchereri, Klebsiella pneumoniae, Klebsiella oxytoca, Klebsiella, or a strain G, Lactococcus lactis, clostridiales, streptococcus mutans, a bacterium D16 of the family ruminococcaceae, a bacterium CAG:102 of the phylum firmicutes, a bacterium tremorgin, clostridium clostridia, bacteroides massiliensis, clostridium lyticum, parabacteroides faecalis, a species CAG:161 of the genus eubacterium, ruminococcus actively, clostridium, or a bacterium comprising a relative abundance of less than 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% (or any derivable range therein) from one or more of the following: citrobacter, Clostridium hirsutum, Hungateichium aldrich, Citrobacter murinus, Eubacterium borgpoensis, Hafniaceae, Citrobacter freundii, Eubacterium hophagi, Enterobacter cloacae, Hafnia alvei, Hafnia, human Raschia, Weissella mesenteroides, Enterobacter, Lactobacillus reuteri, Bacillus, Lactobacillales, Klebsiella aerogenes, Klebsiella, Enterobacter enterobacter, Coprobacter, Intestibacter bartlett, Intestifer, Parastutterella, Parasterella, Alisma propionicum, human Klebsiella anobacter coli, Klebsiella variovorans, Bacteroides thetaiotaomicron, Paraprevorella clara, Oenomelia gynecox, Clostridia pulchereri, Klebsiella pneumoniae, Klebsiella oxytoca, Klebsiella, or a strain G, Lactococcus lactis, Clostridiales, Streptococcus mutans, Ruminococcaceae D16, Mycobacteria CAG:102, Clostridium clostridiforme, Clostridium schizolyticum, parabacteroides faecalis, Eubacterium CAG:161, active Ruminococcus and Clostridium.

In some embodiments, the microbial compositions of the present disclosure do not include bacteria from one or more than one of the families ruminococcaceae, clostridiaceae, pilospiraceae, micrococcidae and/or veillonellaceae and/or comprise less than 1x10⁶、1x10⁵、1x10⁴、1x10³Or 1x10²Cells or CFUs from bacteria from one or more than one of the families ruminococcaceae, clostridiaceae, pilospiraceae, micrococcidae and/or veillonellaceae (or any derivable range therein).

In some embodiments, the microbial compositions of the present disclosure do not include bacteria from one or more than one of the following or comprise less than 1x10⁶、1×10⁵、1×10⁴、1×10³Or 1X10²A cell or CFU from a bacterium of one or more than one of the following (or any derivable range thereof): clostridium coagulans, Clostridium alterniformis, Clostridium aldehydans, Clostridium alterniformis, Clostridium asparaguens, Clostridium cellulosicum, Clostridium sterculans, Clostridium difficile DSM 19732, Clostridium clostridiforme, Clostridium quail, Clostridium coprinum, Clostridium hirsutum, Clostridium harzianum, Clostridium marinum DSM 15053, Clostridium indolens, Clostridium fermentum, Clostridium mollicum, Clostridium methylpentose, Clostridium orotate, Clostridium lyticum DSM 2782, Clostridium populi, Clostridium propionicum, Clostridium saccharolyticum, Clostridium schizolysis, Clostridium sphaericerum, Clostridium faecalis, Clostridium traminisolvens, Clostridium lucidum, Clostridium termitum, Clostridium thermocatenum succinate Clostridium, Clostridium viridans, Clostridium xylanolyticum, Enterobacter devulcanii, Eubacterium procumbens ATCC 33656, Eubacterium longipes, Eubacterium shii ATCC 27750, Eubacterium hophilum, Eubacterium amebic, Eubacterium inertium, Eubacterium gracilis, Ruminococcus strabilis, Acetatofacilato muris, Vibrio cellulolyticus acetate, Vibrio ethanologens, Cholesterol brassicae 0502, Cholesterol parvobacteria, Acinetobacter bovis, Actinobacillus suis, Actinomyces baumannii, Actinomyces dentatus, Actinomyces carinii, Acutalibobacter muris, Aerococcus glaucopiae, Microbacterium fastidiosa, Ardisia fragilis, Alliospirissides, Ardisia sargasseri WAL 8301, Ardisia shigelliformis JC136, Zygosaponicinolilus saccharolytica, Allilus metiriensis, YMF Q.Q 13633, Acellati shigella, Allopreptile rava, Allopretella tannerae, Anaerococcus chartisvenses, Thorescens anabolis, Anaerobiospirillum aethiopica, Anaerobiospirillum acetobacter, Anaerococcus octopamphlei NCTC 9810, Anaerococcus proveniensis, Anaerococcus vaginalis ATCC 51170, Anaerococcus jejunensis, Anaerobiospirillum, human faecalis anaerobacter, Anaerobiosphaera, Anaerobiosphaerella senegens, Anaerobiospirillum anaerobacter, Anaerobiospirillum, Anaerobiostica typus, Anaerobiospirillum hardus, human parahaemophilus anaerobacter, Degrees olphilus, Novadorsalis halobacter, Aquaticus, Anaerophilus paranoii, Arriobacter lottericola, Arriobacter bacteriodes, Anaerobacter lottericola, Anaerolyticus, Anaerobacter lottericola, Anaerolyticus, Anaerophilus, Anaerochaetes, Microbacterium acidiprodii, Microbacterium acidiprotes, Microbacterium acidiprodii, Microbacterium acidiprotes, Microbacterium acidiprodii, DSM 12058, Microbacterium acidiprodiella, Microbacterium acidiprodiella, Microbacterium acidiprodium, Microbacterium acidum acidilactium acidilagineum, Microbacterium acidum acidilactium acidum acidiprodium, Microbacterium acidum acidilactium, Microbacterium acidilactium acidum acidilactium, Microbacterium acidum acidila, Microbacterium acidum acidilactium, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum acidilactium acidum acidilactium acidum acidilactium, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Microbacterium acidum, Bacteroides thetaiotaomicron, Bacteroides monoides, Bacteroides xylanolyticus XB1A, Bacteroides xylanolyticus, Baenscensis enterocolitica, Beduini masselliensis, Bifidobacterium bifidum, Bifidobacterium odontoides, Bifidobacterium longum subspecies infantis, Blautia cacecimuris, faecal Brewsonia globosa, Blautia coprinus, Blautia gluceraa, Blautia chrysogenum DSM 20583, Brewsonia hydrogenotrophica, Blautia sulcata, Blauda bulrush DSM 14534, Blauda bulrush DSM 19850, Brucella aquaticus, Kluyveromyces helveticus, Butycyclomonas paravialis, Vibrio paniculatus, Thermobacter daemonii, Thermus coxiellii, Thermus proteolicus, Caloramacynia quinqueyensis, Campylobacter tenuis, Campylobacter rectus, Campylobacter urellus ureolyticus DSM 20703, Cellophilus gingivalis, Cellophilus carbonarius, Cellulomonus sputigena, Casalelliella malissilis, Catatex caningkongensis, Clostridium trinosum, Clostridium trivialis, Clostridium difficile, Clostridium butyricum, Clostridium perfringens, Clostridium gordonii, Clostridium difficile 966, Clostridium perfringens, Clostridium butyricum, Clostridium perfringens, Clostridium gordonii, Clostridium difficile, Clostridium perfringens, Clostridium difficile, Clostridium diff, Clostridium marinum, Clostridium oryzakii, Clostridium putrefaciens, Clostridium ranciditum, Clostridium perfringens, Clostridium quinqueforti, Clostridium saccharobutanoicum, Clostridium sporogenes, Clostridium ventricum, Coriolis, Comamonas testosteroni, Copro bacterium rostidicus NSB1, enterococcus faecalis, Corynebacterium diphtheriae, Corynebacterium firmus, Corynebacterium mycoides, Corynebacterium pyruvula ATCC BAA-1742, Corynebacterium tuberculosis, Corynebacterium maltophilins, Corynebacterium glutamicum cacumenis, Bifidobacterium saxiphilia, Salmonella foenum-graecum, Streptococcus acidium oxydisulfides, Acidithiobacillus reductium thiofidosus, Desulfossilis fossilis acidovorans, Clostridium saccharolyticum, Clostridium thiolyticum, Acidonatriens, Vibrio-thiobacillus thiofidicus, Clostridium halothioparvum, Desulfoenum-thiobacillus desulfidatum, Desulvum thiooxidans, Desulfothiobacillus thiooxidans, Desulfothiooxidans, Desulfothiobacillus acidi, Desulfoenus acidi Acidovorans, Desulfovibrio acidi, Desulfothiobacillus acidi Acidithiobacillus acidi, Desulfothiobacillus acidi Aciditis, Desulfothiobacillus acidi thiobacillus acidi Acidobacterium thiobacillus acidi, Desulfothiobacillus acidi Aciditis, Desulfothiobacillus acidi thiobacillus acidi, Desulfothiobacillus acidi thiobacillus acidi, Desulfothiobacillus acidi Aciditis, Desulfothiobacillus acidi thiobacillus acidi, Desulfothiobacillus acidi thiobacillus acidi, Desulfothiobacillus acidi thiobacillus acidi, Desul, Producing bacteria of the species Bremia, Dielma rustidiosa, Dietzia alimentaria 72, Dolichia longata, Zymomonas galdtii ATCC BAA-28 6. Zymomonas morganii, Aeromonas sobria, Eisenbergia tayi, Emergenicia timonensis, Enorma maliensis phI, enterococcus faecalis, Enterobacter auricula murris, Haerbin acetogenic bacteria YUAN-3, Eubacterium faecalis, Eubacterium mucosae, Eubacterium Redoxa, Eubacterium Vacudafate ATCC 35585, Eubacterium monoides, Eubacterium ventricoides, Eubacterium xylanophilum, Exbacter muticum, Ezakiella peruensis, Exacter muris, Exzakiella staphylum, Faecalis faecalis acicularis, Faecalicoccus acidiformans, Faecalialella cylindricae, Produce line, Flavonibacter pervirens, Flinibacter butyricus, Frisingiccus calis caligenes cairiculus, Fusobacter coccus incognita, Fusobacter fusobacter, Fusarium jejuniperidiculorum, Clostridium halodurans, Fusarium, Clostridium halodurans, Clostridium halofusobacter, Clostridium halovorax, Clostridium halofusobacter, Clostridium halodurans, Clostridium halofusobacter strain W, Clostridium halofusobacter strain, Clostridium halofusobacter strain, Clostridium halofuscus, Clostridium halofusobacter, Clostridium halofuscus, Clostridium halofusobacter, Clostridium halofuscus, Clostridium halofusobacter, Clostridium halofuscus, Clostridium halofuscum, Clostridium halofuscus, Clostridium halofusobacter, Clostridium halofuscum, Clostridium halofusobacter, Clostridium halofuscus, Clostridium halofusobacter, Clostridium halofuscum, Clostridium halofuscus, Clostridium halofusum, Clostridium halofuscus, Clostridium halofusum, Clostridium halofusca, Clostridium halofuscus, Clostridium halofusum, Clostridium halofuscus, Clostridium halofusum, Clostridium halofusca, Clostridium halofuscus, Clostridium halofusum, Clostridium halofusca, Swine dung herring (Hespollia stercoris), Holdemanella biformis, Himaladinella mosaic AP2, Humulella urealyticum, Hungatella effluvii, Hungatella hatawayi, Anaerobacter saccharina, Ihubacter malsiliensis, Intestibacter bartletti, Intestimonas butyridum, Irregularis, Laminaria mermanii DSM 19542, Kroppenstephagtzhauensis, Lachnoaerullum orales, Lachnoagula umbellata, Lachnocobacter umbens, Lachnocotrichum phyterianum, Lachnococcus phyterians acidophilus, Lactobacillus hypothermis, Lactobacillus anii, Lactobacillus casei, Lactobacillus pentosus, Lactobacillus brevis, ATCC 21132, Lactobacillus plantarum, Lactobacillus brevis, Lactobacillus strain, Lactobacillus brevis, Lactobacillus strain, Lactobacillus brevis, Escherichia coli, Lactobacillus strain, Lactobacillus, Escherichia coli, Bacillus brevis, Escherichia coli, Bacillus brevis, Escherichia coli, Bacillus brevis, Mobillitacea, Campylobacter xylinum, and small, recalcitrant bacteria Bacillus, dystrophia lenta, mulleria, Moorella humifera, Moraxella nonliquefaciens, morsella austriana, morganella morganii, morganella indolens, muribacillus intestinale, murimomonas intestinalis, natraerovirginia pectinvora, negectasia timonesis, neisseria grayi, neisseria oralis, Nocardioides mesophilus, novibacterium thermophilus, human ochrobactrum anthropi, putrescence fetida, olsenia odorifera, Olsenella profundus, lactobacillus gingivalis, pharibacterium asacharyticum ACB7, oral bacillus, bacillus fistulae GH1, tretinobacter valerate, acetobacter pulaceus, pantoea agglomerans, paecillus cinnamomi, ptaceae pleuroticus, ptobacterium parabacter paraguariensis, paragonia, paragonicola, paragonia, etc., bacillus, paragonia, etc., bacillus, and paragonia, etc., bacillus, and paragonia, phocea massilisensis, Pontibacter indicus, Porphyromonas benthica, Porphyromonas pulposus, Porphyromonas pasteri, Prevotella berberidis, Prevotella oralis ATCC 33574, Prevotella denticola, Prevotella suppressalis, Prevotella fusca JCM 17724, Prevotella rosella, Prevotella melanosporum, Prevotella oralis, Klebsiella pallidum ATCC 700821, Prevotella coprinus DSM 06, Prevotella longepensis, Propionilla arctica, Proteus mirabilis, providencia farinosa, Pseudomonas fluorescens, Pseudomonas aeruginosa, Pseudomonas nitroreducens, Klebsiella cellulolytica ATCC 35603, DSM 2933, Vibrio pseudobutyric acid, Pseudomonas aeruginosa ATCC 29799, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas nitroreducens, Nitrosoliella nitroreducens, Rotunicoccus, Rotunicobolus abortus, Rotunica, Rotunicoccula neca, Rotunica, Ralstonia odorifera, Ralstonia, Ralschacteria, Ralstonia, Ralsberghei, Ralsii, Ralstonia, Ralschai, Ralstonia, Ralschai, Ralstonia, Ralschai, Ralschaislandia, Ralschaislanda, and Ralschaislanda, and Ralschaislandia, and Ralsbergheilsberg, and Ralstonia, Ralstonia saccharivorans DSM 16841, Ralstonia carinata ATCC 17931, Ruminociclovir thermocellum, white tumor Pediococcus, Ruminococcus brucei, Lin-Marina, Ruminococcus champinanensis 18P13 ═ JCM 17042, Gastrococci faecalis JCM 15917, Ruminococcus flavus, Ruminococcus gauvreuii, Ruminococcus acidophilus ATCC 29176, Rummeliibib pycnus, Saccharoferrinans acetigenes, Scardovia wiggisiae, Serpentisella thermosiphoniae, Corynebacterium parvum ATCC 35185, Neisseria pavogens ATCC 700122, Slia piriformis YIT 12062, Agrobacterium canadensis, Microbacterium morganii, Spiromonas aquaticum, Spirospongioglobinella, Spiroplasma china, Tabania tabani, Spiroplasma merdae, Streptococcus mutans, Streptococcus parvus, Streptococcus faecalis 13813, Streptococcus mutans, Streptococcus faecalis, Streptococcus parvus, Streptococcus mutans, Streptococcus parvus, Streptococcus lactis, Streptococcus mutans, Streptococcus parvus, Streptococcus lactis, Streptococcus mutans, Streptococcus lactis, Streptococcus mutans, Streptococcus lactis, Streptococcus lactis, Streptococcus lactis, Streptococcus, Sutterella wadsworthensis, saccharotrophic cocci, Cotrophomonas zeylans OL-4, Terrispora mayormei, Thermophilus albus, Treponema denticola, Treponema sovieli, Tyzzerella nexilis DSM 1787, Vallitalea guaymansensis, Vallitalea pronyenensis, Vibrio parahaemophilus, atypical Vellonella, Vellonella destructor, Vellonella dispar, atypical Vellonella, Varda gougeron, Bacillus volcanii, and Weissella confusa.

In some embodiments, the methods further comprise treating a subject predicted to have a non-toxic or effective response with the immune checkpoint blockade combination therapy. In some embodiments, the methods further comprise treating a subject predicted to have a toxic and/or ineffective response with a composition of the present disclosure. In some embodiments, the methods further comprise treating the subject with a combination of (i) a PD-1, PDL1, or PDL2 inhibitor and (ii) a CTLA-4, B7-1, or B7-2 inhibitor.

In some embodiments, a toxic, non-toxic, effective, or ineffective response is predicted when the relative abundance of one or more bacteria of a phylum, order, family, genus, or species described herein is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein). In some embodiments, the total relative abundance of the combination of bacteria is determined to be at least 2%, 3%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, or 90% (or any derivable range therein).

The compositions of the present disclosure may not include one or more than one bacterial genus or species described herein or may comprise less than 1 x 10⁶、1×10⁵、1×10⁴、1×10³Or 1X 10²Cells or CFUs of one or more than one of the bacteria described herein (or any derivable range therein).

The beneficial or unfavorable profiles described herein may not include one or more bacteria described herein or may comprise one or more bacteria described herein in a relative abundance of less than 30%, 25%, 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1% (any derivable range thereof).

In some embodiments, each bacterial population is at least 1 × 10³The concentration of CFU is present in the composition. In some embodiments, the composition is a live bacterial product or a live biotherapeutic product. In some embodiments, the bacteria are lyophilized, freeze-dried, or frozen. In some embodiments, the composition is formulated for oral delivery. In some embodiments, the composition formulated for oral delivery is a tablet or capsule. In some embodiments, the tablet or capsule comprises an acid resistant enteric coating. In some embodiments, the composition is formulated for rectal administration, administration via colonoscopy, administration via sigmoidoscopy through nasogastric tube, or administration as an enema. In some embodiments, the composition can be reformulated as containing a liquid, suspension, gel, geltab, semi-solid, tablet, sachet, lozenge, capsule, or as an enteral formulation for final delivery. In some embodiments, the composition is formulated for multiple administrations . In some embodiments, the composition further comprises a pharmaceutically acceptable excipient.

It is specifically contemplated that any limitation discussed with respect to one embodiment of the invention may apply to any other embodiment of the invention. Further, any of the compositions of the present invention can be used in any of the methods of the present invention, and any of the methods of the present invention can be used to produce or utilize any of the compositions of the present invention. Aspects of the embodiments set forth in the examples are also embodiments that can be practiced in different examples or elsewhere in this application, such as in the context of the embodiments discussed in the summary, detailed description, claims, and drawings.

Drawings

The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

FIG. 1. patient cohort and biological sample model. Clinical outcomes and related biological sample (tumor, peripheral blood mononuclear cells, fecal microbiome) analyses were evaluated in patients with advanced melanoma (n ═ 53) before and after the onset of combined anti-CTLA-4 and anti-PD-1 blockade.

Fig. 2A-2g. molecules and immune predictors of response. (A) Non-synonymous variants (NSV) in pre-treatment tumor samples (n ═ 26) were counted and grouped by binarized best total responses (BOR; R ═ responder, n ═ 20, NR ═ non-responder, n ═ 6). The specific objective response is indicated by the color of each data point (p ═ 0.20, Mann-Whitney test). (B) Copy number loss burden (affected genomic region) in pre-treatment tumor samples (n ═ 26), grouped by binarized optimal total response (p)<0.05, Mann-Whitney test). (C) Genes significantly affected by Copy Number Variation (CNV). (D) CNV landscape assessed by whole exome sequencing (n ═ 26 tumors), showing CNV deletions and additions of selected genes, IFN-signaling genes and antigen processing/presentation genes that affect recurrent mutations in melanoma. (E) Density of infiltrating CD8+ cells in pre-treatment tumors (counts/mm)²) Grouped by binarized response as determined by monochromatic staining immunohistochemistry (n-19R, n-6 NR; p ═ 0.052, one-sided Mann-Whitney test). (F) Entropy of the pre-treatment intratumoral T Cell Receptor (TCR) repertoire R (n ═ 19) was compared to the NR (n ═ 6) repertoire (p ═ 0.058, Mann-Whitney test). (G) Clonality of the pre-treatment intratumoral TCR repertoire was grouped by response (n ═ 19R, n ═ 6 NR; p ═ 0.28, Mann-Whitney test).

Figures 3A-3f. circulating T cell repertoire composition and phenotype prediction toxicity. (A) Comparison of Ki67+ cells within CD8+ T effectors (Teff cells) in early blood samples treated between patients (n ═ 14) grouped by high rank irAE (p <0.01 and p ═ 0.013, Mann-Whitney test, respectively). (B) Diversity of circulating T cell repertoire (retro-Simpson) as measured by TCR sequencing data of pre-treatment peripheral blood lymphocytes (n-24) according to the occurrence (or absence) of grade 3 irAE (p 0.028, Mann-Whitney test). The color of each data point is used to indicate whether systemic immunotherapy was performed. (C) The entropy of the circulating T cell repertoire obtained by TCR sequencing in pre-treatment blood lymphocytes (n 24) was grouped by the occurrence of grade 3 or above grade 3 immune-related adverse events (> grade 3 irAE) (p <0.01, Mann-Whitney test). Color indicates whether systemic immunotherapy has been received. (D) Percentage of CD27+ cells within CD4+ T effectors (Teff) and (E) CD8+ CD28+ cells within Teff in the pre-treatment peripheral blood samples (Mann-Whitney test as indicated; color indicates whether systemic immunotherapy was received). (F) Stacked bar graphs (top) and tabulated (bottom) depict the relationship between risk of high-grade immune-related adverse events after receiving immunotherapy blocked in combination with anti-CTLA-4 and anti-PD-1 (p 0.016, Fisher's exact test).

4A-4F. gut microbiome profiles were correlated with response and toxicity to CICB. (A) Stacked bar graphs depict the target microbial composition of each stool sample analyzed (n-31). (B) α diversity of the fecal microbiome (reverse simpson), grouped by response to blockade of combined immune checkpoints (p ═ 0.14, Mann-Whitney test). R is responder and NR is non-responder. (C) The α diversity of the fecal microbiome (reverse simpson) was grouped by the occurrence of high-grade immune-related adverse events after the start of CICB (p ═ 0.59, Mann-Whitney test). (D) LEfSe pattern of bacterial taxa, correlated with differences in response or no response to CICB. LDA is linear discriminant analysis. (E) LEfSe patterns of bacterial taxa, correlated with the occurrence or non-occurrence of high grade (. gtoreq.3 grade) immune-related adverse events (iraE). (F) Correlation heatmap (Spearman's rho) between key toxicity-associated or non-toxicity-associated bacterial taxa and circulating immune subpopulations was quantified by multiparameter flow cytometry of baseline blood samples (n ═ 9). Teff ═ T effector cells, TCM ═ T central memory, TEM ═ T effector memory, Treg ═ regulatory T cells.

FIGS. 5A-5E. the change in microbiome with CICB correlates with tumor size. (A) Tumor size of MCA205 sarcoma (left panel) or RET melanoma (right panel) after 4 injections of the indicated antibody (x-axis). In general, mice received 6 or 5 injections of anti-PD-1 and anti-CTLA-4, respectively, or related isotype controls. Experimental conditions included anti-PD-1 treated mice (n-6 MCA205, n-6 RET), CICB treated mice (n-6 MCA205, n-10 RET) or isotype treated mice (n-6 MCA205, n-8 RET). Responders are indicated in blue and non-responders in red, which corresponds to tumors that escape despite treatment. Using the Mann-Whitney test: p <0.05, p <0.01, p < 0.001. (B) Beta-diversity of fecal microbiota over time in MCA205 (left panel) and RET (right panel) (orange: before treatment started, blue: 48 hours after 2 injections, red: 48 hours after 5 injections). (C) Beta-diversity, stained for tumor size in MCA205 (left panel) and RET (right panel), with intensity of purple indicating increased tumor size. ANOSIM defines group separation; the p-value defines the significance of this separation after 999 permutations of samples. (D) Venn panel, showing an overlay of bacterial species shared or not shared between tumor models prior to treatment initiation, corresponding to responders to CICB (left panel). Flavolytica Percoll is preferentially found in R mice (right panels) shared with melanoma patients (FIGS. 4D-4E). (E) Phase of relative abundance of taxa detected before treatment in mice, it was found that these taxa have differences in abundance between R (blue) and NR (red) in both patient and murine tumor types, with changes in abundance over time showing significant differences in species level.

Figures 6A-6i microbiota dependent ileitis and colitis in tumor bearing mice. (A-B) representative micrographs (scale bar: 50 μm) of ileum from Haemoda 205 mice treated with isotype control, CICB or CICB + ATB and then reimplanted with Erysipelotridium ramosum or Bacteroides intestinalis. Detailed scores of ileum were stained with H & E from MCA205 tumor-bearing mice singly colonized with isotype control or CICB, or with antibiotics alone or subsequently with Bacteroides Intestinalis (BI), Dielma husidiosa (DF), or E.ramosum (ER), n-9-22/group. And (5) testing by students: p < 0.05. (C) Relative ileal IL1 β expression in MCA205 and RET-bearing mice with or without antibiotics or single colonization with the indicated bacteria. n-5-22. (D) The H & E stained ileum scores of MCA205 tumor-bearing mice treated with either CICB or the blockade of CICB with the IL1R1 receptor, n-10-12 mice/group. (E) Pathological assessment of inflammatory areas in the lamina propria after H & E staining of the colon in RET or MCA205 tumor-bearing mice treated with isotype control or CICB, n-7-16/group. (F) Beta diversity of fecal microbiota assessed in 16S rDNA sequencing of gene amplicons (Bray-Curtis dissimilarity), stained on the score of colonic inflammatory infiltrates in RET tumor-bearing mice, with intensity of purple indicating increased inflammatory infiltrates score (left panel). The relative abundance of bacteroides enterobacter and vibrio pyrenoidovorans shared between mice and patients (right panel) was compared based on responses (NR, red; R, blue) and hosts exhibiting colonic toxicity and hosts not exhibiting colonic toxicity (high inflammation score, green; low inflammation score, yellow). (G) Experimental setup used in (H) and (I): FMT was performed 3 days after antibiotic use in SPF mice with faeces of non-responder (NR) renal cancer patients. Two weeks later, RENCA cells expressing luciferase were implanted in situ. Five days later, isotype control or CICB was administered with or without oral tube feeding of akkermansia muciniphila (Akk) or FMT with stool from responder (R) patients who did not experience grade 3-4 irAE. (H-I) pathological assessment of inflammatory regions in the solid layer of colon after H & E staining was performed on day 15 in RENCA-bearing mice treated with isotype control or CICB. (I) ELISA of fecal lipocalin-2 levels (n-16/panel). C. D, E, H, I Mann-Whitney test: p <0.05, p <0.01, p < 0.001.

FIGS. 7A-7B, associated with FIG. 1 and Table 3: the outcome of the treatment of the patient. (A) Kaplan-Meier curves of progression-free survival in patient cohort (n-53). (B) Swim-pattern (swimmer plot) indicating the optimal total response (color shading), duration of optimal total response (length of shaded portion of bar) and total follow-up duration (shaded portion of bar + unshaded portion) for each patient, measured from the day of the first dose immune checkpoint blockade combination therapy.

FIGS. 8A-8E, relating to FIG. 2: in combination with molecular markers blocked by immune checkpoints. (A) A landscape of nonsynonymous variants (NSVs) identified by whole exome sequencing (n ═ 26 tumors), which affect selected genes, IFN-signaling genes and antigen processing/presentation genes that are repeatedly mutated in melanoma. (B) NSV counts were compared between BRAF V600-mutant patients (n ═ 19) and wild-type (n ═ 6) patients (p <0.001, permutation test). (C) The difference in counts for total predicted neoantigens and fully-, strongly-and weakly-bound neoantigens in patients were grouped by optimal total response (R ═ responder (blue), n ═ 20; NR ═ non-responder (red), n ═ 6; all p >0.05, Mann-Whitney test). (D) Bar graphs of the number of genes affected by the chromosomal copy number deletion show significant loads within chromosomes 5, 10 and 15. (E) The whole genome SGOL score indicates an enrichment of CNVs affecting chromosome 10.

FIGS. 9A-9E, relating to FIGS. 2 and 3: immune markers of CICB response and toxicity. (A) Comparison of CD8+ cell density within tumors before (pre-treatment) and after (post-treatment) the start of CICB (n ═ 19R, n ═ 6 NR; p ═ ns, Mann-Whitney test; the best total response (BOR) is indicated by the color of each data point). (B) The box plots of the number of significantly expanded T cell clones (pre-treatment to treatment) detected by TCR sequencing of the peripheral blood immune repertoire were grouped by the presence or absence of high-grade immune-related adverse events (grade 3 irAE; n ═ 7 present, n ═ 9 absent; p ═ 0.22, Mann-Whitney test). (C) Comparison of Ki67+ cells within T Central Memory (TCM) cells in early efficacy treatment (on-treatment) blood samples between patients (n-14) grouped by high-grade irAE (p <0.01 and p-0.013, Mann-Whitney test, respectively). (D) The percentage of CD4+ CD28+ cells within Teff and (E) CD8+ CD27+ cells within Teff before treatment began, shown as high-grade irAE (p ═ 0.014, p >0.05, Mann-Whitney test, respectively). Percentage of CD27+ of CD28+ cells within CD4+ Teff.

