阅读说明：本技术 基于t细胞的免疫疗法的基因靶标 (Gene targets for T cell-based immunotherapy ) 是由 A·马森 E·士夫洛特 J·卡内瓦利 A·阿诗沃斯 于 2019-07-09 设计创作，主要内容包括：本文提供了相较于野生型T细胞经刺激时展现出增殖增加的遗传修饰的T细胞,生成这类T细胞的方法,和使用该T细胞治疗疾病诸如癌症的方法。(Provided herein are genetically modified T cells that exhibit increased proliferation when stimulated as compared to wild-type T cells, methods of generating such T cells, and methods of using the T cells to treat diseases such as cancer.)

1. A genetically modified hematopoietic cell comprising a genetic modification to a T cell inhibitory gene that inhibits the expression or activity of a polypeptide product encoded by the T cell inhibitory gene, wherein the expression or activity of the polypeptide product is inhibited by at least 60% compared to a control wild-type hematopoietic cell.

2. The genetically modified hematopoietic cell of claim 1, wherein the genetic modification to the T cell suppressor gene inactivates the gene.

3. The genetically modified hematopoietic cell of claim 1 or 2, wherein the genetically modified hematopoietic cell is a T cell.

4. The genetically modified cell of claim 3, wherein said T cell is a CD8+ T cell or a CD4+ T cell.

5. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T cell suppressor gene is suppressed using a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system.

6. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T-cell suppressor gene is suppressed using a transcription activator-like effector nuclease (TALEN) system.

7. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T cell suppressor gene is suppressed using a zinc finger nuclease system.

8. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T cell suppressor gene is suppressed using a meganuclease system.

9. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T cell suppressor gene is suppressed using an inhibitory RNA.

10. The genetically modified hematopoietic cell of any one of claims 1,2, 3, or 4, wherein the T cell suppressor gene is inhibited using shRNA, siRNA, microRNA, or antisense RNA.

11. The genetically modified cell of claim 1, wherein the T cell inhibitory gene is FAM105A, ARID1A, RASA2, TCEB2, SOCS1, CBLB, or TMEM 222.

12. The genetically modified cell of any one of claims 2 to 10, wherein said T-cell inhibitory gene is FAM105A, ARID1A, RASA2, TCEB2, SOCS1, CBLB or TMEM 222.

13. The genetically modified cell of claim 12, wherein the T cell suppressor gene is RASA2, TCEB2, SOCS1, CBLB or TMEM 222.

14. The genetically modified cell of claim 12, wherein the T cell inhibitory gene is FAM105A.

15. The genetically modified cell of claim 12, wherein the T cell suppressor gene is ARID 1A.

16. The genetically modified cell of claim 12, wherein said T cell inhibitory gene is CD5, TNFAIP3, DGKZ, or UBASH 3A.

17. The genetically modified cell of any one of claims 1 to 10, wherein said T-cell inhibitory gene is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB 1.

18. A population of cells comprising the genetically modified T cell of any one of claims 1-17.

19. A method of treating cancer, comprising administering to a subject having cancer a cell population comprising the genetically modified hematopoietic cell of any one of claims 1-17.

20. A genetically modified T cell having a modulated immune function as compared to a control wild-type T cell and comprising a genetic modification to inhibit expression of a polypeptide encoded by a T gene, wherein expression of the polypeptide is inhibited by at least 60% as compared to the control wild-type T cell; and the gene is selected from the group consisting of: CYP2R1, LCP2, RPP21, VAV1, EIF2B 1, RPP 1, EXOSC 1, RPN1, VARS, CD 31, GRAP 1, TRMT112, ALG1, VAV1, EXOSC 1, SH2D 11, HSPA 1, ZAP 1, DDX 1, CD247, ALDOA, ZNF131, WDR 1, AK1, LCP 1, CD247, VHL, EIF2B 1, GRPEL1, NAA1, ALDOA, ALG1, MARS, C4orf 1, RAC 1, LCK, SUPT4H1, SLC25A 1, LUC7L 1, C3orf1, RPP 1, GCTAEDS 1, CANDC 1, EPDESC 1, EPD 1, EPDODE 1, EPDOSC 1, EPDODE 1, EPDE 1, EPDE 1, EPDE 1, EPDE 36, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM1, GATA 1, CCND 1, TTI 1, THG 11, TAF 11, URI1, TRMT112, EIF 31, CCND 1, GCYALM, RBSN, 36RS, TAF1, THG 11, TAFS 1, TAFTRAFT 1, TAFTPTEB 1, TAFTTP 1, TAFTRAFT 1, TARG 1, TAFTFO 1, TAFTC 1, TAFTP 1, TAOCB 1, POD 36.

21. The genetically modified T-cell of claim 20, wherein said gene is any one of: HSPA, RPP, EXOSC, LCP, MYC, CD247, NOP, VAV, RHOH, TAF1, TRMT112, CCND, SH2D1, MARS, CD3, LUC7L, EIF2B, ORAOV1.VARS, NOL, ZBTB8, SLC35B, NAA, EIF2B, DHX, LAT, EMG, ALDOA, GRPEL, ARMC, POLR2, NOP, PSENEN, RELA, SUPT4H, VHL, GFER, BPTF, RAC, RACR, TAF, PMPCA, EIF, STT3, POP, GMPB, TP53, CCNH, TEX, DHX, QARS, EID, IRF, TAF, IARS, GTF3, NOP, IMP, UTL, EIF4G4, AGN, ALP, PHBD, PHBV, PHBR, POLR, ORP, EPOR 2, OROCR, SAL, SARG, SARP 3, SARP 1, SARP 3, SARP, SALT 3, SALT 2, SALT 3, SALT 3, SALT.

22. The genetically modified T-cell of claim 20 or 21, wherein said gene is inactivated.

23. The genetically modified T cell of claim 22, wherein the T cell is a CD8+ or CD4+ T cell.

24. The genetically modified T-cell of any one of claims 20-23, wherein said gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a meganuclease system, a siRNA, an antisense RNA, a microrna, or a hairpin RNA.

25. A cell culture comprising the genetically modified T cell of any one of claims 20-24.

26. A method of treating an autoimmune disease or treating or preventing transplant rejection, the method comprising administering to a subject having an autoimmune disease or undergoing a tissue transplant the T cell population of any one of claims 20-24.

27. A method of generating a population of genetically modified cells for treating a subject having cancer, the method comprising:

obtaining hematopoietic cells from a patient;

inhibiting expression of a T cell inhibitory gene selected from the group consisting of: FAM105A, ARID1A, RASA2, TCEB2, SOCS1, CBLB and TMEM 222;

selecting a hematopoietic cell in which the T cell suppressor gene is suppressed,

expanding the selected hematopoietic cell population ex vivo.

28. A method of generating a population of genetically modified cells for treating a subject having cancer, the method comprising:

obtaining hematopoietic cells from a patient;

inhibiting expression of a T cell inhibitory gene selected from the group consisting of: CD5, TNFAIP3, DGKZ, and UBASH 3A;

selecting a hematopoietic cell in which the T cell suppressor gene is suppressed,

expanding the selected hematopoietic cell population ex vivo.

29. The method of claim 27 or 28, wherein said hematopoietic cells are hematopoietic stem cells.

30. The method of claim 27 or 28, wherein the hematopoietic cell is a T cell.

31. The method of claim 27 or 28, wherein the hematopoietic cell is a CD8+ or CD4+ T cell.

32. The method of any of claims 27, 28,29,30, or 31, wherein the T cell inhibitory gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a meganuclease system, a siRNA, an antisense RNA, a microrna, or a hairpin RNA.

Background

Cytotoxic T cells play an important role in immune-mediated tumor control and autoimmunity. Immunotherapy, such as checkpoint blockade or engineered cell-based therapies, is drastically changing cancer treatments, achieving a persistent response in a subset of patients with other refractory malignancies. However, despite significant efficacy in some patients, most patients do not respond to available immunotherapy.

Next generation adoptive cell therapies are being developed using CRISPR-Cas9 genome engineering. Cas9 ribonucleoproteins can be delivered to primary human T cells to efficiently knock out checkpoint genes (Ren et al, 2017; Rupp et al, 2017; Schumann et al, 2015) or even to rewrite endogenous genomic sequences (Roth et al). Although deletion of the typical checkpoint gene encoding PD-1 may enhance response to some cancers (Ren et al, 2017; Rupp et al, 2017), an expanded set of targets would provide additional therapeutic opportunities. Progress in immunotherapy depends on further understanding of the genetic program that determines how T cells respond when they encounter their target antigens. Promising gene targets can enhance cell proliferation and effective effector responses following stimulation. In addition, immunosuppressive cells and soluble molecules such as cytokines and metabolites may accumulate within the tumor and impede effective anti-tumor T cell responses. Gene targets that affect the ability of T cells to overcome the immunosuppressive tumor microenvironment can extend adoptive cell therapy to solid organ cancers.

Decades of animal models and cell line studies have identified modulators of T cell suppression and activation, but there remains a lack of systematic strategies to fully analyze the function of genes that modulate human T cell responses. Gene knockdown using the selected RNA interference libraries was directed to targets that enhance antigen-reactive T cell proliferation in vivo in mouse models (Zhou et al, 2014). More recently, CRISPR-Cas9 opened a new era for functional genetics studies (Doench, 2018). Large libraries of single guide rnas (sgrnas) are easily designed to target genomic sequences. Transduction of cells with lentiviruses encoding these sgrnas produces a pool of cells with different genomic modifications that can be followed by the sgRNA sequences in the integrated provirus. This approach has been used in stable Cas9 expression engineered cell lines as well as in Cas9 transgenic mouse models (Parnas et al, 2015; Shang et al, 2018). Convergent CRISPR screening has revealed gene targets that modulate T cell immunotherapy responses in human cancer cells (mangusso et al, 2017; Pan et al, 2018; Patel et al, 2017).

Disclosure of Invention

The present disclosure is based, in part, on a novel approach to identify modulators of stimulatory responses in primary human T cells using sgRNA lentiviral transfection (SLICE) electroporated with Cas9 protein. Genome-wide loss of function screens identify important T cell receptor signaling components and genes that negatively regulate proliferation upon stimulation. Targeted ablation of a single candidate gene validated hits (hit) and identified perturbations that enhanced cancer cell killing (pertubation). SLICE coupled to single-cell RNA-seq revealed a characteristic stimulus-response gene program altered by key genetic perturbations. SLICE genome-wide screening is also useful for identifying mediators of immunosuppression, revealing genes that control adenosine signaling responses. Accordingly, provided herein are hematopoietic cells, such as stem cells and T cells, modified to suppress expression of a target gene. Thus, for example, the genetic modifications described in this disclosure may be used to modulate CD8+ T cell proliferation and function.

In one aspect, provided herein is a genetically modified hematopoietic cell comprising a genetic modification to a T cell inhibitory gene that inhibits the expression or activity of a polypeptide product encoded by the T cell inhibitory gene, wherein the expression or activity of the polypeptide product is inhibited by at least 60% compared to a control wild-type hematopoietic cell. In some embodiments, the genetic modification to a T cell inhibitory gene inactivates the gene. In some embodiments, the genetically modified hematopoietic cell is a hematopoietic stem cell. In some embodiments, the genetically modified hematopoietic cell is a hematopoietic T cell. In some embodiments, the T cell is a CD8+ T cell or a CD4+ T cell. In some embodiments, the T cell inhibitory gene is inhibited using a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system. Alternatively, a transcription activator-like effector nuclease (TALEN) system, zinc finger nuclease system, or meganuclease system can be used to inhibit a T cell suppressor gene. In some embodiments, the T cell inhibitory gene is inhibited using antisense RNA, siRNA, microrna, or short hairpin RNA. In some embodiments, the modified T cell suppressor gene is RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM 222. In some embodiments, the modified T cell inhibitory gene is CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, or ARID 1A. In some embodiments, the T cell inhibitory gene is CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB 2; or the T cell suppressor gene is SOCS1, TCEB2, RASA 2or CBLB. In some embodiments, the T cell inhibitory gene is SOCS1, TCEB2, or RASA 2. In some embodiments, the T cell inhibitory gene is RASA 2. In some embodiments, the T cell inhibitory gene is TCEB 2. In some embodiments, the T cell inhibitory gene is SOCS 1. In some embodiments, the T cell suppressor gene is CBLB. In some embodiments, the T cell inhibitory gene is FAM105A. In some embodiments, the T cell inhibitory gene is ARID 1A. In some embodiments, the T cell suppressor gene is TMEM 222. In some embodiments, the T cell inhibitory gene is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB 1. In another aspect, provided herein is a population of cells comprising genetically modified hematopoietic cells, e.g., T cells, as described herein (e.g., in this paragraph). In some embodiments, a hematopoietic cell (e.g., a T cell) can comprise two or more genetic modifications as described herein.

In another aspect, provided herein is a method of treating cancer comprising administering a population of cells comprising genetically modified hematopoietic cells as described herein (e.g., in this paragraph).

