阅读说明：本技术 癌症免疫疗法的新型生物标志物 (Novel biomarkers for cancer immunotherapy ) 是由 松本成司 铃木隆二 于 2018-03-14 设计创作，主要内容包括：本发明提供一种在开始癌症免疫疗法之前预测有效患者的技术。本发明提供一种使用受试者的T细胞受体(TCR)多样性作为对上述受试者的癌症免疫疗法的响应性的指标的方法。本发明还提供一种组合物,其是包含免疫检查点抑制剂的组合物,用于对T细胞的TCR多样性高的受试者治疗癌症。本发明还涉及利用这种TCR多样性的伴随药物。(The present invention provides a technique for predicting an effective patient prior to initiating cancer immunotherapy. The present invention provides a method of using T Cell Receptor (TCR) diversity of a subject as an indicator of responsiveness to cancer immunotherapy in the subject. The invention also provides a composition comprising an immune checkpoint inhibitor for use in treating cancer in a subject with high TCR diversity of T cells. The invention also relates to concomitant drugs that exploit this TCR diversity.)

1. A method of using T cell receptor diversity, i.e., TCR diversity, of T cells of a subject as an indicator of responsiveness of the subject to cancer immunotherapy.

2. The method of claim 1, wherein the T cells are CD8+ and one or more T cell inhibitory cell surface markers are positive.

3. the method of claim 1, wherein the T cells are CD8+ and one or more T cell stimulatory cell surface markers are positive.

4. The method of claim 1, wherein the T cell is CD8+ and is positive for one or more cell surface markers selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR).

5. The method of claim 1, wherein the T cell is a CD8+ PD-1+ T cell.

6. The method of any one of claims 1 to 5, wherein the T cells are T cells in the peripheral blood of the subject.

7. The method of any one of claims 1 to 6, wherein the cancer immunotherapy comprises administration of an immune checkpoint inhibitor.

8. The method of claim 7, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

9. The method of claim 8, wherein the PD-1 inhibitor is nivolumab or pembrolizumab.

10. The method of any one of claims 1 to 9, wherein the TCR diversity is expressed in shannon index, simpson index, reverse simpson index, normalized shannon index, Unique50 index, DE30 index, DE80 index or DE50 index.

11. The method according to any one of claims 1 to 10, wherein the TCR diversity is expressed in DE50 index.

12. The method of any one of claims 1 to 11, wherein the TCR is TCR α.

13. The method according to claim 12, wherein a DE50 index of the subject that has been normalized to any one of the number of reads recited in the following table is above a threshold value corresponding to the number of reads recited in the table, indicating that the subject is a patient with onset of action, or is below the threshold value, indicating that the subject is a patient with no onset of action,

[ Table 45]

Normalized number of reads 100 300 1000 3000 10000 30000 80000 ％DE50 17.14 11.04 5.80 2.58 0.96 0.39 0.18

。

14. The method of any one of claims 1 to 11, wherein the TCR is TCR β.

15. The method of claim 14, wherein a DE50 index for the subject that has been normalized to any one of the number of reads set forth in the table below is above a threshold corresponding to the number of reads set forth in the table indicates that the subject is a patient with onset of action, or when less than the threshold indicates that the subject is a patient with no onset of action,

[ Table 46]

Normalized number of reads 100 300 1000 3000 10000 30000 80000 ％DE50 19.05 11.63 3.64 1.55 0.58 0.25 0.11

。

16. The method of any one of claims 1 to 15, further comprising:

Isolating CD8+ PD-1+ T cells from a peripheral blood sample of the subject; and

Determining the TCR diversity of the CD8+ PD-1+ T cells.

17. The method of any one of claims 1 to 16, wherein the TCR diversity is determined by a method comprising large-scale high-efficiency TCR repertoire analysis.

18. A composition comprising an immune checkpoint inhibitor for use in treating cancer in a subject with high TCR diversity of T cells.

19. The composition of claim 18, wherein the T cells are CD8+ and one or more T cell inhibitory cell surface markers are positive.

20. The composition of claim 18, wherein the T cells are CD8+ and one or more T cell stimulatory cell surface markers are positive.

21. The composition of claim 18, wherein the T cell is CD8+ and is positive for one or more cell surface markers selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR).

22. The composition of claim 18, wherein the T cell is a CD8+ PD-1+ T cell.

23. The composition of any one of claims 18 to 22, wherein the T cells are T cells in the peripheral blood of the subject.

24. The composition of any one of claims 18 to 23, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

25. The composition of claim 24, wherein the PD-1 inhibitor is nivolumab or pembrolizumab.

26. The composition of any one of claims 18 to 25, wherein the TCR diversity of the T cells of the subject is represented by a shannon index, a simpson index, a reverse simpson index, a normalized shannon index, a Unique50 index, a DE30 index, a DE80 index, or a DE50 index.

27. The composition of any one of claims 18 to 26, wherein the TCR diversity of the subject's T cells is represented by the DE50 index.

28. The composition of any one of claims 18 to 27, wherein the TCR is TCR α.

29. The composition of claim 28, wherein the DE50 index of the subject that has been normalized to any one of the number of reads recited in the following table is above a threshold value corresponding to the number of reads recited in the table,

[ Table 47]

Normalized number of reads 100 300 1000 3000 10000 30000 80000 ％DE50 17.14 11.04 5.80 2.58 0.96 0.39 0.18

。

30. The composition of any one of claims 18 to 27, wherein the TCR is TCR β.

31. The composition of claim 30, wherein the DE50 index of the subject that has been normalized to any one of the number of reads set forth in the following table is above a threshold value corresponding to the number of reads set forth in the table,

[ Table 48]

Normalized number of reads 100 300 1000 3000 10000 30000 80000 ％DE50 19.05 11.63 3.64 1.55 0.58 0.25 0.11

。

32. The composition of any one of claims 18 to 31, wherein the TCR diversity of the subject is determined by a method comprising large-scale high-efficiency TCR repertoire analysis.

33. A method for diagnosing responsiveness of a subject to cancer immunotherapy, comprising:

Measuring in vitro TCR diversity of T cells of the subject; and

And determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high or determining that the subject has poor responsiveness to cancer immunotherapy when the TCR diversity is low.

34. a method for diagnosing responsiveness of a subject to cancer immunotherapy, comprising:

Obtaining a peripheral blood sample from the subject;

Determining TCR diversity of T cells in peripheral blood of the subject by a method comprising large-scale high-efficiency TCR repertoire analysis; and

And determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high or determining that the subject has poor responsiveness to cancer immunotherapy when the TCR diversity is low.

35. A method for diagnosing responsiveness of a subject to cancer immunotherapy and treating cancer in the subject, comprising:

Obtaining a peripheral blood sample from the subject;

Measuring TCR diversity of T cells in peripheral blood of the subject; and

A procedure for administering cancer immunotherapy to said subject when said TCR diversity is above a baseline value.

36. A method of using the diversity of libraries determined by a method comprising large scale high efficiency library analysis as an indicator of responsiveness of a subject to therapy.

37. The method of claim 36, wherein the therapy is a therapy associated with an immune response.

38. The method of claim 36 or 37, wherein the library analysis is a TCR library analysis.

39. The method according to any one of claims 1 to 17, wherein a diversity index representing TCR diversity of the subject above a threshold is an indication that the subject is a responsive patient, or the diversity index being less than a threshold is an indication that the subject is a non-responsive patient,

The threshold is determined based on ROC analysis, or based on sensitivity, or based on specificity.

40. The method according to any one of claims 1 to 17 or claim 39 wherein a diversity index indicative of TCR diversity of the subject above a threshold is an indication that the subject is a responsive patient or the diversity index being below a threshold is an indication that the subject is a non-responsive patient,

The threshold is a value normalized to the number of reads used in calculating the diversity index of the subject.

Technical Field

The present invention relates to the field of cancer immunotherapy. More particularly, it relates to prediction of responsiveness of a subject to cancer immunotherapy and treatment with cancer immunotherapy based on the prediction. In another aspect, the invention relates to novel uses of large-scale, high-efficiency library analysis. More particularly, the present invention relates to predicting subject responsiveness to cancer immunotherapy using diversity indices obtained by large-scale, high-efficiency library analysis.

Background

Cancer immunotherapy has attracted attention as a treatment for cancer. In particular, for immune checkpoint inhibitors such as Nivolumab (Nivolumab), which is an anti-PD-l antibody, showed greatly improved results in terms of overall survival over Docetaxel (Docetaxel, an existing standard therapy for non-small cell lung cancer), becoming the standard therapy.

However, among patients who received the immune checkpoint inhibitor, there were patients who exhibited significant effects such as cessation of progression of cancer or remission of cancer, while there was an "ineffective group" in which the disease worsened within 3 months in the anti-PD-l antibody clinical trial. However, a method for efficiently determining the invalid group is not known.

Disclosure of Invention

Means for solving the problems

in one embodiment of the invention, a method of using T Cell Receptor (TCR) diversity of T cells as an indicator of responsiveness to cancer immunotherapy is provided. Generally, cancer immunotherapy utilizes biodefense mechanisms, and thus there are individual differences in response to therapy. Thus, biomarkers have been sought that can determine this individual difference prior to treatment (e.g., the proportion of cells in T cells that express a particular surface marker, surface proteins expressed in tumors, etc.). However, the composition of cells that are believed to attack tumors (e.g., the proportion of CD8+ PD-1+ cells, etc.) cannot be used as a biomarker because there is no difference between patients who are effective and those who are not effective for cancer immunotherapy (fig. 4). The inventors of the present invention have surprisingly found that TCR diversity of T cells of a subject can be used to predict the subject's response to therapy. For example, in lung cancer patients, anti-PD-1 antibody (Nivolumab) is effective in 20-30% of patients. Cancer immunotherapy containing immune checkpoint inhibitors is often expensive, and if effective patients can be predicted before starting treatment with anti-PD-1 antibodies or the like, more effective treatment can be achieved, waste of medical costs can be avoided, and a drastic reduction in social security costs can be facilitated. Accordingly, the present invention provides novel biomarkers for predicting responsiveness to cancer immunotherapy.

In one embodiment of the invention, cancer immunotherapy comprises the administration of an immune checkpoint inhibitor. The immune checkpoint inhibitor may be a PD-1 inhibitor. The PD-1 inhibitor may be an anti-PD-1 antibody comprising nivolumab or Pembrolizumab (Pembrolizumab). One embodiment of the invention provides a method of using T Cell Receptor (TCR) diversity of T cells as an indicator of responsiveness to an immune checkpoint inhibitor.

As the TCR diversity, a plurality of indices, for example, Shannon (Shannon) index, Simpson (Simpson) index, reverse Simpson index, normalized Shannon index, DE index (for example, DE50 index, DE30 index, DE80 index), or Unique index (for example, Unique30 index, Unique50 index, Unique80 index), and the like can be utilized. In a preferred embodiment of the invention, the TCR diversity is the DE index. In a more preferred embodiment of the invention, the TCR diversity is the DE50 index.

In one embodiment of the invention, the TCR diversity is the TCR diversity of T cells of the subject. In one embodiment, as T cells, T cells positive for a T cell suppressor cell surface marker may be used. Alternatively, in another embodiment, as the T cell, a T cell positive for a T cell stimulatory cell surface marker may be used. The T cells used were positive for one or more cell surface markers selected from the group consisting of CD8, PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT and CD367 (GITR). In one embodiment, preferably the T cells are CD8 +. In a further embodiment, the T cells are CD8+ and the T cell suppressor cell surface marker is positive. In a further embodiment, the T cell is CD8+ and the T cell stimulatory cell surface marker is positive. In a further embodiment, the T cells are CD8+, and the T cell inhibitory and T cell stimulatory cell surface markers are positive. In one embodiment, the T cell is CD8+ and is positive for one or more cell surface markers selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR). In one embodiment, the T cell is selected from the group consisting of CD8+ PD1+, CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT +, and CD8+ CTLA4+ T cell. Preferably the T cells are CD8+ PD-1+ T cells. The T cells may be T cells in peripheral blood.