FIGS. 10A-10E, relating to FIG. 4: fecal microbiome characteristics associated with CICB response and toxicity. (A) The alpha-diversity of gut microbiota taken before or shortly after the start of treatment in CICB-treated patients (n ═ 31) was measured by a specified metric (p value as shown, Mann-Whitney test). (B-C) volcano plots that were compared in pairs according to the binary response class (B) or OTU (all taxonomic levels) of development of high-level toxicity (C). (D) Correlation heatmap (Spearman's r) (n-9) between the response or non-response associated taxa and circulating immune cell populations at baseline.

11A-11D, relating to FIG. 5: kinetics of treatment-induced changes in intestinal bacterial composition and correlation with tumor size. LEfSe patterns of species were differentiated 48 hours after 2 (A) or 5 (C) mAb injections (orange: isotype control antibody, purple: anti-PD-1 antibody, green: CICB). Linear Discriminant Analysis (LDA) was combined with effect size measurements to indicate the species that were differentially present in each treatment group. LDA score is greater than or equal to 2(B, D). A heatmap of Spearman correlation index, representing the correlation of each bacterial species with tumor size after 2 (B) or 5 (D) injections as determined from LEfSe analysis for each tumor model, listed in alphabetical order. Red indicates a positive correlation with tumor size, while blue indicates a negative correlation with tumor size. P < 0.05.

Figure 12a microbiota-dependent inflammatory cytokine patterns of the ileum and colon in tumor-bearing mice. (A) A heat map of log 2-fold changes in the CICB/isoform control ratio versus pro-inflammatory gene expression in the ileum and colon of MCA205 and RET tumor-bearing mice with or without antibiotics or single colonization with the indicated bacteria. n is 5-22/group. P <0.05, p <0.01, Mann-Whitney test.

Fig. 13A-13f. beneficial changes in gut microbiome by anti-tumor immune infiltrates affected the response to anti-PD 1 therapy in patients with advanced melanoma. A) Comparison of alpha diversity between responders (R) and non-responders (NR) in the gut microbiome. B) LDA effect size histogram shows that bacteria are differentially enriched in R and NR, with the length of the bar indicating the effect size associated with the taxa. C) Spearman correlation matrix between pro-R enterobacteria and intratumoral immune infiltrates (n-15) as quantified by immunohistochemistry. D) Experimental design studied in sterile mice. E) Tumor growth curves in mice treated with anti-PDL 1 after transplantation responder (R-FMT) or non-responder (NR-FMT) faeces or without FMT. F) Fecal levels in feces of R-FMT and NR-FMT mice at day 14 post tumor injection.

FIG. 14: clinical outcomes and related biological assays were evaluated in a cohort of patients with advanced melanoma (n 77) before and after initiation of combined anti-CTLA-4 and anti-PD-1 blockade.

FIGS. 15A-15D: molecules that respond and immune predictors. A) Copy number deletion burden (CNV) (affected genomic region) in pre-treatment tumor samples (n ═ 26), grouped by binarized best total response (p <0.05, Mann-Whitney test). B) CNV landscape assessed by whole exome sequencing of selected genes, IFN-signaling genes and antigen processing/presentation genes that are repeatedly mutated in melanoma (n ═ 26 tumors). C) Upper panel) diversity (reverse simpson) of circulating T cell repertoire before treatment (n 24), entropy of circulating T cell repertoire before treatment (n 24), with generation (or absence) of irAE grade 3 (p 0.028, Mann-Whitney test), and lower panel) of circulating T cell repertoire of peripheral blood lymphocytes after treatment, with generation (or absence) of irAE grade 3. D) Upper panel) CD4+ CD27 expression in T effector cells, and lower panel) CD28 expression in CD8+ T effector cells, grouped by < or > 3 grades irAE (n-15; p <0.01, p <0.05, Mann-Whitney test), respectively).

FIGS. 16A-16C: the gut microbiome was characterized by 16S rRNA sequencing. A) Stacked bar graphs depict the target microbial composition of each stool sample analyzed from the skin and unknown primary cohort (n-40). B) The α diversity of the fecal microbiome taken before or shortly after the start of treatment in patients with skin or unknown primary melanoma treated with CICB (n-40) was grouped by response, measured by a specified metric (p-0.68, Mann-Whitney test; r-responder, NR-non-responder). C) The alpha diversity of the fecal microbiome (reverse simpson) was grouped by the occurrence of high-grade immune-related adverse events in all patients with fecal samples (p ═ 0.71, Mann-Whitney test, n ═ 54).

FIGS. 17A-17B: differential enrichment of bacterial taxa according to LEfSe. Linear discriminant analysis score plots of differentially enriched bacterial taxa in patients, a) LDA score plots of differentially enriched bacterial taxa in patients with CICB as R or NR, from skin and unknown primary cohort (n ═ 40). LDA is linear discriminant analysis. p <0.05, or B) LDA score of bacterial taxa with or without significant association with the occurrence of high grade (> grade 3) immune related adverse events (irAE) in all patients with stool samples (n ≧ 54). p < 0.05.

FIGS. 18A-18C: discovery of candidate bacterial taxa and association with progression-free survival. Pairwise Mann-Whitney comparative volcano plots of relative bacterial abundance according to bootstrap (at all levels), a) pairwise comparative volcano plots of taxonomic groups (at all levels) by dichotomous response classes in the skin/unknown primary cohort (n ═ 40), using the Mann-Whitney test applied to 1000 different bacterial abundance permutations. And B) volcano plots of pairwise comparisons of bacterial taxa (at all levels) by binary high-ranking (. gtoreq.3) immune-related adverse event classes (n ═ 54), using Mann-Whitney test applied to 1000 different bacterial abundance permutations.

FIGS. 19A-19B: associations between circulating immune populations and phenotypically most differentially enriched bacterial taxa. A) Correlation heat map between taxa associated with response or no response at baseline and circulating immune cell population (Spearman's rho) (n ═ 8), and B) correlation heat map between key toxicity-associated or non-toxicity-associated bacterial taxa and circulating immune subpopulation (Spearman's rho), quantified by multiparameter flow cytometry of baseline blood samples (n ═ 8). TEff ═ T effector cells, TCM ═ T central memory, TEM ═ T effector memory, Treg ═ regulatory T cells.

FIGS. 20A-20℃ correlation of gut microbiota with CICB response. (A) Combined with the two tumor models, partial least squares discriminant analysis (PLS-DA) plots of β diversity at T0 between final tumor-free or tumor-bearing CICB-treated mice at sacrifice. LV, latent variable. (B) Variable Importance (VIP) score bar, highlighting that bacterial species present at T0 were significantly enriched in the group defined by the bar color compared to the group defined by the border color ([ p <0.05, [ p <0.01, [ p <0.001 ]), indicating that mice that were ultimately tumor-free and tumor-loaded following CICB treatment (RET and MCA205 models). For each species, the bar color depicts the queue with the highest average relative abundance for the defined species, while the border color indicates the queue with the lowest average relative abundance. The absence of a border indicates that the average relative abundance in the comparison cohort is zero. The green box highlights the same species as the patient data. Mann-Whitney test: p <0.05, p <0.01, p <0.001, ns is not significant. The bar thickness reports the fold ratio of the average relative abundance of each species between the two queues. NA is not applicable. (C) The relative abundance of parabacteroides dymanii (at T0, T2, and T5) in CICB treated mice correlates with Pearson for tumor size at T5.

Figure 21 correlation heatmap (Spearman's rho) between key toxicity-or non-toxicity-associated bacterial taxa and circulating immune subpopulations, quantified by multiparameter flow cytometry of baseline blood samples (n ═ 8). TEff ═ T effector cells, TCM ═ T central memory, TEM ═ T effector memory, Treg ═ regulatory T cells.

22A-22G. gut microbiome profiles were correlated with response and toxicity to CICB. (A) H from MCA205 or RET tumor-bearing mice treated with isotype control or CICB + -antibiotics&E score of stained ileum (range 0-4), n-9-22/group. And (5) carrying out t test on students. (B) Log of proinflammatory immune gene expression (CICB treatment and isotype) in ileum and colon of MCA205 and RET tumor-bearing mice + -antibiotics₂Heat map of fold change (left panel). (C) Relative ileum Il1b expression in tumor-bearing mice treated with isotype/CICB ± antibiotics. Mann-Whitney test. n-10-22 mice/group. (D) Fecal midgut from isotype and CICB treatment groupsqPCR quantification of the relative abundance of bacteroides, in a pre-treatment and post-treatment pairing manner (linkage point). n-21-26 mice/group. Wilcoxon signed rank test. Relative ileum Il1b expression (E) and ileal toxicity score (F) 48 hours after one cic b injection in mice treated with antibiotics and then allowed to naturally re-colonize or fed once with enterobacteroides after antibiotic withdrawal combined with MCA205 (n-5-32/group, grey point) and RET (n-5-26/group, white point). Data represent a collection of two separate experiments using three different bacteroides enteric strains. For the ileal toxicity score, mice were classified according to the chi-square test for low toxicity (score 0 or 1) versus high toxicity (score 2, 3, or 4). Ileum Il1b expression was analyzed using the Mann-Whitney test. (G) Ileal toxicity scores after CICB (or isotype) treatment compared mice engrafted with bacteroides enterobacter (high) and with donor FMT (low). P <0.05,**p<0.01,***p<0.001

Figure 23 patient treatment results. Kaplan-Meier curves for progression-free survival in patient cohorts stratified by melanoma subtype (n 77, n 63 skin/unknown primary, n 8 mucosa, n 6 uvea).

Fig. 24A-24d immune markers of cicb response and toxicity. (A, B) and CD8+ Teff (C, D), from CICB treatment cohort alone, in pre-treatment peripheral blood samples (p values from Mann-Whitney test as shown).

Figure 25. prior immunotherapy and T cell phenotype associated with toxicity. (C) Stacked bar graphs (top) and tabulated (bottom) depict the relationship between risk of developing grade 3 irAE after combined blockade with anti-CTLA-4 and anti-PD-1 in patients with cutaneous or unknown primary melanoma (p 0.028, Fisher's exact test).

Fig. 26A-26B group abundance of firmicutes (B) and clostridiales (C) were compared by response in the skin/unknown primary cohort (n ═ 40).

Fig. 27A-27d. microbiome and response in murine models. (A) Tumor growth kinetics of MCA205 fibrosarcoma (left panel) or RET melanoma (middle panel) after 5 injections of the indicated antibodies and percentage of tumor-bearing or tumor-free mice at sacrifice (right panel). The experimental group consisted of isotype, anti-PD-1 or CICB treatments. Arrows on the x-axis indicate stool acquisition time points: t0-before treatment began, T2-48 hours after 2 treatments and T5-48 hours after 5 treatments. The tumor growth shown represents 2 experiments. n-12-16 mice/group. Statistical analysis was performed using the software detailed in methods: p <0.01, p < 0.001. (B) In MCA205 (left panel) and RET (right panel) tumor-bearing mice, partial least squares discriminant analysis (PLS-DA) of microbial alpha diversity (upper panel) and fecal microbial beta diversity (PLS-DA) in fecal samples taken before treatment initiation (T0, orange), 48 hours after 2 injections of CICB or isotype control (T2, blue), and 48 hours after 5 injections of CICB or isotype control (T5, red) (lower panel) were evaluated by sequencing of 16S rRNA gene amplicons using shannon index. Mann-Whitney U test: p <0.05, p < 0.01. ANOSIM and PERMANOVA defined group separation; the p-value defines the significance of group separation after 999 permutations of samples. (C) Beta-diversity at T0, staining according to tumor size at T5 in CICB-treated MCA205 and RET; the intensity of the purple color indicates an increase in tumor size. For each of the principal axes (PCo1 and PCo2), the variance of the acquisition, the Pearson rho coefficient, and the corresponding p-value are shown. (D) The relative abundance of parabacteroides dymanii in feces sampled from mice at T0, T2, and T5, combined with the two tumor models, showed a clear difference in the final findings of tumor-bearing or tumor-free mice at T5. Mann-Whitney test: p < 0.05.

FIGS. 28A-28H. (A) representative micrographs (scale bar: 50 μm, magnification: 100X) of ileum of Holcus MCA205 and RET mice treated with isotype or CICB (left panel). (C) A correlation heatmap (Pearson's rho) between colon infiltrate score and relative abundance of taxa when combined with T0, T2 and T5 for RET in the discovery and validation cohort data. Red indicates a positive correlation with the colon infiltrate score, while blue indicates a negative correlation with the colon infiltrate score. (D) The beta-diversity order of fecal microbiota assessed by sequencing of 16S rRNA gene amplicons (Bray-Curtis dissimilarity), stained on the score of colonic inflammatory infiltrates in RET tumor-bearing mice, intensity of purple color indicating increased inflammatory infiltrates scores in the cohort found (left panel) and validated (right panel). Bacterial relative abundance and colonic inflammatory infiltrates were both normalized and normalized prior to correlation analysis. Pearson correlations and associated p-values comparing each principal component to inflammatory infiltrates are indicated. (E) qPCR quantification of the relative abundance of bacteroides uniformis and bacteroides fragilis in mouse feces before and after at least one injection of CICB therapy expressed in paired fashion (linkage points) versus the CICB treated group. n-21-26 mice/group. (F) Schematic of the experimental setup used in fig. 3K and H-I. (G) Enterobacteroid abundance in healthy volunteer donor feces (low vs high selected) and grafts in confirmed post-FMT mice, control, enterobacteroid-low and enterobacteroid-high donor recipient mice were compared (p <0.05, Mann-Whitney test). (H) Relative Il1b expression in mice post-CICB after FMT with bacteroides enterobacter-low or-high donor feces (xp <0.05, Mann-Whitney test).

Detailed Description

Treatment with Combined Immune Checkpoint Blockade (CICB) targeting cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed death receptor-1 (PD-1) is associated with clinical benefit in several tumor types, but the incidence of immune-related adverse events (irAE) is high. Thus, biomarkers of response to CICB and the possibility of developing irAE following treatment with CICB are needed. Microbial determinants of response and toxicity to CICB identified in the gut microbiota in the human and mouse cohorts are described herein. The examples of the present application also provide evidence that targeting these can reduce toxicity in preclinical models. Taken together, these findings are of potential significance for clinical management of cancer using CICB.

I. Definition of

As used herein, the term "antibody" refers to immunoglobulins, derivatives thereof that retain specific binding capacity and proteins having a binding domain that is homologous or largely homologous to an immunoglobulin binding domain. These proteins may be derived from natural sources, or produced partially or completely synthetically. The antibody may be monoclonal or polyclonal. The antibody may be a member of any immunoglobulin class, including any human class: IgG, IgM, IgA, IgD and IgE. Antibodies for use with the methods and compositions described herein are generally derivatives of the IgG class. The term antibody also refers to antibody fragments that bind to an antigen. Examples of such antibody fragments include, but are not limited to, Fab ', F (ab')2, scFv, Fv, dsFv diabodies, and Fd fragments. Antibody fragments can be produced by any means. For example, antibody fragments may be produced enzymatically or chemically by fragmentation of an intact antibody, recombinantly from genes encoding part of the antibody sequence, or synthetically, in whole or in part. The antibody fragment may optionally be a single chain antibody fragment. Alternatively, the fragment may comprise multiple chains linked together, for example by disulfide bonds. The fragment may also optionally be a multimolecular complex. Functional antibody fragments retain the ability to bind their cognate antigen with an affinity comparable to that of an intact antibody.

As used herein, the term "monoclonal antibody" refers to an antibody obtained from a population of substantially homogeneous antibodies, e.g., the individual antibodies comprising the population are identical except for possible mutations, e.g., naturally occurring mutations, that may be present in small amounts. Thus, the modifier "monoclonal" indicates that the antibody is not characterized as a mixture of antibodies having different epitope specificities. In certain embodiments, such monoclonal antibodies generally include antibodies comprising a polypeptide sequence that binds a target, wherein the target-binding polypeptide sequence is obtained by a method comprising selecting a single target-binding polypeptide sequence from a plurality of polypeptide sequences. For example, the selection process may be to select unique clones from a pool of multiple clones, such as hybridoma clones, phage clones, or recombinant DNA clones. It is understood that selected target binding sequences may be further altered, for example, to improve affinity for a target, to humanize the target binding sequence, to improve its yield in cell culture, to reduce its immunogenicity in vivo, to produce multispecific antibodies, etc., and that antibodies comprising altered target binding sequences are also monoclonal antibodies of the disclosure. In contrast to polyclonal antibody preparations, which typically contain several different antibodies directed against different determinants (epitopes), each monoclonal antibody of a monoclonal antibody preparation is directed against a single determinant on the antigen. In addition to their specificity, monoclonal antibody preparations are also advantageous in that they are generally uncontaminated by other immunoglobulins.

The expression "pharmaceutical composition" or "pharmaceutically acceptable composition" refers to molecular entities and compositions that do not produce adverse, allergic, or other untoward reactions when administered to an animal such as a human, as appropriate. The preparation of pharmaceutical compositions comprising antibodies or additional active ingredients will be known to those skilled in the art in light of the present disclosure. Further, for administration to animals (e.g., humans), it is understood that the formulation should meet sterility, pyrogenicity, general safety and purity standards as required by FDA office of biological standards.

As used herein, "pharmaceutically acceptable carrier" includes any and all aqueous solvents (e.g., water, alcohol/water solutions, saline solutions, parenteral vehicles such as sodium chloride and ringer's dextrose), non-aqueous solvents (e.g., propylene glycol, polyethylene glycol, vegetable oils, and injectable organic esters such as ethyl oleate), dispersion media, coatings, surfactants, antioxidants, preservatives (e.g., antibacterial or antifungal agents, antioxidants, chelating agents, and inert gases), isotonic agents, absorption delaying agents, salts, drugs, drug stabilizers, gels, binders, excipients, disintegrants, lubricants, sweeteners, flavoring agents, dyes, fluids, and nutritional supplements, such as similar materials and combinations thereof, as would be known to one of ordinary skill in the art. The pH and exact concentration of the various components of the pharmaceutical composition can be adjusted according to well-known parameters.

The term "unit dose" or "dose" refers to physically discrete units suitable for use in a subject, each unit containing a predetermined amount of a therapeutic composition calculated to produce the desired response discussed herein in relation to its administration, i.e., the appropriate route and treatment regimen. Depending on the number of treatments and the unit dose, the amount to be administered depends on the desired therapeutic effect. The actual dosage amount of the composition of the present embodiment to be administered to a patient or subject may be determined by physical and physiological factors such as the weight, age, health and sex of the subject, the type of disease being treated, the extent of disease penetration, prior or concurrent therapeutic intervention, the patient's idiopathy, the route of administration and the potency, stability and toxicity of the particular therapeutic substance. For example, the dosage per administration may also include from about 1 μ g/kg/body weight to about 1000 mg/kg/body weight (with the range including intermediate dosages) or more than 1000 mg/kg/body weight, and any specific dosage derivable therein. In non-limiting examples of ranges that may be derived from the numbers listed herein, ranges of about 5 μ g/kg/body weight to about 100 mg/kg/body weight, about 5 μ g/kg/body weight to about 500 mg/kg/body weight, and the like may be administered. In any event, the practitioner responsible for administration will determine the concentration of one or more than one active ingredient in the composition and the appropriate dosage or dosages for the individual subject.

A "population" of bacteria may refer to a bacterial composition comprising a single species or a mixture of different species?

The term "immune checkpoint" refers to various stimulatory, co-stimulatory and inhibitory signals that modulate the breadth and magnitude of the immune response, which are essential for maintaining immune homeostasis and host survival. Known immune checkpoint proteins include CTLA-4, PD-1 and its ligands PD-L1 and PD-L2, in addition to LAG-3, BTLA, B7H3, B7H4, TIM3, KIR. Pathways involving LAG3, BTLA, B7H3, B7H4, TIM3 and KIR are considered in the art to constitute immune checkpoint pathways similar to CTLA-4 and PD-1 dependent pathways (see, e.g., Pardoll,2012, Nature Rev Cancer 12: 252-.

The term "inhibitor" refers to a molecule, which may be organic or inorganic, a protein, a polypeptide, an antibody, a small molecule, a carbohydrate, or a nucleic acid, that blocks or reduces one or more functions of a protein. The inhibitor may be a direct inhibitor that acts by interacting directly with the protein or may be an indirect inhibitor that may not interact directly with the protein but still inhibit one or more functions of the protein.

By "immune checkpoint inhibitor" is meant any compound that inhibits the function of an immune checkpoint protein. Inhibition includes reduction and complete blocking of function. In particular, the immune checkpoint protein is a human immune checkpoint protein. Thus, in particular, the immune checkpoint protein inhibitor is an inhibitor of a human immune checkpoint protein.

"subject" and "patient" refer to humans or non-humans, such as primates, mammals, and vertebrates. In particular embodiments, the subject is a human.

As used herein, the terms "treat," "treating," or "ameliorating," when used in reference to a disease, disorder, or medical condition, refer to a therapeutic treatment of the condition, wherein the aim is to reverse, alleviate, ameliorate, inhibit, slow, or stop symptoms or the progression or severity of the condition. The term "treating" includes reducing or alleviating at least one adverse effect or symptom of a condition. A treatment is typically "effective" if one or more symptoms or clinical markers are reduced. Alternatively, a treatment is "effective" if the progression of the condition is slowed or stopped. That is, "treating" includes not only improvement of symptoms or markers, but also stopping or at least slowing the progression or worsening of symptoms that would be expected to occur in the absence of treatment. Beneficial or desired clinical results include, but are not limited to, alleviation of one or more symptoms, reduction in the extent of the deficiency, stable (i.e., not worsening) state of the tumor or malignancy, delay or slowing of tumor growth and/or metastasis, and increased longevity, as compared to that expected in the absence of treatment.

"gut microbiota" or "gut microbiome" refers to a population of microorganisms (and their genomes) that live in the intestine of a subject.

The term "α diversity" is a measure of diversity within a sample and refers to the distribution and assembly pattern of all microbiota within a sample and is calculated as a scalar value for each sample. "beta diversity" is a term for diversity between samples and relates to the comparison of samples to one another that provides a measure of the distance or dissimilarity between each pair of samples.

The term "relative quantity", which may also be referred to as "relative abundance", is defined as the number of bacteria in a particular classification level (phylum to species) in a biological sample as a percentage of the total number of bacteria in that level. For example, such relative abundance can be assessed by measuring the percentage of 16S rRNA gene sequences present in the sample that are attributable to these bacteria. It can be measured by any suitable technique known to the skilled artisan, such as 454 pyrosequencing of a specific bacterial 16s rrna gene marker or quantitative PCR of a specific gene.

Herein, a "good responder to a treatment", also referred to as a "responder" or a "responsive" patient, or in other words a patient "benefiting from" such a treatment, refers to a patient who has cancer and shows or will show clinically significant remission of the cancer after receiving such a treatment. Conversely, "poor responder" or "non-responder" refers to a patient who does not or will not show clinically significant cancer remission after receiving such treatment. The reduced response to treatment can be assessed according to art-recognized criteria, such as immune-related response criteria (irRC), WHO or RECIST criteria. For example, a responsive patient may be a patient identified as having a Complete Response (CR) with all target lesions absent, or identified as having a Partial Response (PR) with at least a 30% decrease in the sum of the Longest Diameter (LD) target lesions (referenced to the baseline sum LD), while a non-responsive patient may be identified as having a Stable Disease (SD) corresponding to either no sufficient decrease to meet the PR, or no sufficient increase to meet the Progressive Disease (PD) (referenced to the minimum sum of LDs since the start of treatment), or may be identified as having a Progressive Disease (PD) -an increase in the sum of LDs of the target lesions (referenced to the minimum sum of LDs recorded since the start of treatment or the appearance of one or more new lesions) of at least 20%.

The term "isolated" encompasses a bacterium or other entity or substance that has been (1) separated from at least some of the components with which it is originally produced (whether in nature or in an experimental setting) and with which it is associated, and/or (2) artificially produced, prepared, purified, and/or manufactured by a human. The isolated bacteria may be separated from at least about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or more than 90% of the other components originally associated therewith. In some embodiments, the isolated bacteria have a purity of greater than about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, or greater than about 99%. As used herein, a substance is "pure" if it is substantially free of other components.

The terms "purified" and "purified" refer to bacteria or other material that has been separated from at least some of the components with which it is associated either as it is initially produced or produced (e.g., whether in nature or in an experimental setting) or during any time after its initial production. A bacterium or population of bacteria can be considered purified if it is isolated, either at the time of manufacture or after manufacture, as from a material or environment containing the bacterium or population of bacteria, and the purified bacterium or population of bacteria can contain up to about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or more than about 90% of other material and still be considered "isolated". In some embodiments, the purified bacteria and bacterial populations possess a purity of greater than about 80%, about 85%, about 90%, about 91%, about 92%, about 93%, about 94%, about 95%, about 96%, about 97%, about 98%, about 99%, or greater than about 99%. In the case of the bacterial compositions provided herein, one or more than one bacterial type present in the composition can be independently purified from one or more than one other bacteria produced and/or present in the material or environment containing that bacterial type. The bacterial composition and its bacterial components are typically purified from residual habitat products.

The term "determined to have" refers to a population of patients that have been tested and reported to have a certain outcome, such as microbiome status.

The terms "reduce", "decrease", "reduction" or "inhibition" are all used herein to generally refer to a reduction in a statistically significant amount. However, for the avoidance of doubt, "reduce", "decrease" or "inhibit" refers to a decrease of at least 10% compared to a reference level, for example a decrease of at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including a 100% decrease (i.e. a level not present compared to a reference sample), or any decrease between 10-100% compared to a reference level.

The terms "increase", "enhancement" or "activation" are all used herein to generally refer to an increase in a statistically significant amount; for the avoidance of any doubt, the terms "increase", "enhancement" or "activation" refer to an increase of at least 10% compared to a reference level, for example an increase of at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or up to and including 100%, or any increase between 10-100% compared to a reference level, or at least about 2-fold, or at least about 3-fold, or at least about 4-fold, or at least about 5-fold or at least about 10-fold, or any increase or greater between 2-fold and 10-fold compared to a reference level.

The term "comprising" synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. The expression "consisting of … …" excludes any element, step or ingredient not specified. The phrase "consisting essentially of … …" limits the scope of the described subject matter to the specified materials or steps and to materials or steps that do not materially affect the basic and novel characteristics thereof. The term allows the presence of additional elements that do not materially affect the basic and novel or functional characteristics of one or more of this embodiment of the invention. With respect to pharmaceutical compositions, the term "consisting essentially of … …" includes the recited active ingredient, excluding any other active ingredient, but does not exclude any pharmaceutical excipients or other components not having therapeutic activity. It is contemplated that embodiments described in the context of the term "comprising" may also be implemented in the context of the term "consisting of … …" or "consisting essentially of … …".

The term "consisting of … …" refers to the compositions, methods, and their respective components as described herein, which do not include any elements not described in the description of the embodiments.

As used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "the method" includes one or more than one method, and/or steps of the type described herein and/or as would be apparent to one of skill in the art upon reading this disclosure and so forth.

As used herein, "substantially free" with respect to a particular component is used herein to refer to which no specified component is intentionally formulated into the composition and/or is present only as a contaminant or in trace amounts. The total amount of the specified components resulting from any accidental contamination of the composition is therefore well below 0.01%. Most preferred are compositions in which no amount of a given component is detectable by standard analytical methods.

As used herein, the terms "or" and/or "are used to describe multiple components that are combined or mutually exclusive with each other. For example, "x, y, and/or z" may refer to "x" alone, "to" y "alone," to "z," "x, y, and z", "(x and y) or z," "x or (y and z)" or "x or y or z" alone. It is specifically contemplated that x, y or z may be specifically excluded from the embodiments.

Throughout this application, the term "about" is used according to its ordinary meaning in the field of cell biology to indicate the standard deviation of error of a device or method used to determine the value.