In another aspect, provided herein are genetically modified T cells having modulated (e.g., reduced) immune function as compared to a control wild-type T cell, and comprising a genetic modification to inhibit expression of a polypeptide encoded by a T gene, wherein expression of the polypeptide is inhibited by at least 60% as compared to the control wild-type T cell; and the gene is selected from the group consisting of: CYP2R1, LCP2, RPP21, VAV1, EIF2B 1, RPP 1, EXOSC 1, RPN1, VARS, CD 31, GRAP 1, TRMT112, ALG1, VAV1, EXOSC 1, SH2D 11, HSPA 1, ZAP 1, DDX 1, CD247, ALDOA, ZNF131, WDR 1, AK1, LCP 1, CD247, VHL, EIF2B 1, GRPEL1, NAA1, ALDOA, ALG1, MARS, C4orf 1, RAC 1, LCK, SUPT4H1, SLC25A 1, LUC7L 1, C3orf1, RPP 1, GCTAEDS 1, CANDC 1, EPDESC 1, EPD 1, EPDODE 1, EPDOSC 1, EPDODE 1, EPDE 1, EPDE 1, EPDE 1, EPDE 36, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM1, GATA 1, CCND 1, TTI 1, THG 11, TAF 11, URI1, TRMT112, EIF 31, CCND 1, GCYALM, RBSN, 36RS, TAF1, THG 11, TAFS 1, TAFTRAFT 1, TAFTPTEB 1, TAFTTP 1, TAFTRAFT 1, TARG 1, TAFTFO 1, TAFTC 1, TAFTP 1, TAOCB 1, POD 36. In some embodiments, the gene is one of the following genes: HSPA, RPP, EXOSC, LCP, MYC, CD247, NOP, VAV, RHOH, TAF1, TRMT112, CCND, SH2D1, MARS, CD3, LUC7L, EIF2B, ORAOV1.VARS, NOL, ZBTB8, SLC35B, NAA, EIF2B, DHX, LAT, EMG, ALDOA, GRPEL, ARMC, POLR2, NOP, PSENEN, RELA, SUPT4H, VHL, GFER, BPTF, RAC, RACR, TAF, PMPCA, EIF, STT3, POP, GMPB, TP53, CCNH, TEX, DHX, QARS, EID, IRF, TAF, IARS, GTF3, NOP, IMP, UTL, EIF4G4, AGN, ALP, PHBD, PHBV, PHBR, POLR, ORP, EPOR 2, OROCR, SAL, SARG, SARP 3, SARP 1, SARP 3, SARP, SALT 3, SALT 2, SALT 3, SALT 3, SALT. In some embodiments, the gene is inactivated. In some embodiments, CRISPR systems, TALEN systems, zinc finger nuclease systems, meganuclease systems, sirnas, antisense RNAs, micrornas, or hairpin RNA suppressor genes are used. In another aspect, the invention provides cell cultures comprising genetically modified T cells, e.g., as described in this paragraph.

In another aspect, provided herein is a method of treating an autoimmune disease or treating or preventing graft rejection, the method comprising administering to a subject having an autoimmune disease or undergoing a tissue transplant a population of T cells as described in the preceding paragraph, for example a population of CD8+ or CD4+ T cells.

In other aspects, provided herein are methods of generating a population of genetically modified cells for treating a subject having cancer, the method comprising: obtaining hematopoietic cells from a patient; inhibiting expression of a T cell inhibitory gene selected from the group consisting of: RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A and TMEM 222; selecting a hematopoietic cell in which the T cell inhibitory gene is suppressed; and expanding the selected hematopoietic cell population ex vivo. In some embodiments, the hematopoietic cells are hematopoietic stem cells. In some embodiments, the hematopoietic cell is a T cell, e.g., a CD8+ or CD4+ T cell. In some embodiments, the T cell inhibitory gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a meganuclease system, a siRNA, an antisense RNA, a microrna, or a short hairpin RNA.

In other aspects, provided herein are methods of generating a population of genetically modified cells for treating a subject having cancer, the method comprising: obtaining hematopoietic cells from a patient; inhibiting expression of a T cell inhibitory gene selected from the group consisting of: CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, and ARID 1A; selecting a hematopoietic cell in which the T cell inhibitory gene is suppressed; and expanding the selected hematopoietic cell population ex vivo. In some embodiments, the hematopoietic cells are hematopoietic stem cells. In some embodiments, the hematopoietic cell is a T cell, e.g., a CD8+ or CD4+ T cell. In some embodiments, the T cell inhibitory gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a meganuclease system, a siRNA, an antisense RNA, a microrna, or a short hairpin RNA.

Definition of

As used herein, the singular forms "a", "an" and "the" are intended to refer to the plural forms as well, unless the context clearly indicates otherwise.

The terms "polynucleotide" and "nucleic acid" are used interchangeably and refer to a polymeric form of nucleotides of any length, i.e., deoxyribonucleotides or ribonucleotides. The term includes RNA, DNA and synthetic forms as well as mixed polymers of the foregoing. In particular embodiments, a nucleotide refers to a ribonucleotide, a deoxynucleotide, or any type of modification or analog of a nucleotide, and combinations thereof. In addition, a polynucleotide may include one or both of naturally occurring and modified nucleotides linked together by naturally occurring and/or non-naturally occurring nucleotide linkages. Nucleic acid molecules may be chemically or biochemically modified, or may comprise non-natural or derivatized nucleotide bases. Such modifications include, for example, labels, methylation, substitution of one or more of the naturally occurring nucleotides with an analog, internucleotide modifications, such as uncharged linkages (e.g., methylphosphonates, phosphotriesters, phosphoramidates, carbamates, etc.), charged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), pendant moieties (e.g., polypeptides), intercalators (e.g., acridine, psoralen, etc.), chelators, alkylating agents, and modified linkages (e.g., α -anomeric nucleic acids, etc.). "Polynucleotide" and "nucleic acid" are also intended to include any topological conformation, including single-stranded, double-stranded, partially double-stranded, triple-stranded, hairpin, circular, and padlock (padlock) conformations. Unless otherwise specifically indicated, reference to a nucleic acid sequence includes its complement. Thus, reference to a nucleic acid molecule having a particular sequence is understood to encompass its complementary strand and its complementary sequence. Reference to a "polynucleotide" or "nucleic acid" encoding a polypeptide sequence also includes nucleic acids comprising alternative codons encoding the same polypeptide sequence and codon-optimized nucleic acids.

As used herein, "complementary" or "complementarity" refers to specific base pairing between nucleotides or between nucleic acids. Base pairing may be fully complementary or partially complementary.

The term "gene" may refer to a segment of DNA involved in the production or encoding of a polypeptide chain. It may include regions preceding and following the coding region (leader and trailer) as well as intervening sequences (introns) between individual coding segments (exons). Genes are defined by symbols and Nomenclature for human genes, as specified by the HUGO Gene Nomenclature Committee (HUGO Gene Nomenclature Committee).

A "promoter" is defined as one or more nucleic acid control sequences that direct the transcription of a nucleic acid. As used herein, a promoter includes the necessary nucleic acid sequences near the start site of transcription. Promoters also optionally include distal enhancer or repressor elements, which can be located up to several thousand base pairs from the transcription start site.

The term "inhibiting expression" refers to inhibiting or reducing expression of a gene or protein. To inhibit or reduce expression of a gene (i.e., a gene encoding or regulated by a transcription factor), the sequence and/or structure of the gene can be modified such that the gene is not transcribed (for DNA) or translated (for RNA), or is not transcribed or translated to produce a functional protein (e.g., a transcription factor). Various methods for inhibiting or reducing gene expression are described in further detail herein. Some methods may introduce nucleic acid substitutions, additions and/or deletions into the wild-type gene. Some methods may also introduce single-or double-strand breaks into the gene. To inhibit or reduce expression of a protein (e.g., a T cell inhibitory protein), expression of a gene or polynucleotide encoding the protein can be inhibited or reduced, as described above. In other embodiments, the protein may be directly targeted using, for example, an antibody or protease to inhibit or reduce expression of the protein. By "inhibit" is meant a reduction of at least 10%, e.g., at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or more and including a 100% reduction (i.e., a level that is not present compared to a reference sample) as compared to a reference control level. The term "inactivated", as used herein, refers to preventing the expression of the polypeptide product encoded by the gene. Inactivation may occur at any stage or process of gene expression, including but not limited to: transcription, translation, and protein expression, and inactivation may affect any gene or gene product, including but not limited to: DNA, RNA, such as mRNA, and polypeptides. In some embodiments, "inhibiting expression" reflects a percentage of inactivation in the modified cells, e.g., further comprising at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or more of the cells in the population of cells in which the target gene is not inactivated.

The phrase "modifying" as used herein in the context of modifying the genome of a cell refers to inducing a structural change in a genomic sequence at a target genomic region. For example, the modification may take the form of insertion of a nucleotide sequence into the genome of the cell. For example, a nucleotide sequence encoding a polypeptide can be inserted into a genomic sequence encoding a cell surface protein endogenous to a T cell. The nucleotide sequence may encode a functional domain or a functional fragment thereof. Such modifications can be made, for example, by inducing a double-strand break within the target genomic region, or a pair of single-strand nicks located on opposite strands and flanking the target genomic region. Methods of inducing single-or double-strand breaks at or in a target genomic region include the use of a nuclease domain (e.g., Cas9) or a derivative thereof and a guide (e.g., a guide RNA) to the target genomic region.

The terms "patient," "subject," "individual," and the like are used interchangeably herein and refer to any animal, e.g., a mammal, such as a primate. In certain non-limiting embodiments, the patient, subject, or individual is a human.

The terms "treatment," "treating," and the like, as used herein generally refer to obtaining a desired pharmacological and/or physiological effect. Such an effect may be prophylactic in terms of completely or partially preventing a disease, disorder, or symptom thereof, and/or therapeutic in terms of a partial or complete cure for a disease or disorder and/or adverse effects, such as symptoms, resulting from the disease or disorder. The term "treatment" as used herein includes any treatment of a disease or disorder in a subject and includes: (a) preventing the disease or disorder from occurring in a subject who may be predisposed to the disease or disorder but has not yet been diagnosed with the disease or disorder; (b) inhibiting the disease or disorder (e.g., arresting its development); or (c) ameliorating the disease or disorder (e.g., causing regression of the disease or disorder, providing an improvement in one or more symptoms).

Drawings

FIGS. 1A-C. Framework for unbiased discovery of T cell proliferation modulators using pooled CRISPR screens. Section a provides a diagram of sgRNA lentiviral infection and Cas9 electroporation (SLICE) hybridization systems that enable convergent CRISPR screening in primary T cells. Section B provides illustrative data showing that editing the CD8A gene with SLICE will result in high-potency protein knockdown in two independent donors. Section C describes illustrative data from targeted screening (about 5,000 guides) showing that sgrnas targeting CBLB and CD5 are enriched in proliferating T cells, while LCP2 and CD3D are depleted. The non-targeted sgrnas are evenly distributed in the population.

Fig. 2A-F.A parts: the upper diagram: distribution of log2 fold change (LFC) values of >75,000 guided dividing cells over non-dividing cells in a whole Genome (GW) library. The following figures: LFCs targeting all 4 sgrnas of 3 enriched genes (CBLB, CD5, UBASH3A) and 3 depleted genes (VAV1, CD3D, LCP2) were overlaid on a gray gradient depicting global distribution. Values are the average of two donors. And part B: volcano plots of hits from primary GW screening. The X-axis shows the Z-score (ZS) of the gene level LFC (median LFC of all sgrnas of each gene). The Y-axis shows the p-value calculated by MAGeCK. Labeled genes with LFC-Z scores less than "0" were negative hits (depletion in dividing cells, FDR <0.2 and | ZS | >2), which annotated the TCR signaling pathway by Gene Ontology (GO). Genes with LFC-Z scores greater than "0" for markers were positive hits (rank <20 and | ZS | > 2). All values for both donors were calculated as biological replicates. Part C: gene hits from the secondary GW screen in two independent donors were positively correlated with the primary screen. Overlapping hits are shown for targets that are top 25 ranked positively and negatively. And part D: for 3 GW screens with increased TCR stimulation titers (1 ═ data in (B)), (B) boxplots of scaled LFCs of the top 100 hits in each direction. For both panels, the LFC value goes to 0, indicating that the selection pressure decreases as the TCR signal increases. The horizontal line is the median and the vertical line is the data range. Part E: gene set enrichment analysis showed significant skewing in LFC ranking for screening hits in two selected gene lists: (upper panel) screening of previously found hits by shRNA and (lower panel) TCR signaling pathway by KEGG in melanoma mouse model. The first 8 gene members of each set, enriched in the leading edge, are shown in the text box on the right. The vertical line on the x-axis is a member of the gene set, ordered by its LFC ranking in the GW screen. FDR ═ false discovery rate, alignment check. And part F: modulators of TCR signaling and T cell activation detected in GW screens. Shown on the left are positive modulators of the TCR pathway (FDR <0.25) found in our GW screen. The TCR pathway is based on the Wikipathways WP69 and literature reviews. Shown on the right are negative regulatory genes (known and unknown) found in the GW screen (FDR <0.25) and represent candidate targets for promoting T cell proliferation. Cell localization and interaction margins are based on literature reviews. The gene node (node) is represented by its LFC in the GW screen (red represents positive LFC values and blue represents negative LFC values).

FIGS. 3A-E. RNP arrays were used to validate gene targets that regulate T cell proliferation. Part A: summary of orthogonal validation strategy using Cas9 RNP electroporation. And part B: proliferation assay using CFSE stained CD8T cells. Panels show CFSE signal from TCR-stimulated (left peak) or unstimulated (right-most peak) human CD8T cells. Shown are data for two wizards CBLB and CD5 for two positive hits compared to NT CTRL and negative hit LCP 2. Part C: summary of data in section B: gene target targets (y-axis) are ranked by their ranking in the GW pool screen. The X-axis is the proliferation index (experimental procedure) calculated relative to NT CTRL in each donor (log2 transformation). The bands show the average of 2 independent experiments with 2 donors in each experiment. Error bars are SEM. And part D: early activation markers measured by flow cytometry 6 hours after stimulation. Shown are representative distributions of two guides for each target gene (y-axis) for CD154 (left) and CD69 (right). Part E: (D) summary of data for all tested gene targets in (y-axis). The X-axis is the fold-change increase in the marker positive (CD69/CD154) population relative to NT CTRL. Vertical lines are mean, error bars are SEM; for 4 donors, 2 guides for each gene.

FIGS. 4A-F. Matching SLICE with single-cell RNA-Seq, and performing high-dimensional molecular phenotype analysis on gene knockout in primary cells; part A: UMAP plots of all single cells with identified sgrnas in stimulated and unstimulated T cells from two human donors. Unstimulated cells are on the left; the stimulated cells are on the right. And part B: UMAP with gene expression scaling for 4 genes showing clusters associated with activation status (IL7R, CCR7), cell cycle (MKI67) and effector function (GZMB). Part C: single cells were unsupervised clustered based on gene expression, with 13 clusters identified as labeled. And part D: cells expressing sgrnas for CBLB, CD5, LCP and NT CTRL were clustered on UMAP representation (representation). Part E: the Y-axis shows the over representation or under representation (Y-axis) of sgRNA-expressing cells between clusters (panels), determined by chi-square test. Part F: the heat maps show the average gene expression (y-axis) between cells with different sgRNA targets (x-axis). Data represent one donor.