TCR diversity can be diversity of TCR α or diversity of TCR β. In one embodiment of the invention, such high TCR diversity is indicative of a subject being a patient with efficacy.

Another embodiment of the invention includes administering cancer immunotherapy to a subject with high TCR diversity. In one embodiment, a composition is provided that is a composition comprising an immune checkpoint inhibitor for treating cancer in a subject with high TCR diversity of T cells. In a further embodiment, there is provided a composition comprising an immune checkpoint inhibitor for use in treating cancer in a subject with high TCR diversity of CD8+ PD-1+ T cells in peripheral blood.

In some embodiments, when the TCR diversity of the subject is above a threshold, the subject is an effective patient for cancer immunotherapy. In another embodiment, when the TCR diversity of the subject is below a threshold, the subject is an ineffective patient for cancer immunotherapy. In the case of using a DE index, the subject is indicated as a patient with an onset of cancer immunotherapy by using a DE index in which the number of reads is normalized or by comparing with a threshold in which the number of reads is adjusted. In one embodiment, a subject is a patient with efficacy when the DE50 index normalized to the 30000 reading of the subject's TCR α is 0.39% or greater. In another embodiment, a subject is a patient with efficacy when the DE50 index normalized to the 30000 reading of the subject's TCR β is 0.24% or greater. A subject is indicated as a patient with or without effect by a comparison between the DE50 index normalized with respect to any number of reads and a threshold corresponding to the number of reads described above. Examples of combinations between the number of reads and the threshold are provided in this specification.

In some embodiments, the threshold is determined based on ROC analysis. In some embodiments, the threshold is determined based on specificity, e.g., a value above the maximum value of non-response is set as the threshold. In some embodiments, the threshold is determined based on sensitivity, e.g., the value of the lowest line below the operator is set as the threshold. In the present invention, a process of determining these threshold values may be included, and the threshold values thus determined may be used.

One embodiment of the present invention is a method comprising a step of isolating a T cell from a subject and a step of measuring TCR diversity of the T cell, wherein the T cell is positive for at least one cell surface marker selected from the group consisting of CD8, PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367(GITR) is isolated. In one embodiment, preferably the T cells are CD8 +. In a further embodiment, the T cells are CD8+ and the T cell suppressor cell surface marker is positive. In one embodiment, the T cell is selected from the group consisting of CD8+ PD1+, CD8+4-1BB +, CD8+ T1M3+, CD8+ OX40+, CD8+ TIGIT +, and CD8+ CTLA4+ T cell. In a further embodiment, the T cell is CD8+ and the T cell stimulatory cell surface marker is positive. Particularly preferred is a method comprising a step of isolating CD8+ PD-1+ T cells from a peripheral blood sample of a subject and a step of measuring the TCR diversity of CD8+ PD-1+ T cells.

Large-scale, high-efficiency TCR library analysis is preferred because it allows identification of low frequency (1/10000-1/100000 or below) genes. One embodiment of the invention includes a procedure for determining TCR diversity by a method that includes large-scale, high-efficiency TCR repertoire analysis. One embodiment of the invention is a method of using TCR diversity determined by large-scale, high-efficiency TCR repertoire analysis as an indicator of a subject's medical status, particularly responsiveness to therapy.

One embodiment of the present invention is a method for diagnosing responsiveness of a subject to cancer immunotherapy, the method comprising: a step of measuring the TCR diversity of T cells of a subject in vitro (in vitro); and determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high. Or when TCR diversity is low, the subject can also be determined to be poorly responsive to cancer immunotherapy. The T cells may be CD8+ PD-1 +. In addition, the T cells may be derived from the peripheral blood of the subject.

Another embodiment of the present invention relates to a method for diagnosing responsiveness of a subject to cancer immunotherapy, the method comprising: obtaining a peripheral blood sample from a subject; a step of measuring the TCR diversity of T cells in peripheral blood of a subject by a method comprising large-scale high-efficiency TCR repertoire analysis; and determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high. Alternatively, a subject may also be determined to be poorly responsive to cancer immunotherapy in cases where TCR diversity is low. The T cells may be CD8+ PD-1 +.

In a further embodiment of the present invention, there is provided a method for diagnosing responsiveness of a subject to cancer immunotherapy and treating cancer in a subject, the method comprising: obtaining a peripheral blood sample from a subject; measuring TCR diversity of T cells in peripheral blood of the subject; and, administering cancer immunotherapy to the subject when the TCR diversity is above a baseline value. The T cells may be CD8+ PD-1 +.

The present invention also provides a technique for predicting responsiveness of a subject to cancer immunotherapy using diversity indices obtained by large-scale, high-efficiency library analysis.

For example, the present invention provides the following.

Scheme 1: a method of using T Cell Receptor (TCR) diversity of T cells of a subject as an indicator of responsiveness of the subject to cancer immunotherapy.

Scheme 2: the method according to the above aspect, wherein the T cell is CD8+, and the one or more T cell inhibitory cell surface markers are positive.

Scheme 3: the method according to any one of the preceding claims, wherein the T cells are CD8+ and one or more T cell stimulatory cell surface markers are positive.

Scheme 4: the method according to any one of the above embodiments, wherein the T cell is CD8+, and the cell surface marker is positive for at least one selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR).

Scheme 5: the method according to any one of the preceding claims, wherein said T cells are CD8+ PD-1+ T cells.

Scheme 6: the method according to any one of the preceding claims, wherein said T cells are T cells in peripheral blood of said subject.

Scheme 7: the method of any of the above regimens, wherein the cancer immunotherapy comprises administration of an immune checkpoint inhibitor.

scheme 8: the method of any of the above protocols, wherein the immune checkpoint inhibitor is a PD-l inhibitor.

Scheme 9: the method according to any of the preceding schemes, wherein the PD-l inhibitor is nivolumab or pembrolizumab.

Scheme 10: the method according to any of the preceding aspects, wherein the TCR diversity is represented by shannon index, simpson index, reverse simpson index, normalized shannon index, Unique50 index, DE30 index, DE80 index or DE50 index.

Scheme 11: the method of any one of the above protocols, wherein the TCR diversity is represented by the DE50 index.

Scheme 12: the method of any of the above embodiments, wherein the TCR is TCR α.

Scheme 13: the method according to any one of the preceding protocols, wherein a DE50 index for the subject normalized to any one of the number of reads set forth in the table below is greater than or equal to a threshold value corresponding to the number of reads set forth in the table indicates that the subject is a patient with onset of action, or wherein less than the threshold value indicates that the subject is a patient with no onset of action.

TABLE 1A

Normalized number of reads	100	300	1000	3000	10000	30000	80000
								％DE50	17.14	11.04	5.80	2.58	0.96	0.39	0.18

Scheme 14: the method of any of the above embodiments, wherein the TCR is TCR β.

Scheme 15: the method of any one of the above regimens, wherein the subject is indicated as a patient with onset of action when the DE50 index for the subject normalized to any one of the number of reads listed in the table below is above the threshold corresponding to the number of reads listed in the table, or is indicated as a patient with no onset of action when less than the threshold.

TABLE 1B

Normalized number of reads	100	300	1000	3000	10000	30000	80000
								％DE50	19.05	11.63	3.64	1.55	0.58	0.25	0.11

Scheme 16: the method according to any of the above aspects, further comprising:

Separating CD8+ PD-1+ T cells from a peripheral blood sample of the subject; and

And (3) determining the TCR diversity of the CD8+ PD-1+ T cells.

Scheme 17: the method of any of the above protocols, wherein the TCR diversity is determined by a method comprising large-scale high-efficiency TCR repertoire analysis.

Scheme 18: a composition comprising an immune checkpoint inhibitor for use in treating cancer in a subject with high TCR diversity of T cells.

Scheme 18A: a composition according to the above aspects having the features described in any one or more of the above aspects.

Scheme 19: the composition of any one of the preceding claims, wherein the T cells are CD8+ and one or more T cell suppressor cell surface markers are positive.

Scheme 20: the composition of any one of the preceding claims, wherein the T cells are CD8+ and one or more T cell stimulatory cell surface markers are positive.

scheme 21: the composition according to any of the above embodiments, wherein the T cell is CD8+, and the cell surface marker is positive for at least one selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR).

Scheme 22: the composition of any one of the preceding claims, wherein said T cells are CD8+ PD-l + T cells.

Scheme 23: the composition according to any one of the preceding claims, wherein said T cells are T cells in peripheral blood of said subject.

Scheme 24: the composition according to any of the preceding claims, wherein the immune checkpoint inhibitor is a PD-l inhibitor.

Scheme 25: the composition according to any of the above embodiments, wherein the PD-l inhibitor is nivolumab or pembrolizumab.

Scheme 26: the composition of any of the above protocols, wherein the TCR diversity of the T cells of the subject is represented by shannon index, simpson index, reverse simpson index, normalized shannon index, Unique50 index, DE30 index, DE80 index, or DE50 index.

Scheme 27: the composition of the above protocol, wherein the TCR diversity of the T cells of the subject is represented by the DE50 index.

Scheme 28: the composition of any one of the preceding claims, wherein the TCR is TCR α.

Scheme 29: the composition of any of the above protocols, wherein the DE50 index for the subject normalized to any one of the numbers of reads set forth in the table below is greater than or equal to the threshold value corresponding to that number of reads set forth in the table.

TABLE 1C

Normalized number of reads	100	300	1000	3000	10000	30000	80000
								％DE50	17.14	11.04	5.80	2.58	0.96	0.39	0.18

Scheme 30: the composition of any one of the preceding claims, wherein the TCR is TCR β.

Scheme 31: the composition of any of the above protocols, wherein the DE50 index for the subject normalized to any one of the numbers of reads set forth in the table below is greater than or equal to the threshold value corresponding to that number of reads set forth in the table.

TABLE 1D

Normalized number of reads	100	300	1000	3000	10000	30000	80000
								％DE50	19.05	11.63	3.64	1.55	0.58	0.25	0.11

Scheme 32: the composition of any of the above protocols, wherein TCR diversity in the subject is determined by a method comprising large-scale high-efficiency TCR repertoire analysis.

Scheme 33: a method for diagnosing responsiveness of a subject to cancer immunotherapy, the method comprising:

Measuring the TCR diversity of the subject's T cells in vitro (in vitro); and

and determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high, or determining that the subject has poor responsiveness to cancer immunotherapy when the TCR diversity is low.

Scheme 33A: the method according to any one or more of the preceding claims, having the features recited in any one or more of the preceding claims.

Scheme 34: a method for diagnosing responsiveness of a subject to cancer immunotherapy, comprising:

Obtaining a peripheral blood sample from the subject;

Measuring the TCR diversity of T cells in peripheral blood of the subject by a method comprising large-scale high-efficiency TCR repertoire analysis; and

And determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high, or determining that the subject has poor responsiveness to cancer immunotherapy when the TCR diversity is low.

Scheme 34A: the method according to any one or more of the preceding claims, having the features recited in any one or more of the preceding claims.

Scheme 35: a method for diagnosing responsiveness of a subject to cancer immunotherapy and treating cancer in said subject, comprising:

Obtaining a peripheral blood sample from the subject;

Measuring TCR diversity of T cells in peripheral blood of the subject; and

And administering cancer immunotherapy to the subject when the TCR diversity is above a baseline value.

Scheme 35A: the method according to any one or more of the preceding claims, having the features recited in any one or more of the preceding claims.

Scheme 36: a method of using the diversity of libraries determined by a method comprising large-scale high-efficiency library analysis as an indicator of responsiveness of a subject to treatment.

Scheme 36A: the method according to any one or more of the preceding claims, having the features recited in any one or more of the preceding claims.

Scheme 37: the method of any of the above regimens, wherein the treatment is a treatment associated with an immune response.

Scheme 38: the method of any one of the above protocols, wherein the library analysis is a TCR library analysis.