The expression "effective amount" or "therapeutically effective amount" or "sufficient amount" refers to a dose of a drug or agent sufficient to produce the desired result. The desired result may be a reduction in tumor size, a reduction in the growth rate of cancer cells, a reduction in metastasis, an increase in CD8+ T lymphocytes in a tumor or tumor immune infiltrate, an increase in CD45+, CD3+/CD20+/CD56+, CD68+ and/or HLA-DR + cells in a tumor, an increase in CD3, CD8, PD1, FoxP3, granzyme B, and/or PD-L1 expression in a tumor immune infiltrate, a reduction in ROR γ T expression in a tumor immune infiltrate, an increase in effectors CD4+, CD8+ T, monocytes, and/or myeloid dendritic cells in systemic or peripheral blood, a reduction in B cells, regulatory T cells, and/or myeloid derived suppressor cells in the systemic or peripheral blood of a subject, or any combination of the foregoing.

Checkpoint inhibitors and combination therapies

Embodiments relate to combination therapies comprising a) a CTLA-4, B7-1, and/or B7-2 inhibitor and B) a PD-1, PDL1, and/or PDL2 inhibitor. In some embodiments, the treatment is with an inhibitor that will block the interaction between CTLA-4 and B7-1 or B7-2 in combination with an inhibitor that will block the interaction of PD-1 and PDL1 or PDL 2.

In some embodiments of any one of the methods, compositions, or kits provided, the immune checkpoint inhibitor is a small molecule inhibitor. In some embodiments of any one of the methods, compositions, or kits provided, the immune checkpoint inhibitor is a polypeptide that will inhibit an immune checkpoint pathway. In some embodiments of any of the provided methods, compositions, or kits, the inhibitor is a fusion protein. In some embodiments of any one of the methods, compositions, or kits provided, the immune checkpoint inhibitor is an antibody. In some embodiments of any of the provided methods, compositions, or kits, the antibody is a monoclonal monomer.

PD-1, PDL1 and PDL2 inhibitors

PD-1 may play a role in the tumor microenvironment where T cells are affected by infection or tumors. Activated T cells will upregulate PD-1 and continue to express it in peripheral tissues. Cytokines such as IFN- γ will induce the expression of PDL1 on epithelial cells and tumor cells. The main role of PD-1 is to limit the activity of effector T cells in the periphery and to prevent excessive damage to tissues during immune responses. The inhibitors of the present disclosure may block one or more functions of PD-1 and/or PDL1 activity.

Alternative names for "PD-1" include CD279 and SLEB 2. Alternative names to "PDL 1" include B7-H1, B7-4, CD274, and B7-H. Alternative names for "PDL 2" include B7-DC, Btdc, and CD 273. In some embodiments, PD-1, PDL1, and PDL2 are human PD-1, PDL1, and PDL 2.

In some embodiments, the PD-1 inhibitor is a molecule that will inhibit the binding of PD-1 to its ligand binding partner. In a particular aspect, the PD-1 ligand binding partner is PDL1 and/or PDL 2. In another embodiment, the PDL1 inhibitor is a molecule that will inhibit the binding of PDL1 to its binding partner. In a particular aspect, the PDL1 binding partner is PD-1 and/or B7-1. In another embodiment, the PDL2 inhibitor is a molecule that will inhibit the binding of PDL2 to its binding partner. In a particular aspect, the PDL2 binding partner is PD-1. The inhibitor may be an antibody, an antigen-binding fragment thereof, an immunoadhesin, a fusion protein or an oligopeptide. Exemplary antibodies are described in U.S. patent nos. 8735553, 8354509, and 8008449, all of which are incorporated herein by reference. Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art, as described in U.S. patent application nos. US2014/0294898, US2014/022021, and US2011/0008369, all of which are incorporated herein by reference.

In some embodiments, the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some embodiments, the anti-PD-1 antibody is selected from nivolumab, pabollizumab, and pidilizumab. In some embodiments, the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1 binding portion of PDL1 or PDL2 fused to a constant region (e.g., the Fc region of an immunoglobulin sequence)). In some embodiments, the PDL1 inhibitor comprises AMP-224. Nivolumab, also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558 andis an anti-PD-1 antibody described in WO 2006/121168. Pabolizumab, also known as MK-3475, Merck 3475, lambrolizumab,And SCH-900475, which is an anti-PD-1 antibody described in WO 2009/114335. Pidilizuzumab, also known as CT-011, hBAT or hBAT-1, is an anti-PD-1 antibody described in WO 2009/101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble as described in WO2010/027827 and WO2011/066342A sex receptor. Additional PD-1 inhibitors include MEDI0680, also known as AMP-514 and REGN 2810.

In some embodiments, the immune checkpoint inhibitor is a PDL1 inhibitor such as bevacizumab (also known as MEDI4736), acilizumab (also known as MPDL3280A), acilinumab (also known as MSB00010118C, MDX-1105, BMS-936559), or a combination thereof. In certain aspects, the immune checkpoint inhibitor is a PDL2 inhibitor such as rHIgM12B 7.

In some embodiments, an antibody described herein (e.g., an anti-PD-1 antibody, an anti-PDL 1 antibody, or an anti-PDL 2 antibody) further comprises a human or murine constant region. In some embodiments, the human constant region is selected from IgGl, IgG2, IgG2, IgG3, and IgG 4. In yet another specific aspect, the human constant region is IgG 1. In yet another aspect, the murine constant regions are selected from IgGl, IgG2A, IgG2B, and IgG 3. In yet another specific aspect, the antibody has reduced or minimal effector function. In yet another specific aspect, minimal effector function results from production in prokaryotic cells. In yet another specific aspect, minimal effector function results from "null effector Fc mutation" or aglycosylation.

In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, palivizumab, or pidilizumab. Accordingly, in one embodiment, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of nivolumab, pabollizumab, or pidlizumab, and the CDR1, CDR2, and CDR3 domains of the VL region of nivolumab, pabollizumab, or pidlizumab. In another embodiment, the antibody competes with the above antibody for binding to and/or to the same epitope on PD-1, PDL1 or PDL 2. In another embodiment, the antibody has at least about 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 99% (or any derivable range thereof) variable region amino acid sequence identity to an antibody described above.

Accordingly, the antibodies used herein may be aglycosylated. Glycosylation of antibodies is usually N-linked or O-linked. N-linked refers to the attachment of a carbohydrate moiety to the side chain of an asparagine residue. The tripeptide sequences asparagine-X-serine and asparagine-X-threonine, where X is any amino acid other than proline, are recognition sequences for the enzymatic attachment of a carbohydrate moiety to an asparagine side chain. Thus, the presence of any of these tripeptide sequences in a polypeptide will create potential glycosylation sites. O-linked glycosylation refers to the attachment of one of the sugars N-acetylgalactosamine, galactose or xylose to a hydroxyamino acid, most commonly serine or threonine, although 5-hydroxyproline or 5-hydroxylysine may also be used. Removal of the glycosylation sites from the antibody is conveniently accomplished by altering the amino acid sequence such that one of the above-described tripeptide sequences (for N-linked glycosylation sites) is removed. Changes can be made by replacing an asparagine, serine, or threonine residue within a glycosylation site with another amino acid residue (e.g., glycine, alanine, or a conservative substitution).

The antibody or antigen-binding fragment thereof can be prepared using methods known in the art, for example, by a method comprising culturing a host cell containing a nucleic acid encoding any of the previously described anti-PDLl, anti-PD-1 or anti-PDL 2 antibodies or antigen-binding fragments in a form suitable for expression under conditions suitable for the production of such an antibody or fragment, and recovering the antibody or fragment.

CTLA-4, B7-1 and B7-2

Another immune checkpoint that may be targeted in the methods provided herein is cytotoxic T-lymphocyte-associated protein 4(CTLA-4), also known as CD 152. The complete cDNA sequence of human CTLA-4 has Genbank accession number L15006. CTLA-4 is present on the surface of T cells and acts as an "off" switch when bound to B7-1(CD80) or B7-2(CD86) on the surface of antigen presenting cells. CTLA4 is a member of the immunoglobulin superfamily that is expressed on the surface of helper T cells and transmits inhibitory signals to T cells. CTLA4 is similar to the T cell costimulatory protein CD28, both molecules bind to B7-1 and B7-2 on antigen presenting cells. CTLA-4 transmits inhibitory signals to T cells, while CD28 transmits costimulatory signals. Intracellular CTLA-4 is also found in regulatory T cells and may be important for its function. Activation of T cells via T cell receptors and CD28 results in increased expression of CTLA-4, an inhibitory receptor for the B7 molecule. The inhibitors of the present disclosure may block one or more functions of CTLA-4, B7-1, and/or B7-2 activity. In some embodiments, the inhibitor will block the CTLA-4 and B7-1 interaction. In some embodiments, the inhibitor will block the CTLA-4 and B7-2 interaction.

In some embodiments, the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen-binding fragment thereof, an immunoadhesin, a fusion protein, or an oligopeptide.

Anti-human CTLA-4 antibodies (or VH and/or VL domains derived therefrom) suitable for use in the methods of the invention can be generated using methods well known in the art. Alternatively, art-recognized anti-CTLA-4 antibodies may be used. For example, anti-CTLA-4 antibodies disclosed in the following references can be used in the methods disclosed herein: US 8119129; WO 01/14424; WO 98/42752; WO 00/37504(CP675206, also known as tremelimumab, formerly teximumab); U.S. patent nos. 6207156; hurwitz et al, 1998. The teachings of each of the foregoing publications are incorporated herein by reference. Antibodies that compete with any of these art-recognized antibodies that bind CTLA-4 can also be used. For example, humanized CTLA-4 antibodies are described in International patent application Nos. WO2001/014424, WO2000/037504, and U.S. Pat. No. 8017114; all of which are incorporated herein by reference.

Yet another anti-CTLA-4 antibody that may be used as a checkpoint inhibitor in the methods and compositions of the present disclosure is ipilimumab (also known as 10D1, MDX-010, MDX-101, and Or antigen-binding fragments and variants thereof (see, e.g., WO 01/14424).

In some embodiments, the inhibitor comprises the heavy and light chain CDRs or VRs of tremelimumab or ipilimumab. Thus, in one embodiment, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of tremelimumab or ipilimumab and the CDR1, CDR2, and CDR3 domains of the VL region of tremelimumab or ipilimumab. In another embodiment, the antibody competes with the above antibody for binding to and/or to the same epitope on PD-1, B7-1, or B7-2. In another embodiment, the antibody has at least about 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 99% (or any derivable range thereof) variable region amino acid sequence identity to an antibody described above.

Other molecules for modulating CTLA-4 include soluble CTLA-4 ligands and receptors, as described in US patent nos. US5844905, US5885796 and international patent application nos. WO1995001994 and WO1998042752, all of which are incorporated herein by reference; and immunoadhesins, as described in US patent No. US8329867, which is incorporated herein by reference.

Microbial modulators

In some aspects, the present disclosure relates to methods comprising detecting one or more of the following in a subject: bacteroides faecalis, Bacteroides intestinalis, Listeria fragilis, Vibrio fluvialis, Tyr zeylanicum, Flavonoides pernici, Dielma rustidiosa, Butyrimonas farichis, Heterobacter, Ackermanella viscophila, Lactobacillus reuteri, Prevotella faecalis, Prevotella saxifragi, Citrobacter, Clostridium hirsutum, Hugateicatrididium aldrich, Citrobacter murinum, Eubacterium crenatum, Hafniaceae, Citrobacter freundii, Eubacterium hopanii, Enterobacter cloacae, Hafnia alvei, Hafnia, Rosematerium human Roseburia, Weissella mesenteroides, Enterobacter, Bacteroides, Lactobacillus, Klebsiella aerogenes, Klebsiella, Diprobacter Coprobacter, Intestibacter intestinitella, Paraselis, Klebsiella pneumoniae, Forniella faecalis, Forniella, Klebsiella, Enterobacter, Klebsiella, Enterobacter strain, Klebsiella, Enterobacter, Klebsiella, Enterobacter strain, Klebsiella, or Klebsiella, Enterobacter strain, Klebsiella, or Enterobacter strain, etc, Yersinia diconii, Hungateicylium thermocellum, Donella longum, Thermus chrysanthemi, Muricomes intestini, Geobacillus underground and Anaerotigm lactitifimentans or the bacterial species disclosed in FIG. 28C.

In another aspect, the present disclosure is directed to a method comprising detecting one or more of the following in a subject: bacteroides faecalis, Bacteroides intestinalis, Allierella terrestris, Bacteroides fragilis, Vibrio fluvialis, Tazilla tarkii, Bacteroides faecalis, Flavonoidea perniciae, Dielma rustidiosa, Achima viscodermatum, Lactobacillus reuteris, Prevotella faecalis, Prevotella sarmentosa, Mastigomycota pachynia, Clostridiaceae, Aliskips indentin, Bacteroides sterosorosis, Clostridium lactitifaciens, Acysivirida alkaniphila, Acetactor murinus, cellulose acetate vibrio, Vibrio ethanologens, Acidobacterium bovis, Acidobacterium leucobacter xylinum, Acidovorax, Acidovicella shigella, Acidobacterium equorum, Acidophila muciniphilus, Acidobacterium viscosus, Acidobacterium acidipritides, Acidobacterium acidipritium, Acidobacterium sp, Acidobacterium lacticola, Acidobacterium lacticum, Acidobacterium lacticola, Acidobacterium lactides, Acidobacterium, human anaerobic coliform bacteria, Anaerotruncus rubinfants, Dermatophagoides, Bacteroides acidovorans, Bacteroides caecidum caecimuris, Bacteroides dolichiensis, Bacteroides faecilothia, Bacteroides farichilensis, Bacteroides stercorarosoisonosis, Bacteroides xylanisolvens, Enterobacter partans, Beduini malsiliensis, Bifidobacterium pseudolongum, Blauteria mucida, Breznaikia blattii, Breznaiella, Breznaikia paphododae, Corynebacterium parvum, Vibrio pentosaceus, Catabecter hongkongensis, Christensenella masselliensis, Christentenella minuta, Christennesensis, Clostridium sticklandii, Clostridium sticklandicum, Clostridium difficile, Clostridium lacticum, Clostridium difficile, Clostridium bifidum, Clostridium bifidulus latum, Clostridium bifidulus latarundiculicile, Clostridium perfringens, Clostridium bifidum, Clostridium berculum, Clostridium bifidum, Clostridium berculicile, Clostridium difficile, Clostridium berculicile, Clostridium sterculum, Clostridium sterculmorbifidum, Clostridium berculum, Clostridium bifidum, Clostridium sterculum, Clostridium sterculmorbifidum, Clostridium berculmorbifidum, Clostridium berculum, Clostridium berculmorbifidum, Clostridium berculum, Clostridium berculmorbifidum, Clostridium berculum, Clostridium berculmorbifidum, Clostridium berculum, Clostridium berculmorbifidum, Clostridium berculum, Clostridium ber, Clostridium lyticum, Clostridium straminilyticum, Clostridium viridans, Clostridium delignificanum, Coprobobacter secunduns, enterococcus acutus, Curvularia maliensis, Delluvatalea saccharophila, Gomphagi gondii, Desulfobacter metallothionein, Campylobacter orientalis, Desulfovibrio sulphureus, Vibrio simplex, Vibrio formis, Eisenbergiella massilis, Emergenecia timonensis, enterococcus enterobacter enterocolitica, Enterobacterium enterobacter, Enterobacter musculus, Erysipelotricum ramosus, Erysipelothrix lutescens, Escherichia coli, sterol-producing Bacillus faecalis, Eubacterium longipes, Eubacterium inertium, Eubacterium multivorans, Lactobacillus ventricusflexus, Lactobacillus paracasei, Lactobacillus casei, Flaviviparus, Lactobacillus sanotis, Lactobacillus sanfrancisella, Lactobacillus casei, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanwicia, Lactobacillus sanwicelitis, Lactobacillus sanillustrating strain, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanwicelitis, Lactobacillus sanobacter, Lactobacillus sanillustrating strain, Lactobacillus sanobacter strain, Lactobacillus sanillustrating strain, Lactobacillus sanobacter, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanobacter, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, Lactobacillus sanillustrating strain, Lactobacillus sanbucillus, strain, Lactobacillus sanillustrating strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, strain, human milk bacillus, intestinal lactobacillus, Lactobacillus johnsonii, Lactobacillus reuteri, Lactobacillus taiwanensis, Lawsonia intracellularis, Longibamulummuris, Marvinbryantia formatexins, Millilonella maliensis, Serissa henryi, Muribactum intestinale, Murimonas intestin, Natranavira pectinora, Neglecta timelensis, Microbacterium odoratum, Olseneella profunsa, Cinchotan, Dermatoptericum valerate, Pectinathi pisiferus, Parazidoides guli, Paraseteria paraguariensis, human Paraseteria parasarensis, Paraviella, Pectiococcus nigra, Phaseochaelis, Phocae maliensis, Porphyromonas porteri oralis, Prevotella praeruptorum, Pectinosus paranois, Runococcus, Rutacea, Pecticola, Pectinatum, Pectinale, Pectinal, Ruthenium lactatiformans, Sphingomonas kyeonggiensis, Spiroplasma stenotrophomonas, Termite spore bacillus, Stomatobaculum longum, Acidococcus oligocens, Streptococcus danieliae, Cotrophomonas Volvhicus, Thermomonas taiwanensis, Tydinium californica, Tyridium lake Tyndaria, Hemicturium, Turicomonas muris, Tyzzerella nexilis, Vallitalea pronyensis, and Vibrio parahaemophilus and/or the bacteria disclosed in FIG. 28C.

In another aspect, the present disclosure relates to compositions comprising at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens.

In some embodiments, the composition comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

In another aspect, the present disclosure relates to compositions comprising at least two isolated or purified populations of bacteria belonging to one or more than one of the genera or species of: flavonoids, Dielma, Akkermansia, Allomycins, Bacteroides, butyric acid monads, haemophilus, Tazier, Parabacteroides dieldii, Fournierella maliensis, Eisenbergiella tayi, Disjonella, Hungatricidiam themocellum, Dolerella longata, Thermus coxiella, Muricomes, Geobacillus, Prevotella marmoraxella, Lactobacilli, Bacteroides fingiensis, Lactobacillus johnsonii, Dermatopterium composte, and Anaeroticum gntatatisfaciens. In some embodiments, the composition comprises at least two isolated or purified populations of bacteria belonging to one or more than one of the genera or species of: flavonoids, Bacteroides, butyric acid monads, Dielma, Akkermansia, Arthrobacter, Bacteroides faecalis, Deuterobacteroides, Fournierella massilisensis, Bacteroides coprophilus, Eisenbergiella tayi, Disjor bacterium, Hungatricellium thermocellum.

In another aspect, the present disclosure relates to compositions comprising isolated or purified populations of bacteria of at least one, at least two, or 3, 4, 5, 6, 7, 8, 9, 10, 15, or 20 (or any derivable range therein) of: parabacteroides destructor, Fournierella malilensis, Eisenbergiella tayi, Yersinia dicentri, Hungateiclysis tribulus, Thermoascus longchain, Pyrococcus coxiella, Muricomes, Geobacillus, Martensivorax, Lactobacillus acidophilus, Bacteroides fenthizakii, Lactobacillus johnsonii, Dermatopterium, Bacteroides flavobacterium, Butyribacterium butyricum, Dielma, Akermanniella, Arthrobacter, Anaerognum lactacidentamoellensis, Bacteroides faecalis, Enterobacter intestinalis, Aristobacter neritidis, Bacteroides fragilis, Haemophilus, Tyrosomus, Bacteroides faecalis, Akkeriphiliella, Acteribacter fragilis, Acidobacter fragilis, Actericola, Acidophilium, Actericola, Clostridium acetobacter asiaticum, Clostridium sporogenes, Lactobacillus sporogenes, Lactobacillus sporogenes, Lactobacillus, etc., Lactobacillus, etc., or Lactobacillus, Vibrio ethanoacetate, bovine acholens, Ceratodes, Acidovorax radialis, Edwardsiella equestrianus, Akkermansia muccinphila, Alisipes industris, Alisipes obesi, Lepidobacter putrescentis, Alligata spp, Eleocharides, Eleocharis monteiformis, Saccharine bacilli, Alkalibacterium bacchia, Aneurobacterium canicola, Aneurobacterium chaetolvens, Aneurogluca lulosum, Aneurogluca lulinosa Luosilytica, Anaerobiospora, Aneuragena torta, human Colophonium anaerobacter, Aneurunculus rubicunensis, Aneuritoides, Pseudovorax snifferii, Pseudoceroticus alvarezii, Bacteroides calinioculeus, Bacteroides calinicureus, Bacteroides, Clostridium toruloides, Clostridium difficile, Clostridium berberidactylencephalus, Clostridium difficile, Clostridium berberissimum, Clostridium berberidactylum, Clostridium berriensis, Clostridium beralopecorhizianum, Clostridium beraloides, Clostridium beralopecorubidus, Clostridium beralobylonicera, Clostridium bereichizianum, Clostridium beraloides, Clostridium bereichizianum, bereichizium, bereichizianum, bereichii, bereichizianum, bereichizium, bereichizianum, bereichizium, bereichizii, bereichizium, bereichizii, bereichizium, bereichizii, bereichizium, bereichizii, bereichizium, bereichizii, bereichizium, fast growing Clostridium, cellobiose producing Clostridium, cellulolytic Clostridium, Clostridium torulosum, Clostridium spirochetum, Clostridium quail, Clostridium marindi, Clostridium indolens, Clostridium jizhou, Clostridium lacticum, Clostridium laval, Clostridium methyl pentose, Clostridium orotate, Clostridium oryzium, Clostridium maceralfatum, Clostridium polysaccharolyticum, Clostridium populi, Clostridium saccharolyticum, Clostridium sarudiense, Clostridium schizolyticum, Clostridium stromamisollosicum, Clostridium viridans, Clostridium xylanisolyticum, Copropbacter secundus, Clostridia faecalis, Curvularia massilisensis, defluvilatifolia, Clostridium gibberellatum, Acidithiobacillus metallothionein Orientia, curvulus thioparvus, Vibrio desulfuricola desulfurizatio, Vibrio simply desulfurizatio, Dolerella formigenes, Eisengiliella maliensis, Emergeniensis, Escherichia coli, enterococcus minitans, Enterobacter enterobacter, Enterobacter flavus, Enterobacter faecalis, Salmonella choleraesurus, Salmonella, and Salmonella, and Salmonella, and Salmonella, rodent, inert, polyclonal, Escherichia, Eubacterium ventricosum, faecal, Flavivirida pacifica, Flaviviridobacter, Flintibacter butryicus, Gordonibacter faecalis, Cellulobacter halodurans, Harryflintia acetispora, Holland, Aneurobacter saccharolyticum, Ihubacter masseliensis, Intestimonas butyrilduucens, Irregularibacter mularia, Lachnocristridium pacans, Lactobacillus animalis, Lactobacillus gasseri, Lactobacillus reuteri, Lactobacillus paracasei, Longiensis, Marvinnbryanu, Lactobacillus casei, Lactobacillus reuteri, Lactobacillus paracasei, Lactobacillus, Porphyromonas catorii, Prevotella oralis, Prevotella coprinus, Prevotella protemontinii, Vibrio ruminobutyrate, Pseudoflavonolactobacillus hirsutus, Pseudomonas aeruginosa, Raoulobacter trichothecensis, Raoulobacter timonensis, Rhizobium strainoryzae, Raosbai raeberrea, Raosbai rabypresently, Raosbai enterobacter, Ruminostrothridium thermocellum, Ruminococcus championellis, Ruminococcus coprinus, Ruminococcus flavus, Ruthenium lactanformation, Sphingomonas kyenopgiensis, Sphingomonas, Microspira, Termite, Stomatamulus longum, Streptococcus parvus, Streptococcus paracoccus, Streptococcus faecalis, Thermoplasma, Tyllus, Tyrospira, Vanilla, or Klinella ureae.

In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more than one of the following categories: flavonoids, Bacteroides faecalis, Butyrimonas faecalis, Dielma, Akkermansia and Alisipes insistintus. In some embodiments, the composition does not comprise bacteroides cacteus. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more of Dielma and akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more of the genera alistipes, dielmas and akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified bacterial population belonging to the genus akkermansia. In some embodiments, the composition comprises or further comprises at least one isolated or purified akkermansia muciniphila population. In some embodiments, the composition comprises or further comprises a population of bacteria comprising one or more of akkermansia muciniphila and Dielma rustidiosa and Alistipes indigentinostus. In some embodiments, the bacteria of the genus flavonolactobacillus include flavonolactobacillus pernici. In some embodiments, the composition comprises or further comprises at least one isolated or purified population of bacteria belonging to one or more than one of the genera or species of: bacteroides fragilis, Vibrio, Taziella, long chain bacteria, Coxietz, Muricomes intestini, Geobacillus, Anerargignum lactatimenteans. In some embodiments, the composition comprises or further comprises at least one isolated or purified bacteroides enteric flora. In some embodiments, the composition comprises or further comprises at least one isolated or purified bacterial population belonging to the phylum firmicutes, order clostridiales and family ruminococcaceae. In some embodiments, the composition comprises or further comprises flavonolactobacillus perniciae and/or Dielma rustidia. In some embodiments, the composition comprises or further comprises bacteroides faecalis, butyricomonas faecalimine, flavonolyticus perniciae, Dielma fascidiosa, Alistipes indestinctus, and akkermansia muciniphila.

In some embodiments, the composition comprises less than 1 x 10⁵1, 1 × 10⁴1, 1 × 10³Or 1 x 10²Individual CFUs or cells (or any derivable range thereof) classified as bacteria of the firmicutes, clostridiales and ruminococcaceae families. In some embodiments, the composition comprises less than 1 x 10⁵1, 1 × 10⁴1, 1 × 10³Or 1 x 10²Belonging to the families Ruminococcaceae, Clostridiaceae, Trichospiraceae, Micrococcaceae and/or VeillonellaceaeCFU or cells of bacteria (or any derivable range thereof).

In another aspect, provided herein are microbial modulator compositions for use in the treatment of cancer and methods for altering the microbiome of a subject who has been or will be treated with an immune checkpoint inhibitor combination therapy.

The present disclosure also provides a pharmaceutical composition comprising one or more than one population of microorganisms as described above and e.g. in the summary of the invention. Thus, the bacterial species are present in the form of viable bacteria, whether in dry, lyophilized or sporulated form. This may preferably be suitable for suitable administration; for example, in tablet or powder form, possibly with an enteric coating, for oral treatment.

In particular aspects, the compositions are formulated for oral administration. Oral administration may be achieved using chewable formulations, dissolving formulations, encapsulating/coating formulations, multi-layered lozenges (to separate the active ingredient and/or active ingredient from the excipients), slow-release/timed-release formulations, or other suitable formulations known to those skilled in the art. Although the term "tablet" is used herein, the formulation may take a variety of physical forms, which forms may be generally referred to by other terms, such as troches, pills, capsules, and the like.

While the compositions of the present disclosure are preferably formulated for oral administration, other routes of administration may be employed, including, but not limited to, subcutaneous, intramuscular, intradermal, transdermal, intraocular, intraperitoneal, mucosal, vaginal, rectal, and intravenous.

A desired dose of a composition of the present disclosure can be provided in multiple (e.g., two, three, four, five, six, or more) sub-doses administered at appropriate time intervals throughout the day.

In one aspect, the disclosed compositions can be prepared as capsules. The capsule (i.e., carrier) can be a hollow, generally cylindrical capsule formed from a variety of substances such as gelatin, cellulose, carbohydrates, and the like.

In another aspect, the disclosed compositions may be prepared as suppositories. Suppositories may contain, but are not limited to, bacteria and one or more carriers such as polyethylene glycol, gum arabic, acetylated monoglycerides, carnauba wax, cellulose acetate phthalate, corn starch, dibutyl phthalate, docusate sodium, gelatin, glycerin, iron oxide, kaolin, lactose, magnesium stearate, methyl paraben, pharmaceutical glaze, povidone, propyl paraben, sodium benzoate, sorbitan monooleate, sucrose talc, titanium dioxide, white wax and colorants.

In some aspects, the disclosed microbial modulator compositions can be prepared as tablets. Tablets may comprise bacteria and one or more than one tableting (i.e. carrier) such as dibasic calcium phosphate, stearic acid, croscarmellose, silicon dioxide, cellulose and a cellulose coating. The tablets may be formed using a direct compression process, but those skilled in the art will appreciate that the tablets may be formed using a variety of techniques.

In other aspects, the disclosed microbial modulator compositions can be formed into, or as an additive to, a food or beverage to which a suitable amount of bacteria is added to render the food or beverage carrier.

The microbiology regulator composition of the present disclosure may further comprise one or more than one prebiotic known in the art, such as lactitol, inulin, or a combination thereof.

In some embodiments, the microbial modulator composition may further comprise a food product or nutritional supplement effective to stimulate the growth of bacteria of the order clostridia present in the gastrointestinal tract of a subject. In some embodiments, the nutritional supplement is produced by bacteria associated with the healthy human gut microbiome.

Additional therapies

The current methods and compositions of the present disclosure may include one or more additional therapies known in the art and/or described herein. In some embodiments, the additional therapy comprises an additional cancer treatment. Examples of such treatments are described herein.