FIGS. 5A-D. The best screening hit promoted in vitro tumor killing by engineered human T cells. Part A: graph of high throughput experimental strategy to test gene targets that promote tumor killing in vitro. And part B: representative images taken 36 hours after co-culture of human CD8T cells and a375 tumor cells. Shown are red fluorescence channels representing wells, annotated at the bottom left of each group of figures. The scale bar is 500 um. Part C: a375 was cleared by antigen-specific CD8T cells after 36 hours. Values were normalized to a375 cell count in wells without T cells. For each gene target 2 wizards in 4 donors and two technical replicates, the horizontal line is the mean and the error bars are SEM. Denotes p <0.001, verxon (Wilcoxon) rank sum test. And part D: time traces of a375 cell counts were measured by IncuCyte software for selected hits. The line is the average of 4 donors, 2 guides for each target gene. Error bars are SEM.

FIGS. 6A-C. SLICE was altered to reveal resistance to immunosuppressive signaling in primary T cells. Part A: a z-score scatter plot of the whole genome screen for resistance to the adenosine A2A selective agonist CGS-21680. And part B: the upper diagram: log2 fold change distribution of all sgrnas in GW libraries of CGS-21680 treated T cells. The following figures: LFCs were stimulated only for individual sgrnas in the (vehicle) screen (green) sgrnas as compared to the CGS-21680 resistance screen (red). Part C: the gene targets from the adenosine resistance screen were validated with RNP-edited T cells. Compared to NT CTRL, knockdown of ADORA2A or FAM105A allows cells to proliferate in the presence of adenosine agonists. Each panel shows two independent sgrnas for 2 donors.

FIGS. 7A-J. In situSlide was established in human T-cell generation, and is associated with fig. 1. Part A: to optimize SLICE for large-scale screening, we investigated whether increasing infection rates using polybrene (mCherry reporter) would yield higher absolute numbers of viable edited cells. Although the addition of polybrene increases the transduction efficiency (bottom marker) appreciably-neglecting its viability improvement and resulting in a higher total number of transduced cells (top marker). And part B: FACS plots showing antibody staining (BV-570, y-axis) from transduced primary human CD8 compared to the targeted protein CD8A⁺Expression of fluorescent reporter by T cells (x-axis). After electroporation of Cas9 protein (3ul in 20ul cell suspension, stock 20uM), cells expressing sgRNA were mostly CD8 negative. Part C: CD8A protein (y-axis) was edited from cells of (a) and electroporated with Cas9 protein at different stock concentrations. The edit was calculated as the ratio of CD8 negative cells to CD8 positive cells in successfully transduced cells. Titers show the dependence of editing efficiency on Cas9 concentration. Data show 2 human donors. And part D: the proliferation dye VPD450 measures the response to a second stimulus (day 9) by the anti-CD 3 and anti-CD 28 complexes according to the first stimulus (day 0) in the SLICE system. Stimulation with anti-CD 3/CD28 beads (lower panel) at a 1:1 bead to T cell ratio will block the ability of CD8T cells to respond to restimulation, probably due to activation-induced depletion and cell death. The first stimulus using the antibody bound to the plate (upper panel) maintained the ability of the cell response, allowing TCR-dependent proliferation screening. Part E: the gating strategy for cells is shown in FIG. 1B. And part F: SLICE can effectively knock out primary human CD8⁺And CD4⁺Candidate target in T cells (CD 45). No targeting control sgRNA (nt.ctrl, grey) indicates that the knockout is specific for cells transduced with a CD45 targeting guide (blue). And part G: CFSE signals from pre-sorted cells in the pilot screen are shown in figure 1C. Part H: distribution of read counts after deep sequencing of sgrnas of sorted cell populations in two donors in the pilot screen. Part I: absolute fold change in abundance of the first two guides, for hits in fig. 1C. Each line is a unique sgRNA targeting a given gene, marked at the top of each panel. Among two guides and two donors, guide enrichment and depletion modesThe formula is identical. Part J: log fold change ranking comparisons from parallel screens using the same experimental timeline and sgRNA libraries as fig. 1C, but without CFSE-based enrichment sorting. This growth-based screen has a low signal-to-noise ratio, represented by the proximity of the diagonal of the TCR-related guide in fig. 1C. Gray dots are the targeting guide alone and black dots are the non-targeting control (nt.ctrl) guide.

FIGS. 8A-F. Whole genome pooling CRISPR screening in primary T cells, correlated with figure 2. Part A: gating strategy for estimation of transduction efficiency in primary GW screen by puromycin titration. And part B: transduced cells in part a were cultured for 2 days with or without puromycin at various seeding densities. In various inoculation dilutions, the total number of viable cells as described in section a was compared between the two conditions. The fitted line is a linear regression with a slope of 0.51 (R)²0.99), indicating efficient transduction. Part C: gating and sorting strategies for representative samples for GW screening. We sorted the populations as shown in the CFSE panel so that the separation between bins (bins) is clearly visible. And part D: hits found in primary GW screening using two donors (green bars) were compared, as well as pooled data from duplicate screens using two donors (orange bars). Hits are defined as FDRs on various aspects<0.25 by the MAGECK RRA algorithm. The label at the top of the column is the number of hits in each group. Part E: TCR stimulation titer (x-axis) effect on 3 positive and negative regulators of T cell stimulation on fold change in gene level. And part F: enrichment of fold changes in gene levels by GSEA resulting in FDR<A KEGG pathway of 0.01. In addition, gene sets from shrnas from published T cell tumor infiltrates were included. Each point is an annotated gene set (y-axis), and the x-axis shows a Normalized Enrichment Score (NES) compared to the mean of a randomized gene set of the same size. The size of each dot is inversely proportional to the p-value of each enrichment.

FIGS. 9A-D. RNP arrays validated hits from whole genome screening, which correlates with fig. 3. Part A: CFSE trace hit in section C of fig. 3. Each panel shows two unique guides for each gene target in two technical replicates. Data represent one donor. And part B: curve fitting of representative CFSE trajectories, as in part C of fig. 3. Panels show fitted gaussians of CFSE peaks, determined by flowFit R software package. The parameter extracted is the proliferation index, defined as the total count of cells in all generations divided by the number calculated for the original parent cell. Part C: the distribution of the activation marker CD154 was among all targets tested. The line shows the measured expression of stimulated (red) and unstimulated (blue) cells, compiled with 2 guides targeting the genes marked at the top of each panel. Data represent one donor. And part D: same as part B for CD 69.

FIGS. 10A-F. SLICE coupled to single-cell RNA-Seq, related to FIG. 4. Part A: transduction efficiency of CropSeq experiments was measured by puromycin selection. By linear regression, we estimated the infection rate to be 13.9% (R)²0.76, blue line, and 95% CI in the shaded area). It is expected that this intentionally low transduction rate will enrich for cells with only a single integrated sgRNA cassette. And part B: the volcano plot of DESeq2 differential gene expression of the synthetic batch RNA-Seq profile (collapsed UMI count for each sample) of stimulated cells compared to unstimulated cells. The points are genes, the y-axis indicates the significance of enrichment, and the x-axis indicates the magnitude and direction of the log2 fold change (LFC) in transcript abundance. Will have | LFC>2.5 and adjusted p-value<1e-18, blue indicates genes up-regulated in stimulated cells, and red indicates down-regulated genes. Part C: gene annotation in clusters was based on enrichment of single cell RNA expression profiles. Each dot represents the enrichment of the eactome annotation (y-axis) in each cluster (x-axis). Genes associated with each cluster were determined by differential gene expression testing, and all cells in the cluster were compared to all other cells. The size of each dot is proportional to the significance of the enrichment, and the color indicates whether the average expression level of the gene in the corresponding annotation in each cluster is up-or down-regulated (red and blue, respectively). And part D: CROP-Seq experiments gene editing at the single cell level. Each panel shows the mean and standard error of the mean (SEM) of the targeted gene unique molecular marker (UMI) counts in the library. Calculation of individual target transcripts for all cells expressing the target sgRNA (target) or non-targeted control guide (nt.ctrl). The average of four samples was calculated, two guides for each gene. Most gene targets showed lower levels of transcripts of cells expressing the targeted guide compared to the control guide, indicating a successful knockout. We note that some gene targets are expressed at low levels, so edits may be difficult to detect at the single cell level. Part E: the gene expression profile of the selected sgrnas of the second donor was the same as in section F of fig. 4. The genes shown were enriched in clusters 8-12 associated with stimulated cells (| logFC>1 and adjusted p<0.05). The system dendrogram uses the Ward D2 algorithm based on euclidean distance as implemented in hclust in R. And part F: similarity in cluster relatedness of cells expressing sgrnas targeted. Shown are the pearson correlation coefficients for the chi-squared residuals in all clusters. The system dendrogram computation is divided into three levels as described in section E.

FIGS. 11A-D. In vitro tumor killing of engineered human T cells, correlated with figure 5. Part A: t cells transduced with the 1G4TCR lentivirus showed high transduction rates according to staining with HLA-A2+ restricted NY-ESO-1 peptidoglycan-PE. Sorting cells to obtain tetramer-positive tumor-specific CD8⁺A pure population of T cells. And part B: caspase levels measured on IncuCyte demonstrated that T cells induced apoptosis in a375 melanoma cells expressing the target NY-ESO in titrations with increased T cell to tumor cell ratio (upper panel). For the same T cell to tumor cell ratio, the tumor cells killed nuclear counts corresponding to a375 RFP markers over time (lower panel). Part C: a375 clearance of all gene targets tested. The Y-axis represents a375 counts by IncuCyte software, normalized by counts in nt.ctrl wells for each donor, time point and gene target. The line shows the average (n-4) for each donor in both wizards and in both technical replicates. Error bars are SEM. And part D: the data in (B) were quantified for two CD8 to a375 ratios at 36 hours. 1:4 is the data summarized in FIG. 5C. Dots represent a single well in the array of all donors (n-4), two guides and two replicates.

FIGS. 12A-E. Screening of primary T cells Using SLICEResistance to immunosuppressive signals, is associated with fig. 6. Part A: dose titration for optimal effect of the adenosine receptor agonist CGS-21680 on inhibition of T cell proliferation determined that 20 μ M was sufficiently inhibitory. Representative CFSE curves are shown for CGS-21680 (as labeled at the top of each panel) at different concentrations. Data for CFSE-labeled CD8 from two human donors⁺T cells. And part B: sorting gating and CFSE profiles for whole genome screening are shown in fig. 6A. Part C: comparison of the first 25 hits where the two screening conditions (CGS-21680 and vehicle) overlapped. We note that although many hits from the vehicle screen also promote proliferation (diagonally) in the presence of immunosuppressive adenosine agonists, ADORA2A and FAM105A sgRNA were selectively enriched in the context of adenosine agonists. ADORA2A and FAM105A were the only hits in the top 0.1% LFC under CGS-21680 treated conditions and in the top 0.1% LFC difference between CGS-21680 and vehicle GW screening conditions. And part D: paralogous genes FAM105A and OTULIN (FAM105B) have 40% homology and are encoded as contiguous dots on chromosome 5. They share the OTU deubiquitinase domain, which is predicted to be catalytically inactive in FAM105A. Non-synonymous Single Nucleotide Polymorphisms (SNPs) in FAM105A have been associated with allergic disease through genome-wide association studies (Ferreira et al, 2017). Rare mutations in OTULIN are associated with single-gene severe inflammatory diseases (dammarard et al, 2016). Part E: clearance of RFP-labeled a375 cells by antigen-specific CD8T cells edited by sgRNA targeting ADORA2A, FAM105A and non-targeted controls with increased CGS-21680 dose. The Y-axis shows the relative clearance level compared to vehicle at each CGS-21680 concentration. The X axis shows the different sgrnas targeted. Horizontal lines are median and whiskers (whisker) are data limits, which are in two guides and 4 donors per target.

FIGS. 13A-B. SLICE for in vivo pooling screening of immunotherapies in humanized mouse models. Part A: principal component analysis (primary component analysis) of pilot counts of T cells collected from spleens (four leftmost points) and tumors (four rightmost points) 7 days after transfer. Each spot was one sample (2 donors out of 4 mice). There was clearly a clear separation for the guidance of enrichment in each tissue. And part B: log fold change in vivo SLICE experiments. The vertical lines are mean values and the horizontal lines are SEM (each tissue n is2, each gene target n is2 guides).

Detailed Description

In one aspect, the present disclosure provides engineered T cells that exhibit enhanced cytotoxicity to a target of interest (e.g., a tumor cell). Such T cells are modified to inhibit expression of T cell inhibitory genes. As used herein, a "T cell inhibitory" gene refers to a gene that will negatively affect proliferation upon stimulation. The modification of a T cell inhibitory gene according to the invention need not be performed only on T cells. Although the modifications are T Cell Receptor (TCR) dependent, they may be applied to other hematopoietic cells. Thus, in some embodiments, the cell modified according to the invention is a T cell, such as a CD8+ T cell. In some embodiments, the cell is a hematopoietic stem cell. In other embodiments, the T cell is a stem memory T cell, an effector memory T cell, a central memory T cell, or a naive T cell. In some embodiments, the modification according to the invention is performed on CD4+ T cells or NK cells or γ Δ T cells. Reviews of T cell subsets are described, for example, in Sallusto et al, Annual Rev.Immunol.22745-763,2004; mueller et al, Annual Rev. Immunol 31:137-161,2013, reviewed in Gattinoni, et al, Nature Med.23:18-27,2018 for memory stem T cells. A description of subgroups by markers can be found in the OMIP Wiley online library (see, e.g., Wingeder and Kronenberg, OMIP-030: Characterization of human T cell subgroups by surface markers (OMIP-030: Characterization of human T cell subgroups via surfaces markers) Cytometry Part A87A: 1067-.

The expression of the target gene may be inhibited or, in some embodiments, may be inactivated such that the gene does not express the active protein product. In some embodiments, the cell population can be enriched for cells in which the gene is inactivated.

In some embodiments, the T cell inhibitory gene modified to inhibit expression is CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2 (also referred to as ELOB, extensin B in the HUGO nomenclature), UBASH3A, or ARID 1A. In some embodiments, the T cell inhibitory gene is CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB 2. In some embodiments, the T cell inhibitory gene is SOCS1, TCEB2, RASA2, or CBLB. In some embodiments, the T cell inhibitory gene is SOCS1, TCEB2, or RASA 2. In some embodiments, the T cell suppressor gene modified to suppress expression is RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM 222. In some embodiments, the T cell inhibitory gene modified to inhibit expression is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB 1. In some embodiments, the hematopoietic cell (e.g., T cell) further comprises a second modification that inhibits expression of a T cell inhibitory gene.