Scheme 39: a method according to any one of the above aspects, wherein a diversity index indicating TCR diversity of the subject above a threshold value is an index of the subject being a patient with an onset of action, or wherein a diversity index below a threshold value is an index of the subject being a patient with no onset of action,

The threshold is determined based on ROC analysis, or based on sensitivity, or based on specificity.

Scheme 40: a method according to any one of the above aspects, wherein a diversity index indicating TCR diversity of the subject above a threshold value is an index of the subject being a patient with an onset of action, or wherein a diversity index below a threshold value is an index of the subject being a patient with no onset of action,

The threshold is obtained by normalizing the number of reads used in calculating the diversity index of the subject.

In the present invention, one or more of the above-described features may be provided in further combination in addition to the combinations explicitly described. Still other embodiments and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, as needed.

ADVANTAGEOUS EFFECTS OF INVENTION

In the present invention, the diversity index obtained by performing TCR library analysis of easily sampled peripheral blood cells can be used as a biomarker for predicting the effect of cancer immunotherapy. This enables individual improvement with treatment (treatment) and reduction of social insurance costs, and enables the individual to receive reliable treatment.

Drawings

Fig. 1 is a diagram showing an exemplary sequence of TCR library analysis for predicting the therapeutic effect of a patient receiving treatment with an anti-PD-l antibody.

Fig. 2A is a view showing clinical evaluation based on CT image diagnosis before and after the start of treatment of patient # 1and patient #2 who received treatment with an anti-PD-l antibody.

Fig. 2B is a view showing clinical evaluation based on CT image diagnosis before and after the start of treatment of patient #3 and clinical evaluation based on FDG-PET image diagnosis before and after the start of treatment of patient #4, which received treatment with the anti-PD-l antibody.

Fig. 3 is a graph showing the results of FACS analysis for patient # 1.

Fig. 4 is a graph showing a comparison of PD1 positive cells between patients with anti-PD-l antibody effect and patients without effect. The proportion of PD1+ cells in CD8+ T cells before treatment (left) and the proportion of CD8+ PD1+ cells in the lymphocyte fraction of peripheral blood cells (right) of patients with an anti-PD-l antibody treatment (Responder, n ═ 6) and patients with no effect (Non-Responder, n ═ 6) are shown. The proportion of PD1+ cells in CD8+ T cells was almost non-different between the two groups.

Fig. 5 is a graph showing a comparison of diversity index of TCR α between patients with and without effect of PD-1 antibody before treatment. Prior to treatment of an anti-PD-1 antibody-treated patient (n-12), peripheral blood mononuclear cells (peripheral blood mononuclear cells) were isolated from the patient's whole blood and CD8+ PD-1+ cells were fractionated by FACS sorter. RNA was extracted from CD8+ PD1+ cells and subjected to large-scale high-efficiency TCR library analysis to calculate their diversity index (TCR α). Using the diversity index of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D) and DE50 index (E), a comparison was made between patients with a PD-1 antibody effect (Responder, n ═ 6) and patients without an effect (Non-Responder, n ═ 6). In all diversity indices, patients with a PD-1 antibody treatment that is efficacious exhibit a higher diversity than those without.

Fig. 6 is a graph showing a comparison of diversity index of TCR β between patients with and without effect of PD-1 antibody before treatment. RNA was extracted from pre-treatment CD8+ PD1+ cells of anti-PD-1 antibody-treated patients and subjected to large-scale high-efficiency TCR repertoire analysis to calculate their diversity index (TCR β). Using the diversity index of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D) and DE50 index (E), a comparison was made between patients with a PD-1 antibody effect (Responder, n ═ 6) and patients without an effect (Non-Responder, n ═ 6). As a result, patients who respond to the PD-1 antibody treatment showed higher diversity than those who do not respond to the treatment in all diversity indices.

FIG. 7 is a ROC curve obtained by plotting sensitivity and 1-specificity when the threshold value is varied in a plurality of ways using the respective diversity indices of TCR α and TCR β of each patient used in example 1 as an index. The upper row uses the diversity index of TCR α and the lower row uses the diversity index of TCR β.

Fig. 8 is a graph showing changes corresponding to the number of reads for each diversity index (shannon index, normalized shannon index, simpson index, reverse simpson index, DE30 index, DE50 index, DE80 index, Unique30 index, Unique50 index, and Unique80 index) for TCR α. The diversity index corresponding to each number of reads was calculated by random resampling of each number of reads from the data obtained in example 1, and the median value for 100 random resampling per subject was plotted. Each dot represents each subject of example 1 of the present specification (n-12). The horizontal axis represents the number of resampled reads (logarithm), and the vertical axis represents the value of the diversity index. For the DE index, the vertical axis is also represented in logarithmic axis. For each individual, the same color is used in the respective indices.

Fig. 9 is a graph showing changes in the respective diversity indices (shannon index, normalized shannon index, simpson index, reverse simpson index, DE30 index, DE50 index, DE80 index, Unique30 index, Unique50 index, and Unique80 index) for TCR β according to the number of reads. The diversity index corresponding to each number of reads was calculated by random resampling each number of reads from the data obtained in example 1, and the median value for 100 random resampling for each subject was plotted. Each dot represents each subject of example 1 of the present specification (n-12). The horizontal axis represents the number of resampled reads (logarithm), and the vertical axis represents the value of the diversity index. For the DE index, the vertical axis is also represented by the logarithmic axis. For each individual, the same color is used in the respective indices.

Fig. 10A fig. 10 is a graph showing a comparison of diversity index normalized to 30000 reads for TCR α between patients with and without effect of PD-1 antibody prior to treatment. Prior to treatment of anti-PD-1 antibody-treated patients (n-12), peripheral blood mononuclear cells were isolated from patient whole blood and CD8+ PD-1+ cells were fractionated by FACS sorter. RNA was extracted from CD8+ PD1+ cells and its diversity index (TCR α) was calculated by performing large-scale high-efficiency TCR repertoire analysis. A comparison was made between patients with PD-1 antibody effect (Responder, n ═ 6) and patients with no effect (Non-response, n ═ 6) using diversity indices of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D), DE30 index (E), DE50 index (F), DE80 index (G), Unique30 index (H), Unique50 index (I) and Unique80 index (J). In all diversity indices normalized to 30000 reads, patients with a therapeutic response to PD-1 antibody showed higher diversity than those without a response.

Fig. 10B fig. 10 is a graph showing a comparison of diversity index normalized to 30000 reads for TCR α between patients with and without effect of PD-1 antibody prior to treatment. Prior to treatment of anti-PD-1 antibody-treated patients (n-12), peripheral blood mononuclear cells were isolated from patient whole blood and CD8+ PD-1+ cells were fractionated by FACS sorter. RNA was extracted from CD8+ PD1+ cells and its diversity index (TCR α) was calculated by performing large-scale high-efficiency TCR repertoire analysis. A comparison was made between patients with PD-1 antibody effect (Responder, n ═ 6) and patients with no effect (Non-response, n ═ 6) using diversity indices of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D), DE30 index (E), DE50 index (F), DE80 index (G), Unique30 index (H), Unique50 index (I) and Unique80 index (J). In all diversity indices normalized to 30000 reads, patients with a therapeutic response to PD-1 antibody showed higher diversity than those without a response.

Fig. 11A fig. 11 is a graph showing a comparison of diversity index normalized to 30000 reads for TCR β between patients with and without effect of PD-1 antibody prior to treatment. Prior to treatment of anti-PD-1 antibody-treated patients (n-12), peripheral blood mononuclear cells were isolated from patient whole blood and CD8+ PD-1+ cells were fractionated by FACS sorter. RNA was extracted from CD8+ PD1+ cells and its diversity index (TCR α) was calculated by performing large-scale high-efficiency TCR repertoire analysis. A comparison was made between patients with PD-1 antibody effect (Responder, n ═ 6) and patients with no effect (Non-response, n ═ 6) using diversity indices of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D), DE30 index (E), DE50 index (F), DE80 index (G), Unique30 index (H), Unique50 index (I) and Unique80 index (J). In all diversity indices normalized to 30000 reads, patients with a therapeutic response to PD-1 antibody showed higher diversity than those without a response.

fig. 11B fig. 11 is a graph showing a comparison of diversity index normalized to 30000 reads for TCR β between patients with and without effect of PD-1 antibody prior to treatment. Prior to treatment of anti-PD-1 antibody-treated patients (n-12), peripheral blood mononuclear cells were isolated from patient whole blood and CD8+ PD-1+ cells were fractionated by FACS sorter. RNA was extracted from CD8+ PD1+ cells and its diversity index (TCR α) was calculated by performing large-scale high-efficiency TCR repertoire analysis. A comparison was made between patients with PD-1 antibody effect (Responder, n ═ 6) and patients with no effect (Non-response, n ═ 6) using diversity indices of shannon index (a), normalized shannon index (B), simpson index (C), reverse simpson index (D), DE30 index (E), DE50 index (F), DE80 index (G), Unique30 index (H), Unique50 index (I) and Unique80 index (J). In all diversity indices normalized to 30000 reads, patients with a therapeutic response to PD-1 antibody showed higher diversity than those without a response.

Fig. 12 is a graph showing that the threshold based on ROC analysis for each diversity index (shannon index, normalized shannon index, simpson index, reverse simpson index, DE30 index, DE50 index, DE80 index, Unique30 index, Unique50 index, and Unique80 index) of TCR α varies depending on the number of reads. The horizontal axis represents the number of resampled reads (logarithmic axis), and the vertical axis represents the value of each index. For the threshold of the DE index, the vertical axis is also represented in logarithmic axis. It is understood that the threshold of the DE index has a linear relationship with the number of reads in both log axes.

Fig. 13 is a graph showing that the threshold based on ROC analysis for each diversity index (shannon index, normalized shannon index, simpson index, reverse simpson index, DE30 index, DE50 index, DE80 index, Unique30 index, Unique50 index, and Unique80 index) of TCR β varies depending on the number of reads. The horizontal axis represents the number of resampled reads (logarithmic axis), and the vertical axis represents the value of each index. For the threshold of the DE index, the vertical axis is also represented in logarithmic axis. It is understood that the threshold of the DE index has a linear relationship with the number of reads in both log axes.

Fig. 14 is a graph showing changes in estimated values of the threshold values of the DE50 indices of TCR α and TCR β, which are obtained by linear regression in two logarithmic axes depending on the number of reads.

FIG. 15 is a graph showing correlation analysis of the number of reads between T cell fractions. The X-axis represents the number of reads for each TCR β clone in the CD8+ PD-1+ fraction, and the Y-axis represents the number of reads in each cell fraction (CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT +, CD8+ CTLA4 +). Dots indicate each TCR β clone. R represents correlation coefficient of Pearson.

FIG. 16 shows calculated values (middle) for the Shannon index, normalized Shannon index, reverse Cimpson index,% DE50 index of TCR alpha chains in FACS sorted CD8+ PD-1+, CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT +, and CD8+ CTLA4+ fractions from PBMCs of treatment-responders. Also shown are the diversity indices of CD8+ PD-1+ for patients with (n-6, left) and patients without (n-6, right) treatment benefit. The CD8+ PD-1+ cells of the patients with treatment effect are obviously higher than those of the patients without treatment effect, and the CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT + and CD8+ CTLA4+ fractions show the diversity with the same degree as that of the CD8+ PD-1+ cells.

FIG. 17 shows calculated values for the shannon index, normalized shannon index, reverse-simpson index,% DE50 index of TCR β chains in FACS-sorted CD8+ PD-1+, CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT + and CD8+ CTLA4+ fractions from PBMCs of treatment-responders (middle). Also shown are diversity indices in CD8+ PD-1+ for patients with (n-6, left) and patients without (n-6, right) with therapeutic benefit. As with the TCR α chain, each T cell fraction showed the same degree of diversity as CD8+ PD-1+ cells.