A. Immunotherapy

In some embodiments, the additional therapy comprises further cancer immunotherapy. Cancer immunotherapy (sometimes referred to as immunooncology, abbreviated IO) utilizes the immune system to treat cancer. Immunotherapy can be classified as active, passive, or mixed (active and passive). These methods take advantage of the fact that cancer cells typically have on their surface molecules detectable by the immune system, called Tumor Associated Antigens (TAAs); they are typically proteins or other macromolecules (e.g., carbohydrates). Active immunotherapy directs the immune system to attack tumor cells by targeting TAAs. Passive immunotherapy enhances existing anti-tumor responses and involves the use of monoclonal antibodies, lymphocytes and cytokines. Immunotherapy is known in the art, and some are described below.

1. Inhibition of co-stimulatory molecules

In some embodiments, the immunotherapy comprises an inhibitor of a costimulatory molecule. In some embodiments, the inhibitors include inhibitors of B7-1(CD80), B7-2(CD86), CD28, ICOS, OX40(TNFRSF4), 4-1BB (CD 137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18), and combinations thereof. Inhibitors include inhibitory antibodies, polypeptides, compounds and nucleic acids.

2. Dendritic cell therapy

Dendritic cell therapy activates lymphocytes by causing them to present tumor antigens to the lymphocytes, causing them to kill other cells presenting the antigen, thereby eliciting an anti-tumor response. Dendritic cells are Antigen Presenting Cells (APCs) in the immune system of mammals. In cancer therapy, they contribute to cancer antigen targeting. An example of a dendritic cell-based cell cancer therapy is sipuleucel-T, which is sold by provenge (r).

One method of inducing presentation of tumor antigens by dendritic cells is vaccination with autologous tumor lysates or short peptides (small portions of proteins corresponding to protein antigens on cancer cells). These peptides are often administered in combination with adjuvants (highly immunogenic substances) to increase the immune and antitumor response. Other adjuvants include proteins or other chemicals that attract and/or activate dendritic cells, such as granulocyte macrophage colony-stimulating factor (GM-CSF).

Dendritic cells can also be activated in vivo by allowing tumor cells to express GM-CSF. This can be achieved by genetically engineering tumor cells to produce GM-CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.

Another strategy is to remove dendritic cells from the patient's blood and activate them in vitro. Dendritic cells are activated in the presence of a tumor antigen, which may be a single tumor-specific peptide/protein or a tumor cell lysate (a solution of lysed tumor cells). These cells are infused (with optional adjuvants) and an immune response is elicited.

Dendritic cell therapy involves the use of antibodies that bind to receptors on the surface of dendritic cells. Antigens may be added to the antibody and may induce dendritic cell maturation and provide immunity to the tumor. Dendritic cell receptors such as TLR3, TLR7, TLR8 or CD40 have been used as antibody targets.

CAR-T cell therapy

Chimeric antigen receptors (CARs, also known as chimeric immune receptors, chimeric T cell receptors, or artificial T cell receptors) are engineered receptors that combine novel non-MHC restriction specificities with immune cells to target cancer cells. Typically, these receptors graft the specificity of monoclonal antibodies onto T cells. These receptors are called chimeras because they are fused from portions derived from different sources. CAR-T cell therapy refers to treatment using such transformed cells for therapeutic purposes, e.g., to treat cancer.

The rationale for CAR-T cell design involves recombinant receptors that combine antigen binding and T cell activation functions. A general premise of CAR-T cells is that T cells are artificially generated that target markers found on cancer cells. Scientists may remove T cells from humans, genetically modify them, and then place them back into the patient so that they attack cancer cells. Once a T cell is engineered to be a CAR-T cell, it will act as a "live drug". CAR-T cells will establish a link between the extracellular ligand recognition domain and the intracellular signaling molecule, which in turn will activate the T cell. The extracellular ligand recognition domain is typically a single chain variable fragment (scFv) derived from an antibody. An important aspect of the safety of CAR-T cell therapy is how to ensure that only cancerous tumor cells are targeted, while normal cells are not. The specificity of CAR-T cells is determined by the choice of the molecule to be targeted.

Exemplary CAR-T therapies include tisagenlecucel (kymeriah (r)) and axicbtagene ciloleucel (yescata (r)). In some embodiments, the CAR-T therapy targets CD 19.

4. Cytokine therapy

Cytokines are proteins produced by many types of cells present within a tumor. They can modulate immune responses. Tumors often use them to allow them to grow and reduce immune responses. These immunomodulating effects make them useful as drugs for stimulating immune responses. Two commonly used cytokines are interferons and interleukins.

Interferons are produced by cells of the immune system. They are usually involved in antiviral responses, but also have an effect on cancer. They are divided into three groups: type I (IFN. alpha. and IFN. beta.), type II (IFN. gamma.) and type III (IFN. lambda.).

Interleukins have a range of immune system effects. IL-2 is an exemplary interleukin cytokine therapy.

5. Adoptive T cell therapy

Adoptive T cell therapy is a form of passive immunization by the transfer of T cells (adoptive cell transfer). T cells are found in blood and tissues and are usually activated when they find a foreign pathogen. In particular, T Cell Receptors (TCRs) are activated when they encounter cells that display a portion of the foreign protein on their surface antigen. These cells may be infected cells or Antigen Presenting Cells (APC). They are found in normal tissues and in tumor tissues, where they are called Tumor Infiltrating Lymphocytes (TILs). They are activated by the presence of APCs, such as dendritic cells presenting tumor antigens. Although these cells can attack the tumor, the environment within the tumor is highly immunosuppressive, which will prevent immune-mediated tumor death.

Various methods have been developed to generate and obtain tumor-targeted T cells. T cells specific for tumor antigens can be removed from Tumor Samples (TILs) or hemofiltered. Subsequent activation and culture was performed ex vivo and the resulting activated T cell preparation was re-infused. Activation can be performed by exposing T cells to a tumor antigen.

B. Oncolytic virus

In some embodiments, the additional therapy comprises an oncolytic virus. An oncolytic virus is a virus that preferentially infects and kills cancer cells. When infected cancer cells are destroyed by oncolytic action, they release new infectious or small viral particles to help destroy the remaining tumor. Oncolytic viruses are thought to not only cause direct destruction of tumor cells, but also stimulate the host's anti-tumor immune response for long-term immunotherapy.

C. Polysaccharides

In some embodiments, the additional therapy comprises a polysaccharide. Certain compounds found in mushrooms, mainly polysaccharides, can up-regulate the immune system and may have anticancer properties. For example, β -glucans such as lentinan have been shown in laboratory studies to stimulate macrophages, NK cells, T cells and immune system cytokines and have been studied in clinical trials as immune adjuvants.

D. Novel antigens

In some embodiments, the additional therapy comprises neoantigen administration. Many tumors express mutations. These mutations potentially create new targetable antigens (neoantigens) for use in T cell immunotherapy. As identified using RNA sequencing data, the presence of CD8+ T cells in cancer lesions was higher in tumors with high mutation load. In many human tumors, the transcriptional levels associated with the cytolytic activity of natural killer and T cells are positively correlated with the mutation burden.

E. Chemotherapy

In some embodiments, the additional therapy comprises chemotherapy. Suitable classes of chemotherapeutic agents include (a) alkylating agents, such as nitrogen mustards (e.g., mechlorethamine, cyclophosphamide, ifosfamide, melphalan, chlorambucil), ethylenimines and methylmelamines (e.g., hexamethylmelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomustine, cloxacin (chlorozotocin), streptozocin), and triazines (e.g., dacarbazine), (b) antimetabolites, such as folic acid analogs (e.g., methotrexate), pyrimidine analogs (e.g., 5-fluorouracil, floxuridine, cytarabine, azauridine), and purine analogs and related materials (e.g., 6-mercaptopurine, 6-thioguanine, pentostatin), (c) natural products, such as vinca alkaloids (e.g., vinblastine, vincristine), epipodophyllotoxins (e.g., etoposide, teniposide), antibiotics (e.g., dactinomycin, daunorubicin, doxorubicin, bleomycin, plicamycin, and mitoxantrone), enzymes (e.g., L-asparaginase), and biological response modifiers (e.g., interferon- α), and (d) other agents such as platinum coordination complexes (e.g., cisplatin, carboplatin), substituted ureas (e.g., hydroxyurea), methylhydrazine derivatives (e.g., procarbazine), and adrenocortical suppressants (e.g., paclitaxel and mitotane). In some embodiments, cisplatin is a particularly suitable chemotherapeutic agent.

Cisplatin has been widely used to treat cancer, such as metastatic testicular or ovarian cancer, advanced bladder cancer, head and neck cancer, cervical cancer, lung cancer, or other tumors. Cisplatin is not absorbed orally and must therefore be delivered via other routes, such as intravenous, subcutaneous, intratumoral or intraperitoneal injection. Cisplatin can be used alone or in combination with other agents, and the effective dose for clinical use comprises about 15mg/m²To about 20mg/m²Every three weeks for 5 days, a total of three courses of treatment are contemplated in certain embodiments. In some embodiments, the amount of cisplatin delivered to a cell and/or subject with a construct comprising an Egr-1 promoter operably linked to a polynucleotide encoding a therapeutic polypeptide is less than the amount delivered with cisplatin alone.

Other suitable chemotherapeutic agents include anti-microtubule agents, such as paclitaxel ("Taxol") and doxorubicin hydrochloride ("doxorubicin"). The combination of Egr-1 promoter/TNF α construct delivered via adenoviral vector and doxorubicin was determined to be effective in overcoming resistance to chemotherapy and/or TNF- α, indicating that the combination treatment of this construct and doxorubicin overcomes resistance to both doxorubicin and TNF- α.

Doxorubicin is poorly absorbed and is preferably administered intravenously. In certain embodiments, a suitable intravenous dose for an adult comprises about 60mg/m every about 21 days²To about 75mg/m²Or about 25mg/m for each of 2 or 3 consecutive days²To about 30mg/m²Repeated every about 3 to about 4 weeks, or about 20mg/m once a week². In elderly patients, the lowest dose should be used when there is past myelosuppression or neoplastic myeloinvasion caused by past chemotherapy, or when combined with other myelosuppressive drugs.

Nitrogen mustard is another suitable chemotherapeutic agent that may be used in the methods of the present disclosure. The nitrogen mustard may include, but is not limited to, mechlorethamine (HN)₂) Cyclophosphamide and/or ifosfamide, melphalan (L-sarcolysin) and chlorambucil. CyclophosphamideAvailable from Mead Johnson and available,available from Adria) is another suitable chemotherapeutic agent. For adults, suitable oral dosages include, for example, from about 1 mg/kg/day to about 5 mg/kg/day, and intravenous dosages include, for example, from about 40mg/kg to about 50mg/kg initially administered in divided doses over a period of from about 2 days to about 5 days, or from about 10mg/kg to about 15mg/kg about every 7 days to about 10 days, or from about 3mg/kg to about 5mg/kg twice a week, or from about 1.5 mg/kg/day to about 3 mg/kg/day. The intravenous route is preferred due to gastrointestinal adverse effects. Drugs are also sometimes administered intramuscularly, by osmosis, or into body cavities.

Additional suitable chemotherapeutic agents include pyrimidine analogs such as cytarabine (cytosine arabinoside), 5-fluorouracil (fluorouracil; 5-FU) and fluorouridine (fluorouracil deoxynucleoside; FudR). 5-FU may be present at about 7.5mg/m²To about 1000mg/m²Is administered to the subject. Furthermore, the 5-FU dosing regimen may be directed to multipleA time period, for example up to six weeks, or as determined by one of ordinary skill in the art to which this disclosure pertains.

Gemcitabine diphosphateEli Lilly&Co., "gemcitabine") is another suitable chemotherapeutic agent that is recommended for the treatment of advanced and metastatic pancreatic cancer, and therefore may also be used in the present disclosure to treat these cancers.

The amount of chemotherapeutic agent delivered to the patient may be variable. In a suitable embodiment, when chemotherapy is administered with the construct, the chemotherapeutic agent may be administered in an amount effective to cause the cessation or regression of cancer in the host. In other embodiments, the chemotherapeutic agent may be administered in any amount 1/2 to 1/10000 that is a chemotherapeutic effective dose of the chemotherapeutic agent. For example, the chemotherapeutic agent may be administered in an amount of about 1/20, about 1/500, or even about 1/5000 of the effective dose of the chemotherapeutic agent. The chemotherapeutic agents of the present disclosure in combination with the constructs can be tested in vivo for the desired therapeutic activity, as well as for determining an effective dose. For example, such compounds may be tested in a suitable animal model system including, but not limited to, rat, mouse, chicken, cow, monkey, rabbit, etc., prior to testing in humans. In vitro tests may also be used to determine suitable combinations and dosages, as described in the examples.

F. Radiotherapy

In some embodiments, the additional or previous therapy comprises radiation, such as ionizing radiation. As used herein, "ionizing radiation" refers to radiation that includes particles or photons that have sufficient energy or can generate sufficient energy via nuclear interactions to produce ionization (gain or loss of electrons). An exemplary and preferred ionizing radiation is x-radiation. Means for presenting x-radiation to a target tissue or cell are well known in the art.

In some embodiments, the amount of ionizing radiation is greater than 20 gray (Gy) and is administered in one dose. In some embodiments, the amount of ionizing radiation is 18Gy and is administered in three doses. In some embodiments, the amount of ionizing radiation is at least, at most, or exactly 2Gy, 4Gy, 6Gy, 8Gy, 10Gy, 15Gy, 16Gy, 17Gy, 18Gy, 19Gy, 20Gy, 21Gy, 22Gy, 23Gy, 24Gy, 25Gy, 26Gy, 27Gy, 18Gy, 19Gy, 30Gy, 31Gy, 32Gy, 33Gy, 34Gy, 35Gy, 36Gy, 37Gy, 38Gy, 39Gy, 40Gy, 41Gy, 42Gy, 43, 44Gy, 45, Gy 46Gy, 47, 48Gy, 49Gy, or 40Gy (or any derivable range therein). In some embodiments, the ionizing radiation is administered in at least, at most, or exactly 1 dose, 2 doses, 3 doses, 4 doses, 5 doses, 6 doses, 7 doses, 8 doses, 9 doses, or 10 doses (or any derivable range therein). When more than one dose is administered, each dose may be separated by about 1 hour, 4 hours, 8 hours, 12 hours, or 24 hours, or 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, or 8 days, or 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 12 weeks, 14 weeks, or 16 weeks, or any derivable range therein.

In some embodiments, the amount of IR can be presented as a total dose of IR, which is then administered in divided doses. For example, in some embodiments, the total dose is 50Gy, administered in 10 divided doses of 5Gy each. In some embodiments, the total dose is 50-90Gy, administered in 20-60 divided doses, each of 2-3 Gy. In some embodiments, the total dose of IR is at least, at most, or about 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 125, 135, 130, or 140 (any range therein). In some embodiments, the total dose is administered in fractionated doses of at least, at most, or exactly 1Gy, 2Gy, 3Gy, 4Gy, 5Gy, 6Gy, 7Gy, 8Gy, 9Gy, 10Gy, 12Gy, 14Gy, 15Gy, 20Gy, 25Gy, 30Gy, 35Gy, 40Gy, 45Gy, or 50Gy (or any derivable range therein). In some embodiments, at least, up to, or exactly 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 divided doses (or any derivable range therein) are administered. In some embodiments, at least, up to, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) divided doses are administered per day. In some embodiments, at least, up to, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any derivable range therein) divided doses are administered weekly.

G. Surgery

About 60% of cancer patients will undergo certain types of surgery, including prophylactic, diagnostic or staging, curative and palliative surgery. Curative surgery includes resection, in which all or part of the cancerous tissue is physically removed, excised, and/or destroyed and may be used in conjunction with other therapies, such as treatments, chemotherapies, radiation therapies, hormonal therapies, gene therapies, immunotherapies, and/or alternative therapies according to embodiments of the present invention. Tumor resection refers to physically removing at least a portion of a tumor. In addition to tumor resection, treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and micromanipulation (morse surgery).

After resection of some or all of the cancerous cells, tissue, or tumor, a cavity may form in the body. Treatment may be accomplished by perfusion, direct injection, or regional local application of additional anti-cancer therapies. Such treatment may be repeated, for example, every 1 day, 2 days, 3 days, 4 days, 5 days, 6 days or 7 days or every 1 week, 2 weeks, 3 weeks, 4 weeks and 5 weeks or every 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 7 months, 8 months, 9 months, 10 months, 11 months or 12 months. These treatments may also have different dosages.

H. Other agents

It is contemplated that other agents may be used in combination with certain aspects of the present embodiments to enhance the efficacy of the treatment. These additional agents include agents that affect the upregulation of cell surface receptors and GAP junctions, cytostatic and differentiation agents, cell adhesion inhibitors, agents that increase the sensitivity of hyperproliferative cells to apoptosis-inducing agents, or other biological agents. Increasing intercellular signaling by increasing the number of GAP junctions increases the anti-hyperproliferative effect on the adjacent hyperproliferative cell population. In other embodiments, cytostatic or differentiation agents may be used in combination with certain aspects of the present embodiments to improve the anti-hyperproliferative efficacy of the treatments. Cell adhesion inhibitors are expected to improve the efficacy of embodiments of the present invention. Examples of cell adhesion inhibitors are Focal Adhesion Kinase (FAK) inhibitors and lovastatin. It is also contemplated that other agents that increase the sensitivity of hyperproliferative cells to apoptosis, such as antibody c225, may be used in combination with certain aspects of the present embodiments to improve therapeutic efficacy.

Administration of therapeutic compositions

The therapies provided herein include administering an immune checkpoint inhibitor in combination with a microbial modulator. The therapy may be administered in any suitable manner known in the art. For example, the immune checkpoint inhibitor (e.g., a PD-1 inhibitor and/or a CTLA-4 inhibitor) and the microbial modulator can be administered sequentially (at different times) or simultaneously (at the same time). In some embodiments, the immune checkpoint inhibitor and the microbial modulator are in separate, separate compositions. In some embodiments, the immune checkpoint inhibitor is in the same composition as the microbial modulator.

Embodiments of the present disclosure relate to compositions and methods comprising one or more CTLA-4, B7-1, and/or B7-2 inhibitors in combination with one or more PD-1, PDLl, and/or B7-2 inhibitors. The immune checkpoint inhibitor may be administered in one composition or in more than one composition, such as 2 compositions, 3 compositions, or 4 compositions. Various combinations of inhibitors may be employed, for example, a CTLA-4, B7-1, or B7-2 inhibitor is "a" and a PD-1, PDL1, or PDL2 inhibitor is "B":

in some embodiments, the methods comprise administering one or more of a CTLA-4, B7-1, and/or B7-2 inhibitor concurrently with one or more of a PD-1, PDLl, and/or PDL2 inhibitor. In some embodiments, one or more of the CTLA-4, B7-1, and/or B7-2 inhibitors is administered prior to one or more of the PD-1, PDLl, and/or PDL2 inhibitors. In some embodiments, one or more of the CTLA-4, B7-1, and/or B7-2 inhibitors is administered at least, up to, or exactly 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) before one or more of the PD-1, PDL1, and/or PDL2 inhibitors. In some embodiments, one or more of the PD-1, PDLl and/or PDL2 inhibitors is administered prior to one or more of the CTLA-4, B7-1 and/or B7-2 inhibitors. In some embodiments, one or more of the inhibitors of PD-1, PDL1, and/or PDL2 is administered prior to one or more of the inhibitors of CTLA-4, B7-1, and/or B7-2 for at least, up to, or exactly 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein). In some embodiments, one or more of the CTLA-4, B7-1, and/or B7-2 inhibitors is administered within 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, 10 weeks, 12 weeks, 14 weeks, 16 weeks, 18 weeks, or 20 weeks (or any derivable range therein) of the one or more of the PD-1, PDL1, and/or PDL2 inhibitors.

In some embodiments, the microbial modulator composition is administered prior to the immune checkpoint inhibitor. In some embodiments, the microbial modulator composition is administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) prior to the immune checkpoint inhibitor. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) doses of the microbial modulator composition are administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) prior to administration of the immune checkpoint inhibitor. In some embodiments, the microbial modulator composition is administered after the immune checkpoint inhibitor. In some embodiments, the microbial modulator composition is administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks or 8 weeks (or any derivable range therein) after the immune checkpoint inhibitor or after at least one of the immune checkpoint inhibitors or after at least 2 of the immune checkpoint inhibitors. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) agents of the microbial modulator composition are administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) after the immune checkpoint inhibitor or after at least one of the immune checkpoint inhibitors or after at least 2 of the immune checkpoint inhibitors.

The combination therapies of the present disclosure also include a microbial modulator composition. In some embodiments, the microbial modulator composition is administered prior to one or more of the PD-1, PDL1, and/or PDL2 inhibitors. In some embodiments, the microbial modulator composition is administered prior to (or any derivable range therein) one or more of the PD-1, PDL1 and/or PDL2 inhibitors for at least, up to, or exactly 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) agents of the microbial modulator composition are administered prior to one or more of the PD-1, PDL1, and/or PDL2 inhibitors for at least, up to, or exactly 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein). In some embodiments, the microbial modulator composition is administered after one or more of the PD-1, PDL1, and/or PDL2 inhibitors. In some embodiments, the microbial modulator composition is administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks or 8 weeks (or any derivable range therein) after one or more of the PD-1, PDL1 and/or PDL2 inhibitors or after at least one or at least two of PD-1, PDL1 or PDL 2. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) doses of the microbial modulator composition are administered at least, up to or exactly 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) after one or more of the PD-1, PDL1, or PDL2 inhibitors or after at least one or at least two of the PD-1, PDL1, or PDL2 inhibitors.

The combination therapies of the present disclosure also include a microbial modulator composition. In some embodiments, the microbial modulator composition is administered prior to one or more than one of the CTLA-4, B7-1 and/or B7-2 inhibitors. In some embodiments, the microbial modulator composition is administered prior to (or any derivable range therein) one or more than one of the CTLA-4, B7-1, and/or B7-2 inhibitors for at least, up to, or exactly 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) agents of the microbial modulator composition are administered prior to one or more of the CTLA-4, B7-1 and/or B7-2 inhibitors for at least, up to, or exactly 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein). In some embodiments, the microbial modulator composition is administered after one or more of the CTLA-4, B7-1, and/or B7-2 inhibitors. In some embodiments, the microbial modulator composition is administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks or 8 weeks (or any derivable range therein) after one or more of the CTLA-4, B7-1 and/or B7-2 inhibitors or after at least one or at least two of the CTLA-4, B7-1 or B7-2 inhibitors. In some embodiments, at least 1, 2, 3, 4, 5, 6, or 7 (or any derivable range therein) agents of the microbial modulator composition are administered at least, up to or just 1 hour, 2 hours, 3 hours, 5 hours, 6 hours, 12 hours, 24 hours, or 2 days, 3 days, 4 days, 6 days, 8 days, 10 days, or 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, or 8 weeks (or any derivable range therein) after one or more of the CTLA-4, B7-1 and/or B7-2 inhibitors or after at least one or at least two of the CTLA-4, B7-1 or B7-2 inhibitors.

In some embodiments, the microbial modulator composition is formulated for oral administration. Those skilled in the art are aware of various formulations which may contain live or killed micro-organisms and which may be present as a food supplement (e.g. pills, tablets etc.) or as a functional food such as a beverage or fermented yoghurt.

The immune checkpoint inhibitor and the microbial modulator may be administered by the same route of administration or by different routes of administration. In some embodiments, the immune checkpoint inhibitor is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intracamerally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. In some embodiments, the microbial modulator is administered intravenously, intramuscularly, subcutaneously, topically, orally, transdermally, intraperitoneally, intracamerally, by implantation, by inhalation, intrathecally, intraventricularly, or intranasally. In particular aspects, the immune checkpoint inhibitor is administered intravenously and the microbial modulator is administered orally. An effective amount of an immune checkpoint inhibitor and a microbial modulator can be administered to prevent or treat a disease. Suitable dosages of immune checkpoint inhibitor and/or microbial modulator may be determined based on the type of disease to be treated, the severity and course of the disease, the clinical condition of the individual, the clinical history of the individual and the response to treatment, and the judgment of the attending physician.

For example, a therapeutically effective amount or sufficient amount for administration of each of the at least one isolated or purified bacterial population or each of the at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15 isolated or purified bacterial populations of the microbial modulator compositions of embodiments to a human will be at least about 1 x 10³Individual bacterial Colony Forming Units (CFU) or at least about 1X 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Individual CFUs (or any derivable range therein). In some embodiments, a dose will contain at least, at most, or exactly 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵Bacteria (e.g., a particular bacteria or species, genus or family as described herein) in an amount specified for the bacterial CFU (or any derivable range therein). In some embodiments, a dose will contain at least, at most, or exactly 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10 ⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵Total bacterial CFU (or any derivable range therein). In particular embodiments, the bacteria are provided in the form of spores or as sporulated bacteria. In particular embodiments, the concentration of spores of each isolated or purified bacterial population, e.g., each species, subspecies, or strain, is at least, at most, or exactly 1 x 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵(or any derivable range therein) viable bacterial spores per gram of composition or per application. In some embodiments, the composition comprises, or the method comprises, administering at least, up to, or just 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 40, or 50 (or any derivable range thereof) different bacterial species, different bacterial genera, or different bacterial families.

In some embodiments, the therapeutically effective or sufficient amount of each of the at least one isolated or purified bacterial population or each of the at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 isolated or purified bacterial populations of the microbial modulator composition of embodiments administered to a human will be at least about 1 x 10 ³Bacterial cells or at least about 1X 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹A plurality of,1×10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Individual cells (or any derivable range therein). In some embodiments, a dose will contain at least, at most, or exactly 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵An amount of bacteria (e.g., a particular bacteria or species, genus or family as described herein) that specifies the cells of the bacteria (or any derivable range thereof). In some embodiments, a dose will contain at least, at most, or exactly 1 × 10⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵The cells of (or any derivable range thereof) a total bacteria. In particular embodiments, the bacteria are provided in the form of spores or as sporulated bacteria. In particular embodiments, the concentration of spores of each isolated or purified bacterial population, e.g., each species, subspecies, or strain, is at least, at most, or exactly 1 x 10 ⁴1, 1 × 10⁵1, 1 × 10⁶1, 1 × 10⁷1, 1 × 10⁸1, 1 × 10⁹1, 1 × 10¹⁰1, 1 × 10¹¹1, 1 × 10¹²1, 1 × 10¹³1, 1 × 10¹⁴1, 1 × 10¹⁵Is one or more than 1 x 10¹⁵(or any derivable range therein) viable bacterial spores per gram of composition or per application. In some embodiments, the composition comprises, or the method comprises, administering at least, up to, or just 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 40, or 50 (or any derivable range thereof) different bacterial species, different bacterial genera, or different bacterial families.

Intratumoral injection or injection into tumor vessels is particularly contemplated for discrete, solid, readily available tumors. Local, regional or systemic administration may also be suitable. For tumours of >4cm, the amount to be administered is about 4-10ml (especially 10ml), whereas for tumours of <4cm, an amount of about 1-3ml (especially 3ml) will be used. Multiple injections delivered in a single dose comprise an amount of about 0.1 to about 0.5 ml. For example, the adenovirus particles may advantageously be contacted by administering multiple injections to a tumor.

Treatment regimens may also vary and generally depend on tumor type, tumor location, disease progression, and the patient's health and age. Clearly, certain types of tumors require more aggressive treatment, while at the same time, certain patients cannot tolerate more onerous treatment regimens. Clinicians will be best suited to make such decisions based on the known efficacy and toxicity (if any) of the therapeutic formulation.

In certain embodiments, the treated tumor may be unresectable, at least initially unresectable. Treatment with therapeutic viral constructs may increase the resectability of tumors due to shrinkage at the margins or elimination by certain particularly aggressive moieties. After treatment, resection may be feasible. Additional treatment after resection will help to eliminate microscopic residual disease at the tumor site.

Treatment may include various "unit doses". A unit dose is defined as containing a predetermined amount of the therapeutic composition. The determination of the amount to be administered and the specific route and formulation is within the judgment of the skilled artisan in the clinical field. The unit dose need not be administered as a single injection, but may comprise a continuous infusion over a set period of time. In some embodiments, a unit dose comprises a single administrable dose.

Depending on the number of treatments and the unit dose, the amount to be administered depends on the desired therapeutic effect. An effective amount is understood to mean the amount necessary to achieve a particular effect. In the practice of certain embodiments, it is contemplated that doses in the range of 10mg/kg to 200mg/kg may affect the protective ability of these agents. Thus, contemplated doses include doses of about 0.1, 0.5, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195 and 200, 300, 400, 500, 1000 μ g/kg, mg/kg, μ g/day, or mg/day, or any range derivable therein. Further, such doses may be administered multiple times a day and/or over days, weeks, or months.