Any number of tests may be used to evaluate function. An exemplary assay measures a T cell proliferative response, e.g., in response to T Cell Receptor (TCR) stimulation. Exemplary experiments are described in the examples section. Assays include, but are not limited to: CFSE (or other similar dye) dilution, growth-based assays, in vivo expansion of specific sites, or sorting of other markers of activation or effector function (e.g., cytokine production, induction of cell surface markers, or production of granular enzymes).

In another aspect, the present disclosure provides engineered T cells modified to inhibit T cell gene expression to inhibit T cell function, e.g., to target genes for the treatment of autoimmune diseases or other diseases where inhibition of T cell function is desired (e.g., transplant rejection). In some embodiments, the T cell gene to be modified is: CYP2R1, LCP2, RPP21, VAV1, EIF2B 1, RPP 1, EXOSC 1, RPN1, VARS, CD 31, GRAP 1, TRMT112, ALG1, VAV1, EXOSC 1, SH2D 11, ZAP 1, DDX 1, CD247, ALDOA, ZNF131, WDR 1, AK1, LCP 1, CD247, VHL, EIF2B 1, PREPEL 1, GRPEL1, NAA1, ALDOA, ALG1, MARS, C4orf 1, RAC 1, LCK, SUPT4H1, SLC25A 1, LUC7L 1, C3orf1, RPP 1, RPRS 1, EPRODE 1, CANDC 1, EPDCASC 1, EPD 1, EPDCASD 1, EPFLEXFAR 1, EPRODE 1, EPTC 368, EPTC 1, EPTC 368, EPTC 1, CADE 1, 368, EPTC 368, 1, 368, 1, 368, 1, 368, 1, 368, 1, 368-1, 368-368, 1, 368, 1, 368, 1, 368, 1, 368, 1, 368, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNNB 4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXCC 7, NOB1, EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND2, TTI2, THG 12, TAF 12, URI 2, TRMT112, EIF 32, CCND2, GCLM, RBSN, QARS, 2, TAF2, HUWE 2, CARWES, 364A 2, 36785, PRNDF 7872, AOWD 2, AOD 2, PADDSLC 2, PHB2, PHTHCP 2, POD 2, PHTABTX 2, PHTAORP 2, PHTHCP 2, PHTABTF 2, PHTAOCR 72, PHTADG 2, PHTHTP 2, PHTAOCTAOCTAR 72, PHTAOCTAR 72, PHTAD 2, PHTAORS 2, PHTHAT 2, PHTAOCTAR 72, PHTAR 72, PHTHAT 2, PHTAORS 2, PHTHAT 2, PHTHS 2, PHTHAT 2, PHTAORS 2, PHTHAT 2, PHTAORS. In some embodiments, the gene is any one of the following genes: HSPA, RPP, EXOSC, LCP, MYC, CD247, NOP, VAV, RHOH, TAF1, TRMT112, CCND, SH2D1, MARS, CD3, LUC7L, EIF2B, ORAOV1.VARS, NOL, ZBTB8, SLC35B, NAA, EIF2B, DHX, LAT, EMG, ALDOA, GRPEL, ARMC, POLR2, NOP, PSENEN, RELA, SUPT4H, VHL, GFER, BPTF, RAC, RACR, TAF, PMPCA, EIF, STT3, POP, GMPB, TP53, CCNH, TEX, DHX, QARS, EID, IRF, TAF, IARS, GTF3, NOP, IMP, UTL, EIF4G4, AGN, ALP, PHBD, PHBV, PHBR, POLR, ORP, EPOR 2, OROCR, SAL, SARG, SARP 3, SARP 1, SARP 3, SARP, SALT 3, SALT 2, SALT 3, SALT 3, SALT.

In another aspect, the T cell is modified to inhibit expression of a gene involved in resistance to an immunosuppressive agent. In some embodiments, the gene is FAM105A (also known as OTULINL (OTU deubiquitinase with linear ligation specificity) in the HUGO nomenclature).

In some embodiments, the T cell inhibitory gene is inactivated by gene deletion. As used herein, "gene deletion" refers to the removal of at least a portion of a DNA sequence of or adjacent to a gene. In some embodiments, the sequence undergoing gene deletion comprises an exon sequence of a gene. In some embodiments, the sequence subject to gene deletion comprises a promoter sequence of the gene. In some embodiments, the sequence undergoing gene deletion comprises flanking sequences of the gene. In some embodiments, a portion of the gene sequence is removed from the gene. In some embodiments, the entire gene sequence is removed from the chromosome. In some embodiments, the host cell comprises a gene deletion, as described in any of the embodiments described herein. In some embodiments, a gene is inactivated by deletion of at least one nucleotide or nucleotide base pair in the gene sequence, resulting in a non-functional gene product. In some embodiments, a gene is inactivated by a gene deletion, wherein at least one nucleotide deletion of the gene sequence produces a gene product that no longer has the function or activity of the original gene product; or the gene is a dysfunctional gene product. In some embodiments, a gene is inactivated by gene addition or substitution, wherein addition or substitution of at least one nucleotide or nucleotide base pair in the gene sequence produces a non-functional gene product. In some embodiments, a gene is inactivated by inactivation of the gene, wherein the inclusion or substitution of at least one nucleotide into the gene sequence results in a gene product that no longer has the function or activity of the original gene product; or the gene is a dysfunctional gene product. In some embodiments, the gene is inactivated by gene addition or substitution, wherein incorporation of at least one nucleotide into the gene sequence or substitution results in a dysfunctional gene product. In some embodiments, the host cell comprises a gene deletion, as described in any of the embodiments described herein.

Methods and techniques for inactivating a T cell suppressor gene in a host cell or a target gene that suppresses T cell function as described herein include, but are not limited to: small interfering RNA (siRNA), small hairpin RNA (shRNA; also known as short hairpin RNA), Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), transcription activator-like effector nucleases (TALEN), Zinc Finger Nucleases (ZFN), homologous recombination, non-homologous end joining and meganucleases. See, e.g., O' Keefe, Mater Methods,3,2013; doench et al, Nat Biotechnol,32,2014; gaj et al, Trends Biotechnol,31,2014; and Silva et al, Curr Gene Ther,11,2011.

Inhibitory RNA

In some embodiments, the T cell inhibitory gene, or the gene to be modified to inhibit T cell function, is inactivated by a small interfering rna (sirna) system. siRNA sequences that inactivate a target gene can be identified using considerations such as: the length of the siRNA, e.g., 21-23 nucleotides or less; avoiding regions with start and stop codons of 50-100 nucleotides, avoiding intronic regions; a segment avoiding four or more identical nucleotides; regions with GC content less than 30% or greater than 60% are avoided; avoiding repetitive and low sequence complexity regions; avoiding single nucleotide polymorphism sites, and avoiding sequences complementary to sequences in other off-target genes (see, e.g., the siRNA design Rules for RNA interference (Rules of siRNA design for RNA interference), Protocol Online,2004, 5.29; and Reynolds et al, Nat Biotechnol,22: 3236-.

In some embodiments, the siRNA system comprises an siRNA nucleotide sequence that is about 10 to 200 nucleotides in length, or about 10 to 100 nucleotides in length, or about 15 to 100 nucleotides in length, or about 10 to 60 nucleotides in length, or about 15 to 60 nucleotides in length, or about 10 to 50 nucleotides in length, or about 15 to 50 nucleotides in length, or about 10 to 30 nucleotides in length, or about 15 to 30 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is about 10-25 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is about 15 to 25 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is at least about 10, at least about 15, at least about 20, or at least about 25 nucleotides in length. In some embodiments, the siRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a target mRNA molecule. In some embodiments, the siRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a target pre-mRNA (pro-mRNA) molecule. In some embodiments, the siRNA system comprises a double stranded RNA molecule. In some embodiments, the siRNA system comprises a single-stranded RNA molecule. In some embodiments, the host cell comprises an siRNA system, as described in any of the embodiments described herein. In some embodiments, the host cell comprises a pre-siRNA nucleotide sequence that is processed into an active siRNA molecule, as described in any of the embodiments described herein. In some embodiments, the host cell comprises an siRNA nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a target mRNA molecule. In some embodiments, the host cell comprises an expression vector encoding an siRNA molecule, as described in any of the embodiments described herein. In some embodiments, the host cell comprises an expression vector encoding a pre-siRNA molecule, as described in any of the embodiments described herein.

In some embodiments, the siRNA system comprises a delivery vehicle. In some embodiments, the host cell comprises a delivery vector. In some embodiments, the delivery vehicle comprises a pre-siRNA and/or an siRNA molecule.

In some embodiments, the T cell inhibitory gene is inactivated by a small hairpin RNA (shRNA; also referred to as short hairpin RNA) system. Genes can be inactivated by the shRNA system. In some embodiments, the shRNA system comprises a nucleotide sequence that is about 10-200 nucleotides in length, or about 10-100 nucleotides in length, or about 15-100 nucleotides in length, or about 10-60 nucleotides in length, or about 15-60 nucleotides in length, or about 10-50 nucleotides in length, or about 15-50 nucleotides in length, or about 10-30 nucleotides in length, or about 15-30 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is about 10-25 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is about 15-25 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is at least about 10, at least about 15, at least about 20, or at least about 25 nucleotides in length. In some embodiments, the shRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a region of a T cell inhibitory nucleic acid mRNA molecule. In some embodiments, the shRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to the pre-mRNA molecule. In some embodiments, the shRNA system comprises a double stranded RNA molecule. In some embodiments, the shRNA system comprises a single-stranded RNA molecule. In some embodiments, the host cell comprises an shRNA system, as described in any of the embodiments described herein. In some embodiments, the host cell comprises a pre-shRNA nucleotide sequence that is processed into an active shRNA nucleotide sequence, as described in any of the embodiments described herein. In some embodiments, the pre-shRNA molecule consists of DNA. In some embodiments, the pre-shRNA molecule is a DNA construct. In some embodiments, the host cell comprises an shRNA nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a T cell inhibitory gene mRNA molecule. In some embodiments, the host cell comprises an expression vector encoding an shRNA molecule, as described in any of the embodiments described herein. In some embodiments, the host cell comprises an expression vector encoding a pre-shRNA molecule, as described in any of the embodiments described herein.

In some embodiments, the shRNA system comprises a delivery vector. In some embodiments, the host comprises a delivery vector. In some embodiments, the delivery vector comprises a pre-shRNA and/or shRNA molecule. In some embodiments, the delivery vector is a viral vector. In some embodiments, the delivery vector is a lentivirus. In some embodiments, the delivery vector is an adenovirus. In some embodiments, the vector comprises a promoter.

CRISPR

In some embodiments, inhibiting expression of a T cell inhibitory gene is achieved using the CRISPR/CAS method. Illustrative methods for reducing gene expression using a CRISPR/Cas system are described in various publications, e.g., U.S. patent application publication No. 2014/0170753. The CRISPR/Cas system includes a Cas protein and at least 1-2 ribonucleic acids that hybridize to and direct the Cas protein to target motifs in T cell inhibitory genes. Any CRISPR/Cas system capable of altering a target polynucleotide sequence in a cell can be used. In some embodiments, the CRISPR Cas system is a type I CRISPR system, in some embodiments, the CRISPR/Cas system is a type II CRISPR system. In some embodiments, the CRISPR/Cas system is a type V CRISPR system.

Cas proteins for use in the present invention are naturally occurring Cas proteins or functional derivatives thereof. "functional derivatives" include, but are not limited to: fragments of the native sequence and derivatives of the native sequence polypeptide and fragments thereof, provided that they share biological activity with the corresponding native sequence polypeptide. The biological activity envisaged herein is the ability of the functional derivative to hydrolyze a DNA substrate into fragments. The term "derivative" includes amino acid sequence variants of the polypeptide, covalent modifications, and fusions thereof, such as derivatized Cas proteins. Suitable derivatives of Cas polypeptides or fragments thereof include, but are not limited to, mutants, fusions, covalently modified forms of Cas proteins, or fragments thereof.

There are 3 major types of Cas nucleases (type I, type II and type III), and 10 subtypes, including 5 type I proteins, 3 type II proteins and 2 type III proteins (see, e.g., Hochstrasser and Doudna, Trends Biochem Sci,2015:40(1): 58-66). Type II Cas nucleases include Cas1, Cas2, Csn2 and Cas 9. These Cas nucleases are well known to those skilled in the art. For example, the amino acid sequence of the streptococcus pyogenes wildtype Cas9 polypeptide is shown, e.g., in NBCI sequence number NP _269215, and the amino acid sequence of the streptococcus thermophilus wildtype Cas9 polypeptide is shown, e.g., in NBCI sequence number WP _ 011681470. Some CRISPR-associated endonucleases that can be used in the methods described herein are disclosed, for example, in U.S. patent application publication nos. 2014/0068797, 2014/0302563 and 2014/0356959. Non-limiting examples of Cas nucleases include: cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7 (also known as Csn 7 and Csx 7), Cas7, Csy 7, Cse 7, Csc 7, Csa 7, Csn 7, Csm 7, Cmr 7, Csb 7, Csx 36x 7, Csx 36f 7, Csf 7, Csx 36f 7, Csx 36x 7, cs.

Cas9 homologs are present in a variety of eubacteria, including, but not limited to, bacteria of the following taxonomic groups: actinomycetes (actinobacilla), aquatics (Aquificae), bacteroides-chloromycetes (bacteroides-Chlorobi), chlamydia-Verrucomicrobia (chlamydia-Verrucomicrobia), chloroflexus (chloflexi), Cyanobacteria (Cyanobacteria), Firmicutes (Firmicutes), Proteobacteria (Proteobacteria), spirochetes (Spirochaetes), and thermomyces (thermoanaerobae). An exemplary Cas9 protein is the Streptococcus pyogenes (Streptococcus pyogenes) Cas9 protein. Other Cas9 proteins and their homologs are described, for example, in Chylinksi, et al, RNA biol.2013, 5 months and 1 days; 10(5) 726-; nat. rev. microbiol.2011 for 6 months; 9(6) 467-477; hou, et al, Proc Natl Acad Sci U S A.2013, 24.9 months; 110(39) 15644-9; sampson et al, Nature.2013, 5, month, 9; 497(7448) 254-7; and Jinek, et al, science.2012 8 month 17; 337(6096):816-21. Any of the variants of Cas9 nuclease provided herein can be optimized to provide high efficiency activity or enhance stability in a host cell. Thus, engineered Cas9 nucleases are also contemplated. Cas9 from Streptococcus pyogenes (Streptococcus pyogenes) contains 2 endonuclease domains, including a RuvC-like domain that cleaves target DNA non-complementary to crRNA and a HNH nuclease domain that cleaves target DNA complementary to crRNA. The double-stranded endonuclease activity of Cas9 also involves a short conserved sequence (2-5 nucleotides), referred to as a protospacer-associated motif (PAM), which immediately follows the 3' of the target motif in the target sequence.