Detailed Description

The present invention will be described below with reference to the most preferred embodiments. Throughout this specification, it should be understood that, unless otherwise specifically stated, expressions in the singular form include concepts in the plural form thereof. Thus, it should be understood that the singular forms of articles (e.g., "a," "an," "the," etc. in english) also include the plural forms of concepts unless specifically stated otherwise. Also, it should be understood that the terms used in the specification are used in the meaning conventionally used in the art unless otherwise specifically noted. Accordingly, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification, including definitions, will control.

The definitions and/or the basic technical contents of the terms specifically used in the present specification are appropriately described below.

(cancer immunotherapy)

In the present specification, "cancer immunotherapy" refers to a method of treating cancer by utilizing an immune mechanism possessed by an organism. Cancer immunotherapy is broadly divided into: cancer immunotherapy by enhancing the immune function against cancer, and cancer immunotherapy by inhibiting the immune evasion function of cancer. In addition, cancer immunotherapy includes: active immunotherapy for activating immune function in vivo, and passive immunotherapy for returning immune cells activated or proliferated in immune function in vitro to the body.

It has been found that the diversity of TCR repertoires can be used as an index to predict responsiveness to the therapeutic effect of such cancer immunotherapy according to the methods described herein.

As examples of cancer immunotherapy, nonspecific immunopotentiators, cytokine therapy, cancer vaccine therapy, dendritic cell therapy, adoptive immunotherapy, nonspecific lymphocyte therapy, cancer antigen-specific T cell therapy, antibody therapy, immune checkpoint inhibition therapy, CAR-T therapy, and the like can be cited.

In recent years, immune checkpoint (inhibition) therapies using immune checkpoint inhibitors have been spotlighted (Pardol DM. the Block of immune checkpoints in cancer immunotherapy. Nat Rev cancer.2012Mar 22; 12(4): 252-64.). Cancer cells express various proteins on their surface, but since these proteins escape attack by immune cells such as T cells, it is considered that cancer tissues cannot be eliminated only by the immune function of the organism under normal conditions. The immune checkpoint inhibitor can effectively eliminate cancer based on the immune function of an organism by inhibiting ligand-receptor interaction and the like, which is responsible for the transduction of inhibitory signals from such cancer tissues to the immune function. One embodiment of the invention is a method of using T Cell Receptor (TCR) diversity of T cells (e.g., CD8+ PD-1+ T cells) as an index to predict responsiveness of a subject to an immune checkpoint inhibitor as described below. Another embodiment of the invention is a method of administering an immune checkpoint inhibitor as shown below to a (responsive) subject selected based on T Cell Receptor (TCR) diversity. In another embodiment, methods are provided for discontinuing or avoiding administration of an immune checkpoint inhibitor to a responsive subject that has been determined to be non-responsive based on T Cell Receptor (TCR) diversity.

A typical example of an immune checkpoint inhibitor is a PD-1 inhibitor. Examples of PD-1 inhibitors include, but are not limited to, Nivolumab (sold as OPDIVOTM) and Pembrolizumab (sold as KEYTRUDATM) which are anti-PD-1 antibodies. In a preferred embodiment, nivolumab may be selected as such an inhibitor. While not wishing to be bound by theory, one of the reasons for the preference of therapy with nivolumab is that: it is shown in the examples that responsive subjects can be clearly identified from non-responsive subjects when using the diversity index calculated from the large-scale high-efficiency TCR repertoire analysis of the present invention, it is understood that responsiveness can be clearly distinguished from non-responsiveness, particularly by using a specific threshold value for the DE50 index. It is of course believed that the diversity index can also be utilized to the same extent for other PD-1 inhibitors.

The anti-PD-1 antibody is thought to exert an anti-cancer effect by releasing the inhibition of activation of T cells by PD-1 signaling. It is believed that when PD-1(programmed death 1) interacts with PD-L1 or PD-L2, SHP-2 (which is a type of tyrosine dephosphorylating enzyme) is taken up into the cytoplasmic domain of PD-1, inactivating ZAP70 (which is a T cell receptor signaling protein), thereby inhibiting activation of T cells (Okazaki, T., Chikuma, S., Iwai, Y.et a.l.: A rheostat for immune responses, the unique properties of PD-1and the ir assays for clinical application Nat. Immunol.,14,1212-1218 (2013)). In addition, PD-L1 is also believed to interact with CD80 to inhibit T cell activation (button, M.J., Keir, M.E., Phamduy, T.B.et al: PD-L1 intermediates specificity with B7-1 to inhibit T cell promotion. immunity,27,111-122 (2007)).

It is considered that PD-1 is highly expressed in killer T cells and natural killer cells infiltrating into cancer tissues, and the immune response is weakened by the action of PD-L1 on tumors. When the above-described reduction of the immune response caused by the PD-1 signal is suppressed by an anti-PD-1 antibody, an enhancement effect of the anti-tumor immune response can be obtained.

as other examples of the immune checkpoint inhibitor, a PD-L1 inhibitor (e.g., avilumab (Avelumab), devaluzumab (Durvalumab), or atezumab (Atezolizumab) as an anti-PD-L1 antibody) can be exemplified.

PD-L1 inhibitors inhibit the above PD-1 pathway by binding to the PD-L1 side and generate an anti-tumor immune response.

As further examples of immune checkpoint inhibitors, CTLA-4 inhibitors (e.g., Yipimama (Iplimumab) or Techiumumab (Tremelimumab), which is an anti-CTLA-4 antibody) may be exemplified.

CTLA-4 inhibitors activate T cells in a different pathway than PD-l inhibition and generate an anti-tumor immune response. T cells are activated by the interaction of surface CD28 with CD80 or CD 86. However, even in T cells that are transiently activated, CTLA-4 (cytotoxic T-1 lymphocyte-associated antigen 4, cytotoxic T-1ymphocyte-associated antigen 4) expressed on the surface of the cells preferentially interacts with CD80 or CD86 with higher affinity than CD20, and thus activation is considered to be inhibited. CTLA-4 inhibitors prevent the inhibition of the interaction between CD20 and CD80 or CD86 by inhibiting CTLA-4, thereby generating an anti-tumor immune response.

in further embodiments, the immune checkpoint inhibitor may also target immune checkpoint proteins such as T1M-3(T-cell immunoglobulin and mucin associating protein-3, T-cell immunoglobulin and mucin-3), LAG-3(lymphocyte activating gene-3), B7-H3, B7-H4, B7-H5(VISTA), or TIGIT (T cell immunoreceptor with Ig and ITIM domains).

Although the immune checkpoint described above is considered to suppress an immune response to a self-tissue, the immune checkpoint is also increased in T cells when an antigen such as a virus is present in an organism for a long period of time. Since tumor tissues are also antigens that exist in the living body for a long time, it is considered that these immune checkpoints allow escape of antitumor immunity, and the immune checkpoint inhibitor as described above invalidates this escape function and exerts an antitumor effect.

In one embodiment of the invention, a predictor of responsiveness of a subject with cancer to cancer immunotherapy is provided.

In the present invention, examples of the target cancer include, but are not limited to, lung cancer, non-small cell lung cancer, renal (renal cell) cancer, prostate cancer, stomach cancer, testicular cancer, liver (liver) cancer, skin cancer, esophageal cancer, melanoma, pancreatic cancer, bone tumor-osteosarcoma, colorectal cancer, soft tissue tumor, biliary tract cancer, multiple myeloma, malignant lymphoma (hodgkin's lymphoma and non-hodgkin's lymphoma), bladder cancer, laryngeal cancer, uterine cancer (body/neck), head and neck cancer, ovarian cancer, and breast cancer. In one embodiment of the invention, a method is provided for using TCR diversity of a subject having lung cancer as an indicator of responsiveness of the subject to cancer immunotherapy.

(TCR diversity)

The biological defense mechanisms based on the immune system are largely dependent on specific immunity provided primarily by T cells or B cells. T cells or B cells do not react with their own cells or molecules, but specifically recognize foreign pathogens such as viruses or bacteria to attack. Therefore, T cells or B cells have a mechanism that recognizes and distinguishes self antigens from various antigens derived from other organisms by receptor molecules expressed on the cell surface. In T cells, a T Cell Receptor (TCR) serves as an antigen receptor. Intracellular signals are transmitted by stimulation from these antigen receptors, which increases the production of inflammatory cytokines, chemokines, and the like, enhances cell proliferation, and elicits various immune responses.

The TCR recognizes antigen peptides while distinguishing self from non-self by recognizing peptides (peptide-MHC complex, pMHC) bound to the peptide binding groove of Major Histocompatibility Complex (MHC) expressed on antigen presenting cells (Cell 1994,76, 287-299). TCRs are heterodimeric receptor molecules consisting of two TCR polypeptide chains, and there are α β -type TCRs expressed by general T cells and γ δ -type TCRs with special functionality. The α and β chain TCR molecules form complexes with multiple CD3 molecules (CD3 ζ chain, CD3 ∈ chain, CD3 γ chain, CD3 δ chain) and transmit intracellular signals upon antigen recognition to elicit various immune responses. Following viral infection, endogenous antigens such as viral antigens that proliferate within cells or cancer antigens derived from cancer cells are presented on MHC class I molecules as antigenic peptides. Furthermore, antigens from foreign microorganisms are taken up into antigen presenting cells by endocytosis, processed and then presented on MHC class II molecules. These antigens are recognized by TCRs expressed by CD8+ T cells or CD4+ T cells, respectively. It is also known that co-stimulatory molecules such as CD28, ICOS, OX40 molecules are important in stimulation by TCR molecules.

The TCR gene is composed of multiple V (variable, V), J (joining, J), D (multiple, D) and C (constant, C) regions encoded as distinct regions on the genome. During the differentiation of T cells, these gene segments are genetically recombined in various combinations, α chain and γ chain TCRs expressing genes formed from V-J-C, and β chain and δ chain TCRs expressing genes formed from V-D-J-C. Diversity is created by recombination of these gene segments, and random amino acid sequences are formed by insertion or deletion of 1 or more bases between V and D or D and J gene segments to create TCR gene sequences with higher diversity.

The regions of the TCR molecule that bind directly to the surface of the pMHC complex (TCR footprints) include the diverse Complementary Determining Regions (CDRs) CDR1, CDR2 and CDR3 within the V region. Among them, the CDR3 region includes a part of V region, V-D-J region formed by random sequence and a part of J region to form the most diversified antigen recognition site. On the other hand, the other region is called FR (framework region) and plays a role of forming a structure as a framework of the TCR molecule. During the differentiation maturation of T cells in the thymus, the β chain TCR is first genetically recombined to associate with the pT α molecule to form a pre-TCR complex molecule. The α chain TCR is then recombined to form an α β TCR molecule, and recombination is caused at another α chain TCR gene allele when a functional α β TCR is not formed. It is known to receive positive/negative selection in the thymus and thus select TCRs with appropriate affinity for antigen specificity (Annual Review Immunology, 1993, 6, 309-326).

T cells produce 1 TCR with high specificity for a particular antigen. A plurality of antigen-specific T cells exist in an organism, thereby forming various TCR libraries (reporters), and can effectively serve as a defense mechanism against various pathogens.

In the present specification, "TCR diversity" refers to the diversity of T cell receptor repertoires (repotorenes) of a subject, and can be determined by one skilled in the art using various methods known in the art. The index indicating TCR diversity is referred to as "TCR diversity index". As the TCR diversity index, any index known in the art may be used, and diversity indices such as Shannon-Weaver index (Shannon-Weaver index), Simpson index (Simpson i index), reverse Simpson index (Inverse Simpson index), Normalized Shannon-Weaver index (Normalized Shannon-Weaver index), DE index (e.g., DE50 index, DE30 index, or DE80 index), or Unique index (e.g., Unique50 index, Unique30 index, Unique80 index) may be applied to or used for the TCR.

One method is as follows: the proportion of T cells expressing each V β chain was analyzed by flow cytometry using specific V β chain-specific antibodies for how many each V chain was used for T cells in the sample (FACS analysis).

In addition, TCR library analysis by molecular biology techniques was studied based on information of TCR genes obtained from human genome sequences. There is a method of extracting RNA from a cell sample, synthesizing complementary DNA, and then performing PCR amplification on a TCR gene to quantify the DNA.