In some embodiments, a therapeutically effective or sufficient amount of an immune checkpoint inhibitor, such as an antibody and/or a microbial modulator, administered to a human, whether by one or more administrations, will be in the range of about 0.01 to about 50mg/kg of patient body weight. In some embodiments, the inhibitor is used, for example, in a daily administration of about 0.01 to about 45mg/kg, about 0.01 to about 40mg/kg, about 0.01 to about 35mg/kg, about 0.01 to about 30mg/kg, about 0.01 to about 25mg/kg, about 0.01 to about 20mg/kg, about 0.01 to about 15mg/kg, about 0.01 to about 10mg/kg, about 0.01 to about 5mg/kg, or about 0.01 to about 1 mg/kg. In some embodiments, the inhibitor is administered at 15 mg/kg. However, other dosage regimens may be used. In one embodiment, the inhibitor described herein is administered to a subject at a dose of about 100mg, about 200mg, about 300mg, about 400mg, about 500mg, about 600mg, about 700mg, about 800mg, about 900mg, about 1000mg, about 1100mg, about 1200mg, about 1300mg, or about 1400mg on day 1 of a 21-day cycle. The dose may be administered as one dose or as multiple doses (e.g., 2 or 3 doses), such as an infusion. The progress of the therapy can be readily monitored by conventional techniques.

In some embodiments, an effective dose of the pharmaceutical composition is a dose that provides a blood level of about 1 μ M to 150 μ M. In another embodiment, the effective amount provides a blood level of about 4 μ M to 100 μ M; or about 1 μ M to 100 μ M; or about 1 μ M to 50 μ M; or about 1 μ M to 40 μ M; or about 1 μ M to 30 μ M; or about 1 μ M to 20 μ M; or about 1 μ M to 10 μ M; or about 10 μ M to 150 μ M; or about 10 μ M to 100 μ M; or about 10 μ M to 50 μ M; or about 25 μ M to 150 μ M; or about 25 μ M to 100 μ M; or about 25 μ M to 50 μ M; or about 50 μ M to 150 μ M; or about 50 μ M to 100 μ M (or any range derivable therein). In other embodiments, the dose may provide the following blood levels of agent resulting from administration of the therapeutic agent to the subject: about, at least about, or at most about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 μ M, or any range derivable therein. In certain embodiments, a therapeutic agent administered to a subject is metabolized in vivo to a metabolized therapeutic agent, in which case blood levels can refer to the amount of the metabolized therapeutic agent. Alternatively, to the extent that a therapeutic agent is not metabolized by a subject, the blood levels discussed herein may refer to the therapeutic agent not metabolized.

The precise amount of the therapeutic composition will also depend on the judgment of the practitioner and will be specific to each individual. Factors that affect dosage include the physical and clinical state of the patient, the route of administration, the intended therapeutic goal (whether symptomatic relief or cure), and the efficacy, stability, and toxicity of the particular therapeutic substance or other therapy that the subject may be receiving.

It will be understood and appreciated by those skilled in the art that the dosage units μ g/kg or mg/kg body weight may be converted and expressed in equivalent concentration units of μ g/ml or mM (blood level), such as 4 μ M to 100 μ M. It is also understood that absorption is species and organ/tissue dependent. Suitable scaling factors and physiological assumptions relating to absorption and concentration measures are well known and will allow one of skill in the art to scale one concentration measure to another and make reasonable comparisons and conclusions regarding the dosages, efficacies, and results described herein.

Methods of treatment

Provided herein are methods for treating or delaying progression of cancer in an individual comprising administering to a subject individual who has been administered or is being administered immune checkpoint therapy an effective microbial improver composition. Also provided herein are methods of selecting a subject that will respond favorably to immune checkpoint therapy by assessing the subject's microbial profile and administering an immune checkpoint inhibitor to a subject identified as having a favorable microbial profile.

In some embodiments, the treatment results in a sustained response in the individual after cessation of treatment. The methods described herein can be used to treat conditions where enhanced immunogenicity is desired, such as increasing tumor immunogenicity to treat cancer. Also provided herein are methods of enhancing immune function, such as in an individual having cancer, comprising administering to the individual an effective amount of an immune checkpoint inhibitor (e.g., a PD-1 inhibitor and/or a CTLA-4 inhibitor) and a microbial modulator. In some embodiments, the subject is a human.

In some embodiments, the individual has a cancer that is resistant (has proven to be resistant) to one or more anti-cancer therapies. In some embodiments, the resistance to anti-cancer therapy comprises relapse of cancer or refractory cancer. Recurrence may refer to the reoccurrence of the cancer at the original site or new site after treatment. In some embodiments, the resistance to the anti-cancer therapy comprises progression of the cancer during treatment with the anti-cancer therapy. In some embodiments, the cancer is at an early stage or at an advanced stage.

In some embodiments of the methods of the present disclosure, the cancer has a low level of T cell infiltration. In some embodiments, the T cell infiltrate of the cancer is not detectable. In some embodiments, the cancer is a non-immunogenic cancer (e.g., a non-immunogenic colorectal cancer and/or ovarian cancer). Without being bound by theory, combination therapy may increase T cells (e.g., CD 4) relative to prior to administration of the combination ⁺T cell, CD8⁺T cells, memory T cells), priming, activation, proliferation, and/or infiltration.

The cancer may be a solid tumor, a metastatic cancer or a non-metastatic cancer. In certain embodiments, the cancer may be derived from bladder, blood, bone marrow, brain, breast, urinary system, cervix, esophagus, duodenum, small intestine, large intestine, colon, rectum, anus, gingiva, head, kidney, liver, lung, nasopharynx, neck, ovary, prostate, skin, stomach, testis, tongue, or uterus.

The cancer may in particular be of the following histological type, but is not limited to these: malignant neoplasms; cancer; undifferentiated bladder cancer, blood cancer, bone cancer, brain cancer, breast cancer, urinary tract cancer, esophageal cancer, thymoma, duodenal cancer, colon cancer, rectal cancer, anal cancer, gum cancer, head cancer, kidney cancer, soft tissue cancer, liver cancer, lung cancer, nasopharyngeal cancer, neck cancer, ovarian cancer, prostate cancer, skin cancer, stomach cancer, testicular cancer, tongue cancer, uterine cancer, thymus cancer, skin squamous cell cancer, non-colorectal gastrointestinal cancer, colorectal cancer, melanoma, merkel cell cancer, renal cell cancer, cervical cancer, hepatocellular cancer, urothelial cancer, non-small cell lung cancer, head and neck cancer, endometrial cancer, esophageal gastric cancer, small cell mesothelioma, ovarian cancer, esophageal gastric cancer, glioblastoma, adrenal cortex cancer, uveal cancer, pancreatic cancer, germ cell cancer, giant cell cancer, and spindle cell cancer; small cell carcinoma; papillary carcinoma; squamous cell carcinoma; lymphatic epithelial cancer; basal cell carcinoma; hair matrix cancer; transitional cell carcinoma; papillary transitional cell carcinoma; adenocarcinoma; malignant gastrinomas; bile duct cancer; hepatocellular carcinoma; combined hepatocellular carcinoma and cholangiocarcinoma; trabecular adenocarcinoma; adenoid cystic carcinoma; adenocarcinoma within adenomatous polyps; adenocarcinoma; familial polyposis coli; a solid cancer; malignant carcinoid tumors; bronchioloalveolar adenocarcinoma; papillary adenocarcinoma; a cancer of the chromophobe; eosinophilic carcinoma; eosinophilic adenocarcinoma; basophilic carcinoma; clear cell adenocarcinoma; granular cell carcinoma; follicular adenocarcinoma; papillary and follicular adenocarcinomas; non-enveloped sclerosing cancers; adrenocortical carcinoma; endometrial cancer; skin appendage cancer; adenocarcinoma of the apocrine gland; sebaceous gland cancer; cerumen adenocarcinoma; mucoepidermoid carcinoma; cystic carcinoma; papillary cystadenocarcinoma; papillary serous cystadenocarcinoma; mucinous cystadenocarcinoma; mucinous adenocarcinoma; signet ring cell carcinoma; invasive ductal carcinoma; medullary carcinoma; lobular carcinoma; inflammatory cancer; paget's disease of the breast; acinar cell carcinoma; adenosquamous carcinoma; with squamous metaplasia of adenocarcinoma; malignant thymoma; malignant ovarian stromal tumors; malignant thecal cell tumor; malignant granulosa cell tumors; malignant testicular blastoma; sertoli cell carcinoma; malignant Leydig cell tumors; malignant lipocytoma; malignant paraganglioma; malignant external paraganglioma of mammary gland; pheochromocytoma; hemangiospherical sarcoma; malignant melanoma; no melanoma; superficial diffusible melanoma; malignant melanoma which is a giant pigmented nevus; epithelial-like cell melanoma; malignant blue nevus cutaneous melanoma; a sarcoma; fibrosarcoma; malignant fibrous histiocytoma; myxosarcoma; liposarcoma; leiomyosarcoma; rhabdomyosarcoma; embryonal rhabdomyosarcoma; alveolar rhabdomyosarcoma; stromal sarcoma; malignant mixed tumor; mullerian mixed tumor; nephroblastoma; hepatoblastoma; a carcinosarcoma; malignant mesenchymal tumor; malignant tumor of brenner's disease; malignant phyllo-tumor; malignant synovial sarcoma; clonal cell tumors; an embryonic carcinoma; malignant teratoma; malignant ovarian thyroid tumors; choriocarcinoma; malignant mesonephroma; angiosarcoma; malignant vascular endothelioma; kaposi's sarcoma; malignant vascular endothelial cell tumors; lymphangioleiomyosarcoma; osteosarcoma; paracortical osteosarcoma; chondrosarcoma; malignant chondroblastoma; mesenchymal chondrosarcoma; giant cell tumor of bone; ewing sarcoma; malignant odontogenic tumors; amelogenic cell dental sarcoma; malignant ameloblastic tumors; amelogenic cell fibrosarcoma; malignant pineal tumor; chordoma; malignant glioma; ependymoma; astrocytoma; primary plasma astrocytoma; fibrillar astrocytomas; astrocytomas; glioblastoma; oligodendroglioma; oligodendroglioma; primitive neuroectodermal tumors; cerebellar sarcoma; ganglionic neuroblastoma; neuroblastoma; retinoblastoma; olfactive neurogenic tumors; malignant meningioma; neurofibrosarcoma; malignant schwannoma; malignant granulosa cell tumors; malignant lymphoma; hodgkin's disease; hodgkin's lymphoma; granuloma paratuberis; small lymphocytic malignant lymphoma; large cell diffuse malignant lymphoma; malignant follicular lymphoma; mycosis fungoides; other non-hodgkin's lymphoma as specified; malignant tissue cell proliferation; multiple myeloma; mast cell sarcoma; immunoproliferative small bowel disease; leukemia; lymphoid leukemia; plasma cell leukemia; erythroleukemia; lymphosarcoma cell leukemia; myeloid leukemia; basophilic granulocytic leukemia; eosinophilic leukemia; monocytic leukemia; mast cell leukemia; megakaryoblastic leukemia; myeloid sarcoma; and hairy cell leukemia.

In some embodiments, the cancer comprises cutaneous squamous cell cancer, non-colorectal and colorectal gastrointestinal cancer, merkel cell cancer, anal cancer, cervical cancer, hepatocellular cancer, urothelial cancer, melanoma, lung cancer, non-small cell lung cancer, head and neck cancer, kidney cancer, bladder cancer, hodgkin's lymphoma, pancreatic cancer, or skin cancer.

In some embodiments, the cancer comprises lung cancer, pancreatic cancer, metastatic melanoma, renal cancer, bladder cancer, head and neck cancer, or hodgkin's lymphoma.

The methods may involve determining, administering, or selecting an appropriate cancer "management regimen" and predicting the outcome thereof. As used herein, the expression "management protocol" refers to a management plan that specifies the types of examinations, screens, diagnoses, monitors, care, and treatments (e.g., dosages, schedules, and/or treatment durations) provided to a subject in need thereof (e.g., a subject diagnosed with cancer).

The term "treatment" or "treating" refers to any treatment of a disease in a mammal, including: (i) prevention of disease, i.e., the clinical symptoms of disease are prevented from occurring by administering a protective composition prior to the induction of the disease; (ii) suppression of the disease, i.e., the clinical symptoms of the disease are not developed by administering a protective composition after the induction of the event but before the clinical appearance or reoccurrence of the disease; (iii) inhibiting the disease, i.e., arresting its development by administering a protective composition after the initial appearance of clinical symptoms; and/or (iv) remission of the disease, i.e. regression of clinical symptoms after their initial appearance by administration of a protective composition. In some embodiments, treatment may not include prevention of the disease.

In certain aspects, further cancer or metastasis examinations or screens or further diagnoses such as contrast-enhanced Computed Tomography (CT), positron emission tomography-CT (PET-CT), and Magnetic Resonance Imaging (MRI) can be performed to detect cancer or cancer metastasis in patients determined to have a certain composition of gut microbiome.

Method for determining the composition of a microbiome

In some embodiments, the methods involve obtaining a microbiome profile. In some embodiments, obtaining a microbiome profile comprises the following steps or ordered steps: i) obtaining a sample obtained from a subject (e.g., a human subject), ii) isolating one or more than one bacterial species from the sample, iii) isolating one or more than one nucleic acid from at least one bacterium, iv) sequencing the isolated nucleic acids, and v) comparing the sequenced nucleic acids to a reference nucleic acid sequence. In performing a method requiring genotyping, any genotyping assay may be used. This can be done, for example, by sequencing the 16S or 23S ribosomal subunits or by macrogenomic shotgun sequencing associated with macrotranscriptomics.

Methods of determining the composition of a microbiome may include one or more than one microbiological method such as sequencing, next generation sequencing, immunoblotting, comparative genomic hybridization, PCR, ELISA, and the like.

VIII. kit

Certain aspects of the present disclosure also include kits for performing the methods of the present disclosure, such as detection, diagnosis, or treatment of cancer and/or detection and qualitative or quantitative characterization of microorganisms. Such kits can be prepared from readily available materials and reagents. For example, such kits may comprise any one or more than one of the following materials: enzyme, reaction tube, buffer solution, surfactant, primer, probe and antibody. In a preferred embodiment, these kits allow a practitioner to obtain a sample of tumor cells in blood, tears, semen, saliva, urine, tissue, serum, stool, sputum, cerebrospinal fluid, and supernatant from cell lysate. In another preferred embodiment, these kits comprise the required equipment for performing RNA extraction, RT-PCR and gel electrophoresis. The kit may further comprise instructions for performing the assay.

In a particular aspect, the kits can comprise a plurality of reagents for evaluating or identifying a microorganism, wherein the kit is contained in a container. The kit may further comprise instructions for using the kit to evaluate the sequence, means for transforming and/or analyzing the sequence data to generate a prognosis. The reagents in the kit for measuring biomarker expression may comprise a plurality of PCR probes and/or primers for qRT-PCR and/or a plurality of antibodies or fragments thereof for assessing expression of the biomarker. In another embodiment, the reagents for measuring biomarker expression in a kit can comprise an array of polynucleotides complementary to mRNA of a biomarker of the invention. Possible means for converting the expression data into expression values and for analyzing the expression values to generate a score for predicting survival or prognosis may also be included.

The kit may comprise a container with a label. Suitable containers include, for example, bottles, vials, and test tubes. The container may be formed from a variety of materials, such as glass or plastic. The container may contain a composition that includes probes useful for prognostic or non-prognostic applications, as described above. The label on the container may indicate that the composition is for a particular prognostic or non-prognostic application, and may also indicate whether it is for in vivo or in vitro use, such as those described above. The kit may comprise the above-described container and one or more other containers containing commercially and user-desirable materials, including buffers, diluents, filters, needles, syringes, and package inserts with instructions for use.

Further kit embodiments relate to kits comprising the therapeutic compositions of the present disclosure. Kits may be useful in the treatment methods of the present disclosure and include instructions for use.

IX. example

The following examples are introduced to illustrate preferred embodiments of the present invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

Example 1-molecular, immunological and microbiological factors in response and toxicity to CTLA-4 and PD-1 Combined blockade

The therapeutic paradigm for cancer is rapidly evolving, driven by parallel advances in the ability to understand and deeply characterize tumors at the genomic and immune levels. Checkpoint blockade immunotherapy against the negative regulatory pathways that lead to an ineffective anti-tumor immune response in patients is now a practical and effective strategy for widespread clinical use. A variety of new agents are under development that are designed to block immunosuppression or activate immunostimulatory molecular targets. Efforts to increase the response rate to checkpoint blockade are currently dominated by combination drug strategies, an example of which is the combination of CTLA-4 and PD-1 inhibitors (combined immune checkpoint blockade, CICB). While more effective at inducing objective responses (Larkin et al, 2015), this combination is associated with significant immune-related adverse events (irAE) (Hammers et al, 2017; Sznol et al, 2017) and may be neither necessary nor appropriate for up to 40% of non-selected patients who are predicted to respond to PD-1 blockade alone with an accompanying low risk of severe irAE (Robert et al, 2015 a; Robert et al, 2015 b). Despite recent efforts, a reliable predictor of pre-treatment toxicity has not been identified and is urgently needed.

The inventors sought to identify potential tumor-derived and systemic molecular, immune and intestinal microbial biomarkers of response and immune-related toxicity in melanoma patients receiving CICB. A group of patients with advanced melanoma who received concurrent anti-CTLA-4 antibody ipilimumab and anti-PD-1 antibody (nivolumab or parbolzumab) was recruited. Patients were classified according to response and toxicity, response was determined using objective radiology assessment and toxicity was determined using incidence of high-grade (grade 3 or above grade 3) irAE.

Lower toxicity was associated with a lower diversity of peripheral T cell repertoire, and the immunophenotype indicated more antigen-stimulated T cells. Strikingly, this phenotype is also associated with the acceptance of existing immunotherapy agents, which predict lower levels of toxicity. The median of fecal microbial alpha diversity among responders was numerically higher, and differences in abundance of bacteroides faecalis, akkermansia muciniphila, prevotella faecalis, and bacteroides fragilis were associated with response and toxicity. The causal role of intestinal microbiota in promoting CICB-mediated subclinical ileitis and colitis was examined in two tumor mouse models, which confirm the common point of driving immune stimulation or inhibition by different commensal ecosystems between mammalian species.

A. Results

1. Study protocol and biological sample Collection

The inventors summoned a group of metastatic melanoma patients receiving CICB in clinical trials or as standard of care (SOC) therapy between 1/2014 to 31/8/2017 (fig. 1, table 1). Patients were excluded from the main study cohort if they had mucosal or uveal melanoma subtypes, lacked the appropriate biological samples associated with the treatment period, or if there was insufficient data to determine radiological response and toxicity data.

Patients were classified as "responders" (R) or "non-responders" (NR) based on their best overall response to CICB (BOR) as measured by RECIST v1.1, and also as having any irAE grade 3 or above grade 3 with iraes below grade 3. Tumor and peripheral blood samples were collected for relevant molecular and immunological analysis before and at treatment, while stool samples were collected and frozen before or at early treatment using the OMNIgene-GUT kit, and then microbiome profiles were determined by 16S rDNA sequencing (Table 1).

2. Patient characteristics, clinical efficacy and toxicity of CICB

The cohort contained 53 patients with predominantly stage IV disease (n-45, 85%), most of which received no previous systemic treatment for advanced disease (n-39, 74%) (table 2). One fifth (n-11, 20.8%) of patients had received some form of previous immunotherapy; ipilimumab or anti-PD-1 (not as a combination therapy), anti-PD-L1 agent, a cytokine agent (alone or as part of a biochemical treatment regimen for adjuvant or palliative purposes) (table 2).

The median number of ipilimumab + anti-PD-1 agent combination was 3 (range 1-4) (table 3), and the median number of anti-PD-1 agent monotherapies after initial combination administration was 1 (range 0-44). The overall response rate was 77.4% (41/53 patients), and progression occurred in 21 patients after a median follow-up time of 15.6 months (median PFS not reached, median time to progression in 3.0 months; fig. 1). Almost all patients (n ═ 51, 96.2%) experienced any level of treatment-related ("possible", "likely" or "clear" association) Adverse Events (AEs), with high levels of treatment-related, immune-related AEs occurring in 28 (52.8%) patients (grade ≧ 3), most commonly diarrhea/colitis, transaminations, hypothyroidism/hyperthyroidism, other endocrine diseases and skin toxicities (rashes, pruritus) (table 3). Treatment-related toxicity resulted in discontinuation of treatment in 21 (39.6%) patients, but no treatment-related deaths.

3. Molecules and immunodeterminants for response and resistance to CICB

Since the mutation burden varies widely between different tumor types and has previously been shown to affect the objective response to CTLA-4 or PD-1 blocking monotherapy (Hugo et al, 2016; Snyder et al, 2014; Van Allen et al, 2015), some evidence has also been reported in the context of CICB treatment for non-small cell lung cancer (Hellmann et al, 2018), the inventors first investigated the relationship between mutation burden and response to CICB. Whole exome sequencing was performed in available pre-treatment tumor samples (n-26, table 1). As expected for cutaneous melanoma, all cases showed a predominance of UV damage features characterized by C > T conversion (data not shown). No statistically significant difference was observed between total exon mutations or non-synonymous variants (NSV) between responders (R, n ═ 20) and non-responders (NR, n ═ 6) to CICB (fig. 2A), but significant overlap was noted in the low mutation burden range (<1000NSV), indicating that high mutation burden may allow responses, but is not a prerequisite for responses.

Next, the inventors examined whether specific mutant drivers of melanoma or immune-related signaling pathways are associated with CICB responses. Common melanoma-driving mutations are evenly distributed throughout the patientIn the tumor of origin, regardless of the response status, there was no clear pattern of affected genes or gene groups classified according to mutation type (e.g., missense, nonsense, indels), as melanoma driver, IFN-. gamma. -pathway and antigen processing pathway gene sets (FIG. 8A). As expected, BRAF^V600Mutations were significantly associated with a lower overall somatic mutation burden (p)<0.001) (fig. 8B), but not in response.

Given that immunogenicity associated with neoantigen loading is expected to be proportional to potential non-synonymous mutant loading, the present inventors performed in silico neoantigen prediction using netMHCpan algorithm (Nielsen et al, 2007) which revealed no significant difference in the number of total or high binding affinities between the two response groups predicting neoantigens (n-26, fig. 8C). Since there was no correlation between the mutated or predicted neoantigen load and the response, the inventors subsequently examined whether genomic copy number Changes (CNA) would affect the response. Unlike the mutation load, the copy number deletion load shows a statistical correlation with the response (p 0.04, fig. 2B), which is driven by the higher of the NR major chromosomal copy number deletion loads affecting chromosomes 5, 15 and particularly 10 (fig. 2C, fig. 8D-8E). Several genes previously implicated in resistance to immune checkpoint blockade monotherapies appear to be completely (CD74) or predominantly (PDIA3, B2M, PTEN) affected by copy number deletions in NR tumors (FIG. 2D), suggesting a potential immunogenomic mechanism for resistance to CICB (Ekmekcioglu et al, 2016; Peng et al, 2016; Roh et al, 2017; Tanese et al, 2015; Zaretsky et al, 2016).

To further explore the complex microenvironment interactions between tumors and infiltrating immune cells that shape the outcome of CICB, the inventors next examined intratumoral and systemic immune populations to identify potential response markers. As expected, numerically higher CD8+ densities were observed in baseline tumor immune infiltrates of R compared to NR tumors, however this did not reach statistical significance, most likely due to the limited cohort size and the relatively small proportion of non-responders (n 19R, n 6 NR; p 0.052, one-sided Mann-Whitney test, fig. 2E). Notably, CD8+ T cell density tended to increase following CICB treatment regardless of treatment response (fig. 9A). Analysis of the intratumoral T cell repertoire by T Cell Receptor (TCR) sequencing (n 25, table 1) revealed a strong trend of higher entropy of tumor T cell infiltrates of R (fig. 2F). TCR sequencing of baseline tumors demonstrated no significant difference in clonality between R and NR (p 0.28, fig. 2G), however this was again limited by cohort size and R/NR ratio (n 19R, n 6 NR).

4. Antigen stimulated T cell repertoire and previous immunotherapy were associated with the absence of grade 3-4 irAE

Severe irAE is particularly common in patients receiving CICB, and although the clinical response is good, the occurrence of grade 3 or above grade 3 irAE often results in discontinuation of treatment. Lack of precise immune mechanisms and reliable predictive biomarkers for irAE from CICB (Carlino and Long, 2016). The inventors investigated the correlation between systemic immune parameters and toxicity, assuming that the systemic circulation represents the most accessible compartment from which potentially autoreactive immune cells can be sampled and thereby identify the immune profile of a patient's susceptibility to irAE undergoing CICB. The inventors performed a comprehensive immunoassay of peripheral blood leukocytes using multiparameter flow cytometry and evaluated the circulating T cell repertoire using TCR sequencing. Consistent with the previously reported findings that treatment-induced expansion of circulating CD8+ T cell clones following ipilimumab therapy can predict toxicity in prostate cancer patients (Subudhi et al, 2016), TCR sequencing analysis (n ═ 16) also showed that, although the cutoff of 55 expanded clones in peripheral blood correlates with high-grade toxicity, there was no useful negative predictive value for the TCR repertoire bearing lower numbers of expanded clones (overall p ═ 0.22, fig. 9B). Patients experiencing toxicity also had a higher Ki67 proliferation index in early-stage effects of treatment and central memory CD8+ T lymphocytes, consistent with accelerated expansion of cytotoxic T cells contributing to immune-related toxicity (p ═ 0.0044, n ═ 14; fig. 3A, fig. 9C). Notably, peripheral blood lymphocytes collected prior to the start of CICB (n-24) revealed significantly higher T cell repertoire diversity (p-0.028, fig. 3B) and significantly higher entropy in patients who subsequently experienced a high grade irAE (p-0.0068, fig. 3C). Together, these results suggest that a less concentrated T cell repertoire containing a greater number of potentially autoreactive clones may contribute to toxicity against CICB.

To gain a more insight into the phenotype of these circulating lymphocytes, the inventors performed multiparameter flow cytometry on baseline peripheral blood samples (n-14-18). Significantly lower expression of surface CD28 and CD27 was noted in circulating CD4+ and CD8+ T lymphocytes of patients who did not develop severe irAE (CD 27 in CD4 Teff, p 0.0022; CD28 in CD4 Teff, p 0.014; CD27 in CD8 Teff, p 0.072; CD28 in CD8 Teff, p 0.04; fig. 3D-3E, fig. 9D-9E), indicating that patients with more antigen-stimulated T cell repertoire had lower incidence of subsequent toxicity upon treatment with CICB.

Since this immune profile suggests a profile of past immune activation, the inventors next compared T lymphocyte expression of CD27 and CD28 between patients who received immunotherapy and those who did not. It is speculated that previous immunotherapy led to a more antigen-stimulated T cell repertoire with a phenotype more similar to terminally differentiated, repeatedly stimulated T cells. The inventors first stratified the patient cohort according to the patient's past history of immunostimulatory therapy and irAE status. The inventors observed that the risk of high grade irAE (RR 0.29, 95% CI 0.08-0.81) was significantly reduced in patients exposed to any previous immunotherapy (p 0.016, Fisher's exact test) (fig. 3F). In addition, consistent with speculation, patients who did not receive immunotherapy did have significantly higher CD27 and/or CD28 expression on the effector CD4/8T cell population at baseline as compared to patients who received immunotherapy (n ═ 12 did not receive immunotherapy, n ═ 3 had undergone immunotherapy; CD27 in CD 4T effector cells, p ═ 0.0044, CD28 in CD 8T effector cells, p ═ 0.018; fig. 3D-3E, previous immunotherapy indicated with color).