Furthermore, Cas nucleases, such as Cas9 polypeptides, may be derived from a variety of bacterial species, including but not limited to: sarmentpresence typica (Veillonella typica), Fusobacterium nucleatum (Fusobacterium nucleaum), Trench Proteus (Filifoctor glocus), Morobacterium moorei (Solobacterium moorei), enterococcus faecalis (Coprococcus cathus), Borrelia denticola (Treponema dencola), Deuterotrophic bacterium (Peptophilus duerdei), Streptomyces mitilis (Catenibacter suokai), Streptococcus mutans (Streptococcus mutans), Listeria innocua (Listeria innoculus), Staphylococcus pseudomesogenes (Staphylococcus aureus), enterococcus entericus (Acidococcus neococci), Pseudomonas gingivalis (Lactobacillus), Lactobacillus paraguatus (Lactobacillus), Lactobacillus plantarum), Lactobacillus casei (Lactobacillus), Lactobacillus plantarum), Lactobacillus casei (Lactobacillus casei), Lactobacillus plantarum (Lactobacillus casei), Lactobacillus casei (Mycoplasma lactis), Lactobacillus (Mycoplasma lactis), Mycoplasma lactis (Mycoplasma lactis), Mycoplasma gracilium), Mycoplasma (Mycoplasma lactis), Mycoplasma lactis (Mycoplasma lactis), Mycoplasma lactis, Mycoplasma lactis, Mycoplasma, Mycopla, Eubacterium rectal (Eubacterium rectangle), Streptococcus thermophilus (Streptococcus thermophilus), Eubacterium elongatum (Eubacterium dolichum), Lactobacillus subsp.corynebacterium subsp.contorti (Lactobacillus subsp.Torrens), Corynebacterium cremoris (Corynebacterium polytropus), Ruminococcus albus (Ruminococcus albus), Exiguobacterium muciniphilus (Akkermanicola), Thermomyces cellulolyticus (Acidorhizophilus cellulolyticus), Bifidobacterium longum (Bifidobacterium longum), Bifidobacterium odonta (Bifidobacterium bifidum), Corynebacterium diphtheriae (Corynebacterium diphyteria), Micrococcus parvum (Elusiricusbusi), Rhodococcus nitrificans (Nitrosporus sarcinalis), Streptococcus thermophilus (Corynebacterium parvum), Streptococcus faecalis (Lactobacillus paracasei), Streptococcus thermophilus (Corynebacterium parvus), Streptococcus succinogenes), Streptococcus faecalis (Corynebacterium parvus), Streptococcus faecalis (Bacillus subtilis), Streptococcus faecalis strain (Bacillus subtilis), Streptococcus faecalis strain (Corynebacterium parvurica), Streptococcus faecalis strain (Bacillus subtilis), Streptococcus faecalis, Streptococcus strain, Streptococcus, Flavobacterium columniformis (Flavobacterium columniformis), Aminomonas paucimobilis (Aminomonas paucivorans), Rhodospirillum rubrum (Rhodospirillum rubrum), candidate Helicobacter pylori (Candidatus Punicifluum marinum), Pheretima lumbricus (Verminephthobacter eisheniae), Klebsiella persimilis (Ralstonia syzygii), Verticillium japonicum (dinoflagellaceae), Rhodospirillum japonicum (dinoflagellate shibae), Azospirillum azotobacillum (Azospillum), Nitrosobacter handii (Nitrobacter hamurus), Chronic rhizobacteria (Bradyrhizobium), Wolbasidium succiniciproducens (Wolina sucinogenes), Campylobacter jejunii subspecies (Campylobacter xylinus), Clostridium sporotrichineum, Salmonella typhimurii, Salmonella choleraesurus, Salmonella cholerae, or, "Theloschestes warselis" (Sutterella wadswortheis), Proteobacteria (proteobacterium), Legionella pneumophila (Legionella pneumoniae), Spiraria exuberans (Parastutteriiihosis), Wollachia succinogenes (Wolinella succinogenes) and Francisella neoterrestris (Francisella novicida).

Other RNA-mediated nucleases include Cpf1 (see, e.g., Zetsche et al, Cell, Vol.163, stage 3, p759-771, 22/10/2015) and homologs thereof.

As used herein, the term "Cas 9 ribonucleoprotein" complex or like terms refer to such a complex: a complex between a Cas9 protein and a guide RNA, a complex between a Cas9 protein and a crRNA, a complex between a Cas9 protein and a trans-activating crRNA (tracrRNA), or a combination thereof (e.g., a complex comprising a Cas9 protein, a tracrRNA, and a crRNA guide RNA). It is understood that in any of the embodiments described herein, Cas9 nuclease may be replaced with another RNA-mediated nuclease, such as an alternative Cas protein or Cpf1 nuclease.

In some embodiments, the Cas protein is introduced into the T cell as a polypeptide. Thus, for example, in certain embodiments, the Cas protein may be coupled to or fused to a cell penetrating polypeptide or peptide well known in the art. Non-limiting examples of cell penetrating peptides include those provided in Milletti F, "Drug discov. today 17: 850-. In some cases, T cells can be engineered to produce Cas proteins.

In some embodiments, the Cpf1 nuclease or Cas9 nuclease and gRNA are introduced into the cell as a Ribonucleoprotein (RNP) complex.

In some embodiments, the RNP complex may be introduced at about 1X 10⁵About 2X10⁶Individual cell (e.g., 1X 10)⁵One cell-about 5X 10⁵One cell, about 1X 10⁵One cell-about 1X 10⁶1X 10 cells, cell⁵Individual cell-about 1.5X 10⁶1X 10 cells⁵One cell-about 2X10⁶One cell, about 1X 10⁶Single cell s-about 1.5X 10⁶Individual cell or about 1X 10⁶One cell-about 2X10⁶Individual cells). In some embodiments, the cells are cultured under conditions effective to expand the modified cell population. Also provided herein are cell populations in which the genome of at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99% or more of the cells comprise a genetically modified or heterologous polynucleotide that inhibits expression of a T cell inhibitory gene as described herein. In some embodiments, the population comprises subpopulations of cells, each subpopulation having a different genetic modification to inhibit expression of a T-cell inhibitory gene described herein.

In some embodiments, the RNP complex is introduced into the T cell by electroporation. Methods, compositions, and devices for electroporating cells to introduce RNP complexes are known in the art, see, e.g., WO 2016/123578, WO/2006/001614, and Kim, j.a. et al biosens.bioelectrron.23, 1353-1360 (2008). Other or additional methods, compositions, and devices for electroporating cells to introduce RNP complexes may include those described below: U.S. patent application publication numbers 2006/0094095; 2005/0064596, respectively; or 2006/0087522; li, l.h. et al Cancer res.treat.1, 341-350 (2002); U.S. patent nos.: 6,773,669, respectively; 7,186,559, respectively; 7,771,984, respectively; 7,991,559, respectively; 6,485,961, respectively; 7,029,916, respectively; and U.S. patent application publication No. 2014/0017213; and 2012/0088842; geng, t, et al, j.control Release 144, 91-100 (2010); and Wang, J., et al Lab. chip 10, 2057-.

In some embodiments, the Cas9 protein may be in an activated endonuclease form, so that when bound to a target nucleic acid as part of a complex with a guide RNA or part of a complex with a DNA template, a double strand break is introduced into the target nucleic acid. In the methods provided herein, a Cas9 polypeptide or a nucleic acid encoding a Cas9 polypeptide can be introduced into a T cell. Double strand breaks can be repaired with HDR to insert DNA templates into the genome of T cells. The methods described herein can utilize various Cas9 nucleases. For example, Cas9 nuclease can be utilized that requires a NGG Protospacer Adjacent Motif (PAM) immediately 3' to the region targeted by the guide RNA for Cas9 nuclease. Such Cas9 nuclease may target exon 1 of TRAC or a region of exon 1 of TRAB that comprises the NGG sequence. As another example, Cas9 proteins with orthogonal PAM motif requirements can be used to target sequences that do not have adjacent NGG PAM sequences. Exemplary Cas9 proteins with orthogonal PAM sequence specificity include, but are not limited to, those described in Evelt et al (Nature Methods 10: 1116-1121 (2013)).

In some cases, the Cas9 protein is a nickase, thus introducing single strand breaks or nicks into the target nucleic acid upon binding to the target nucleic acid as part of a complex with the guide RNA. A pair of Cas9 nickases, each binding a structurally different guide RNA, can target 2 proximal sites of a target genomic region and thus introduce a pair of proximal single-stranded breaks into the target genomic region, e.g., exon 1 of the TRAC gene or exon 1 of the TRBC gene. Nickase pairs can provide enhanced specificity because off-target action can result in a single nick, which is usually repaired atraumatically by base excision repair mechanisms. Illustrative Cas9 nickases include Cas9 nuclease with D10A or H840A mutations (see, e.g., Jinek et al, Science 337:816-821, 2012; Qi et al, Cell,152(5):1173-1183, 2012; Ran et al, Cell 154:1380-1389, 2013). In one embodiment, the Cas9 polypeptide from streptococcus pyogenes comprises at least one mutation at position D10, G12, G17, E762, H840, N854, N863, H982, H983, a984, D986, a987, or any combination thereof. A description of such dCas9 polypeptides and variants thereof is provided, for example, in international patent publication No. WO 2013/176772. The Cas9 enzyme may comprise mutations at D10, E762, H983, or D986, as well as mutations at H840 or N863. In some cases, the Cas9 enzyme may comprise a D10A or D10N mutation. In other embodiments, the Cas9 enzyme may comprise H840A, H840Y, or H840N. In some embodiments, the Cas9 enzyme may comprise D10A and H840A; D10A and H840Y; D10A and H840N; D10N and H840A; D10N and H840Y; or D10N and H840N. The substitution can be conservative or non-conservative to catalytically inactivate the Cas9 polypeptide and enable binding to the target DNA.

In some embodiments, the Cas nuclease may be a high fidelity or specificity enhancing Cas9 polypeptide variant, with reduced off-target effects and robust on-target cleavage. Non-limiting examples of Cas9 polypeptide variants with improved target-specificity include SpCas9(K855A), SpCas9(K810A/K1003A/R1060A) (also known as eSpCas9(1.0)), and SpCas9(K848A/K1003A/R1060A) (also known as eSpCas9(1.1)) variants described in Slaymaker et al, Science,351(6268):84-8(2016), and SpCas9 variants described in Kleinstiver et al, Nature,529(7587):490-5 (2016)) comprising 1,2, 3, or 4 of the following mutations: N497A, R661A, Q695A, and Q926A (e.g., SpCas9-HF1 contains all 4 mutations).

In some embodiments, the target motif can be selected to minimize off-target effects of the CRISPR/Cas system of the invention. For example, in some embodiments, the target motif is selected such that it comprises at least two mismatches relative to all other genomic nucleotide sequences in the cell. In some embodiments, the target motif is selected such that it comprises at least one mismatch compared to all other genomic nucleotide sequences in the cell. One skilled in the art will appreciate that a variety of techniques can be used to select suitable target motifs for minimizing off-target effects (e.g., bioinformatic analysis).

As used throughout, a guide rna (gRNA) sequence is a sequence that interacts with a site-specific or targeted nuclease and specifically binds or hybridizes to a target nucleic acid within the genome of a cell, thereby co-localizing the gRNA and targeted nuclease to the target nucleic acid in the genome of the cell. Each gRNA includes a DNA targeting sequence or protospacer sequence of about 10 to 50 nucleotides in length that specifically binds or hybridizes to a target DNA sequence in the genome. For example, the targeting sequence may be about 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23,24,25,26, 27, 28,29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides in length. In some embodiments, the gRNA comprises a crRNA sequence and a trans-activating crRNA (tracrrna) sequence. In some embodiments, the gRNA does not comprise a tracrRNA sequence.

One skilled in the art will appreciate that the sgrnas can be selected depending on the particular CRISPR/Cas system employed and the sequence of the target polynucleotide. As indicated above, in some embodiments, 1-2 ribonucleic acids may also be selected to minimize hybridization to nucleic acid sequences other than the target polynucleotide sequence. In some embodiments, the 1-2 ribonucleic acids hybridize to a target motif comprising at least two mismatches compared to all other genomic nucleotide sequences in the cell. In some embodiments, the 1-2 ribonucleic acids hybridize to a target motif comprising at least one mismatch compared to all other genomic nucleotide sequences in the cell. In some embodiments, the 1-2 ribonucleic acids are designed to hybridize to a target motif directly adjacent to a deoxyribonucleic acid motif recognized by the Cas protein. In some embodiments, each of the 1-2 ribonucleic acids is designed to hybridize to a target motif directly adjacent to a deoxyribonucleic acid motif recognized by the Cas protein flanking a mutant allele located between the target motifs. Guide RNAs can also be designed using readily available software, for example, that available at website crispr. mit. edu. One or more sgrnas can be transfected into T cells in which the Cas protein is presented by transfection, according to methods known in the art.

In some cases, the DNA targeting sequence may incorporate wobble or degenerate bases to bind multiple genetic elements. In some cases, the 19 nucleotides at the 3 'or 5' end of the binding region are perfectly complementary to one or more target genetic elements. In some cases, the binding region may be altered to increase stability. For example, non-natural nucleotides may be included to increase the resistance of the RNA to degradation. In some cases, the binding region may be altered or designed to avoid or reduce secondary structure formation in the binding region. In some cases, the binding region can be designed to optimize the G-C content. In some cases, G-C content is preferably from about 40% to about 60% (e.g., 40%, 45%, 50%, 55%, 60%).