Extraction of nucleic acid from a cell sample can be performed using a tool known in the art, such as RNeasy Plus Universal Mini Kit (QIAGEN). Total RNA extraction and purification from cells dissolved in TRIzol LS reagent can be performed using the RNeasy Plus Universal Mini Kit (QIAGEN).

The synthesis of complementary DNA from the extracted RNA can be carried out using any reverse transcriptase known in the art, such as Superscript IIITM (Invitrogen).

For PCR amplification of the TCR gene, one skilled in the art can suitably perform this using any polymerase known in the art. However, amplification of a gene having a large variation such as a TCR gene has an advantageous effect for accurate measurement if amplification can be performed in a "non-biased" manner.

As the primers used for PCR amplification, a method of designing a plurality of primers specific to each TCR V chain and quantifying each TCR V chain by a real-time PCR method or the like, or a method of simultaneously amplifying these specific primers (Multiple PCR) method is used. However, even when the amount of each V chain is determined by endogenous control, accurate analysis cannot be performed if a large amount of primers are used. In addition, in the multiplex PCR method, there is a disadvantage that a difference in amplification efficiency between primers causes a bias in PCR amplification. To overcome the disadvantages of the multiplex PCR method described above, Adaptor-ligation PCR method has been reported by Heita et al, in which all γ β TCR genes are amplified by common Adaptor primers and C region-specific primers after an Adaptor is added to the 5' end of the double-stranded complementary DNA of the TCR genes (Journal of Immunological Methods, 1994, 169, 17-23). In addition, Reverse dot blot (Reverse dot blot) Methods (Journal of Immunological Methods, 1997, 201, 145-15.) or Microplate hybridization assay (Human Immunology, 1997, 56, 57-69) have been developed which also apply to amplification of α β TCR genes, quantification of individual V chain-specific oligonucleotide probes.

In a preferred embodiment of the present invention, TCR diversity is determined by amplifying TCR genes comprising all genes of the same type or subtype without changing the frequency of presence, using a set of primers consisting of one forward primer and one reverse primer, as described in WO2015/075939 (repertore genes Inc.). Primer design as described below is advantageous for amplification with non-bias.

With a view to the gene structure of the TCR or BCR gene, it is not necessary to set primers in V regions having high diversity, and genes including all V regions are amplified by adding an adaptor sequence to the 5' end thereof. The above-mentioned adapter is of any length and sequence on the base sequence, and about 20 base pairs is most preferable, but a sequence of 10 bases to 100 bases can be used. The adapter added to the 3' end was removed by restriction enzymes and all TCR genes were amplified by adapter primers of the same sequence as the 20 base pair adapter and a reverse primer specific for the C region of the common sequence.

Complementary strand DNA is synthesized from TCR or BCR gene messenger RNA by reverse transcriptase, followed by double-stranded complementary DNA. Double-stranded complementary DNA containing V regions of different lengths is synthesized by a reverse transcription reaction or a double-stranded synthesis reaction, and an adaptor consisting of 20 base pairs and 10 base pairs is added to the 5' -end of these genes by a DNA ligase reaction.

Reverse primers can be set in the C regions of the α chain, β chain, γ chain, and δ chain of the TCR to amplify these genes. The reverse primer set in the C region coincides with the sequence of each of C β, C α, C γ, and C δ of the TCR, and a primer having a mismatch (mismatch) to the extent that it does not cause is set in the other C region sequences. The reverse primer of the C region is optimally prepared in consideration of the base sequence, base composition, DNA melting temperature (Tm), presence or absence of self-complementary sequence, so that the reverse primer of the C region can be amplified with the adaptor primer. Primers are set in regions other than the base sequences different between the allele sequences of the C region sequences, so that all alleles can be amplified uniformly. In order to increase the specificity of the amplification reaction, a multi-step nested PCR (nested PCR) was performed.

The length (number of bases) of the primer candidate sequence for any primer not including a sequence different between allele sequences is not particularly limited, and is 10 to 100 bases, preferably 15 to 50 bases, and more preferably 20 to 30 bases.

Such non-biased amplification is advantageous for identifying a gene having a low frequency (1/10000 to 1/100000 or less), and is preferable.

By sequencing the TCR genes amplified in the manner described above, TCR diversity can be determined from the read data obtained.

By performing PCR amplification of TCR genes from a human sample and performing TCR library analysis of small scale and limited information such as V chain use frequency using the next generation sequence analysis technique, it is possible to realize large-scale and high-efficiency TCR library analysis in which more detailed gene information at the clone level is obtained and analyzed.

The sequencing method is not limited as long as the sequence of the nucleic acid sample can be determined, and any method known in the art can be used, but Next Generation Sequencing (NGS) is preferably used. Examples of the next-generation sequencing include, but are not limited to, pyrosequencing, sequencing-by-synthesis (sequencing-by-synthesis), sequencing by ligation (ligation), and ion semiconductor sequencing.

By matching the obtained reads to a reference sequence comprising the V, D, J gene, unique numbers of reads can be derived, and TCR diversity determined.

In one embodiment, a reference database is prepared for each V, D, J gene region used. Typically, a nucleic acid sequence data set for each region and each allele disclosed by IMGT is used, but the present invention is not limited thereto, and any data set may be used as long as a unique ID is assigned to each sequence.

The obtained reads (including data subjected to appropriate processing such as trimming as necessary) were used as an input sequence group to search for homology with the reference database of each gene region, and the closest reference allele and alignment with the sequence thereof were recorded. Here, in the homology search, an algorithm having high mismatch tolerance is used in addition to C. For example, when general BLAST is used as a homology search program, settings such as reduction of window size, reduction of mismatch penalty, and reduction of gap penalty are made for each region. In selecting the closest reference allele, the homology score, the alignment length, the core length (the length of a continuous nucleotide sequence), and the number of base sequences to be matched are used as indices, and the method is applied to the selection according to a predetermined preferred order. For the input sequences for which V and J have been identified used in the present invention, the CDR3 sequence is extracted with the beginning of CDR3 on reference V and the end of CDR3 on reference J as symbols. These were translated into amino acid sequences for classification of the D region. In the case where a reference database of D region can be prepared, the combination of the results of homology search and the results of translation of amino acid sequences is used as the classification result.

From the above, each allele of V, D, J was assigned to each sequence in the input combination. The TCR library is then derived by calculating the frequency of occurrence of V, D, J, or a combination thereof, throughout the input combination. The frequency of occurrence is calculated in units of allele or gene name, depending on the accuracy required for classification. The latter can be achieved by converting each allele into a gene name.

After assigning V region, J region and C region to the read data, the matched reads are summed up, and for the unique reads (reads not having the same sequence), the ratio (frequency) of the number of detected reads in the sample and the total number of reads is calculated.

The diversity index or similarity index is calculated using data such as the number of samples, the type of reads, and the number of reads using statistical analysis software such as ESTIMATES or r (vegan). In a preferred embodiment, TCR library analysis software (Reertore genetics Inc.) is utilized.

The diversity index can be obtained from the number of reads for each unique read obtained as described above. For example, Shannon-Weaver index (Shannon-Weaver index) (also simply called Shannon index), Simpson index (Simpson index), Normalized Shannon-Weaver index (Normalized Shannon-Weaver index), and DE50 index can be calculated according to the following mathematical formula. N: total number of reads, nj: number of reads for the i-th unique read, S: number of unique reads, S50: the number of best unique reads that accounts for 50% of the total reads.

[ mathematical formula 1]

Simpson's index (1-lambda)

[ mathematical formula 2]

Shannon-Weaver index (Shannon-Weaver index) (H')

[ mathematical formula 3]

Normalized Shannon Fabric index (Normalized Shannon-Weaver index) (H')

[ mathematical formula 4]

DE50(D)

[ math figure 5]

Unique50(U)

U＝S

as other indexes of diversity, there may be used a reverse Simpson index (1/λ), a β index under the forest, a balance index of McIntosh, a dominance index of McNaughton, 1/α of Yuancun, a diversity index of Fisher, eH 'of Sheldon, a balance index of Pielou, 1/σ 2 of Preston, a prosperity index of N β under the forest, and H' N of Pielou. The DE index including the DE50 index may also be described by a ratio or percentage (%), and the meaning of the numerical values described may be clearly and appropriately understood by those skilled in the art, and the present invention may be practiced by converting the threshold value, etc. The DE index can be calculated as the number of optimal unique reads/number of unique reads at an arbitrary ratio (1-99%) of the total reads, and can be used as a diversity index in the present invention.

As the DE index, in addition to the DE50 index, a DEX index based on Sx (x is an arbitrary number of 0 to 100) may be used instead of S50 (the number of optimal (top) unique reads that account for 50% of the total reads). DE30 and DE80 indices, for example, using S30 (number of best unique reads accounting for 30% of the total reads) and S80 (number of best unique reads accounting for 80% of the total reads) may also be used. In addition, the DE index may utilize values that are normalized to the number of reads (e.g., normalized to 80000 reads, 30000 reads, 10000 reads, etc.).

In addition, the Unique X index, which is a molecule of DE index, directly utilizing Sx can also be used. Examples of the Unique index include Unique30, Unique50 and Unique80 indexes.

(Large Scale high efficiency TCR library analysis)

In a preferred embodiment of the invention, TCR diversity is determined using large-scale, high-efficiency TCR repertoire analysis. In the present specification, "large-scale high-efficiency library analysis" is described in WO2015/075939 (the disclosure of which is incorporated by reference in its entirety as needed), and when the object is a TCR, it is referred to as "large-scale high-efficiency TCR library analysis". The large-scale high-efficiency library analysis is a method for quantitatively analyzing a library (reportoire) (variable region of T Cell Receptor (TCR) or B Cell Receptor (BCR)) of a subject using a database, the method comprising the steps of: a step (1) of providing a nucleic acid sample that is amplified non-preferentially from the subject and that contains a nucleic acid sequence for a T Cell Receptor (TCR) or a B Cell Receptor (BCR); a step (2) of determining the nucleic acid sequence contained in the nucleic acid sample; and a step (3) of calculating the frequency of occurrence of each gene or a combination thereof based on the determined nucleic acid sequence, and deriving a library of the subject, wherein the step (1) includes:

A step (1-1) for synthesizing a complementary DNA using an RNA sample derived from a target cell as a template;

A step (1-2) of synthesizing a double-stranded complementary DNA using the complementary DNA as a template;

A step (1-3) of adding a common adaptor primer sequence to the double-stranded complementary DNA to synthesize adaptor-added double-stranded complementary DNA;

And (1-4) performing a first PCR amplification reaction using the adapter-added double-stranded complementary DNA, a common adapter primer formed from the common adapter primer sequence, and a first TCR-or BCR-C region-specific primer, wherein the first TCR-or BCR-C region-specific primer is designed to: a C region having sufficient specificity for the target TCR or BCR, and containing a sequence having no homology with other gene sequences, and including discordant bases between subtypes downstream when amplification is performed;

and (1-5) performing a second PCR amplification reaction using the PCR amplification product of step (1-4), the common adaptor primer, and a second TCR-or BCR-C region-specific primer, wherein the second TCR-or BCR-C region-specific primer is designed to: a sequence having a sequence completely matching the C region of the TCR or BCR in a sequence downstream of the sequence of the C region-specific primer of the first TCR but including a sequence having no homology with other gene sequences and including non-identical bases downstream between subtypes when amplified; and

And (1-6) performing a third PCR amplification reaction using the PCR amplification product of step (1-5), a common adaptor primer added thereto, wherein the common adaptor has a nucleic acid sequence including a first added adaptor nucleic acid sequence, and a C region-specific primer of a third TCR to which an adaptor is added, wherein the C region-specific primer of the third TCR is designed to: the sequence downstream of the sequence of the C region-specific primer of the second TCR or BCR has a sequence that completely matches the C region of the TCR or BCR, but includes a sequence that is not homologous to other gene sequences, and includes inconsistent bases between downstream subtypes when amplified. The first add-on adaptor nucleic acid sequence is a sequence suitable for binding to a DNA capture bead and for an emPCR reaction, the second add-on adaptor nucleic acid sequence is a sequence suitable for an emPCR reaction, and the molecular identification (MID Tag) sequence is a sequence used to confer uniqueness to enable identification of the amplification product. Specific contents of the above-mentioned method are described in WO2015/075939, and those skilled in the art can appropriately refer to this document and examples of the present specification to perform the analysis.