5. Gut microbiome profile is associated with efficacy and toxicity of CICB

The present inventors next sought to investigate the effect of differential diversity and composition of gut microbiota on the clinical outcome of immune checkpoint blockade monotherapy in patients treated with CICB. Fecal microbiome samples were collected at the beginning of treatment when feasible in patients undergoing CICB treatment as described (fig. 1) and sequenced using 16S rDNA (n-31, including n-24R, n-7 NR, n-19 ≧ 3 grades irAE, n-12 no ≧ 3 grades irAE; table 1). Stool microbial composition varied widely throughout the cohort, with bacteroidales and clostridiales being most abundant in all stool samples (fig. 4A). Based on previous studies showing that the gut microbiome in R has higher diversity against PD-1 blockade (Gopalakrishnan et al, Science 2018), the inventors first compared the median α diversity to CICB in R and NR and observed a similar trend in retention across multiple diversity metrics (p ═ 0.14, fig. 4B, fig. 10A). The inventors next evaluated the relationship between the diversity of the gut microbiome and toxicity against CICB, confirming that there was no significant trend between patients with severe irAE and patients without severe irAE (p ═ 0.59, fig. 4C). The inventors then used linear discriminant analysis of effect size (LEfSe) (Segata et al, 2011) and pairwise comparisons of relative taxonomic abundance to evaluate the compositional difference between R and NR for CICB. Several bacterial taxa enriched CICB in R, including akkermansia muciniphila, bacteroides faecalis, and Dielma fastidiosa et al (p ═ 0.032, p ═ 0.019, p ═ 0.018, respectively, according to Mann-Whitney test; fig. 4D, fig. 10B). Instead, it was noted that there was additional bacterial taxa enrichment in NR, including lactobacillus reuteri, bacteroides fragilis and prevotella faecalis (fig. 4D) (p ═ 0.040, p ═ 0.033, respectively, according to Mann-Whitney test, fig. 10B). In addition, consistent with previous findings, firmicutes, clostridiales (p 0.21), ruminococcaceae (p 0.56) also tended to be higher in responders, however the association was relatively weaker, probably due to a distinct microbial response association in the case of anti-PD-1 monotherapy compared to the use of CICB (fig. 10B). Several bacterial taxa were also associated with high grade irAE, including bacteroides faecalis (also associated with responding patients), bacteroides faecalis, and listeria (p ═ 0.040, p ═ 0.033, p ═ 0.038, respectively, according to Mann-Whitney test; fig. 4E, fig. 10C). Bacterial taxa enriched in patients without irAE included bacteroides fragilis, haemophilus and tazilian etc. (LEfSe, fig. 4E; pairwise p ═ 0.014, p ═ 0.018, fig. 10C, respectively).

To determine whether a particular systemic immune population mediates the microbial impact on the development of a treatment-related irAE, the inventors then examined the association between circulating immune cell subpopulations and key taxa associated with the response in cases with matched fecal microbiome data and pre-treatment peripheral blood immunophenotype (n-9) and fecal microbiome data. The previously identified key response-related taxa bacteroides faecalis, flavonols perniciae, Dielma fascidiosa and akkermansia muciniphila (fig. 4D) were directly related to the pre-treatment abundance of the circulating PD-1+ T cell population and a measure of the overall CD 8T cell abundance (total CD8, inversely related to the CD4: CD8 ratio; fig. 10D) was consistent with the systemic immune profile enriched for potential tumor-reactive lymphocytes and prepared for responses after CICB treatment. The inventors then performed a similar analysis between the circulating immune population and the bacterial taxa associated with the occurrence or non-occurrence of high-grade irAE. A consistent positive correlation was observed between the abundance of several virulence associated bacteroides taxa and PD-1+ T cell populations, consistent with the potential (re-) activation of multiple immunospecificities leading to toxicity after the start of CICB (fig. 4F). Unexpectedly, the toxicity-associated bacteroides species showed a non-uniform association with CD4+ and CD8+ T cell expression of previously identified CD27 and CD 28; notably, bacteroides faecalis, which is inversely related to CD27/CD28 levels, is also highly correlated with responses. In addition, the inconsistent correlation between several toxicity-associated bacteroides species and the CD27/28+ fraction within the T cell subpopulation became more pronounced in the samples at the time of treatment (n-9, fig. 10E). These data indicate that there is a distinct bacterial immune association between taxa more clearly associated with toxicity and those associated with both toxicity and/or response (fig. 4F).

CICB-induced bacterial changes in the gut microbiome associated with responsiveness

Due to the correlation of gut microbial composition in influencing the efficacy and toxicity of CICB in melanoma patients, the inventors next investigated whether the microbial pattern would influence the outcome of ICB monotherapy or CICB in mice. The inventors treated the established MCA205 sarcoma and RET melanoma with either anti-PD-1 antibody alone or in combination with anti-CTLA-4 antibody for 2 weeks (6 and 5 administrations, respectively) and observed prolonged and/or complete regression in MCA205 with each treatment modality (2/6 anti-PD-1 treated mice and 6/6 CICB treated mice are responders (R) defined as mice with tumor regression or no increase in size in two consecutive measurements; FIG. 5A, left panel). Similar studies in the RET melanoma model showed similar results but with a higher number of non-responder (NR) mice, defined as mice whose tumors increased in two consecutive measurements (NR for anti-PD-1 treated mice 5/6 and for CICB treated mice 2/10; FIG. 5A, right panel).

To test the differences in microbial composition during treatment between the two tumor models, the inventors performed a principal coordinate analysis of microbial beta diversity, which provides a measure of the overall correlation (or lack thereof) between samples. When longitudinal stool samples collected before and after 2 or 5 doses of CICB were considered, the present inventors found out significant changes in microbiome composition over time in both tumor models (fig. 5B, left and right panels), and the most significant clustering effect was observed in the samples obtained after administration of 5 doses of CICB in the RET model (fig. 10A-D). In the same mixture of longitudinal samples, principal coordinate analysis revealed a significant association between fecal microbiota and tumor size at each sample collection time point for both tumor models (p 0.001 in MCA205 and 0.039 in RET; fig. 5C). Taken together, these data identify dynamic interactions between microbiota, immunotherapy and tumor growth kinetics.

The inventors next compared the bacteria present in the gut microbiota before treatment of mice subsequently responding to CICB in either of the two tumour models (MCA205 and RET) and found that there were 169 taxa including Alistipes indistinctus and akkermansia muciniphila in both models. Furthermore, flavonoles perniciae were also enriched in human patients who responded to CICB (fig. 4D, fig. 5D, table 4). To determine further commonality of bacteria in patient samples and murine tumor models, the inventors compared taxa associated with the response identified by LEfSe in patients with those identified using LEfSe in two murine tumor models as differentially enriched for CICB between R and NR mice. Furthermore, in view of the findings that gut microbial composition is significant over time and changes depending on the model, the present inventors attempted to enrich for both taxonomic findings and additional biological information by focusing on the human response-associated taxa that became or remained differentially enriched in any mouse model during CICB and that were correlated (or inversely correlated) with tumor size (fig. 11A-D). The inventors examined two time points in an attempt to determine whether treatment and tumor size were associated with gut microbiota and whether gut microbiota remained stable after treatment was initiated. In the early stages of treatment (after 2 injections), flavonols (flavonols peroneus in MCA 205) and Dielma (d.rustiosa in MCA 205) were identified as overlapping with the microbiota fingerprint of CICB responder patients (fig. 11A-B). After 5 injections, akkermansia (akkermansia muciniphila in MCA 205), bacteroides (b. stercoriosoris in MCA 205), and Dielma (d. rustidia in RET) were common in responding mice and patients (fig. 11C). Notably, akkermansia muciniphila was inversely related to tumor size in RET tumors and to previously reported commensal species (e.g., e.hirae) (fig. 11D) (route et al, 2018). In contrast, different lactobacilli (e.g. lactobacillus animalis) were positively correlated with tumor growth in CICB-treated RET and anti-PD-1-treated MCA205 tumors (fig. 11D) and were noted to correlate with non-response to CICB in human patients (fig. 4D). When responders (R) to CICB were isolated from non-responsive (NR) mice at different time points, the inventors found that not only akkermansia muciniphila and Dielma rustidia but also Alistipes indestinctus were associated with responses in melanoma patients, regardless of tumor type (fig. 5E, table 4).

7. The intestinal symbiont causes or reduces CICB-induced subclinical ileitis or colitis

Next, the inventors focused on intestinal toxicity (colon and ileum) to analyze potential correlations between intestinal microbiota and toxicity in mouse models. Notably, the murine model does not reproduce well the apparent colonic irAE (e.g., weight loss, mucosal bleeding, fecal mass and consistency changes), so the inventors first scored histological abnormalities of the intestinal epithelial cells and lamina propria (irregularities or destruction or length of villi and crypts, presence of inflammatory infiltrates) after CICB administration alone or in combination with a broad spectrum Antibiotic (ATB), with or without single colonization of specific commensal bacteria as reported by parallel human analysis. In fact, CICB induced subclinical ileal toxicity, which was highly alleviated by sterilization of the mouse intestine with ATB (fig. 6A-B). Supplementation of ATB-treated mice with different commensals (e.g., erysipelas trisodium ramosum) restored ileal toxicity, while others (bacteroides enterobacter) appeared to have protective effects (fig. 6A-B). Next, the inventors performed gene expression profiling of the ileum and colon of treated mice, indicating that this ileal toxicity is accompanied by a rapid and selective up-regulation of the transcription of the pro-inflammatory cytokine IL-1 β, but not TNF α and IL-6, and only in the presence of the intestinal microflora or single colonization by e.ramosum instead of bacteroides intestinalis or d.rustidia (fig. 6C, fig. 12). Consistent with the specific role of IL-1 β in the development of ileitis in response to CICB, IL-1R1 blockade with CICB reduced ileal toxicity (FIG. 6D).

In contrast to ileal toxicity, the development of subclinical mouse colitis was tumor-associated and was observed during CICB against RET melanoma, but not on MCA205 fibrosarcoma (fig. 6E). Furthermore, the composition of the fecal microbiome correlated with the presence of immune infiltrates in the colonic mucosa in the RET tumor model (fig. 6F left panel). Of the ten species associated with high colonic immune infiltrates suggestive of colitis in mice, only bacteroides intestinalis were also associated with toxicity in the human cohort, indicating that the definition of high grade irAE among patients is not limited to gastrointestinal toxicity, and only 7/31 patients in the microbiome cohort experienced gastrointestinal toxicity (fig. 4E, fig. 6F right panel). In contrast, high abundance of c.chlorella haemophilus is associated with low immune infiltrates in the colon of treated mice, and is also noted in patients to be associated with a lower probability of high-level toxicity (fig. 6F right panel, fig. 4E). Clostridium lacticum, associated with lower toxicity in patients (fig. 4E), was also present in mice responsive to CICB (table 4).

Finally, the inventors tested the effect of gut microbiome on sub-clinical colon toxicity by transferring fecal material from Renal Cell Carcinoma (RCC) patients who did not respond to ICB and did not experience toxicity into ATB-sterilized avatar mice, followed by implantation of RENCA tumors in situ and treatment with CICB as described previously (fig. 6G) (Routy et al, 2018). Transplantation of fecal material into tumor-bearing mice resulted in colonic inflammation following CICB treatment, which could be prevented by oral tube feeding akkermansia muciniphila (akkermansia muciniphila has previously been identified as a species associated with anti-PD-1 responses (Routy et al, 2018)) or fecal material from RCC patients who responded to treatment and did not experience grade 3-4 irAE, as monitored by immunohistochemistry and fecal levels of antimicrobial peptide lipocalin-2 (fig. 6H-I). Metagenomic analysis of the feces of responding RCC patients indicated the presence of a. indestinctus, which is also present in the fecal microbiota of responding melanoma patients (fig. 4D) and responding mice (fig. 5E), but absent in the feces of non-responding RCC patients.

These microbiological analyses together demonstrate a causal role of the bacterial ecosystem in modulating therapeutic responses and toxicities to CICB, demonstrating a strong correlation between gut microbiome composition and CICB-induced compositional changes and treatment outcome or toxicity prior to treatment. These findings also highlight the unexpected commonality of different bacterial communities across tumor types and mammalian species (human, mouse).

B. Discussion of the related Art

Combined anti-CTLA-4 and anti-PD-1 therapy provides superior response rates in the treatment of advanced melanoma compared to monotherapy, however, this strengthening regimen is hampered by the high incidence of severe irAE (Carlino and Long, 2016). The studies described in this example identified novel biomarkers of response and irAE in the context of combined immune checkpoint blockade. Higher copy number deletion loads were found to be predictors of CICB resistance. Genes preferentially deleted in non-responders were concentrated on chromosomes 5, 10 and 15. After careful knock-out, genes previously implicated in tumor inflammation or in response to immunotherapy, including PTEN, B2M and CD74(Ekmekcioglu et al, 2016; Peng et al, 2016; Tanese et al, 2015; Zaretsky et al, 2016), were found in these regions affected by copy number changes.

Analysis of gut microbiota provides a heuristic insight into the potentially modifiable biomarkers of response. The inventors determined that members of the genera Bacteroides and Akkermansia will be preferentially enriched in responders, while members of the genus Lactobacillus will be enriched in non-responders to CICB. During CICB, akkermansia and akkermansia muciniphila strains also occur with high frequency in sarcoma-bearing mice (and both respond to CICB) and remain at least stable in responders, while cross-tumor models are reduced in non-responders. A. indestinctus was also found in the responsive patient group in this cohort of metastatic melanoma patients treated with CICB and in the responsive mouse group across the tumor model during treatment with CICB. Famidiosa is also a common feature in current patient cohorts and in CICB-responsive mice during treatment and highlights the importance of the culture omics as a tool to study microbiota.

To date, most biomarkers have focused on objective anti-tumor responses, however, the safe and optimal use of these agents, particularly in combination regimens, requires more consideration of the biomarkers of toxicity. In this study, the inventors found that a more diverse repertoire of TCRs could predict a high level of irAE. These data highly suggest that treatment-related autoimmunity may be driven by latent, low-abundance, self-reactive T cell clones, whose presence is proportional to the overall diversity of the circulating lymphocyte pool. Most importantly, the discovery of more terminally differentiated/antigen stimulated T cell repertoires with features consistent with the concept of more "focused", less diverse repertoires is associated with a significantly lower likelihood of high-level toxicity. The population-level structure of the circulating lymphocyte pool may influence the relative likelihood of activating tumor-reactive (desired) rather than autoreactive (undesired) T cell clones, and may be the basis for the link between the response and toxicity observed in patients treated with immunotherapy. The concurrent observation that previous immunotherapy exposures may promote T cell phenotypes that favor toxicity avoidance provides attractive preliminary evidence that pre-treatment pharmacological manipulation may be able to mitigate toxicity of high-intensity immunotherapy regimens. A larger cohort with well documented and more uniform past acceptance (or non-acceptance) for modern immunotherapeutic agents would provide valuable opportunities to study the interaction of circulating T cell repertoires, immunomodulatory interventions, microbiota and irAE. In addition, a large cohort will be required to dissect the etiological differences between associations with any type of irAE or organ-system specific (e.g., transamination, colitis, thyroiditis) irAE.

The gut microbiota may also represent a key and modifiable impact on the likelihood of development of checkpoint immunotherapy-related autoimmune toxicity. The present inventors identified several taxa that are strongly associated with the occurrence or non-occurrence of high-grade immune-related toxicity. Importantly, most response-associated bacterial taxa, except bacteroides faecalis, are not highly associated with toxicity, and there is a clear species-level variation in the correlation of systemic lymphocyte subpopulations associated with toxicity. This data indicates that the potentially different immunomodulatory microbiological mechanisms underlying the association with irAE extend to very low taxonomic (species) levels, and thus even closely related taxonomic groups may have conflicting associations with irAE. In mice, CICB is more toxic in the ileum than in the colon, with obvious causal effects of different ecosystems. In fact, therapy with broad-spectrum antibiotics or fecal microbial transplants can improve ileitis or colitis scores. Some bacterial species appear to be more toxic than others (e.g. ramosum). CICB-induced ileitis may be attributed to the ability of toxicity-associated symbionts to induce mucosal IL-1 β, as IL-1R1 blockade alleviates CICB-induced ileitis. In terms of symbionts associated with gut toxicity, commonalities among mammals have also been found. Enterobacteroides enterobacter are symbionts associated with grade 3-4 toxicity in the current cohort of metastatic melanoma patients treated with CICB and in (C) ICB-treated RET/MCA205 tumor-bearing mice. However, in the model system, bacteroides enterobacter alone failed to induce ileitis or colitis, either because the strain was not inherently cytotoxic or because it required the appropriate ecosystem (and other strains) to alter the mucosal barrier.

In addition to their role in predictive biomarker identification, these comprehensive molecular, immunological and microbiological studies have found a promising approach to explore the manipulation of the gut microbiota and the modeling of the lymphocyte repertoire with the aim of decoupling responses and toxicities in the case of CICB. These findings have important clinical significance in the personalized medicine age and need to be validated in a larger data set and a variety of cancer types.

C. Experimental models and subject details

1. Patient cohort

Advanced stage (stage III/IV) melanoma patients receiving at least one dose of ipilimumab in combination with a PD-1 checkpoint blocker (nivolumab or paribrizumab) as a Combined Immune Checkpoint Blockade (CICB) treatment at UT MD Anderson cancer center during the period of 1-23 months 2014 to 8-31 months 2017 were determined from a detailed retrospective and prospective study of clinical records. CICB treatment is provided as part of a clinical trial or extended admission plan protocol (NCT01844505, NCT02186249, NCT02089685, NCT01621490, NCT02519322, NCT02320058) or as a standard of care therapy. Because of the known differences in potential biological and immunotherapeutic responses between melanoma subtypes, only cutaneous melanoma was included (i.e., mucosal and uveal melanoma were excluded from this study). To allow for transformation analysis, patients with unavailable biological samples associated with the CICB treatment period, or patients who do not have sufficient data available to determine the radiological response and toxicity results, are excluded. Overall, an initial cohort of 40 patients meeting the above criteria was determined and the molecular and immune correlations of response and toxicity were studied. Due to the initially limited fecal microbiota sampling and the wider involvement during the study interval, an additional 13 patients from whom available fecal samples were obtained were subsequently identified, incorporated into the microbiome-related transformation assay and fully integrated into all clinical assays.

2. Mouse

All mouse experiments were approved by the local institutional committee and performed according to government and institutional guidelines and regulations. Female C57Bl/6 and BALB/C were purchased from Harlan (France) and Janvier (France), respectively. Mice between 8 and 16 weeks of age were used. All mouse experiments were performed in a Gustave Roussy Cancer campas and mice were housed under specific pathogen free conditions or kept in isolators.

3. Cell lines

MCA205 and RET melanoma (transgenic forced expression of RET protooncogene under control of metallothionein-1 promoter, driving spontaneous melanoma development, kindly provided by professor Viktor Umansky) (homologous to C57BL/6J mice) and luciferase-transfected renal carcinoma (RENCA) cell line (homologous to BALB/C mice, kindly provided by Transgene of Elegashi, France) were transfected at 37 ℃, 5% CO₂The cells were cultured in RPMI-1640 medium supplemented with 10% heat-inactivated Fetal Bovine Serum (FBS), 1% penicillin/streptomycin, 2mM L-glutamine and 1% sodium pyruvate and non-essential amino acids (all from Gibco-Invitrogen), referred to herein as complete RPMI medium. RENCA was maintained in complete RPMI supplemented with 0.7mg/ml geneticin (Invitrogen, Life technologies). Cell lines were examined regularly for mycoplasma contamination and bacteria passaged over 10 passages were not used.

D. Details of the method

1. Clinical evaluation and biological samples

Clinical response annotation (MCA, PAP, HT) was performed independently by at least two clinical researchers per patient. Treatment responses were defined using the Best Overall Response (BOR) according to RECIST 1.1 criteria (Eisenhauer et al, 2009), comparing tumor burden for restenotic imaging performed in standard disease reevaluation time point studies with baseline (pre-treatment) studies. Longitudinal restated scans were evaluated throughout the treatment period until the start of subsequent treatments or the last known follow-up date. The imaging modalities are matched as closely as possible to facilitate contrast enhanced CT of the chest, abdomen and pelvis, contrast enhanced MRI or CT of the brain, and imaging of the neck or extremities, as indicated by known disease sites. A patient is classified as a "responder" (R) if the patient achieves an objective complete response (CR; 100% reduction in tumor burden) or partial response (PR; 30% reduction in tumor burden) attributable to CICB. Patients were classified as non-responders if they achieved BOR with progressive disease (PD; disease burden increase ≧ 20%) or stable disease (SD; non-compliance with the CR/PR/PD criteria). Mice were defined as responders (R) if their tumors regressed or stabilized during treatment, or as non-responders (NR) when tumors increased in size in two consecutive measurements.

Immune-related adverse events (irAE) were scored according to the NCI adverse event general terminology criteria (CTCAE)4.0 criteria and the immune-related relationships ("possible", "likely", "determined" association) of CICB therapy were divided according to consensus opinions of at least two independent clinical researchers (MCA, HT, WSC).

Available tumor and peripheral blood samples are determined by the querying institution investigating the biological sample units and, if necessary, the archival pathology units from the diagnostic samples. Tumor biopsies are obtained as punch, hollow needle or excisional biopsies and saved as snap-frozen (for RNA/DNA extraction) or formalin-fixed paraffin-embedded (FFPE; for immunohistochemistry or DNA extraction) specimens. Peripheral blood samples were subjected to density gradient centrifugation to isolate Peripheral Blood Mononuclear Cells (PBMCs) and then cryopreserved until germline DNA extraction or flow cytometry was required. Biological specimens were retrieved, collected and analyzed according to the protocol approved by the UT MD Anderson cancer central agency review committee in the declaration of helsinki. Stool samples were obtained at the outpatient clinic using the OMNIgene-GUT kit (DNA Genotek Inc (Ottawa, Canada)) following detailed interpretation and guidance by the treating clinician according to the manufacturer's recommendations. Stable stool samples were returned either personally or by mail within 30 days after collection.

2. Genomic analysis

Whole exome sequencing analysis Whole Exome Sequencing (WES) was performed using the same protocol as previously described (Roh et al, 2017). A total of 26 pre-treatment samples (19R, 7NR) were included. After pathological assessment and confirmation of tumor content, DNA was extracted from tumor samples. Matched peripheral blood leukocytes were collected as germline DNA controls. The initial genomic DNA input for the shearing step was 750 ng. End repair, base addition, linker ligation using bifurcated Illumina paired end linkers and library enrichment Polymerase Chain Reaction (PCR) were performed using the KAPA Hyper Prep kit (# KK8504), followed by solid phase reverse immobilized bead cleanup and clustering. Library construction was performed according to the manufacturer's instructions. Target Enrichment was performed using the Agilent SureSelectXT Target Enrichment (#5190-8646) protocol using 650-750ng of the prepared library according to the manufacturer's instructions. The enriched libraries were normalized to equal concentrations using an Eppendorf Mastercycler EP Gradient instrument, pooled in equimolar amounts on the Agilent Bravo B platform, and quantified using the KAPA library quantification kit (# KK 4824). The pooled libraries were adjusted to 2nM, denatured with 0.2M NaOH, diluted with Illumina hybridization buffer, and cluster amplified using HiSeq v3 cluster chemistry and Illumina multiplex sequencing primer kit according to the manufacturer's instructions. The library was then sequenced on the Illumina HiSeq 2000/2500v3 system using a 76bp double-ended read and analyzed using RTA v.1.13 or higher. The mean coverage of exome data was 221 x in tumors and 100 x in germline. The aligned BAM file is then processed using Picard and GATK software to identify duplicates, realignments, and recalibrations. MuTect (v1.1.4) was used to identify somatic point mutations and Pindel (v0.2.4) was used to identify small insertions/deletions. Additional post-call filters are then applied, including: (a) total read count in tumor samples >30, (b) total read count in matched normal samples >10, (c) VAF (variant allele frequency) >0.05 in tumor samples, (d) VAF <0.01 in matched normal samples, and (e) removal of SNVs reported in dbSNP129 and thousand human genome Project (1000Genomes Project).

Copy number variation analysis was performed as described previously (Roh et al, 2017). Basically, the Sequenza (v2.1.2) algorithm was applied to the aligned BAM data to obtain a log of each tumor sample₂Copy number ratio (tumor/normal). The increase in copy number (log) at the gene level was determined using the R package "CNTools" (v1.24.0)₂Copy ratio>log₂1.5) and deletions (log)₂Copy ratio<-log₂1.5). The copy number increase or deletion load is defined as the total number of genes whose copy number is increased or deleted per sample. To define recurrent CNA, the R package "cghMCR" (v1.26.0) was applied to the calculated log₂Copy ratio (tumor/normal) to identify genomic regions of recurrent CNA (minimal common region, MCR). To identify genes preferentially deleted or added in responders versus non-responders, Fisher's exact test was performed at each gene position and p adjusted by FDR<0.05 defines statistical significance. Genes with CNA in less than 3 samples were excluded.

Non-synonymous exon mutations from WES (NSEM) were reviewed, and neoantigen prediction was performed using all possible 8-to 12-mer peptides comprising NSEM and compared to the wild-type peptide. PHLAT was used to predict HLA for each case (Bai et al, 2014). Binding affinity was assessed by NetMHCpan (v2.8) algorithm (Hoof et al, 2009) taking into account patient HLA. Predicted candidate peptides with IC50<500nM were considered HLA-bound.

3. Immunoassay

Peripheral Blood Mononuclear Cells (PBMCs) obtained from study patients were analyzed by members of the MD Anderson immunotherapy platform. Blood samples were drawn before and after treatment for immunophenotypic analysis of PBMCs. PBMC samples were from 20 patients, including 10 patients ≧ 3 stages of iraE and 10<Grade 3 irAE patients. Multiparameter flow cytometry analysis of PBMCs by several plates using fluorescently conjugated monoclonal antibodies: CD4 AF532(SK3, eBioscience), CD3 PerCP-Cy5.5(UCHT1, Biolegend), CD8 AF700(RPA-T8, BD Biosciences), CD127 BV711(HIL-7R-M21, BD Biosciences), ICOS PE-Cy7(ISA-3, eBioscience), PD-1BV650(EH12.1 BD Biosciences), and FOXP3 PE-e610(PCH 101; eBioscience); CD3 PE-CF594, CD4 Pe-Cy5.5, CD8 AF532, CD45RA BV650(HI100, Biolegend), CCR7 BV785(G043H7, Biolegend), CD27 PeCy5(0323, eBioscience), CD28 APC-e780(CD28.2 eBioscience)nce), PD-1BV650(EH12.1 BD Biosciences), EOMES e660(WD1928, eBioscience) and TBET BV605(4B10 Biolegend). Live/Dead can fix a yellow stain from Thermo Fisher Scientific. Samples were run using LSR Fortessa (BD Biosciences) and analyzed using FlowJo software program. Following appropriate forward/side scatter and live single cell gating, the inventors determined the frequency of total CD3+ T cells, CD8+ T cells (CD3+ CD8+) and CD4+ T cells (CD3+ CD4 +). Among CD4, there are CD4+ effector T cells (CD4+ FOXP3-) and CD4+ regulatory T cells (CD4+ FOXP3+ CD 127-/low). PD-1 and ICOS expression was evaluated in these populations. CD45RA and CCR7 expression on CD4 and CD 8T cells was used to define the initial T Central Memory (TCM), T Effect Memory (TEM), and effect T (teff) subsets. PD-1, CD28, CD27, EOMES and TBET expression were evaluated in each of these compartments.

Immunohistochemistry hematoxylin and eosin (H) were obtained from each FFPE tumor sample&E) Stained slides to confirm the presence of tumors. The heavily pigmented samples were pretreated by bleaching the melanin with low concentrations of hydrogen peroxide. Selected antibody sets included programmed death ligand 1(PD-L1) clone E1L3N (1:100, Cell Signaling Technology), PD-1 clone EPR4877(1:250, epotomics), CD3 polyclonal (1:100, DAKO), CD4 clone 4B12(1:80, Leica Biosystems), CD8 clone C8/144B (1:25, Thermo Scientific), FOXP3 clone 206D (1:50, BioLegend), and granzyme B clone 11F1 (Ready, Leica Microsystems). The limiting antibody group was subjected to IHC staining using a Leica Bond Max automated staining instrument (Leica Biosystems, Buffalo Grove, IL). The IHC reaction was performed using a Leica Bond Polymer Refine detection kit (Leica Biosystems) and Diaminobenzidine (DAB) was used as the chromogen. Counterstaining with hematoxylin. All IHC slides were scanned using Aperio AT Turbo (Leica Biosystems) before all downstream IHC analyses. Five randomly selected 1mm from within the tumor area were analyzed using Aperio Image Toolbox analysis software (Leica Biosystems) ²The mean value for each standard in the field was selected for digital analysis as previously described (Chen et al, 2016). PD-L1 expression was evaluated by H-scoreIt evaluates the percentage of positive cells (0 to 100) and the staining intensity (0 to 3+), with a total score ranging from 0 to 300. The remaining markers were scored at cell density.

TCR sequencing Using QIAamp DNA FFPE tissue kit (Qiagen) from available FFPE tumor tissue (19R, 6NR) and PBMC (15 patients ≧ 3 grades of iraE, 12 patients)<Grade 3 irAE) to extract DNA. Next generation TCR sequencing of CDR3 variable regions was performed using the ImmunoSeq hsTCRB kit (Adaptive Biotechnologies), followed by sequencing on MiSeq 150 × (Illumina) and analysis using the ImmunoSeq Analyzer software v3.0(Adaptive Biotechnologies), considering only samples where a minimum of 1000 unique templates were detected. Clonality is an index inversely related to TCR diversity and is expressed as 1- (entropy)/log₂(number of productively unique sequences) metric. Preferential clonal expansion is defined as the number of T cell clones that expand significantly after treatment compared to a blood sample before treatment.