In some embodiments, the sequence of the gRNA, or a portion thereof, can be designed to be complementary (e.g., perfectly complementary) or substantially complementary (e.g., 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% complementary) to a target region in a T cell inhibitory gene. In some embodiments, the portion of the gRNA that is complementary to and binds to the target region in the polynucleotide is at or about 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23,24,25,26, 27, 28,29,30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 or more nucleotides in length. In some cases, the portion of the gRNA that is complementary to and binds to the target region in the polynucleotide is between about 19 and about 21 nucleotides in length. In some cases, the gRNA may incorporate wobble or degenerate bases to bind to the target region. In some cases, the gRNA may be altered to increase stability. For example, non-natural nucleotides may be included to increase the resistance of the RNA to degradation. In some cases, the gRNA may be altered or designed to avoid or reduce secondary structure formation. In some cases, grnas can be designed to optimize G-C content. In some cases, the G-C content is about 40% to about 60% (e.g., 40%, 45%, 50%, 55%, 60%). In some cases, the binding region may comprise modified nucleotides, such as, but not limited to, methylated or phosphorylated nucleotides.

In some embodiments, the gRNA may be optimized for expression by substitution, deletion, or addition of one or more nucleotides. In some cases, nucleotide sequences that provide for inefficient transcription of the encoding template nucleic acid may be deleted or substituted. For example, in some cases, a gRNA is transcribed from a nucleic acid that is operably linked to an RNA polymerase III promoter. In such cases, gRNA sequences that result in inefficient transcription by RNA polymerase III may be deleted or substituted, such as Nielsen et al, science.2013jun 28; 340(6140) 1577-80. For example, one or more consecutive uracils of the gRNA sequence can be deleted or deleted. In some cases, if uracil hydrogen bonds to the corresponding adenine, the gRNA sequence may be altered to exchange for adenine and uracil. This "a-U flip" can preserve the overall structure and function of the gRNA molecule while improving expression by reducing the number of consecutive uracil nucleotides.

In some embodiments, grnas can be optimized for stability. Stability can be enhanced by optimizing the stability of the gRNA to nuclease interaction, optimizing the assembly of the gRNA to nuclease complex, removing or altering RNA destabilizing sequence elements or adding RNA stabilizing sequence elements. In some embodiments, the gRNA comprises a 5' stem loop structure that is proximal or near the region of gRNA-mediated nuclease interaction. Optimization of the 5' stem-loop structure can enhance stability or assembly of the gRNA nuclease complex. In some cases, the 5' stem-loop structure is optimized by increasing the length of the stem portion of the stem-loop structure.

grnas can be modified by methods known in the art. In some cases, modifications may include, but are not limited to, the addition of one or more of the following sequence elements: a 5' cap (e.g., a 7-methyl guanylic acid cap); a 3' poly a tail; a riboswitch sequence; a stability control sequence; a hair clip; a subcellular localization sequence; detecting the sequence or marker; or one or more binding sites for proteins. Modifications may include the introduction of one or more non-natural nucleotides including, but not limited to: fluorescent nucleotides and methylated nucleotides.

Also provided herein are expression cassettes and vectors for producing grnas in host cells. The expression cassette can comprise a promoter (e.g., a heterologous promoter) operably linked to a polynucleotide encoding a gRNA. Promoters may be inducible or constitutive. The promoter may be tissue specific. In some cases, the promoter is a U6, H1, or Spleen Focus Forming Virus (SFFV) long terminal repeat promoter. In some cases, the promoter is a weak mammalian promoter compared to the human elongation factor 1 promoter (EF 1A). In some cases, the weak mammalian promoter is a ubiquitin C promoter or a phosphoglycerate kinase 1 Promoter (PKG). In some cases, the weak mammalian promoter is a TetOn promoter in the absence of an inducing agent. In some cases, when a TetOn promoter is used, the host cell is also contacted with a tetracycline transactivator. In some embodiments, the strength of the selected gRNA promoter is selected to express an amount of gRNA proportional to the amount of Cas9 or dCas 9. The expression cassette may be in a vector (such as a plasmid), viral vector, or lentiviral vector, among others. In some cases, the expression cassette is in a host cell. The gRNA expression cassette can be episomal or integrated into the host cell.

Modification using other targeted nuclease systems

In some embodiments, targeted nucleases, transcription activator-like effector nucleases (TALENs), Zinc Finger Nucleases (ZFNs) and megatals (megatals) are applied to modify T Cells to inhibit T cell suppressive gene expression (see, e.g., Merkert and Martin, "Site-Specific Genome Engineering in Human Pluripotent Stem Cells", int.j.mol.sci.18(7):1000 (2016)).

Zinc finger nuclease for inhibiting expression of T cell inhibitory gene

In some embodiments, modified T cells comprising a T cell inhibitory gene targeted alteration are generated by inhibiting expression using ZFNs. Methods of using ZFNs to reduce gene expression are described, for example, in U.S. Pat. No. 9,045,763, and Durai et al, Nucleic Acid Research 33: 5978-; carroll et al Genetics Society of America 188:773-782, 2011; and Kim et al Proc.Natl.Acad.Sci.USA 93: 1156-1160.

The ZFNs comprise a FokI nuclease domain (or derivative thereof) fused to a DNA binding domain. In the case of ZFNs, the DNA binding domain comprises one or more zinc fingers. Zinc fingers are small protein structural motifs stabilized by one or more zinc ions. The zinc finger may contain, for example, Cys2His2 and may recognize a sequence of approximately 3 bp. A variety of zinc fingers of known specificity can be combined to produce a multi-finger polypeptide that recognizes a sequence of about 6, 9, 12, 15, or 18 bp. There are a variety of selection and modular assembly techniques to generate zinc fingers (and combinations thereof) that recognize specific sequences, including phage display, yeast single-hybrid (one-hybrid) systems, bacterial single-hybrid and two-hybrid systems, and mammalian cells.

ZFNs dimerize to cleave DNA. Thus, a pair of ZFNs is used to target non-palindromic DNA sites. Two separate ZFNs bind opposite strands of DNA with correctly spaced nucleases (see, e.g., Bitinaite et al, Proc. Natl. Acad. Sci. USA 95:10570-5, 1998). ZFNs can create double-strand breaks in DNA, which can produce frameshift mutations if improperly repaired, resulting in reduced expression and expression levels of the target gene in the cell.

TALEN (transcription activator like effector gene) inhibiting T cell inhibitory gene

In some embodiments, the T cell comprising the targeted alteration is generated by inhibiting a desired T cell suppressor gene using a transcription activator-like effector nuclease (TALEN). TALENs are similar to ZFNs in that they bind in pairs around a genomic site and direct a non-specific nuclease (e.g., FoKI) to cut the genome at a specific site, but rather than recognize DNA triplets, each domain recognizes a single nucleotide. Methods of using TALENs to reduce gene expression are disclosed, for example, in U.S. patent nos. 9,005,973; christian et al, "Genetics 186(2):757-761, 2010; zhang et al 2011Nature Biotech.29:149-53, 2011; geibler et al 2011PLoS ONE 6: e19509,2011; boch et al 2009Science 326: 1509-12; moscou et al 2009Science 326: 3501.

To produce TALENs, TALE proteins are typically fused to fokl endonucleases, which can be wild-type or mutant fokl endonucleases. For the use of fokl in TALENs, various mutations have been made to fokl; for example, they will improve cleavage specificity or activity. Cerak et al, Nucl. acids Res.39: e82,2011; miller et al, Nature Biotech.29:143-8, 2011; hockemeyer et al, Nature Biotech.29: 731-one 734, 2011; wood et al, Science 333:307,2011; doyon et al, Nature Methods 8:74-79,2010; szczepek et al, Nature Biotech.25:786-793, 2007; and Guo et al, J.mol.biol.200:96,2010.

The FokI domain functions as a dimer and typically utilizes two constructs with unique DNA binding domains to sites in the target genome, with appropriate orientation and spacing. The number of amino acid residues between the TALE DNA binding domain and the fokl cleavage domain and the number of bases between two separate TALEN binding sites appears to be important parameters for achieving high levels of activity (e.g., Miller et al, 2011, cited above).

Meganuclease (Meganuclase)

A "meganuclease" is a rare-cutting endonuclease or a homing nuclease that can have a high degree of specificity, which recognizes DNA target sites that are at least 12 base pairs in length, e.g., 12-40 base pairs or 12-60 base pairs in length. The meganuclease can be a modular DNA-binding nuclease, such as any fusion protein comprising at least one endonuclease catalytic domain and at least one DNA-binding domain or protein of a specified nucleic acid target sequence. The DNA binding domain may comprise at least one motif that recognizes single-stranded or double-stranded DNA. Meganucleases can be monomers or dimers.

In some embodiments of the methods described herein, meganucleases can be used to inhibit expression of T cell suppressor genes or genes that would inhibit immune function as described herein. In some cases, meganucleases are naturally occurring (occurring in nature) or wild-type, and in other cases meganucleases are non-natural, artificial, engineered, synthetic, or rationally designed. In certain embodiments, meganucleases that can be used in the methods described herein include, but are not limited to: I-CreI meganuclease, I-CeuI meganuclease, I-MsoI meganuclease, I-SceI meganuclease, variants thereof, mutants thereof and derivatives thereof.

Useful meganucleases and their use in Gene editing are described in detail, for example, in Silva et al, Curr Gene Ther,2011,11(1): 11-27; zaslavoski et al, BMC biolnformatics, 2014,15: 191; takeuchi et al, Proc Natl Acad Sci USA,2014,111(11): 4061-; 7,897,372, respectively; 8,021,867; 8,163,514, respectively; 8,133,697, respectively; 8,021,867; 8,119,361, respectively; 8,119,381, respectively; 8,124, 36; and 8,129,134.

The efficiency of inhibiting the expression of any T cell regulatory gene using the methods described herein can be assessed by measuring the amount of mRNA or protein using methods well known in the art, such as quantitative PCR, western blot, flow cytometry, and the like. In some embodiments, the level of protein is assessed to assess the efficiency of the inhibitory efficiency. In certain embodiments, the efficiency of reducing expression of a target gene is at least 5%, at least 10%, at least 20%, at least 30%, at least 50%, at least 60% or at least 80%, or at least 90% or more compared to a corresponding cell without the targeted modification. In certain embodiments, the reduced efficiency is from about 10% to about 90%. In certain embodiments, the reduced efficiency is from about 30% to about 80%. In certain embodiments, the reduced efficiency is from about 50% to about 80%. In some embodiments, the reduced efficiency is greater than or equal to about 80%.

Therapeutic methods and compositions

Any of the methods described herein can be used to modify T cells obtained from a human subject, e.g., CD8+ T cells. T cells modified according to the invention may be used to treat any number of diseases or disorders, including cancer, autoimmune diseases, infectious diseases, or diseases or disorders associated with transplant rejection.

Methods of treating cancer

In some embodiments, the T cell is modified to reduce expression of a T cell inhibitory gene, as described herein. In some embodiments, the modified T cell inhibitory gene is CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, or ARID 1A. In some embodiments, the T cell inhibitory gene is CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB 2. In some embodiments, the T cell inhibitory gene is SOCS1, TCEB2, RASA2, or CBLB. In some embodiments, the T cell inhibitory gene is SOCS1, TCEB2, or RASA 2. In some embodiments, the modified T cell suppressor gene is RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM 222. In some embodiments, the modified T cell inhibitory gene is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB 1. Thus, in some embodiments, provided herein is a method of treating cancer in a human subject, the method comprising: a) obtaining T cells, e.g., CD8+ T cells, from a subject; b) modifying a T cell to reduce expression of a T cell inhibitory gene (e.g., a gene disclosed in this paragraph) using any of the methods provided herein; and c) administering the modified T cells to the subject.

In some embodiments, T cells obtained from a subject with cancer, e.g., CD8+ T cells, may be expanded ex vivo. The characteristics of a subject's cancer may determine a set of customized cellular modifications (e.g., selection of one or more inhibitory gene targets), and these modifications may be applied to T cells using any of the methods described herein. The modified T cells can then be reintroduced into the subject. This strategy takes advantage of and enhances the function of the subject's natural reservoir of cancer-specific T cells, providing a diverse therapeutic pool to rapidly eliminate mutagenized cancer cells.

Any cancer can be treated with the genetically modified T cells described herein. In some embodiments, the cancer is a carcinoma or a sarcoma. In some embodiments, the cancer is a hematologic cancer. In some embodiments, the cancer is breast cancer, prostate cancer, testicular cancer, renal cell carcinoma, bladder cancer, liver cancer, ovarian cancer, cervical cancer, endometrial cancer, lung cancer, colorectal cancer, anal cancer, pancreatic cancer, gastric cancer, esophageal cancer, hepatocellular cancer, kidney cancer, head and neck cancer, glioblastoma, mesothelioma, melanoma, chondrosarcoma, or a skeletal or soft tissue sarcoma. In some embodiments, the cancer is adrenocortical carcinoma, anal carcinoma, appendiceal carcinoma, astrocytoma, basal cell carcinoma, biliary tract carcinoma, bone tumor, brain stem glioma, brain cancer, cerebellar astrocytoma, brain astrocytoma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumors, optic pathway and hypothalamic glioma or bronchial adenoma. In some embodiments, the cancer is acute lymphocytic leukemia, acute myelogenous leukemia, burkitt's lymphoma, central nervous system lymphoma, chronic lymphocytic leukemia, chronic myelogenous leukemia, hairy cell leukemia, chronic myeloproliferative disorder, myelodysplastic syndrome, adult acute myeloproliferative disorder, multiple myeloma, cutaneous T-cell lymphoma, hodgkin's lymphoma or non-hodgkin's lymphoma. In some embodiments, the cancer is proliferative small round cell tumor, ependymoma, epithelial angioendothelioma (EHE), Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, intraocular melanoma, retinoblastoma, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), germ cell tumor, gestational trophoblastic tumor, gastric carcinoid cancer, cardiac disease, hypopharynx cancer, hypothalamic and optic pathway glioma, childhood, intraocular melanoma, islet cell carcinoma, Kaposi's sarcoma, laryngeal cancer, lip and oral cancer, liposarcoma, non-small cell lung cancer, macroglobulinemia, male breast cancer, malignant fibrous histiocytoma of bone, medulloblastoma, melanoma, merkel cell carcinoma, mesothelioma, metastatic cervical cancer, oral cancer, multiple endocrine tumor, mycosis fungoides, chronic, myxoma, cancer of the nasal and paranasal sinuses, cancer of the nasopharynx, neuroblastoma, oligodendroglioma, cancer of the mouth, oropharyngeal carcinoma, osteosarcoma, ovarian epithelial carcinoma, ovarian germ cell tumor, low malignant potential tumor of the ovary, cancer of the sinuses and nasal cavity, cancer of the parathyroid gland, cancer of the penis, pharyngeal carcinoma, pheochromocytoma, pineal astrocytoma, pineal blastocytes, osteoblastoma, supratentorial primitive neuroectodermal tumor, pituitary adenoma, plasmacytoma, pleuropulmonoblastoma, primary central nervous system lymphoma, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland carcinoma, uterine sarcoma, Szezary syndrome, non-melanoma skin cancer, melanoma merkel cell skin cancer, small bowel cancer, squamous cell carcinoma, cervical carcinoma, laryngeal carcinoma, thyroid carcinoma, transitional cell carcinoma of the kidney and pelvis, trophoblastic tumors, pregnancy, urinary tract cancers, uterine cancers, vaginal cancers, vulvar cancers, waldenstrom's macroglobulinemia, and wilms tumors.