In one embodiment of the invention, a method is provided that exploits TCR diversity of a subpopulation of T cells. In one embodiment, as T cells, T cells positive for a T cell suppressor cell surface marker may be used. Alternatively, in another embodiment, as the T cell, a T cell positive for a T cell stimulatory cell surface marker may be used. For example, TCR diversity of subpopulations of T cells positive for one or more cell surface markers selected from CD8, PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367(GITR) may be used. In one embodiment, the T cell is selected from the group consisting of CD8+ PD1+, CD8+4-1BB +, CD8+ TIM3+, CD8+ OX40+, CD8+ TIGIT +, and CD8+ CTLA4+ T cell.

In the present specification, a "T cell stimulatory cell surface marker" refers to a cell surface molecule that transmits a signal that activates T cells. Examples of the "T cell-stimulating cell surface marker" include, but are not limited to, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), and CD 27.

In the present specification, "T cell inhibitory cell surface marker" refers to a cell surface molecule that transmits a signal for inhibiting T cells. Examples of the "T cell inhibitory cell surface marker" include, but are not limited to, PD-1, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), TIGIT, and CD367 (GITR).

Without being limited by theory, it is believed that the high TCR diversity of T cell subsets expressing this cell surface marker is due to: TCRs with surface antigens recognizing cancer tissues do exist in this subset and are therefore readily benefited by treatments based on immune checkpoint inhibitors.

A subset of T cells is for example a population of CD8+ T cells. Preferably CD8+, and is a subpopulation of T cells expressing one or more immune checkpoint molecules, for example, a subpopulation of T cells that is CD8+ and positive for one or more cell surface markers selected from PD-1, CD28, CD154(CD40L), CD134(OX40), CD137(4-1BB), CD278(ICOS), CD27, CD152(CTLA-4), CD366(TIM-3), CD223(LAG-3), CD272(BTLA), CD226(DNAM-1), tig, and CD367 (GITR). In one embodiment, the T cells are CD8 +. In some embodiments, TCR diversity of a subset of T cells for which a T cell stimulatory cell surface marker is positive, TCR diversity of a subset of T cells for which a T cell inhibitory cell surface marker is positive, or TCR diversity of subsets of T cells for which both a T cell stimulatory cell surface marker and a T cell inhibitory cell surface marker are positive may be employed. In some cases, the subpopulation of T cells may be a population of PD-1+ T cells. The diversity of TCRs can be determined for each subpopulation of T cells, which in a preferred embodiment of the invention is a population of CD8+ PD-1+ T cells. Where TCR diversity for an appropriate subpopulation is used as an indicator of a subject's medical status, it can sometimes be used as a more accurate indicator.

methods of isolating subpopulations of T cells are well known in the art and may be performed using a suitable cell sorter, such as a BD FACSAria III cell sorter (BD Bioscience). The person skilled in the art can suitably use labeled antibodies directed against cell surface markers for distinguishing the subpopulations to be isolated. When a TCR repertoire analysis is performed using a nucleic acid sample extracted from the isolated subpopulation in a manner as described above, TCR diversity for a particular T cell subpopulation can be determined.

In a report investigating TCR diversity in PBMCs using a method of unfractionating specific cells (https:// meetinglibrary. asco. org/record/126066/abstrate), the following is reported: patients with an effective versus patients with no effect of the anti-PD-1 antibody could not be significantly strictly distinguished by TCR analysis without fractionation of specific cells. As demonstrated in this specification, the discovery that TCR diversity in a particular population of cells can be used to distinguish between patients who are effective and those who are not effective for cancer immunotherapy would be unexpected to one skilled in the art.

T cells taken from any tissue may be used. The T cells can be obtained from, for example, peripheral blood, tumor sites, normal tissues, bone marrow, thymus, or the like. In a preferred embodiment, TCR diversity of T cells in peripheral blood of a subject is determined. The collection of T cells from peripheral blood is non-invasive and simple.

The TCR chains used to determine the TCR are alpha, beta, gamma and/or delta chains. In one embodiment, a diversity of TCR α is used. In another embodiment, TCR β is used.

(diagnosis)

The response to cancer immunotherapy can be determined based on RECIST v1.1(New response assessment criteria in solid tumor therapy) Revised RECIST guideline (Revised RECIST guidelines) (version 1.1).

The effect of cancer therapy can be judged as Complete (CR), Partial (PR), Progressive (PD) or Stable (SD) depending on the change in tumor size, etc.

In this specification, a "responsive patient" (Responder) refers to a subject who exhibits complete or partial response to cancer treatment. In this specification, a "Non-Responder" refers to a subject who presents a progressive disease or a stable disease for cancer treatment.

Responsiveness of a subject to a cancer treatment includes a subject being a "productive patient" or a subject being a "unproductive patient. Thus, determining responsiveness of a subject to a cancer treatment includes determining whether the subject is a patient that is responsive or not responsive.

One aspect of the invention utilizes TCR diversity to predict or determine a subject as a "responsive patient" or a subject as a "non-responsive patient". The determination period is preferably predicted before the start of treatment, but may be after the start of treatment. This is because it is also medically useful to determine whether or not the currently performed treatment is appropriate. Alternatively, the TCR diversity of the invention can be used to determine prognosis. For example, using the TCR diversity of the present invention, patients predicted to become non-effective, i.e., relapsed. In addition, as the timing of determination, library analysis may be performed over time after cancer immunotherapy (for example, after administration of an immune checkpoint inhibitor), and the prognosis may be determined from the diversity index.

(preferred embodiment)

Preferred embodiments of the present invention will be described below. It should be understood that the embodiments described below are provided for better understanding of the present invention, and the scope of the present invention is not limited to the following descriptions. Therefore, it is obvious that those skilled in the art can make appropriate modifications within the scope of the present invention with reference to the description in the present specification. Also, the following embodiments of the present invention may be used alone or in combination.

(index of responsiveness)

In one aspect of the present invention, there is provided a method for using T Cell Receptor (TCR) diversity of T cells of a subject as an indicator of responsiveness of the subject to cancer immunotherapy, or diagnosis of the responsiveness. Here, TCR diversity is provided as a diversity index. The T cells may be CD8+ PD-1+ in peripheral blood.

In one aspect, the present invention provides a method for diagnosing responsiveness of a subject to cancer immunotherapy, the method comprising: measuring the TCR diversity of the subject's T cells in vitro (in vitro); and determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high. Alternatively, in the case where TCR diversity is low, the subject may be determined to be poorly responsive to cancer immunotherapy. The T cells may be CD8+ PD-1+ T cells in peripheral blood.

In still other aspects, the present invention provides methods for diagnosing responsiveness of a subject to cancer immunotherapy, comprising: obtaining a peripheral blood sample from the subject; measuring the TCR diversity of T cells in peripheral blood of the subject by a method comprising large-scale high-efficiency TCR repertoire analysis; and determining that the subject has good responsiveness to cancer immunotherapy when the TCR diversity is high. Alternatively, in the case where TCR diversity is low, the subject may be determined to be poorly responsive to cancer immunotherapy. The T cells may be CD8+ PD-1+ T cells in peripheral blood.

While not wishing to be bound by theory, the inventors of the present invention found that the high degree of TCR diversity, i.e., the diversity index, of a subject is associated with better responsiveness to cancer immunotherapy at higher values. In particular, for TCR diversity of CD8+ PD-1+ T cells, it is useful as a favorable indicator of responsiveness to treatment with immune checkpoint inhibitors, in particular PD-1 inhibitors (e.g., nivolumab or pembrolizumab).

Higher values of the subject's Shannon-Weaver index (Shannon-Weaver index), reverse-Simpson index (Inverse Simpson index), Simpson index (Simpson index), Normalized Shannon-Weaver index (Normalized Shannon-Weaver index), DEX index (X is 0-100, e.g., DE30 index, DE50 index, DE80 index, etc.), and/or Unique X (X is 0-100, e.g., Unique30 index, Unique50 index, Unique80 index, etc.) can be used as indicators of better responsiveness to cancer immunotherapy.

Without being limited by theory, the results according to the examples of the present invention suggest that: in the CD8+ T cell (killer T cell, or cytotoxic T Cell (CTL)) population that attacks cancer, if there is no TCR that recognizes the cancer tissue surface antigen or there are few TCRs that recognize the cancer tissue surface antigen, an anti-tumor effect is hardly obtained even if the immune checkpoint is suppressed. In subjects with high TCR diversity, TCRs with recognition of cancer tissue surface antigens do exist, and thus it is thought that treatment based on immune checkpoint inhibitors would be readily beneficial.

In addition, it is known that cancer is composed of a plurality of cell populations, not a uniform cell population. These diverse cancer cells are thought to express different cancer antigens on each cell. Therefore, from the results of the examples of the present invention, it can be predicted that immune cells that need to recognize more diversified antigens in the inhibition of cancer cells are required, and it is considered that the effects produced by immune checkpoint suppressors are easily exerted in patients with a variety of T cells.

In one embodiment of the invention, the subject is determined to be a patient with effect, or determined to be a patient with no effect, based on the values of Shannon-Weaver index (Shannon-Weaver index), reverse-Simpson index (Inverse Simpson index), Simpson index (Simpson index), Normalized Shannon-Weaver index (Normalized Shannon-Weaver index), DEX index (X is 0-100, e.g., DE30 index, DE50 index, DE80 index, etc.), and/or Unique X (X is 0-100, e.g., Unique30 index, Unique50 index, Unique80 index, etc.) of the subject's TCR.

In one embodiment of the present invention, a numerical value based on a plurality of analyses described in the present specification can be used as the threshold value. The threshold values described in the present specification are merely examples, and one skilled in the art may determine and utilize further threshold values based on the determination results in further subject groups. In the present specification, a method of determining a threshold value is also disclosed, and the method of the present invention may include a process of determining a threshold value, and a threshold value predetermined according to this method may also be used.

The threshold for the diversity index may be set by calculating a diversity index for a prescribed number of patients with onset and patients with no onset and determining a value for identifying patients with onset and patients with no onset. Examples of the method for determining the value to be identified include: a method using the minimum value of the effective patient group (effective patients are determined to be reliably effective, and the sensitivity is 100%), a method using the maximum value of the ineffective patient group (ineffective patients are not determined to be effective, and the specificity is 100%), or a method using ROC analysis (the effectiveness of determination based on the balance between sensitivity and specificity is maximized).

as another method, a reference range (reference value) is obtained from the numerical values of the non-effective group or effective group, and identification is performed based on an abnormal value exceeding the reference range. The reference range may be, for example, a range of a mean value ± Standard Deviation (SD) or a mean value ± 2SD, and an upper limit or a lower limit of the reference range may be a reference value. In some cases, the threshold value is determined by calculating the average value + SD or the average value +2SD or the like from the values of the non-effective group. In some cases, the threshold may be determined by calculating the mean-SD or mean-2 SD, etc., from the values of the desired group.

When the diversity index or the threshold is affected by the number of reads, there may be mentioned: the threshold value is set by normalizing the number of reads, or by calculating a threshold value in the number of reads that differs depending on resampling (resampling), and by using a prediction expression obtained by regression analysis or the like of the number of reads and the threshold value (for example, when the number of reads is X and the threshold value is Y, it can be represented by the general formula Y ═ aX ^ b, or the like), the threshold value is set.