4. Murine model

Mice were treated with antibiotic solution (ATB) containing ampicillin (1mg/ml), streptomycin (5mg/ml) and colistin (1mg/ml) (Sigma-Aldrich) with or without vancomycin (0.25mg/ml) (using drinking water). The solution and bottles were replaced 3 times and once a week, respectively. Antibiotic activity was confirmed by culturing fecal pellets resuspended in BHI + 15% glycerol at 0.1g/ml for 48 hours at 37 ℃ on COS (Columbia agar containing 5% sheep blood) plates under aerobic and anaerobic conditions. The duration of the ATB treatment varied slightly with the experimental setup. Briefly, mice received 2 weeks of treatment prior to tumor implantation and continued throughout the MCA205 and RET experiments, while in experiments using RENCA, 3 days of ATB treatment were administered prior to fecal microbiota transfer.

Tumor challenge and treatment mice were injected subcutaneously (s.c.) laterally in the flank with 0.8X 10⁶MCA205 or 0.5x10⁶And (3) RET cells. When the tumor reaches 20 to 30mm²Treatment is initiated. Mice were injected intraperitoneally (ip) with anti-PD-1 mAb (250. mu.g/mouse; clone RMP1-14) and/or anti-CTLA-4 mAb (100. mu.g/mouse, clone 9D9), with or without anti-IL 1R (anakinra, 500. mu.g/mouse) or corresponding isotype controls, e.g.adnexaAs shown in the figure. All mabs used in vivo were from BioXcell (west libamon, new hampshire, usa) using the recommended isotype control mAb (Swedish orange Biovitrum, sweden) except anakinra.

Fecal microbiota transfer experiment after 3 days of ATB treatment, fecal microbiota transfer was performed using samples from non-responder patients ((FMT)⁴And RENCA tumor cells. The skin incision is then closed with surgical clips. Treatment was started 5 days after tumor inoculation. Mice were treated with anti-PD-1 mAb and anti-CTLA-4 with or without oral tube feeding of fecal specimens from responding patients or Ackermanella muciniphila. Tumor growth was monitored weekly on IVIS imaging system 50 series (Analytic Jenap).

Ackermanella muciniphila CSURP2261 (supplied by Institut homeoto enterorsitaie M diterrane Infection, France mosaic), Dielma rustidias (isolated from human samples), Erysipelotridium ramosum (isolated from human samples) and Bacteroides enterocolitica (isolated from mouse samples) were incubated on COS plates under anaerobic conditions for 24-72 hours at 37 ℃ using an oxygen-free generator (Biomerieux). 10, which measured at 600nm and had an optical density of 1, was obtained using a fluorescence spectrophotometer (Eppendorf)⁹CFU/mL suspension. Administered 24 hours prior to antibody treatment and 10 with each antibody treatment⁸Or 10⁹Individual CFUs were fed at 100 μ L suspension port tube. Bacteria were verified using a matrix assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometer (Microflex LT analyzer, Bruker daltons, germany).

Cytokine quantification stool samples were collected and stored at-80 ℃ until further processing. The samples were thawed and resuspended (at 100mg/mL) in PBS containing 0.1% tween 20. After incubation at room temperature for 20 minutes with shaking, the samples were centrifuged at 12000rpm for 10 minutes, and the supernatants were harvested and stored at-20 ℃ until analysis. Lipocalin-2 levels were measured using the mouse lipocalin-2/NGAL DuoSet ELISA kit (R & D Systems, Minneapolis, MN) according to the manufacturer's instructions.

Intestinal tissue was preserved in Formalin Fixed Paraffin Embedded (FFPE) or optimal cutting temperature compound (OCT). Ileum and colon were removed at sacrifice, washed in PBS, cut longitudinally, tumbled, and fixed in 4% PFA overnight at 4 ℃, or in some experiments at room temperature for 2 hours. The Tissue was then treated with Tissue- 6 vacuum infiltration processor (Sakura) paraffin embedding, or rehydration in 15% sucrose for 1 hour, then overnight in 30% sucrose, OCT embedding (Sakura) and snap freezing. Hematoxylin, eosin and safranin stain (H) for longitudinal section&E) And (5) counterdyeing.

Histological assessment of intestinal tissue toxicity ileum: inflammatory lesions, the appearance of submucosa, the length of the villi, and the thickness of the lamina propria were scored by a pathologist (P.O.) for each section. The score is defined as: 0-normal, 1-focal and mild lesion; diffuse and mild lesions; diffuse, mild and major lesions; 4-the major lesion with a region containing only connective tissue. Colon: inflammatory infiltrates, scored by the definition of physiological (0) level, low (1) level, moderate (2) level and high (3) level.

5. Microbiome analysis

Patient stool samples baseline stool samples were collected using the OMNIgene GUT kit (DNA Genotek, ottawa, canada). From the initial 40 patient cohorts, 18 stool samples were available for analysis. To expand the microbiome analysis, fecal samples were collected from another 13 patients receiving CICB. A total of 31 fecal samples were subjected to bacterial 16S rDNA sequencing (6R and 24 NR; 19 patients ≧ 3 iraE, 12 <3 iraE). In this cohort, some samples obtained early after the start of CICB were included as surrogate baseline samples, as parallel studies of longitudinal samples taken from patients receiving immune checkpoint blockade monotherapy showed no significant change in fecal microbiota after the start of treatment (Gopalakrishnan et al, 2018).

Human fecal DNA extraction and bacterial 16S rDNA sequencing preparation and sequencing of Human fecal samples was performed in concert with the Alkek Center for reagents and Microbiome Research (CMMR) from Baylor College of Medicine using a method adapted from the NIH-Human Microbiome Project (2012 a, b). Expanded details of the analysis of pipelines have been reported previously (Gopalakrishnan et al, 2018). Briefly, bacterial genomic DNA extracted using the MO BIO PowerSoil DNA isolation kit (MO BIO Laboratories, usa) was PCR amplified of the 16S rDNA V4 region and sequenced using the MiSeq platform (Illumina, Inc, san diego, california). Mass-filtered sequences with > 97% identity were clustered by open-reference OTU packaging into boxes called Operational Taxonomic Units (OTU) and classified at the species level with reference to the NCBI 16S ribosomal RNA sequence database (release date 2.11.2017; NCBI-blast + package 2.5.0). Phylogenetic information was obtained by mapping representative OTU sequences with NCBI taxonomic database (release date 2017, 2 months 16 days) using BLAST.

Raw FASTQ files were analyzed using the mortur pipeline v.1.39.5 for quality checks and filtering (sequencing errors, chimeras) on the workstation DELL T7910(Round Rock, tx). The raw reads (15512959 total, 125104 average per sample) were filtered (6342281 total, 51147 average per sample) and clustered into Operational Taxonomic Units (OTUs), then the low population OTUs were eliminated (up to 5 reads) and de novo OTUs were performed for alignment at 97% pairwise identity using standardized parameters and the SILVA rDNA database v.1.19. A total of 427 bacteria were identified, taking into account RET and MCA samples. The sample coverage was calculated using Mothur, which resulted in an average coverage of more than 99% for all samples, and thus meant to be a suitable normalization procedure for subsequent analysis. Bioinformatics and statistical analysis of the identified OTUs were performed using Python v.2.7.11. The most representative and most abundant reads in each OTU (as confirmed in the previous step with Mothur v.1.39.5) were nucleotide aligned (nucleotid Blast) using the National Center for Biotechnology Information (NCBI) Blast software (NCBI-Blast-2.3.0) and the latest NCBI 16S microbial database accessed at the end of 2018 at 4 months (which can be found at ftp. A bacterial relative abundance matrix was established at each classification level (phylum, class, order, family, genus, species) for subsequent multivariate statistical analysis.

Measurements of alpha diversity (within sample diversity) were calculated at OTU scale using SciKit-leran package v.0.4.1, as observed OTU and shannon index. Exploratory analysis of β -diversity (between sample diversity) uses Bray-Curtis difference metric calculation calculated by Mothur and expressed in principal coordinate analysis (PCoA), while for Hierarchical Cluster Analysis (HCA) the 'Bray-Curtis' metric and 'complete linkage' methods are implemented using custom scripts (Python v.2.7.11). To compare the microbiota taxa with the gene expression dataset, multivariate statistical Spearman correlation analysis (and associated P-values) was performed using custom Python scripts. The Mann-Whitney U and Kruskall-Wallis tests were used to assess the significance of pairwise or multiple comparisons, respectively, with a p-value of 0.05 or less being considered significant.

The paired between-group Mann-Whitney test was used to calculate differentially enriched taxa within patient samples. The effect size is estimated as the ratio of the test statistic to the square root of the sample size. The sparsification limit for calculating alpha diversity is set based on the fewest number of reads in all stool samples. Estimating the taxonomic alpha diversity of the patient sample by using the inverse Simpson index, and the calculation formula is (pi is the proportion of species i in all species S) (Morgan and Huttenhouwer, 2012) and estimates other diversity metrics as indicated in the figure. Similarity analysis (ANOSIM, which represents the difference in the centroid of the dataset) or Pearson correlation coefficients (when noted) were calculated using Python 2.7.11.

Using Kruskal-Wallis test, the LEfSe method was used to compare the abundance of all bacterial clades (statistical significance defined as p <0.05 for response and p <0.1 for toxicity) based on the occurrence of toxicity (i.e.: between patients ≧ 3 and < 3) and the response (i.e.: between R and NR) (Segata et al, 2011). Effect size was calculated using bacterial taxa with differential abundance between study groups as input to Linear Discriminant Analysis (LDA). Lefse analysis was performed on the murine taxa with Mothur v.1.39.5.

E. Quantification and statistical analysis

1. Statistical analysis

Data analysis and presentation was performed using R software (available at the online website of R-project. org.), Microsoft Excel (Microsoft co., redmond 436, washington) or Prism 5(GraphPad, san diego, california). Patient cohort survival curves were generated using the R package "survivval" (Therneau and Grambsch, 2000). In the case of low sample dichotomous variables, an unpaired Mann-Whitney U test or Fisher's exact test was used for inter-group comparisons of patient cohort genomes and immune parameters, with p <0.05 considered statistically significant. All comparisons are two-sided unless strong a priori assumptions warrant a one-sided approach (as appropriate). Permutation checking is performed by randomly permuting the sample tags for a total of 1000 iterations. In the murine study, statistical analysis of more than two groups collected using ANOVA followed by pairwise comparisons with Bonferroni adjustments was performed. Otherwise, statistical analysis was performed using unpaired t-test for both groups. Outliers within a given distribution were tested using the Grubbs test (found at the online site of graph pad. com/quick caps/Grubbs 1. cfm) with a threshold of p < 0.05. All tumor growth curves were analyzed using software developed in the laboratory by professor Guido Kroemer, and information on statistical analysis can be linked as follows: kroemerlab, shinyapps, io/TumGrowth/. Briefly, for longitudinal analysis, the raw tumor measurements were logarithmically transformed prior to statistical testing. When complete tumor regression was observed, the minimum value was divided by 2 to estimate the zero value (zeros). Automatic outlier detection with p <0.1 was retained for longitudinal analysis and Kaplan Meier curves. Survival curves were estimated using Cox regression and multiple tests were considered using Bonferroni adjustment. p values are two-sided with a confidence interval of 95%, considered significant when p < 0.05. Symbol significance: p <0.05, p <0.01, p < 0.001.

F. Table form

Table 1, relating to fig. 1: biological sample usage.

Previous immunotherapy: IL-2, IFN, anti-CTLA-4, anti-PD-1 (Pabollizumab, nivolumab). Grade 3 + irAE: grade 3 or above grade 3 immune-related adverse events. Pre-treatment/post-treatment/early stage: sample sampling time point relative to the start of CICB. BOR: optimal overall response according to RECIST v 1.1.

Table 2, relating to fig. 1: a patient characteristic.

Table S3, relating to fig. 1: clinical results

The best overall response for PR + CR and NR for PD. Percentages are expressed relative to the number of patients within each response group.

Table 4, relating to fig. 5: correlation of the microorganism with the response.

Example 2: the efficacy and tolerability of combined immune checkpoint blockade in metastatic melanoma is influenced by the gut microbiome

The gut microbiome is increasingly being considered as a powerful modulator of anti-PD 1 based cancer immunotherapy. Convincing evidence suggests differential bacterial enrichment and diversity in responders (R) versus non-responders (NR), mediated by profound effects on systemic and anti-tumor immune infiltrates. However, this has not been investigated in the context of treatment with Combined Immune Checkpoint Blockade (CICB), which is associated with excellent response rates but with a higher incidence of potentially debilitating toxicity. The method comprises the following steps: the inventors recruited a panel of metastatic melanoma patients (n-54) who were receiving CICB. All patients were classified as R (n-31, CR + PR) or NR (n-23, SD + PD) based on RECIST v1.1 and as having grade 3 or above grade 3 (T; n-29) or below grade 3 (NT; n-25) immune-related adverse events according to the NCI CTCAE 4.0 criteria. Baseline stool samples were characterized by 16S rRNA sequencing. Matched pre-treatment blood samples were subjected to correlation analysis of the peripheral immune cell population according to flow cytometry (n-12) with a pool of circulating T cells according to TCR sequencing (n-12). As a result: the overall intestinal microbial landscape in these patients varies with the high abundance of bacteroidales and clostridiales. The ordering of the β -diversity distances revealed a lack of clustering of primary tumor subtypes (uvea, mucosa, skin), consistent with a significant impact on tumor-free histology. Although there was no apparent response or toxicity association based on diversity, significant compositional differences were understood. Comparison of the relative abundances of LEfSe (LDA >2, p <0.05) and paired Mann-Whitney tests revealed enrichment of bacteroides faecalis (p ═ 0.03) and parabacteroides dymanii (p ═ 0.04) in R and lactobacillus (p ═ 0.005) in NR. Consistent with previous findings, the clostridial median relative abundance was again higher in R (0.34) than in NR (0.26). On the other hand, bacteroides enterobacter (p ═ 0.01) and anthropogenic lactifermentans (p ═ 0.006) were enriched in T and NT, respectively. Importantly, correlation analysis of circulating immune cell subsets revealed different associations of different bacterial enrichments (including a positive correlation between overall CD8+ T cell abundance and R-taxa) and clustering effects of high or low T cell repertoire entropy. And (4) conclusion: these findings build on previous work and support the view that there is a close link between gut microbiome and the therapeutic outcome of checkpoint blockade therapy. Extensive studies are currently being conducted on matched human biological samples and preclinical models to further understand the mechanisms of interaction with immune markers and to establish causal relationships. Taken together, these data support the critical role of the gut microbiome as a predictive tool and therapeutic target.

anti-CTLA-4 and anti-PD-1 combination therapy provides superior response rates in the treatment of advanced melanoma compared to monotherapy, however, this strengthening regimen is hampered by the high incidence of severe irAE. Figures 13 to 19 show that although there is no large difference in gut diversity due to response or toxicity, there is a significant difference in taxonomic enrichment, particularly bacteroides faecalis in R and lactobacillus reuteri in NR (also associated with PFS); and bacteroides enterobacter in patients with grade 3 (or higher irAE) and analotium lactatifaciens in patients with irAE below grade 3. Correlation analysis revealed a comparative association with circulating immune cell subpopulations in taxa differentially enriched by response and irAE. Further experiments considered the integration of microbiome associations between treatment types in metastatic melanoma patients, mechanistic studies in preclinical murine models, and the investigation of host lifestyle factors and their associations with microbial characteristics.

Example 3: peripheral immune repertoire and intestinal microbiome features are associated with toxicity blockade against CTLA-4 and PD-1 in combination

Treatment with Combined Immune Checkpoint Blockade (CICB) targeting CTLA-4 and PD-1 is associated with clinical benefit in several tumor types, but the incidence of immune-related adverse events (irAE) is also high. There is a need for an in-depth understanding of the biomarkers and the mechanisms of response and toxicity to CICB. To solve this problem, the inventors analyzed the blood, tumor and intestinal microbiome of 77 patients with advanced melanoma treated with CICB, with any grade 3 iraE occurring at a high rate (49%). Immune and genomic biomarkers of response to CICB were similar to those identified for anti-CTLA-4 and anti-PD-1 monotherapies. Toxicity from CICB correlates with a more diverse T cell repertoire and a less antigen stimulated phenotype. Novel microbial determinants of toxicity against CICB have been identified in the patient gut microbiota, such as Bacteroides intestinalis, and validated in a murine model. In summary, these findings are of great significance to clinical management using CICB in terms of potential biomarkers and mechanisms of therapeutic toxicity.

Treatment with CICB is associated with a high objective response rate (Larkin,2015#1), however, a significant proportion of patients experience immune-related adverse events (irAE) (Hammers,2017# 3; Sznol,2017# 2). Interestingly, clinical response rates and irAE appear to be linked (Attia,2005#10), although the different mechanisms behind therapeutic toxicity are not fully understood. Lacking a robust biomarker for response to CICB at present, it is likely that up to 40% of non-selected melanoma patients receiving CICB treatment are expected to respond to PD-1 blockade alone and thus may potentially avoid the increased risk of severe irAE associated with this regimen (Robert,2015# 4; Robert,2015# 5; Larkin,2015# 1).

To help solve this problem, the inventors studied biomarkers of response and toxicity to CICB in a cohort of 77 patients with advanced melanoma (predominantly cutaneous) who received CICB, either in clinical trials or as standard of care treatment (fig. 14, expanded data table 1). Most patients had stage IV disease (n 65, 84%) and did not undergo prior systemic treatment (n 57, 74%) (expanded data table 1, fig. 23). In this cohort, the incidence of irAE at any level was high (n ≧ 72, 93.5%), nearly half of the patients (49%) experienced severe (≧ 3) irAE (extended data table 2), consistent with other published series (Sznol,2017# 2; Wolchok,2013# 9; Postow,2015# 71; Larkin,2015# 1; D' Angelo,2017# 70).

The inventors first performed whole exome sequencing in available pre-treatment tumor samples to assess the association of Total Mutation Burden (TMB) with response to CICB (n 26, expanded data table 3). Overall, the inventors observed a higher TMB in responders to CICB (R, n ═ 20) than in non-responders (NR, n ═ 6) (fig. 2A, p ═ 0.20), consistent with findings from previous studies (Hellmann,2018# 40; Hugo,2016# 14; Snyder,2014# 13; Van Allen,2015# 6). However, there appear to be 2 subgroups within the responder population; one subgroup, high in TMB, may be sufficient for them to be anti-PD-1 monotherapy; while one subset has a lower TMB, which overlaps with the non-responder range, so for them the mutational burden is not a useful response predictor. Thus, there is a subset of patients with low TMB who may respond to CICB, although the mechanism of these patient responses is unclear. Qualitative assessment of the mutant landscape in this cohort did not reveal significant differences in the frequency of mutations in the gene sets for R and NR common melanoma driver IFN- γ -pathway and antigen processing pathway (fig. 8A). Similarly, no significant difference was found in neoantigen loading between R and NR (fig. 8C), but the cohort size of these analyses was relatively small.

Whereas previous findings indicate that high copy number deletion burden is associated with resistance to treatment blocked with successive checkpoints targeting CTLA-4 and PD-1 (Roh,2017#22), the inventors next evaluated the association between copy number deletion and response to CICB. In the current cohort, the inventors observed that NR to CICB had a significantly higher copy number miss load compared to R (p ═ 0.04, fig. 15A). Resistance to CICB was primarily associated with a loss of copy number affecting chromosomes 5, 10 and 15 (FIGS. 8D-8E, 2C). Several genes previously implicated in resistance to immune checkpoint blockade monotherapies appeared to be completely (CD74) or predominantly (PDIA3, B2M, PTEN) affected by copy number deletions in NR tumors (fig. 15B), suggesting a potential immunogenomic mechanism for resistance to CICB (ekmekciglu, 2016# 25; Peng,2016# 23; Roh,2017# 22; Tanese,2015# 26; Zaretsky,2016# 24).

Whereas previous studies highlighted the prognostic significance of the density and distribution of CD8+ T cells in response to ICB monotherapy (Tumeh,2014# 72; Peng,2016#23), the present inventors next evaluated the density of CD8+ T cells in baseline tumor biopsies for the R and NR of CICB. A higher density of CD8+ T cells was observed in R tumors compared to NR (n ═ 19R, n ═ 6 NR; p ═ 0.052, unilateral, figure 2E). The inventors also evaluated T cell repertoires in baseline tumor samples for R and NR of CICB via TCR sequencing. Although there was a limited association with responses, the T cell repertoire entropy in R was higher (p ═ 0.058, fig. 2F), suggesting possible regimen-specific differences compared to ICB monotherapy (Roh,2017# 22).

Following this, the inventors sought to find putative biomarkers for toxicity of CICB, since severe irAE is particularly common and treatment may be limited due to unplanned discontinuation of treatment (Carlino,2016# 16). To this end, the inventors first investigated the correlation between systemic immune parameters via TCR sequencing of Peripheral Blood Lymphocytes (PBLs) at baseline and treatment. In these studies, the inventors observed significantly higher baseline T cell bank diversity in patients subsequently experiencing high grade irAE (p ═ 0.028, n ═ 24; fig. 15C (top)). This is consistent with previously published checkpoint blockade monotherapy reports (Oh,2017# 11; Subudhi,2016#12), and suggests that TCR diversity at baseline can help predict toxicity against CICB, although this needs to be examined in an additional cohort. Polyclonal expansion of T cell clones from baseline to time of treatment was also observed, with > 55 cycles of expansion of CD8+ T cell clones (p ═ 0.22, fig. 9A) in patients experiencing grade 3 or above grade 3 toxicity to CICB compared to patients with < grade 3 irAE, consistent with previously reported findings in prostate cancer patients receiving ICB monotherapy targeting CTLA-4 (Subudhi,2016# 12).

Next, the inventors analyzed the phenotype of PBL at baseline and at treatment via multiparameter flow cytometry in patients with grade 3 or above grade 3 irAE. In these studies, the inventors observed a higher proliferation index of their effect and central memory CD8+ T lymphocytes at the early treatment time point (p 0.0044, n 14, fig. 3A, 9C), suggesting that accelerated expansion of cytotoxic T cells may contribute to immune-related toxicity. Together with TCR sequencing data, these results indicate that a more diverse T cell repertoire containing a greater number of potentially self-reactive clones may contribute to irAE after CICB.

Inspired by these findings, the inventors subsequently evaluated the expression of CD28 and CD27 in PBLs of patients with high and low grade irAE, as these markers are known to be gradually down-regulated in antigen-stimulated T cells, assuming a unique "aging" functional state (Moro-Garcia,2012# 73; Chen,2010# 74). In these studies, the inventors observed significantly lower expression of surface CD28 and CD27 on circulating CD4+ and CD8+ effector T lymphocytes in patients who did not develop severe irAE (CD 27 in CD4 Teff, p 0.0022; CD28 in CD4 Teff, p 0.014; CD27 in CD8 Teff, p 0.072; CD28 in CD8 Teff, p 0.04; fig. 3D, 15D (bottom), fig. 9D-9E). Notably, a similar phenotypic trend was also observed in the second cohort of melanoma patients treated with CICB, which examined pre-treatment peripheral blood lymphocyte samples from patients, comparing grade 3 toxicity to <3 toxicity (FIGS. 24A-24D), characterized by a propensity for reduced expression of CD27/28 in the CD4/8T effector cell subpopulation. Interestingly, in the cohort, this phenotype was observed more frequently in patients previously exposed to systemic immunotherapy (odds ratio 0.21, 95% CI 0.03-0.93, p 0.028, Fisher's exact test) (fig. 3D, 3E, 25), and the association between both previous immunotherapy exposure and the absence of high-grade irAE was still significant when all melanoma subtypes were considered (odds ratio 0.27, 95% CI 0.06-1.02, p 0.047, Fisher's exact test). These data may suggest that prior immunotherapy exposure may drive this phenotype to occur, but further studies are required to validate and possibly exploit this finding.

Following analysis of blood and tumor samples, the inventors next evaluated the association of gut microbiome characteristics with response and toxicity to CICB, in view of the increasing evidence of the role of gut microbiota in response to checkpoint blockade (gopalakrinhan, 2018# 19; Matson,2018# 20; Routy,2018# 18). Importantly, the inventors evaluated the profiles in human patients and conducted studies in preclinical models to conduct cross-species validation of putative microbial contributors to response and/or toxicity. Focusing on first being placed on candidate taxa associated with responses, the inventors analyzed baseline fecal microbiome samples using 16S rRNA gene sequencing (n ═ 54; expanded data table 3, fig. 16A). The inventors first queried the association of gut microbiota with responses by compositional difference studies between R and NR using LEfSE (fig. 17A) and pairwise comparisons (fig. 18A). Several differentially enriched bacterial taxa were found, including bacteroides faecalis, parabacteroides diesei and fourniella massilisensis in R (p ═ 0.03, p ═ 0.005; fig. 17A, 18A, respectively, according to Mann-Whitney test) and klebsiella aerogenes and lactobacillus reuteri in NR (p ═ 0.04, p ═ 0.009; fig. 17A, 18A, respectively, according to Mann-Whitney test). Consistent with previous findings, firmicutes and clostridia tend to be higher in responders (p ═ 0.39, p ═ 0.38; fig. 26A-B, respectively). Contrary to the inventors' findings in patients receiving anti-PD-1 monotherapy, the inventors did not observe any significant difference in alpha diversity for R versus NR in this cohort (fig. 16B), but undeniably limited sample size.

The inventors next analyzed candidate taxa associated with responses in preclinical models treated with CICB (data not shown). In these studies, treatment with CICB was associated with prolonged response and/or complete tumor regression in both tumor models compared to control and mice treated with anti-PD-1 monotherapy (fig. 27A). Interestingly, treatment with CICB was associated with changes in gut microbiota over time, with increased alpha diversity (fig. 27B).

To determine whether microbiome could predict response to CICB, the inventors explored differences in microbiome composition at T0 using supervised analysis (partial least squares discriminant analysis; PLS-DA), compared the final tumor-bearing mice at T2 to tumor-free mice, and noted a clear difference between the two groups (fig. 20A, p ═ 0.001). Comparison of the relative contribution of abundance of each bacterial species to the observed group separation at T0 using the PLS-DA derived Variable Importance (VIP) score (fig. 20B) revealed that parabacteroides diesei before treatment was predictive of CICB response, which was also observed in human patients. Importantly, the relative abundance of parabacteroides dymanii at T0, T2, and T5 correlated negatively with tumor size at T5 (fig. 20C), and appeared at a significantly high frequency at T0 and T2 in final tumor-free CICB-receiving mice (fig. 27D). Note the other taxa associated with the response in each tumor model (expanded data tables 4-5). Taken together, these data confirm the dynamic interaction between CICB and gut commensal microbiota, with a commonality in terms of enriched taxa (e.g., parabacteroides diesei) between the two murine tumor models and human patients, which is positively correlated with a favorable tumor response.

After assessing the potential impact of gut microbiota response, the inventors next queried the association between gut microbiota and toxicity to treatment in patient cohorts and in murine models. In the patient cohort, patients with > 3 class irAE and patients without > 3 class irAE had differential enrichment of several individual bacterial taxa in the baseline gut microbiome samples, including bacteroides enterobacter and Intestinibacter bartletti (p ═ 0.009, respectively, according to the Mann-Whitney test; fig. 17B, 18B). Notably, through LEfSe and pairwise comparisons, taxa enriched in those where no > 3 grades of irAE occurred were also found, including antiaertignum lactatifereneans and long-chain dorferia (p ═ 0.016 and p ═ 0.06, fig. 17B, 18B, respectively).

In the human cohort, the inventors next evaluated the relationship between candidate taxa in the gut and phenotypes of the peripheral immune repertoire in patients with available matching baseline samples (n ═ 13). A consistent positive correlation was observed between bacteroides enterobacter and the abundance of PD-1+ T cell populations, consistent with the potential (re-) activation of multiple immunospecificities leading to toxicity after the start of CICB (fig. 21). Interestingly, taxa associated with no occurrence of grade 3 irAE, such as long-chain dorsalella, Muricomes intestini and antiaerotigum lactatifermentans, were inversely related to the abundance of previously implicated CD4+ and CD8+ T cells expressing CD27 and CD28, suggesting potential immunomodulatory pathways linking these taxa with observed clinical outcomes. In this cohort, the inventors did not detect any association between microbial alpha diversity and ≧ 3-stage iraE (FIG. 16C).

After assessing gut microbiota and toxicity in the human cohort, the inventors next assessed the relationship between gut microbiota and toxicity (colitis and ileitis) in a murine model. Although the current model is limited in toxicity assessment, as the murine model rarely exhibits overt colonic irAE, the inventors carefully assessed histological abnormalities of the intestinal epithelial cells and lamina propria associated with subclinical toxicity (fig. 28A-B) and compared them to candidate taxa in the intestine.