In certain embodiments, the population of individuals with genetically modified T cells, or genetically modified T cell subtypes, is administered to the subject in the following ranges: from about 100 to about 1000 million cells, e.g., from 100 to about 500 million cells (e.g., from about 5 million cells, from about 2500 million cells, from about 500 million cells, from about 10 million cells, from about 50 million cells, from about 200 million cells, from about 300 million cells, from about 400 million cells, or a range defined between any two of the foregoing values), e.g., from about 1000 to about 1000 million cells (e.g., from about 2000 million cells, from about 3000 million cells, from about 4000 million cells, from about 6000 million cells, from about 7000 million cells, from about 8000 million cells, from about 9000 million cells, from about 100 million cells, from about 250 cells, from about 500 million cells, from about 750 million cells, from about 900 million cells, or a range defined between any two of the foregoing values), and in some cases, from about 1 to about 500 million cells (e.g., from about 1.2 million cells, from about 2.5 million cells, about 3.5 million cells, about 4.5 million cells, about 6.5 million cells, about 8 million cells, about 9 million cells, about 30 million cells, about 300 million cells, about 450 million cells) or any value in between these ranges.

In some embodiments, the total cell dose and/or the individual subpopulation of cells dose is in the following ranges: is at or about 10⁴And is or about 10⁹Cells per kilogram (kg) body weight, e.g., between 10⁵And 10⁶Between cells/kg body weight, e.g., at least about 1x 10⁵Cell/kg, 1.5X 10⁵Cells/kg，2x10⁵Cell/kg, 5X 10⁵Cell/kg or 1X 10⁶Cells/kg body weight.

The appropriate dosage will depend upon the type of cancer being treated, the severity and course of the disease, previous therapy, the subject's clinical history and response to the cells, and the discretion of the attendant physician. In some embodiments, the compositions and cells are suitable for administration to a subject at one time point or in a series of treatments.

The cells can be administered by any suitable means, for example, by bolus (bolus) infusion, by injection, e.g., intravenous or subcutaneous injection, intraocular injection, fundus injection, subretinal injection, intravitreal injection, trans-septal injection, subdural injection, intrachoroidal injection, anterior chamber injection, subconjunctival injection, suprascleral injection, retrobulbar injection, periocular injection, or peribulbar delivery. In some embodiments, they are administered parenterally, intrapulmonary and intranasally, and intralesionally if topical treatment is desired. Extraperitoneal infusion includes intramuscular, intravenous, intraarterial, intraperitoneal or subcutaneous administration. In some embodiments, a given dose is administered by a single bolus administration of cells. In some embodiments, the cells are administered by multiple bolus injections, e.g., over a period of no more than 3 days, or by continuous infusion.

In some embodiments, the cells are administered as part of a combination therapy, e.g., simultaneously or sequentially in any order with another therapeutic intervention, such as an antibody or an engineered cell or receptor or agent (such as a cytotoxic or therapeutic agent). In some embodiments, the cells are co-administered with one or more other therapeutic agents or administered in combination with another therapeutic intervention, either simultaneously or sequentially in any order. In some cases, the cells are co-administered with other therapies, in close enough time proximity that the population of cells enhances the effect of the one or more other therapeutic agents, and vice versa. In some embodiments, the cells are administered prior to one or more other therapeutic agents. In some embodiments, the cells are administered after one or more other therapeutic agents.

Methods of treating autoimmune diseases or transplant rejection

Also provided herein are methods of treating autoimmune diseases or other conditions where suppression of the immune system is desired (e.g., transplant rejection) by administering T cells (e.g., CD8+ T cells) modified to suppress expression of the following genes: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP3, EXOSC 3, RPN 3, VARS, CD 33, GRAP 3, TRMT112, ALG 3, VAV 3, EXOSC 3, SH2D 13, HSPA 3, ZAP 3, DDX 3, CD247, ALDOA, ZNF131, WDR3, AK 3, LCP 3, CD247, VHL, EIF2B3, GRPEL 3, NAA 3, ALDOA, ALG 3, MARS, C4orf 3, RAC 3, LCK, SUPT4H 3, SLC25A3, LUC7L3, C3orf 3, RPP3, GCTAEDS 3, CALDOSC 3, CALDEI 3, EPC 3, EPROADEI 3, EPC 3, EPCALDE 3, EPOR 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EP, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND 3, TTI 3, THG 13, TAF 13, URI 3, TRMT112, EIF 33, CCND 3, GCLM, RBSN, 36RS, 3, TAF 3, THG 13, TAFS 4, TAWD 3, TAMTP 72, TAFDTP 3, TAOFFTC 3, TARG 3, TARGET 72, TARG 3, TARGD 3, TARG 3, TAORS 3, TARGD 3, TAORS 3, TARGD 3, TAORBENTE 72, TAORS 3, TARGD 3, TAORS 3, TAORBENTF. In some embodiments, the gene is any one of the following genes: HSPA, RPP, EXOSC, LCP, MYC, CD247, NOP, VAV, RHOH, TAF1, TRMT112, CCND, SH2D1, MARS, CD3, LUC7L, EIF2B, ORAOV1.VARS, NOL, ZBTB8, SLC35B, NAA, EIF2B, DHX, LAT, EMG, ALDOA, GRPEL, ARMC, POLR2, NOP, PSENEN, RELA, SUPT4H, VHL, GFER, BPTF, RAC, RACR, TAF, PMPCA, EIF, STT3, POP, GMPB, TP53, CCNH, TEX, DHX, QARS, EID, IRF, TAF, IARS, GTF3, NOP, IMP, UTL, EIF4G4, AGN, ALP, PHBD, PHBV, PHBR, POLR, ORP, EPOR 2, OROCR, SAL, SARG, SARP 3, SARP 1, SARP 3, SARP, SALT 3, SALT 2, SALT 3, SALT 3, SALT. In one embodiment, the method comprises a) obtaining T cells from a subject in the presence of the condition (e.g., an autoimmune disease, or a transplant); b) t cells are modified using any of the methods provided herein to inhibit expression of: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP3, EXOSC 3, RPN 3, VARS, CD 33, GRAP 3, TRMT112, ALG 3, VAV 3, EXOSC 3, SH2D 13, HSPA 3, ZAP 3, DDX 3, CD247, ALDOA, ZNF131, WDR3, AK 3, LCP 3, CD247, VHL, EIF2B3, GRPEL 3, NAA 3, ALDOA, ALG 3, MARS, C4orf 3, RAC 3, LCK, SUPT4H 3, SLC25A3, LUC7L3, C3orf 3, RPP3, GCTAEDS 3, CALDOSC 3, CALDEI 3, EPC 3, EPROADEI 3, EPC 3, EPCALDE 3, EPOR 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EPCANDC 3, EPC 3, EP, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND 3, TTI 3, THG 13, TAF 13, URI 3, TRMT112, EIF 33, CCND 3, GCLM, RBSN, 36RS, 3, TAF 3, 36363672, THG 13, TAFS 4, TAWD 3, TRMT112, TAFN 3, TAXDR 3, TARGD 3, TAORS 3, TARGD 3, TAORS 3, TARGD 3, TAORS 3, TARGD 3, TAORS 3, TAORBED 36; and c) administering the modified T cells to the subject. In some embodiments, the gene is any one of the following genes: HSPA, RPP, EXOSC, LCP, MYC, CD247, NOP, VAV, RHOH, TAF1, TRMT112, CCND, SH2D1, MARS, CD3, LUC7L, EIF2B, ORAOV1.VARS, NOL, ZBTB8, SLC35B, NAA, EIF2B, DHX, LAT, EMG, ALDOA, GRPEL, ARMC, POLR2, NOP, PSENEN, RELA, SUPT4H, VHL, GFER, BPTF, RAC, RACR, TAF, PMPCA, EIF, STT3, POP, GMPB, TP53, CCNH, TEX, DHX, QARS, EID, IRF, TAF, IARS, GTF3, NOP, IMP, UTL, EIF4G4, AGN, ALP, PHBD, PHBV, PHBR, POLR, ORP, EPOR 2, OROCR, SAL, SARG, SARP 3, SARP 1, SARP 3, SARP, SALT 3, SALT 2, SALT 3, SALT 3, SALT.

In some embodiments, the modified T cells described in the preceding paragraph are administered to a patient to treat or prevent transplant rejection.

In some embodiments, a T cell modified to suppress immune function as described herein is administered to a patient having an autoimmune disease or inflammatory disorder. In some embodiments, the autoimmune or inflammatory disease is osteoarthritis, rheumatoid arthritis, juvenile rheumatoid or idiopathic arthritis, multiple sclerosis, psoriasis, psoriatic arthritis, crohn's disease, inflammatory bowel disease, ulcerative colitis, celiac disease, lupus, graves ' disease, hashimoto's thyroiditis, addison's disease, myasthenia gravis, sjogren's syndrome, type I diabetes, vasculitis, ankylosing spondylitis.

In certain embodiments, the population of individuals with genetically modified T cells, or genetically modified T cell subtypes, is administered to the subject in the following ranges: from about 100 to about 1000 million cells, e.g., from 100 to about 500 million cells (e.g., from about 5 million cells, from about 2500 million cells, from about 500 million cells, from about 10 million cells, from about 50 million cells, from about 200 million cells, from about 300 million cells, from about 400 million cells, or a range defined between any two of the foregoing values), e.g., from about 1000 to about 1000 million cells (e.g., from about 2000 million cells, from about 3000 million cells, from about 4000 million cells, from about 6000 million cells, from about 7000 million cells, from about 8000 million cells, from about 9000 million cells, from about 100 million cells, from about 250 cells, from about 500 million cells, from about 750 million cells, from about 900 million cells, or a range defined between any two of the foregoing values), and in some cases, from about 1 to about 500 million cells (e.g., from about 1.2 million cells, from about 2.5 million cells, about 3.5 million cells, about 4.5 million cells, about 6.5 million cells, about 8 million cells, about 9 million cells, about 30 million cells, about 300 million cells, about 450 million cells) or any value in between these ranges.

In some embodiments, the total cell dose and/or the individual subpopulation of cells dose is in the following ranges: is at or about 10⁴And is or about 10⁹Cells per kilogram (kg) body weight, e.g., between 10⁵And 10⁶Between cells/kg body weight, e.g., at least about 1x 10⁵Cell/kg, 1.5X 10⁵Cell/kg, 2X10⁵Cell/kg, 5X 10⁵Cell/kg or 1X 10⁶Cells/kg body weight.

The appropriate dosage will depend upon the type of cancer being treated, the severity and course of the disease, previous therapy, the subject's clinical history and response to the cells, and the discretion of the attendant physician. In some embodiments, the compositions and cells are suitable for administration to a subject at one time point or in a series of treatments.

The cells can be administered by any suitable means, for example, by bolus (bolus) infusion, by injection, e.g., intravenous or subcutaneous injection, intraocular injection, fundus injection, subretinal injection, intravitreal injection, trans-septal injection, subdural injection, intrachoroidal injection, anterior chamber injection, subconjunctival injection, suprascleral injection, retrobulbar injection, periocular injection, or peribulbar delivery. In some embodiments, they are administered parenterally, intrapulmonary and intranasally, and intralesionally if topical treatment is desired. Extraperitoneal infusion includes intramuscular, intravenous, intraarterial, intraperitoneal or subcutaneous administration. In some embodiments, a given dose is administered by a single bolus administration of cells. In some embodiments, the cells are administered by multiple bolus injections, e.g., over a period of no more than 3 days, or by continuous infusion.

In some embodiments, the cells are administered as part of a combination therapy, e.g., simultaneously or sequentially in any order with another therapeutic intervention (such as an antibody or other immunosuppressive agent). In some embodiments, the cells are co-administered with one or more other therapeutic agents or administered in combination with another therapeutic intervention, either simultaneously or sequentially in any order. In some cases, the cells are co-administered with other therapies, in close enough time proximity that the population of cells enhances the effect of the one or more other therapeutic agents, and vice versa. In some embodiments, the cells are administered prior to one or more other therapeutic agents. In some embodiments, the cells are administered after one or more other therapeutic agents.

The publications cited herein and the materials to which they are cited are specifically incorporated by reference in their entirety.

Examples

The following examples are provided to illustrate, but not to limit, the claimed invention.