In one embodiment, when the shannon-weiver index of the TCR α of the CD8+ PD-1+ T cells of the subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting a prospective or retrospective clinical trial (forward-looking or retrospective clinical trial) on the subject patient, and in one embodiment, the threshold value can be set in a range of about 3.2 to 4.4, and preferably in a range of about 3.3 to about 4.2, and as a specific threshold value, for example, about 3.3, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9, about 4.0, about 4.1, about 4.2, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the reverse simpson index of the TCR α of the CD8+ PD-1+ T cells of the subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 9 to about 19, preferably in a range of about 10 to about 18, as a specific threshold value, for example, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, etc. (any other specific value between these specific values can also be used).

In one embodiment, when the simpson index of the TCR α of the CD8+ PD-1+ T cells of the subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.86 to about 0.96, preferably in a range of about 0.88 to about 0.94, as specific threshold values, for example, about 0.86, about 0.87, about 0.88, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the normalized shannon-weiver index of the TCR α of CD8+ PD-1+ T cells of a subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.41 to about 0.51, and preferably in a range of about 0.42 to about 0.49, and as specific threshold values, for example, can be about 0.42, about 0.43, about 0.44, about 0.45, about 0.46, about 0.47, about 0.48, about 0.49, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the DE50 index of TCR α of CD8+ PD-1+ T cells of a subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.0007 to about 0.0015, preferably in a range of about 0.0008 to about 0.0014, about 0.0009 to about 0.0013, about 0.0010 to about 0.0011, and the like, as specific threshold values, for example, about 0.0007, about 0.0008, about 0.0009, about 0.0010, about 0.0011, about 0.0012, about 0.0013, about 0.0014, about 0.0015, and the like can be enumerated (any other specific numerical values between these can also be used).

In one embodiment, when the shannon-weiver index of the TCR β of CD8+ PD-1+ T cells of a subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 3.2 to about 4.3, preferably about 3.4 to about 4.1, and specific values can be, for example, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9, about 4.0, about 4.1, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the reverse simpson index of TCR β of CD8+ PD-1+ T cells of a subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value may be set in the range of about 8 to 32, preferably about 10 to about 30, and specific values may be, for example, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 21, about 22, about 23, about 24, about 25, about 26, about 27, about 28, about 29, about 30, and the like (any other specific values between these specific values may also be used).

In one embodiment, when the simpson index of the TCR β of the CD8+ PD-1+ T cells of the subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.90 to about 0.96, preferably in a range of about 0.92 to about 0.95, and as a specific threshold value, for example, can be about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, and the like (any other specific value between these specific values can also be used).

In one embodiment, when the normalized shannon-weiver index of TCR α of CD8+ PD-1+ T cells of a subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value may be set in the range of about 0.37 to about 0.48, preferably in the range of about 0.38 to about 0.47, and as specific values, for example, about 0.38, about 0.39, about 0.40, about 0.41, about 0.42, about 0.43, about 0.44, about 0.45, about 0.46, about 0.47, and the like may be cited (any other specific values between these specific values may also be used).

In one embodiment, when the DE50 index of TCR α of CD8+ PD-1+ T cells of a subject is above a threshold value, which indicates that the subject is a patient with efficacy, the threshold value may be set in the range of about 0.0004 to about 0.0012, preferably in the range of about 0.0005 to about 0.0012, and as specific values, for example, about 0.0005, about 0.0006, about 0.0007, about 0.0008, about 0.0009, about 0.0010, about 0.0011, about 0.0012, etc. may be enumerated (any other specific values between these specific values may also be used).

Therefore, the method for diagnosing responsiveness of a subject to cancer immunotherapy of the present invention may include a step of confirming a specific threshold value for determining that responsiveness of the subject to cancer immunotherapy is good. Such a confirmation method can be confirmed by performing a clinical test as exemplified in examples and the like, calculating a diversity index, and performing statistical processing as necessary.

the value of the index described above obtained by measuring and calculating the TCR diversity of a subject can be appropriately rounded (for example, in the case of DE50, rounding 5 decimal places or less, or the like), and compared with a threshold value. In consideration of detection limit, the significant digit may take 2 bits or 1 bit.

Further, the lower numerical limits of mean-SD, mean-2 SD or more of the threshold values calculated from ROC analysis and the groups of patients with effect of the results from the examples of the present specification may be employed. For example, the mean-SD of the patient groups with effect with respect to a part of the diversity index in the examples of the present specification is as follows.

Shannon-weaver index of TCR α: 3.37

Reverse simpson index of TCR α: 9.83

Normalized shannon-weaver index for TCR α: 0.422

DE50 index for TCR α: 0.0013605

Shannon-weaver index of TCR β: 3.69

Reverse simpson index of TCR β: 15.84

Normalized shannon-weaver index for TCR β: 0.433

DE50 index for TCR β: 0.0009545

In addition, the mean value of-2 SD in the patient groups with onset of action is shown below.

Shannon-weaver index of TCR α: 2.66

Reverse simpson index of TCR α: -4.19

Normalized shannon-weaver index for TCR α: 0.366

DE50 index for TCR α: 0.0007697

Shannon-weaver index of TCR β: 3.03

Reverse simpson index of TCR β: 1.43

Normalized shannon-weaver index for TCR β: 0.385

DE50 index for TCR β: 0.0005064

The threshold (cut-off value) can be set by ROC analysis (receiver operating characteristics analysis). Using cut-off values determined by ROC analysis, for example, a prediction of the pre-treatment effect of anti-PD-1 antibody-treated patients can be made.

In the ROC analysis, an ROC curve was prepared by plotting the positive rate at the time of changing the cut-off value as the sensitivity on the vertical axis and the false positive rate (1-specificity) on the horizontal axis. When setting the threshold, the following method is adopted: a method in which a point having the smallest distance from the upper left corner of the ROC curve is set as a critical value; and a method of calculating a point (sensitivity + specificity-1) farthest from the slope line at which the area under the curve (AUC) in the ROC curve is 0.500, and setting a point (Youden index) at which the area becomes the maximum value as a critical value. The ROC curve of each diversity index described in the present specification is shown in fig. 7. The critical values calculated from the Youden index based on example 1 of the present specification are shown in table 12, and the values thus exemplified can be used as the threshold values.

In the present specification, "sensitivity" refers to the probability that a case to be determined as positive is accurately determined as positive, and there is a relationship that false negatives decrease when the sensitivity is high. When the sensitivity is high, it is useful for rejection diagnosis (rule out).

In the present specification, "specificity" refers to the probability of accurately determining a negative condition as negative, and there is a relationship that false positives decrease when the specificity is high. When the specificity is high, it is useful for determining the diagnosis.

For example, as a critical value of the shannon-weaver index of TCR α, about 3.7; as a critical value for the reverse simpson index of TCR α, about 13; as a critical value of the normalized shannon-weaver index of TCR α, about 0.43; as a critical value for the DE50 index for TCR α, about 0.0012; as a critical value of shannon-weaver index of TCR β, 3.8 may be used; as a critical value for the reverse simpson index of TCR β, about 17; as a critical value of the normalized shannon-weaver index of TCR β, about 0.42; as a critical value for the DE50 index for TCR β, about 0.0007 may be used.

However, without being limited to these values exemplified, one skilled in the art can adjust the threshold based on ROC analysis and according to the sensitivity and/or specificity desired. Further, without being limited to the examples described in the present specification, ROC analysis may be performed using further information on the subjects to determine the cut-off value.

In one embodiment, it is predicted that a high therapeutic effect by anti-PD-1 antibodies cannot be expected below these cut-off values.

For the diversity index, there may be cases where it is affected by the amount sampled. That is, a portion of the TCR diversity index varies depending on the number of reads sequenced. When such a diversity index is used, by performing normalization corresponding to the number of specific reads, more accurate evaluation of responsiveness can be achieved. The shannon, simpson and reverse simpson indices are assumed to be almost constant values regardless of the number of reads, and are hardly affected by the number of reads particularly at 1 ten thousand reads or more at the actual analysis level.

The DE index has a tendency to generally decrease with increasing number of reads. It is considered that when the difference between the number of sequencing reads between the subjects or between the subject and the reference subject becomes large (for example, when there is a 10-fold or more variation), more accurate evaluation of the responsiveness can be achieved by using the DE index obtained by normalizing a certain number of reads.

As one method of normalization, the DE index may be approximated as those indices that have a linear relationship with the number of reads on two logarithmic axes, based on which the index may be normalized. Therefore, the evaluation of the responsiveness can be performed by comparing the DE index at a certain number of reads with the threshold value adjusted according to the linear relationship.

As an example of the linear relationship, the linear relationship between the threshold value of the DE index and the number of reads described in the examples of the present specification is as follows:

[ mathematical formula 6]

DE50、TCRα：y＝1892.344x^(-0.8239)

DE50、TCRβ：y＝993.116x^(-0.8072)

DE30、TCRα：y＝260.0x^(-0.7008)

DE30、TCRβ：y＝476.4x^(-0.8032)

DE80、TCRα：y＝4275.6x^(-0.7905)

DE80、TCRβ：y＝6151.8x^(-0.8406)

(wherein y is a threshold value and x is the number of reads)

The person skilled in the art can use the above-mentioned relationship for the normalization. That is, the evaluation of the responsiveness is performed by comparing the DE index obtained based on the number of reads x with the above-described y. In addition, one skilled in the art can derive a new linear relationship for normalization from multiple sequencing results.

In addition, a linear relationship to the threshold may be considered to have a width. For example, the band shape can be expressed when the value of the diversity index is the vertical axis and the number of reads is the horizontal axis. The method can also be used for the following indexes: if the upper limit is above, the drug is determined to be effective, if the lower limit is below, the drug is determined not to be effective, and if the lower limit is in the middle, the drug administration is determined according to the judgment of the doctor. For the variation amplitude, for example, a 95% confidence interval of a fitting curve (fitting curve) may be utilized, and an example is shown in the embodiments of the present specification. By performing the calculations as described above, the specificity or sensitivity can be maximized.

in addition, as another normalization method, normalization may be performed by resampling a certain number of reads from the reads obtained by sequencing, and calculating a diversity index based on the resampled reads. Resampling may be performed by randomly taking reads from the obtained reads. In addition, resampling may be performed a plurality of times, in which case a representative value (median, average, etc.) of the diversity index for each trial may be used as the normalized diversity index.

the number of reads to be used as a standard is not limited, but may be, for example, 1000, 10000, 20000, 40000, 80000, 100000, 200000, or the like (any other specific numerical value between these specific numerical values may be used). In some embodiments, the DE50 index normalized to a 30000 read is used.

In one embodiment, when the shannon-weaver index of the subject's CD8+ PD-1+ T cell TCR α normalized to the 30000 reading is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in the range of about 2.8 to 4.1, and preferably in the range of about 3.9 to about 4.1, and as specific threshold values can be, for example, about 2.8, about 2.9, about 3.0, about 3.1, about 3.2, about 3.3, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9, about 4.0, about 4.1, and the like (any other specific value between these specific values can also be used).

In one embodiment, when the reverse simpson index of the subject's CD8+ PD-1+ T cell TCR α normalized to the 30000 reading is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 8 to about 16, preferably in a range of about 13 to about 15, and as a specific threshold value, can be, for example, about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, and the like (any other specific value between these specific values can also be used).

In one embodiment, when the simpson index of the subject's CD8+ PD-1+ T cell TCR α normalized to the 30000 reading is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.89 to about 0.94, preferably in a range of about 0.92 to about 0.94, and as a specific threshold value, can be, for example, about 0.89, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the normalized shannon-weaver index of the TCR α of CD8+ PD-1+ T cells of the subject is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.41 to about 0.54, preferably in a range of about 0.50 to about 0.52, and as specific threshold values can be, for example, about 0.41, about 0.42, about 0.43, about 0.44, about 0.45, about 0.46, about 0.47, about 0.48, about 0.49, about 0.50, about 0.51, about 0.52, about 0.53, about 0.54, and the like (any other specific value between these specific values can also be used).