The inventors first evaluated toxicity following administration of CICB with or without co-administration with a broad spectrum Antibiotic (ATB). In these studies, treatment with CICB was associated with subclinical ileal toxicity, which was highly alleviated by sterilization of the intestine with ATB (fig. 22A). Notably, this ileitis was accompanied by rapid and selective upregulation of the transcription of the pro-inflammatory cytokine Il1B, but not Tnf or Il6, and only in the presence of the intact gut microflora (fig. 22B-C). Inspired by this finding, the inventors next queried human tissue from patients with colitis and observed a significant increase in IL1B expression in colitis samples compared to healthy colon controls (data not shown).

Since bacteroides enterobacter in the patient cohort was significantly associated with toxicity (fig. 17B), the relative abundance of bacteroides enterobacter was assessed by qPCR in fecal samples taken from mice before and after CICB. CICB induced a significant increase in enterobacteroides (fig. 22D), but not other bacteroides species such as bacteroides uniformis or bacteroides fragilis (fig. 28E). To elucidate the role of enterobacteroides during CICB in melanoma, the inventors either gavaged mice with three different enterobacteroides strains or allowed spontaneous re-colonization of the symbiont after antibiotic treatment, found that enterobacteroides specifically induced ileum Il1b transcription (fig. 22E) and sensitized the ileum to CICB-induced damage (fig. 22F). Similarly, FMT using healthy human donor stools containing low or high endogenous levels of enterobacteroides (fig. 28F-G) on a RET melanoma mouse model reproduced the finding that enterobacteroides-induced ileal sensitivity to CICB-induced lesions (fig. 22G) correlates with elevated Il1b expression (fig. 28H).

Taken together, these studies were based on previous findings of immune checkpoint monotherapy to identify novel biomarkers of response and irAE in the case of CICB, to which unique features can be applied. Checkpoint blockade many predictors of monotherapy can also predict CICB response and resistance (including TMB, CD8+ T cell density and copy number deletion burden), but the cohort size may be insufficient and additional studies are urgently needed. Nevertheless, interesting signs regarding toxicity to the treatment were observed in this cohort: patients who exhibited a more diverse TCR repertoire at baseline were more likely to develop high-grade irAE. This suggests that the population-level structure of the circulating lymphocyte pool may influence the relative likelihood of activation of tumor-reactive (desirable) and potentially autoreactive (undesirable) T cell clones, and as the data suggests, may even be determined by previous treatments as the data shows, but this also needs to be validated in an additional/larger cohort. In addition, these studies have led to heuristic insights into potentially modifiable determinants of response and toxicity within the gut microbiota. In summary, the insights of these studies may provide new strategies in terms of biomarkers of response and toxicity to CICB and new therapeutic targets for potential elimination of toxicity.

A. Details of the method

1. Clinical evaluation and biological samples

Clinical response annotation (MCA, PAP, HT) was performed independently by at least two clinical researchers per patient. Treatment response was defined using the Best Overall Response (BOR) according to RECIST 1.1 criteria (Eisenhauer,2009#29), comparing tumor burden for restenotic imaging performed in standard disease reevaluation time point studies with baseline (pre-treatment) studies. Longitudinal restated scans were evaluated throughout the treatment period until the start of subsequent treatments or the last known follow-up date. The imaging modalities are matched as closely as possible to facilitate contrast enhanced CT of the chest, abdomen and pelvis, contrast enhanced MRI or CT of the brain, and imaging of the neck or extremities, as indicated by known disease sites. A patient is classified as a "responder" (R) if the patient achieves an objective complete response (CR; 100% reduction in tumor burden) or partial response (PR; 30% reduction in tumor burden) attributable to CICB. Patients were classified as non-responders if they achieved BOR with progressive disease (PD; disease burden increase ≧ 20%) or stable disease (SD; non-compliance with the CR/PR/PD criteria) (expanded data Table 2). Mice were defined as responders (R) if their tumors regressed or stabilized during treatment, or as non-responders (NR) when tumors increased in size in two consecutive measurements.

Immune-related adverse events (irAE) were scored according to the NCI adverse event general terminology criteria (CTCAE)4.0 criteria and the immune-related relationships ("possible", "likely", "determined" association) of CICB therapy were divided according to consensus opinions of at least two independent clinical researchers (MCA, HT, WSC). The binary toxicity classification is based on whether the patient experienced any grade 3 or higher irAE with irAE below grade 3 (extended data table 2).

The available pre-treatment and at-treatment tumor and peripheral blood samples are determined by the querying institution by studying biological sample units, and if necessary, by querying archival pathology units from diagnostic samples. Tumor biopsies are obtained as punch, hollow needle or excisional biopsies and saved as snap-frozen (for RNA/DNA extraction) or formalin-fixed paraffin-embedded (FFPE; for immunohistochemistry or DNA extraction) specimens. Peripheral blood samples were subjected to density gradient centrifugation to isolate Peripheral Blood Mononuclear Cells (PBMCs) and then cryopreserved until germline DNA extraction or flow cytometry was required. Biological specimens were retrieved, collected and analyzed according to the protocol approved by the UT MD Anderson cancer central agency review committee in the declaration of helsinki. Stool samples were obtained at the outpatient clinic using the OMNIgene-GUT kit (DNA Genotek Inc (Ottawa, Canada)) following detailed interpretation and guidance by the treating clinician according to the manufacturer's recommendations. Stable stool samples were returned either personally or by mail within 30 days after collection. The patient level sample utilization was as shown in extended data table 3.

2. Genomic analysis

Whole exome sequencing analysis Whole Exome Sequencing (WES) was performed using the same protocol (Roh,2017#22) as previously described. A total of 26 pre-treatment samples (19R, 7NR) were included. After pathological assessment and confirmation of tumor content, DNA was extracted from tumor samples. Matched peripheral blood leukocytes were collected as germline DNA controls. The initial genomic DNA input for the shearing step was 750 ng. End repair, base addition, linker ligation using bifurcated Illumina paired end linkers and library enrichment Polymerase Chain Reaction (PCR) were performed using the KAPA Hyper Prep kit (# KK8504), followed by solid phase reverse immobilized bead cleanup and clustering. Library construction was performed according to the manufacturer's instructions. Target Enrichment was performed using the Agilent SureSelectXT Target Enrichment (#5190-8646) protocol using 650-750ng of the prepared library according to the manufacturer's instructions. The enriched libraries were normalized to equal concentrations using an Eppendorf Mastercycler EP Gradient instrument, pooled in equimolar amounts on the Agilent Bravo B platform, and quantified using the KAPA library quantification kit (# KK 4824). The pooled libraries were adjusted to 2nM, denatured with 0.2M NaOH, diluted with Illumina hybridization buffer, and cluster amplified using HiSeq v3 cluster chemistry and Illumina multiplex sequencing primer kit according to the manufacturer's instructions. The library was then sequenced on the Illumina HiSeq 2000/2500v3 system using a 76bp double-ended read and analyzed using RTA v.1.13 or higher. The mean coverage of exome data was 221 x in tumors and 100 x in germline. The aligned BAM (hg19) file is then processed using Picard and GATK software to identify duplicates, realignments, and recalibrations. MuTect (v1.1.4) was used to identify somatic point mutations and Pindel (v0.2.4) was used to identify small insertions/deletions. Additional post-call filters are then applied, including: (a) total read count in tumor samples >30, (b) total read count in matched normal samples >10, (c) VAF (variant allele frequency) >0.05 in tumor samples, (d) VAF <0.01 in matched normal samples, and (e) removal of SNVs reported in dbSNP129 and thousand human genome Project (1000Genomes Project).

Copy number variation analysis was performed as previously described (Roh,2017# 22). Basically, the sequennza (v2.1.2) algorithm was applied to aligned BAM data to obtain log2 copy number ratio (tumor/normal) for each tumor sample. The increase in copy number at the gene level (log2 copy ratio > log21.5) and the deletion (log2 copy ratio < -log21.5) were determined using the R package "CNTools" (v1.24.0). The copy number increase or deletion load is defined as the total number of genes whose copy number is increased or deleted per sample. To define recurrent CNAs, the R package "cghMCR" (v1.26.0) was applied to the calculated log2 copy ratio (tumor/normal) to identify the genomic region of recurrent CNAs (minimal common region, MCR). To identify genes preferentially deleted or added in responders versus non-responders, Fisher's exact test was performed at each gene position and statistical significance was defined by FDR adjusted p < 0.05. Genes with CNA in less than 3 samples were excluded.

Non-synonymous exon mutations from WES (NSEM) were reviewed, and neoantigen prediction was performed using all possible 8-to 12-mer peptides comprising NSEM and compared to the wild-type peptide. PHLAT was used to predict HLA for each case (Bai,2014# 38). Binding affinities were evaluated by the NetMHCpan (v2.8) algorithm (Nielsen,2007# 15; Hoof,2009#33) taking into account patient HLA. Predicted candidate peptides with IC50<500nM were considered HLA-bound.

3. Immunoassay

Peripheral Blood Mononuclear Cells (PBMCs) obtained from study patients were analyzed by members of the MD Anderson immunotherapy platform. Blood samples were drawn before and after treatment for immunophenotypic analysis of PBMCs. PBMC samples were from 20 patients, including 10 patients ≧ 3 iraE and 10 <3 iraE. Multiparameter flow cytometry analysis of PBMCs by several plates using fluorescently conjugated monoclonal antibodies: CD4 AF532(SK3, eBioscience), CD3PerCP-Cy5.5(UCHT1, Biolegend), CD8 AF700(RPA-T8, BD Biosciences), CD127 BV711(HIL-7R-M21, BD Biosciences), ICOS PE-Cy7(ISA-3, eBioscience), PD-1BV650(EH12.1 BD Biosciences), and FOXP3 PE-e610(PCH 101; eBioscience); CD3 PE-CF594, CD4 Pe-Cy5.5, CD8 AF532, CD45RA BV650(HI100, Biolegend), CCR7 BV785(G043H7, Biolegend), CD27 PeCy5(0323, eBioscience), CD28 APC-e780(CD28.2 eBioscience), PD-1BV650(EH12.1 BD Biosciences), EOMES e660(WD1928, ebiosciences) and TBET 605(4B10 Biolegend). Live/Dead can fix a yellow stain from Thermo Fisher Scientific. Samples were run using LSR Fortessa (BD Biosciences) and analyzed using FlowJo software program. Following appropriate forward/side scatter and live single cell gating, the inventors determined the frequency of total CD3+ T cells, CD8+ T cells (CD3+ CD8+) and CD4+ T cells (CD3+ CD4 +). Among CD4, there are CD4+ effector T cells (CD4+ FOXP3-) and CD4+ regulatory T cells (CD4+ FOXP3+ CD 127-/low). PD-1 and ICOS expression was evaluated in these populations. CD45RA and CCR7 expression on CD4 and CD 8T cells were used to define the primary, T Central Memory (TCM), T Effector Memory (TEM), and effector T (teff) subpopulations. PD-1, CD28, CD27, EOMES and TBET expression were evaluated in each of these compartments.

Hematoxylin and eosin (H & E) stained slides were obtained from each FFPE tumor specimen to confirm the presence of the tumor. The heavily pigmented samples were pretreated by bleaching the melanin with low concentrations of hydrogen peroxide. Selected antibody sets included programmed death ligand 1(PD-L1) clone E1L3N (1:100, Cell Signaling Technology), PD-1 clone EPR4877(1:250, epotomics), CD3 polyclonal (1:100, DAKO), CD4 clone 4B12(1:80, Leica Biosystems), CD8 clone C8/144B (1:25, Thermo Scientific), FOXP3 clone 206D (1:50, BioLegend), and granzyme B clone 11F1 (Ready, Leica Microsystems). The limiting antibody group was subjected to IHC staining using a Leica Bond Max automated staining instrument (Leica Biosystems, Buffalo Grove, IL). The IHC reaction was performed using a Leica Bond Polymer Refine detection kit (Leica Biosystems) and Diaminobenzidine (DAB) was used as the chromogen. Counterstaining with hematoxylin. All IHC slides were scanned using Aperio AT Turbo (Leica Biosystems) before all downstream IHC analyses. The mean value of each standard was selected from five randomly selected 1mm2 areas within the tumor area for digital analysis as previously described using Aperio Image Toolbox analysis software (Leica Biosystems) (Chen,2016# 27). PD-L1 expression was evaluated by H-score, which evaluates the percentage of positive cells (0 to 100) and staining intensity (0 to 3+), with a total score in the range of 0 to 300. The remaining markers were scored at cell density.

TCR sequencing Using QIAamp DNA FFPE tissue kit (Qiagen) from available FFPE tumor tissue (19R, 6NR) and PBMC (15 patients ≧ 3 grades of iraE, 12 patients)<Grade 3 irAE) to extract DNA. Next generation TCR sequencing of CDR3 variable regions was performed using the ImmunoSeq hsTCRB kit (Adaptive Biotechnologies), followed by sequencing on MiSeq 150 × (Illumina) and analysis using the ImmunoSeq Analyzer software v3.0(Adaptive Biotechnologies), considering only samples where a minimum of 1000 unique templates were detected. Clonality is an index inversely related to TCR diversity and is expressed as 1- (entropy)/log₂(number of Productivity-unique sequences) degreeAmount of the compound (A). Preferential clonal expansion is defined as the number of T cell clones that expand significantly after treatment compared to a blood sample before treatment.

4. Murine model

Mice were treated with antibiotic solution (ATB) containing ampicillin (1mg/ml), streptomycin (5mg/ml) and colistin (1mg/ml) (Sigma-Aldrich) with or without vancomycin (0.25mg/ml) (using drinking water). The solution and bottles were replaced 3 times and once a week, respectively. Antibiotic activity was confirmed by culturing fecal pellets resuspended in BHI + 15% glycerol at 0.1g/ml for 48 hours at 37 ℃ on COS (Columbia agar containing 5% sheep blood) plates under aerobic and anaerobic conditions. The duration of the ATB treatment varied slightly with the experimental setup. Briefly, mice received 2 weeks of treatment prior to tumor implantation and continued throughout the MCA205 and RET experiments, while in experiments using RENCA, 3 days of ATB treatment were administered prior to fecal microbiota transfer.

Tumor challenge and treatment mice were injected subcutaneously (s.c.) laterally in the flank with 0.8X 10⁶MCA205 or 0.5 × 10⁶And (3) RET cells. Treatment was started when the tumor reached 20 to 30mm 2. Mice were injected intraperitoneally (ip) once every three days with anti-PD-1 mAb (250. mu.g/mouse; clone RMP1-14, 6 injections in MCA205, 5 injections in RET) and/or anti-CTLA-4 mAb (100. mu.g/mouse, clone 9D9, 5 injections in both MCA205 and RET), with or without anti-IL-1R (anakinra, 500. mu.g/mouse, i.p. injections three times a week) or the corresponding isotype controls, as shown in the figure. All mabs used in vivo were from BioXcell (west libamon, new hampshire, usa) using the recommended isotype control mAb (Swedish orange Biovitrum, sweden) except anakinra.

Fecal microbiota transfer experiment after 3 days of ATB treatment, fecal microbiota transfer ((FMT) was performed using samples from PD-1 inhibitor responders or non-responders patients, frozen fecal samples were thawed and vortexed thoroughly, large granular material was allowed to settle under gravity, 200 μ Ι _ supernatant was administered by single dose fed through a mouth tube, BALB/c mice were anesthetized with isoflurane after two additional 100 μ Ι _ FMT was applied to the fur of each animal, Injection into the subcapsular space of the right kidney of 1X 10 in 30. mu.L PBS⁴And RENCA tumor cells. The skin incision is then closed with surgical clips. Treatment was started 5 days after tumor inoculation. Mice were treated with CICB with or without oral tube feeding of fecal samples from responsive patients who did not experience toxicity. Tumor growth was monitored weekly on IVIS imaging system 50 series (Analytic Jenap).

Enterobacter enterobacter CSURP836 (provided by Institut hospitalo-undivistaire M urterane Infection (French mosaic); isolated from human specimens), Enterobacter enterobacter from everImmune (isolated from feces of lung cancer patients before immunotherapy), and Enterobacter enterobacter (isolated from mouse specimens) were cultured on COS plates under anaerobic conditions for 24-72 hours at 37 ℃ using an oxygen-free generator (Biomerieux). 10 with an optical density of 1 measured at 600nm was obtained using a spectrophotometer (Eppendorf)⁹CFU/mL suspension. Administered 24 hours prior to antibody treatment and 10 with each antibody treatment⁹Individual CFUs were fed at 100 μ L suspension port tube. Bacteria were verified using a matrix assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometer (Microflex LT analyzer, Bruker daltons, germany).

Cytokine quantification stool samples were collected and stored at-80 ℃ until further processing. The samples were thawed and resuspended (at 100mg/mL) in PBS containing 0.1% tween 20. After incubation at room temperature for 20 minutes with shaking, the samples were centrifuged at 12,000rpm for 10 minutes, and the supernatants were harvested and stored at-20 ℃ until analysis. Lipocalin-2 levels were measured using the mouse lipocalin-2/NGAL DuoSet ELISA kit (R & D Systems, Minneapolis, MN) according to the manufacturer's instructions.

Intestinal tissue was preserved in Formalin Fixed Paraffin Embedded (FFPE) or optimal cutting temperature compound (OCT). Ileum and colon were removed at sacrifice, washed in PBS, cut longitudinally, tumbled, and fixed in 4% PFA overnight at 4 ℃, or in some experiments at room temperature for 2 hours. The Tissue was then treated with Tissue- 6 vacuum infiltration processor (Sakura) paraffin embedding, or rehydration in 15% sucrose for 1 hour, then overnight in 30% sucrose, OCT embedding (Sakura) and snap freezing. Hematoxylin, eosin and safranin stain (H) for longitudinal section&E) And (5) counterdyeing.

Histological assessment of intestinal tissue toxicity scoring systems were developed by pathologists (P.O). The ileum: each section was scored for inflammatory foci, appearance of submucosa, length of villi, and thickness of lamina propria. The score is defined as: 0-normal, 1-focal and mild lesion; diffuse and mild lesions; diffuse, mild and major lesions; 4-the major lesion with a region containing only connective tissue. Colon: inflammatory infiltrates, scored by the definition of physiological (0) level, low (1) level, moderate (2) level and high (3) level.

Determination of intestinal immunogene expression by real-time quantitative PCR analysisRNA extraction Using the RNeasy Mini kit (Qiagen) and SuperScript III reverse transcriptase and RNaseOUT^TMRecombinant ribonuclease inhibitors (Life Technologies) were reverse transcribed into cDNA using random primers (Promega, Wisconsin, USA) and PCR-grade deoxynucleotide triphosphate sets (Roche, Basel, Switzerland). TaqMan protocol was used on 7500 Rapid real-time PCR System (Applied Biosystems) according to the manufacturer's instructionsGene Expression analysis and Taqman Universal Master Mix II (Invitrogen) Gene Expression was analyzed by real-time quantitative PCR (RT-qPCR). Expression was normalized to the expression of housekeeping genes of beta-2 microglobulin by the 2-delta Ct method. The following primers (all fromGene Expression Assay,ThermoFisher)：B2m(Mm00437762_m1)、Il1b(Mm00434228_m1)、Il6(Mm00446190_m1)、Tnf(Mm00443258_m1)。

5. Microbiome analysis

Patient stool samples baseline stool samples were collected using the OMNIgene GUT kit (DNA Genotek, ottawa, canada). A total of 54 fecal samples were subjected to bacterial 16S rRNA gene sequencing, including a skin/unknown primary cohort (29R, 11 NR; 24 cases with grade 3 of iraE, 16 cases with <3 of iraE) and a mucosal cohort for toxicity analysis only (3 cases with grade 3 of iraE, 5 cases without) and a uveal melanoma cohort (2 cases with grade 3 of iraE, 4 cases without). In this cohort, some samples obtained early after the start of CICB were included as surrogate baseline samples, as parallel studies of longitudinal samples taken from patients receiving immune checkpoint blockade monotherapy showed no significant change in fecal microbiota early after the start of treatment (Gopalakrishnan,2018# 19).

Human fecal DNA extraction and bacterial 16S rRNA gene sequencing preparation and sequencing of Human fecal samples was performed in concert with the Alkek Center for reagents and Microgenome Research (CMMR) from Baylor College of Medicine using a method adapted from the NIH-Human Microbiome Project (2012 # 34; Human genome Project,2012# 35). Expanded details of the analysis of pipelines have been reported previously (Gopalakrishnan,2018# 19). Briefly, bacterial genomic DNA extracted using the MO BIO PowerSoil DNA isolation kit (MO BIO Laboratories, usa) was subjected to PCR amplification of the 16S rRNA gene V4 region and sequenced using the MiSeq platform (Illumina, Inc, san diego, california). Mass-filtered sequences with > 97% identity were clustered by open-reference OTU packaging into boxes called Operational Taxonomic Units (OTU) and classified at the species level with reference to the NCBI 16S ribosomal RNA sequence database (release date 2.11.2017; NCBI-blast + package 2.5.0). Phylogenetic information was obtained by mapping representative OTU sequences with NCBI taxonomic database (release date 2017, 2 months 16 days) using BLAST.

At least two longitudinal stool samples were collected from mice (n-71) and stored at-80 ℃ until DNA extraction.

Mouse fecal DNA extraction and microbiota characterization preparation and sequencing of mouse fecal samples was performed in IHU M Di rerane Infection, France mosaic. Briefly, two protocols were used to extract DNA. The first protocol involved physical and chemical lysis using glass frit and proteinase K, respectively, followed by treatment using the Macherey-Nagel DNA tissue extraction kit (Duren, germany) (Dridi, 2009# 64). The second protocol was identical to the first protocol, but with the addition of a glycoprotein cleavage and deglycosylation step (Angelakis,2016# 65). The resulting DNA was sequenced and targeted to the V3-V4 region of the 16S rRNA gene as previously described (Million,2016# 63). Raw FASTQ files were analyzed using the mortur pipeline v.1.39.5 for quality checking and filtering (sequencing errors, chimeras) on the workstation DELL T7910(Round Rock, tx, usa). The raw reads (15512959 total, 125104 average per sample) were filtered (6342281 total, 51147 average per sample) and clustered into Operational Taxonomic Units (OTUs), then the low population OTUs were eliminated (up to 5 reads) and de novo OTUs were performed for alignment at 97% pairwise identity using standardized parameters and the SILVA rDNA database v.1.19. Considering RET and MCA samples, a total of 427 bacteria were identified using a prevalence threshold of ≧ 20%. The sample coverage was calculated using Mothur, which resulted in an average coverage of more than 99% for all samples, and thus meant to be a suitable normalization procedure for subsequent analysis. Bioinformatics and statistical analysis of the identified OTUs were performed using Python v.2.7.11. The most representative and most abundant reads in each OTU (as confirmed in the previous step with Mothur v.1.39.5) were nucleotide aligned (nucleotid Blast) using the National Center for Biotechnology Information (NCBI) Blast software (NCBI-Blast-2.3.0) and the latest NCBI 16S microbial database accessed at the end of 2019 at 4 months (which can be found at ftp. A bacterial relative abundance matrix was established at each classification level (phylum, class, order, family, genus, species) for subsequent multivariate statistical analysis.

Genomic DNA was extracted from Stool samples using QIAamp DNA pool Mini kit (Qiagen) according to the manufacturer's instructions. Targeted qPCR systems were applied using either TaqMan technology (for systems targeting all bacterial domains) or SYBR Green for different bacteroid species. The following primers and probes were used:

for mouse experiments, raw data were first normalized and then normalized using the QuantileTransformer and StandardScaler method in the Sci-Kit learning package v0.20.3. Normalization using the output _ distribution ═ normal' option converts each variable to a strictly gaussian shape distribution, whereas normalization results in a mean of zero and a variance of one for each normalized variable. These two normalization steps after normalization will ensure the correct comparison of variables with different dynamic ranges, such as bacterial relative abundance, tumor size or colon infiltrate score.

Measurements of alpha diversity (within sample diversity) were calculated at OTU scale using the SciKit-lern package v.0.4.1, as observed OTU and the shannon index. Exploratory analysis of β -diversity (between sample diversity) uses Bray-Curtis difference metric calculation calculated by Mothur and expressed in principal coordinate analysis (PCoA), while for Hierarchical Cluster Analysis (HCA) the 'Bray-Curtis' metric and 'complete linkage' methods are implemented using custom scripts (Python v.2.7.11). The inventors performed partial least squares discriminant analysis (PLS-DA) and subsequent variable importance maps (VIP) as a supervised analysis in order to identify the most discriminative bacterial species in tumor-bearing and tumor-free mice and between different time points (T0, T2, T5). As depicted in 2D, the bar thickness reports the ratio of the multiples of the average relative abundance (FR) of each species between the two cohorts, while the not applicable (N/a) refers to the comparison with the zero relative abundance group. The absence of a border indicates that the average relative abundance in the comparison cohort is zero. To compare the microbiota taxa with the gene expression dataset or tumor size and colon toxicity, multivariate statistical Spearman (or Pearson, for mouse data) correlation analysis (and associated P-values) was performed using custom Python scripts. The Mann-Whitney U and Kruskal-Wallis tests were used to assess significance for pairwise or multiple comparisons, respectively, with p-values <0.05 considered significant.

Pairwise comparisons of relative abundance between identified taxa within patient samples were performed using the Mann-Whitney test followed by 1000 permutations of bootstrap. Only taxa present in at least 40% of all samples were considered. The sparsification limit for calculating alpha diversity is set based on the fewest number of reads in all stool samples. Estimating the taxonomic alpha diversity of the patient sample by using the inverse Simpson index, and the calculation formula is(pi is the proportion of species i in the total species S) (Morgan,2012#36), and other diversity metrics as indicated in the figure are estimated. Spearman's rho was used to estimate the correlation between the relative abundance of candidate taxa and peripheral immune markers. Similarity analysis (ANOSIM, which represents the difference in the centroid of the dataset) or Pearson correlation coefficients (when noted) were calculated using Python 2.7.11.

Using Kruskal-Wallis test, the LEfSe method was used to compare the abundance (defined as statistical significance as p <0.05) of all bacterial clades (Segata,2011#21) in terms of response (i.e.: between R and NR) and occurrence of toxicity (i.e.: between patients ≧ 3 and <3 > iraE). Effect size was calculated using bacterial taxa with differential abundance between study groups as input to Linear Discriminant Analysis (LDA). Lefse analysis was performed on the murine taxa with Mothur v 1.39.5.

B. Quantification and statistical analysis

1. Statistical analysis

Data analysis and presentation was performed using R software (available from world wide web R-project. org.), Microsoft Excel (Microsoft co., redmond 436, washington) or Prism 5(GraphPad, san diego, california, usa). Patient cohort survival curves were generated using the R package "survivval" (Therneau,2000# 37). In the case of low sample dichotomous variables, an unpaired Mann-Whitney U test or Fisher's exact test was used for inter-group comparisons of patient cohort genomes and immune parameters, with p <0.05 considered statistically significant. All comparisons are two-sided unless strong a priori assumptions warrant a one-sided approach (as appropriate). Permutation checking is performed by randomly permuting the sample tags for a total of 1000 iterations. In the murine study, statistical analysis of more than two groups collected using ANOVA followed by pairwise comparisons with Bonferroni adjustments was performed. Otherwise, statistical analysis was performed using unpaired t-test for both groups. Outliers within a given distribution were tested using the Grubbs test (found online in graph pad. com/quick caps/Grubbs 1. cfm) with a threshold of p < 0.05. All tumor growth curves were analyzed using software developed in the laboratory by professor Guido Kroemer, and information on statistical analysis can be found online in the link (https) kroemlab. Briefly, for longitudinal analysis, the raw tumor measurements were logarithmically transformed prior to statistical testing. When complete tumor regression was observed, the minimum value was divided by 2 to evaluate the zero value (zeros). Automatic outlier detection with p <0.1 was retained for longitudinal analysis and Kaplan Meier curves. Survival curves were estimated using Cox regression and multiple tests were considered using Bonferroni adjustment. p values are two-sided with a confidence interval of 95%, considered significant when p < 0.05. Symbol significance: p <0.05, p <0.01, p < 0.001.

C. Table form

Extended data table 1: a patient characteristic.

Extended data table 2: clinical results

Percentages are expressed relative to the number of patients within each indicated group. Rounding in the median is represented by a non-integer value.

The best overall response for PR + CR and for NR + SD + PD.

Extended data table 3: overview of biological sample usage.

0 is not applicable and 1 is used.

Extended data table 4: association of microorganisms with tumor-free mice.

Extended data table 5: association of microorganisms with tumor-bearing mice.

All of the methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More particularly, it will be apparent that certain agents that are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

Reference to the literature

The following references, to the extent they provide exemplary procedures or other details supplementary to those set forth herein, are expressly incorporated herein by reference.

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