In one embodiment, when the DE50 index (%) of the TCR α of CD8+ PD-1+ T cells of a subject normalized to the 30000 reading is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value may be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value may be appropriately determined in the range of about 0.36 to about 0.40, etc., and as the specific threshold value, for example, about 0.36, about 0.37, about 0.38, about 0.39, about 0.40, etc. (any other specific numerical value between these specific numerical values may also be used).

In one embodiment, when the shannon-weaver index of the TCR β of the CD8+ PD-1+ T cells of the subject, normalized to the 30000 reading, is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by conducting prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 3.2 to about 4.0, preferably in a range of about 3.7 to about 3.9, and specific values can include, for example, about 3.2, about 3.3, about 3.4, about 3.5, about 3.6, about 3.7, about 3.8, about 3.9, about 4.0, and the like (any other specific values between these specific values can also be used).

In one embodiment, when the reverse simpson index of the TCR β of CD8+ PD-1+ T cells of the subject normalized to the 30000 reading is greater than or equal to a threshold value, which indicates that the subject is a patient with efficacy, the threshold value may be set in the range of about 10 to 23, preferably about 12 to about 22, and specific values include, for example, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, about 20, about 21, about 22, about 23, and the like (any other specific values between these specific values may also be used).

In one embodiment, when the simpson index of the TCR β of the CD8+ PD-1+ T cells of the subject normalized to the 30000 reading is above a threshold value, indicating that the subject is a patient with efficacy, the threshold value can be determined by prospective or retrospective clinical trials on the subject patient, and in one embodiment, the threshold value can be set in a range of about 0.90 to about 0.97, preferably in a range of about 0.92 to about 0.96, and as a specific threshold value, for example, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, and the like (any other specific value between these specific values can also be used).

In one embodiment, when the normalized shannon-weaver index of the TCR α of CD8+ PD-1+ T cells of the subject normalized to the 30000 reading is equal to or greater than the threshold value, indicating that the subject is a patient with efficacy, the threshold value may be set in the range of about 0.42 to about 0.53, preferably in the range of about 0.47 to about 0.52, and specific values may include, for example, about 0.42, about 0.43, about 0.44, about 0.45, about 0.46, about 0.47, about 0.48, about 0.49, about 0.50, about 0.51, about 0.52, about 0.53, and the like (any other specific values between these specific values may also be used).

In one embodiment, when the DE50 index (%) of the TCR α of CD8+ PD-1+ T cells of a subject normalized to the 30000 reading is equal to or higher than a threshold value, which indicates that the subject is a patient with efficacy, the threshold value may be set in the range of about 0.22 to about 0.26, preferably about 0.23 to about 0.25, and specific values include, for example, about 0.22, about 0.23, about 0.24, about 0.25, about 0.26, and the like (any other specific values between these specific values may also be used).

For the normalized diversity index, a threshold value calculated based on the ROC analysis as described above may be used, and for example, for the diversity index normalized for 30000 reads, a threshold value exemplified in the present specification may be used.

for example, as a critical value of the shannon-weaver index for TCR α normalized to 30000 reads, about 3.9; as a threshold value for the reverse simpson index of TCR α normalized to 30000 reads, about 14; as a threshold value of the simpson index for TCR α normalized to 30000 reads, about 0.92; as a critical value for the normalized shannon-weaver index for TCR α normalized to 30000 reads, about 0.51; as a threshold value for the DE50 index of TCR α normalized to the 30000 reading, about 0.39; as a critical value of shannon-weaver index of TCR β normalized to 30000 reads, 3.8 can be used; as a threshold value for the reverse simpson index of TCR β normalized to 30000 reads, about 22; as a critical value of the simpson index for TCR β normalized to 30000 reads, about 0.95; as a critical value of the normalized shannon-weaver index for TCR β normalized to 30000 reads, about 0.51; as a threshold value for the DE50 index for TCR β normalized to the 30000 reading, about 0.24 can be used.

The threshold value may be adjusted and selected according to the purpose, and may be determined, for example, according to the purpose of (i) eliminating non-effective patients (from the viewpoint of social security costs) or (ii) eliminating omission of effective patients (from the viewpoint of doctors and treatment). The object (i) can be achieved by setting the value of the highest line higher than the non-effective patient as a threshold value, and the object (ii) can be achieved by setting the value of the lowest line lower than the effective patient as a threshold value. These values can be determined by one skilled in the art based on the diversity index of a population of subjects, or set thresholds based on the examples set forth in this specification of the maximum or minimum of diversity indices presented by non-responding and responding subjects.

In general, biomarkers become data with deviations, and the two groups being compared rarely separate clearly. In general, when a threshold value of a normal value can be set from a plurality of data and whether the abnormal value is present or not can be determined based on the threshold value, the abnormal value can be used as a marker, for example, in the application of Keytruda (PD-L1 antibody), PD-1 high positive is used as a marker, but the actual efficiency is about 50%. Since the marker is sufficiently valuable even if it cannot be predicted by 100%, a marker that enables two-group separation at 100% in both sensitivity and specificity (for example, DE50 index of TCR diversity) is very advantageous.

In a preferred embodiment, the TCR is TCR α. In another preferred embodiment, TCR is TCR β. More preferably, it is TCR β. While not wishing to be bound by theory, the reason for this is that the diversity index of TCR β is not repeated in the values displayed by responsive subjects and non-responsive subjects. However, the present invention is not limited thereto, and may be TCR α. While not wishing to be bound by theory, the reason for this is, for example, that it appears that it can be strictly distinguished by using the DE50 index.

In one embodiment, the diversity utilized in the present invention can be calculated by a process of analyzing CD8+ PD-1+ T cells from a peripheral blood sample of a subject and a process of measuring, determining or calculating the TCR diversity of CD8+ PD-1+ T cells.

One embodiment of the present invention is a method for diagnosing responsiveness of a subject to cancer immunotherapy, including a step of determining that responsiveness of the subject to cancer immunotherapy is good when the TCR diversity is high.

Preferably, the TCR diversity calculated here is advantageous when using large-scale high-efficiency TCR repertoire analysis (WO2015/075939) as detailed in the present specification. While not wishing to be bound by theory, in the case of using other TCR repertoires analysis, it is not possible to detect a portion of the unique reads that can be detected using a large-scale, high-efficiency TCR repertoire analysis. Therefore, the diversity index calculated by large-scale high-efficiency TCR library analysis is more refined and more accurately reflects the status of the subject. While not wishing to be bound by theory, it is believed that the diversity index in the large-scale high-efficiency TCR repertoire analysis clearly distinguishes between patients with or without effect, whereas the differentiation between patients with or without effect is not sufficiently performed by a repertoire analysis other than the conventional large-scale high-efficiency TCR repertoire analysis. Thus, when TCR diversity determined using large-scale, high-efficiency library analysis is used, more accurate evaluation results can be obtained than in the prior art.

In the method for diagnosing responsiveness of a subject to cancer immunotherapy of the present invention, after TCR diversity of T cells of the subject is measured by a method including large-scale high-efficiency TCR repertoire analysis, the responsiveness of the subject to cancer immunotherapy is determined to be good in the case where the TCR diversity is high. Whether the TCR diversity is high or not can be determined relatively, or whether the diversity is high or not can be determined by comparing with a predetermined threshold value of the diversity index (for example, a threshold value described in the present specification). When the diversity index is high, it is judged that the cancer immunotherapy is responsive or responsive, and the subsequent treatment can be performed as appropriate. The T cell to be used may be any one or more of the T cells described in the present specification, and is preferably CD8+ PD-1+ T cells in peripheral blood.

A further embodiment of the present invention is a method of diagnosing responsiveness of a subject to cancer immunotherapy and treating cancer in the subject (so-called concomitant diagnosis or concomitant treatment), comprising: measuring the TCR diversity of T cells of the subject; and administering cancer immunotherapy to the subject when the TCR diversity is above a baseline value. The reference value or threshold value of TCR diversity can be determined appropriately by those skilled in the art based on the description of the present specification, and the specific numerical value of the diversity index is exemplified in the present specification and can be used appropriately. The T cells that can be used are one or more of any of the types of T cells described in the present specification, and preferably the T cells are CD8+ PD-1+ T cells in peripheral blood.

(concomitant use of immune checkpoint inhibitors)

In a further aspect of the invention, the invention provides a composition comprising an immune checkpoint inhibitor for use in the treatment of cancer in a subject with high TCR diversity of T cells. Such immune checkpoint inhibitors are advantageous when administered to subjects with high TCR diversity of T cells, as discovered by the inventors of the present invention. Furthermore, subjects with low TCR diversity for T cells can be determined as non-effective patients, and can also be determined as not being administered with an immune checkpoint inhibitor, or being discontinued or discontinued. The T cell for measuring TCR diversity may be any one or more of the T cells described in the present specification, and is preferably CD8+ PD-1+ T cells in peripheral blood.

The composition of the present invention is preferably a pharmaceutical composition, and examples of the immune checkpoint inhibitor contained as an active ingredient thereof include a PD-1 inhibitor. As the PD-1 inhibitor, nivolumab or pembrolizumab which is an anti-PD-1 antibody can be cited.

The composition can be formulated into any dosage forms, such as aerosol, liquid, extract, elixir, capsule, granule, pill, ointment, powder, tablet, solution, suspension, emulsion, etc. The composition may comprise any pharmaceutically acceptable additive and/or excipient known in the art.

The composition of the present invention can be administered by any appropriate route determined by those skilled in the art, but is not limited thereto, and examples thereof include intravenous injection, intravenous drip, oral administration, parenteral administration, and transdermal administration.

In one embodiment, a composition is provided for treating cancer in a subject with a high shannon index, simpson index, normalized shannon index, or DE50 index for the TCR of a T cell. In a preferred embodiment, there is provided a composition for treating cancer in a subject having a high DE50 index for TCR of T cells.

The present invention provides a composition for treating cancer in a subject having a DE50 index of 0.39% or more normalized to a 30000 reading for TCR alpha of CD8+ PD-1+ T cells in peripheral blood.

the present invention provides a composition for treating cancer in a subject having a DE50 index of 0.24% or greater normalized to a 30000 reading for TCR β of CD8+ PD-1+ T cells in peripheral blood.

(novel use of large-Scale high-efficiency TCR library analysis)

In one aspect, a method is provided that uses the diversity of libraries determined by a method that includes large-scale, high-efficiency TCR library analysis as an indicator of a subject's responsiveness to treatment. In the above method, a TCR gene or BCR gene comprising all isoforms or subtypes of genes is amplified without changing the frequency of existence using a set of primers comprising one forward primer and one reverse primer, thereby determining the diversity of the library. As described in the present specification, and also in WO2015/075939, the above primer design is advantageous for amplification with non-bias.

In the case of using other library analyses, a portion of the unique reads that can be detected with a large-scale, high-efficiency library analysis cannot be detected. Therefore, the diversity index calculated by the large-scale high-efficiency library analysis is more refined and reflects the state of the subject more accurately. While not wishing to be bound by theory, it is believed that the diversity index in the large-scale high-efficiency library analysis clearly distinguishes between patients with and without benefit of treatment, whereas library analysis other than the conventional large-scale high-efficiency library analysis does not sufficiently distinguish between patients with and without benefit of treatment.

In one embodiment, the targeted therapy is a therapy associated with an immune response. In another preferred embodiment, the library analysis utilized is a TCR library analysis.

(Note)

In the present specification, "or" is used when "at least one (more) than (at least one (one) or more) of the items listed herein may be employed. The same applies to "or". When a range is explicitly described as "two values" in the present specification, the range includes the two values themselves.

All references cited in the present specification, such as scientific literature, patents, and patent applications, are incorporated herein by reference in their entirety to the same extent as if each reference were specifically and individually indicated to be incorporated herein by reference.

The present invention has been described above to illustrate preferred embodiments for the convenience of understanding. The present invention will be described below with reference to examples, but the above description and the following examples are only for illustrative purposes and are not intended to limit the present invention. Accordingly, the scope of the invention is limited only by the claims and not by the embodiments or examples specifically described in this specification.