Example 1 hybrid method for introducing traceable genetic perturbations into Primary human T cells

We set out to establish a high-throughput CRISPR screening platform that acts directly in ex vivo human hematopoietic cells. Current convergent CRISPR screening methods rely on the establishment of cell lines with stably integrated Cas9 expression cassettes. We attempted to stably express streptococcus pyogenes Cas9 in primary T cells by lentivirus resulting in very low transduction efficiency. This low efficiency prevents large-scale convergent screening of primary cells that are not immortalized and can only be expanded in culture for a limited time. We previously showed efficient gene editing of primary human T cells by electroporating Cas9 protein pre-loaded with small guide rnas (sgrnas) in vitro (Patel et al, 2017). We conceived a hybrid system that introduced traceable sgRNA cassettes by lentivirus followed by electroporation with Cas9 protein (fig. 1A). To test this strategy, we targeted the gene encoding a candidate cell surface protein, i.e., the alpha chain of the CD8 receptor (CD8A), as it is in human CD8⁺High and uniform expression in T cells. We optimized multiple steps in cell stimulation, lentiviral transduction, and Cas9 electroporation to deliver various components efficiently while maintaining cell viability (fig. 7A-D). Briefly, CD8⁺T cells were isolated from peripheral blood of healthy donors, stimulated, and then transduced with lentiviruses encoding the sgRNA cassette and the mCherry fluorescent protein reporter. Following transduction, T cells were transfected with recombinant Cas9 protein by electroporation. 4 days after electroporation, most of the transduced cells (mCherry +) were ((mCherry +))>80%) was CD8 negative (fig. 1B and 7E), indicating successful targeting of the Cas9-sgRNA combination. Loss of CD8 protein was specifically programmed by targeting the sgRNA, as cells transduced with non-targeted control sgrnas retained high levels of CD8 expression. By targeting PTPRC (CD45) with the same delivery strategy, we confirmed successful knockdown at the secondary target and demonstrated this CD8⁺And CD4⁺Efficacy of the system in T cells (fig. 7F). Consistent with the observed loss of target protein expression, by targeting the genomeSequencing of the target site confirmed efficient gene editing (fig. 7G). We derive the use ofCas9 proteinElectric powerIs perforated forsgRNASlowVirusFeeling ofStaining (SLICE) led to the conclusion that the target gene was efficiently and specifically destroyed.

We next tested whether SLICEs could be expanded to allow large-scale loss of function screening in primary cells with a library of lentivirus-encoded sgrnas. We performed a screen to identify gene targets that modulate T cell proliferation in response to T Cell Receptor (TCR) stimulation. For preliminary studies, we generated a custom sgRNA plasmid library that targets all annotated cell surface proteins and multiple standard members of the TCR signaling pathway (approximately 5000 guides, total 1211 genes targeted, and 48 non-targeting guides). CD8 isolated from two healthy human donors⁺T cells were transduced with lentiviruses encoding this sgRNA library, electroporated with Cas9, and then kept in culture (experimental procedure). On day 10 after electroporation, cells were labeled with CFSE to follow cell division, followed by TCR stimulation. Four days after stimulation, CFSE levels indicated that the cells underwent multiple divisions. Cells were sorted by FACS into two populations: (1) non-proliferating cells (CFSE high) and (2) highly proliferating cells (CFSE low) (fig. 1A, fig. 7G and experimental procedure). We quantified sgRNA abundance of each population by deep sequencing of the amplified sgRNA cassettes. Consistent with good coverage of sgrnas in the experimental procedure, we were able to detect infected CD8⁺All library guidance in T cells, where sgRNA abundance distribution was relatively uniform in individual donors and across biological replicates (fig. 7H). To identify sgrnas that regulate T cell proliferation, we calculated an abundance-based ranking difference between highly dividing and non-dividing cells. Sgrnas that are highly enriched in dividing or non-dividing cells are directed to key biological pathways. We found that sgRNAs targeting the essential components of TCR signaling, such as CD3D and LCP2, would inhibit cell proliferation as expected (de Saint Basile et al, 2004; Shen et al, 2009). We have also found that human T cell proliferation can be enhanced by targeting CD5 or CBLB, a role in down-regulating T cell stimulatory responses has been reported (Azzam et al, 2001; Naramura et al, 2002; Voisine et al, 2016). Targeting these genesThe ranking difference of sgrnas of (a) was the first 1% in both biological replicates (fig. 1C). Furthermore, multiple sgrnas targeting these genes had consistent effects, increasing our confidence that the phenotype was not due to off-target effects (fig. 7I). Importantly, sorting dividing and non-dividing primary cells according to CFSE provided much stronger enrichment of sgRNA sequences than simple growth-based screening using the otherwise identical experimental timeline (fig. 7J). Cell-doubling based screening has been largely successful using immortalized cell lines that can be cultured for long periods of time, but this has not been transferred to screening in human primary T cells (Shalem et al, 2014; Wang et al, 2014). Taken together, these data indicate that slide-pooled CRISPR screening can be used to discover positive and negative regulators in primary human T cell proliferation.

Whole genome pool CRISPR screening reveals regulatory factors of TCR response

To take full advantage of this platform, we expanded targeted lead screening to the whole Genome (GW) scale (Doench et al, 2016) and transduced a library of 77,441 sgrnas (19,114 genes) into T cells from two healthy donors. After confirming successful transduction of these primary human T cells (fig. 8, a-B), the cells were restimulated and then FACS sorted into non-proliferating and highly proliferating populations according to CFSE levels (fig. 8C and experimental procedure). MAGECK software (Li et al, 2014) was used to systematically identify positively or negatively selected genes in proliferating T cell populations. The top positive and negative regulators from the primary screen and many other hits were confirmed in two biological replicates of the GW screen (fig. 2A, B). To train the top candidate list, we performed independent secondary screens in cells from two additional human blood donors. The results showed a good correlation between primary and secondary screening (fig. 2C). Furthermore, the combined analysis of two independent screens performed on a total of 4 human blood donors improved the ability to target discovery, particularly for negative regulators of T cell proliferation (fig. 8D). To confirm that the hits were actually dependent on TCR stimulation, we performed GW screening with increasing TCR stimulation levels. Although similar gene targets appeared as positive and negative modulators throughout the conditions, at higher levels of TCR stimulation, the intensity of the effect was diminished, suggesting that stronger TCR stimulation effects may cover the effects of these gene perturbations (fig. 2D and 8E). The dose response observed confirms that most screening targets are dependent on TCR stimulation and help to modulate the resulting proliferative response. Taken together, these screens identified tens of genetic perturbations that positively and negatively regulate T cell proliferation.

Genes identified in the integrated screening assay were enriched with the annotation pathway associated with TCR stimulation. Gene Set Enrichment Analysis (GSEA) revealed overexpression of gene targets depleting self-proliferating cells in the TCR signaling pathway (FDR <0.01, fig. 2E and fig. 8F). We also found a large enrichment in dividing cells for genes abundant in hits from published shRNA screens designed to find gene targets (FDR <0.01) that promote T cell proliferation in tumor tissues in vivo (Zhou et al, 2014). This is surprising because the studies were conducted with different gene perturbation platforms in different organisms, but there was a significant enrichment in our screen for high ranking positive hits by the hit list found in vivo animal models. These global analyses confirm that our functional screens can identify key gene targets, and can now be implemented directly in primary human cells on a genome-wide basis.

The targets depleted of self-proliferating cells in this GW screen encode key protein complexes essential for TCR signaling (fig. 2F). For example, gene targets that impair TCR-dependent proliferation include the delta and zeta chains of the TCR complex itself (negative 18 and 6, respectively), LCK (negative 20), which directly phosphorylates and activates TCR ITAM and the central signaling mediator ZAP70(Dave et al, 1998; Tsuchihashi et al, 2000; Wang et al, 2010). LCK and ZAP70 are translocated to the immune synapse by RhoH (negative rank 2) (Chae et al, 2010). ZAP70 target LCP2 (negative rank 4) is an adaptor protein required for TCR-induced activation and mediates integration of TCR and CD28 costimulatory signals required for TCR-induced calcium flux and signaling by activating VAV1 (negative rank 8) (Dennehy et al, 2007; Raab et al, 1997; Tybuliwicz, 2005). LAT (negative rank 38) is another ZAP70 target that recruits multiple key adaptor proteins after phosphorylation for signaling downstream of TCR binding (Bartelt and Houtman).

Genes that down-regulate T cell proliferation have therapeutic potential to enhance T cell function. While functions have been assigned to some negative regulators, many negative regulators are poorly annotated in the standard TCR signaling pathway. Diacylglycerol (DAG) kinases, DGKA (rank 17) and DGKZ (rank 1) (negative regulators of DAG-mediated signals) were found to inhibit human T cell proliferation after stimulation (Aranz-Nicol a. et al, 2018; Chen et al, 2016; Gharbi et al, 2011). E3 ubiquitin protein ligase CBLB (rank 4) and its interaction partner CD5 (rank 12) act together to inhibit TCR activation by ubiquitination leading to TCR degradation (Voisinne et al, 2016). TCEB2 (rank 5) was complexed with RNF7 (rank 34), CUL5 (rank 162) and SOCS1 (rank 3), which are key inhibitors of JAK/STAT signaling in activated T cells (Kamura et al, 1998; Liau et al, 2018). UBASH3A (rank 10), TNFAIP3 and its partner TNIP1 (ranks 13 and 24, respectively) inhibit TCR-induced NFkB signaling, which is for CD8⁺The key survival and growth signals of T cells (D ü wel et al, 2009; Ge et al, 2017). In addition to these key complexes, genes encoding other less characteristic cell surface receptors (e.g., TMEM222, GNA13), cytosolic signaling components (e.g., RASA2, FIBP) and nuclear factors (e.g., CDKN1B, ARIH2, ZFP36L1) were also found to inhibit proliferation (fig. 2F), revealing a promising resource set of candidate targets to promote the effects of T cell stimulation.

Array delivery of Cas9 RNP revealed that hits altered the stimulatory response

We next confirmed the biological role of the high-molecular genes in promoting T cell activation and proliferation by array electroporation of a single Cas9 Ribonucleoprotein (RNP) (Hultquist et al, 2016; Schumann et al, 2015). Our validation focused primarily on a highly top-ranked group of negative proliferation modulators, as they have therapeutic potential to enhance T cell function when targeted. We further examined how much positive hits affected T cell proliferation after TCR stimulation. Briefly, CD8+ T cells from 4 human blood donors were stimulated, electroporated with RNP, left for 10 days, labeled with CFSE and restimulated (fig. 3A and experimental procedure). High-throughput flow cytometry determined proliferative responses in edited cells and control cells based on CFSE dilution. This demonstrated the ability of many of the tested gene targets to increase T cell proliferation after stimulation, consistent with their robust role in the pool screen (fig. 9A). For example, CBLB and CD5 knockout cells showed a significant increase in the number of divisions after stimulation compared to controls, persisting in guide RNA and blood donors (fig. 3B). To systematically quantify cell proliferation, we performed CFSE distribution fitting on our samples using a mathematical model (Roederer, 2011) (fig. 9B). This analysis shows that multiple negative regulators of perturbing T cell stimulation increase proliferation index scores compared to controls (7 of the 10 gene agitation negative regulators are shown here). Both UBASH3A, CBLB, CD5 and RASA2 knockout T cells showed an increase in proliferation index of more than 2-fold compared to non-targeted control cells (fig. 3C). Notably, targeting these genes did not increase the proliferation of unstimulated cells, suggesting that they are not general regulators of proliferation, but rather appear to modulate proliferation induced by TCR signaling. In contrast, guidance against gene targets consumed in proliferating cells in the pooled screen showed a decrease in proliferation index compared to the non-targeted control. Thus, we demonstrated by an orthogonal gene targeting system that most of the top-ranked genes identified by our screening would robustly modulate stimulus-dependent proliferation in human CD8T cells.

We next examined whether these hits, in addition to cell proliferation, modulated the typical response to TCR stimulation. The phenotype of cells compiled in an array format can be assessed at different time points using multiple markers. We analyzed two different cell surface markers for early CD8+ T cell activation, CD69 and CD154 (Lopez-Cabrera et al, 1993; Shipkova and Wieland, 2012). Cells were assessed at 6 hours after restimulation, day 10 after electroporation. We found that T cells engineered to lack negative regulators of proliferation, such as SOCS1, CBLB, CD5, etc., also showed increased surface expression levels of CD69 and CD154 compared to non-targeted control cells (fig. 3D and fig. 8C-D). In contrast, the positive modulator of TCR signaling, LCP2, would decrease expression of CD69 and CD154 in stimulated cells. In summary, the percentage of cells expressing these activation markers in each case was higher for positive hits compared to the non-targeted control guide, consistent with two sgrnas for each gene, for 4 donors (fig. 3E). Thus, array editing and phenotypic analysis characterize the effects of genetic perturbation and reveal targets that promote stimulus-dependent proliferation and activation programs.

SLICE paired with Single-cell RNA-Seq for molecular phenotypic analysis of modified Primary human cells

We then further characterized the stimulus-dependent transcription program altered by excision of key target genes in human T cells. Recently, the combination of the pooled CRISPR screening with single-cell RNA-seq enabled high-content analysis of transcriptional changes due to genetic perturbations in immortalized cell lines (Adamson et al, 2016; Datlinger et al, 2017; Dixit et al, 2016) or in cells from transgenic mice (Jaitin et al, 2016). Here, we combined SLICE with droplet-based single-cell transcriptome reading for high dimensional profiling of perturbations pooled in human primary T cells. We selected the CROP-Seq platform as it provides barcodeless pooled CRISPR screening and single cell RNA-Seq using a readily available 10X Genomics platform (Datlinger et al, 2017). For a total of 48 sgrnas, we generated custom libraries targeting top-ranked hits from our GW screen (2 sgrnas per gene), known checkpoint genes (PDCD1, TNFRSF9, C10orf54, HAVCR2, LAG3, BTLA), and 8 non-targeting controls. Human T cells from two donors were transduced with this library, electroporated with Cas9 protein, and enriched with puromycin selection (experimental procedure) (fig. 10A). Single cell transcriptome analysis was performed on cells with or without restimulation to characterize changes in cell state and stimulatory responses due to various genetic modifications.

First, we analyzed the transcriptional status of over 15,000 resting and stimulated single cells, where we could identify sgRNA barcodes. The resultant large gene expression profile shows that stimulated cells up-regulate multiple cell cycle genes, indicating a response to TCR stimulation (fig. 10B). Then, we used a Uniform Manifold Approximation and Projection (UMAP) (McInnes and Healy,2018) to show the distribution of these single cell transcriptomes at reduced size (fig. 4A). While unstimulated T cells have a donor-dependent basal gene expression pattern, stimulated cells from both donors tend to share transcriptional characteristics and cluster together. For example, stimulated cells generally induced cell cycle gene (e.g., MKI67) and cytolytic granzyme (e.g., GZMB) expression (FIG. 4B). In contrast, unstimulated cells express markers of resting state, such as IL7R and CCR7 in large amounts. Thus, TCR stimulation has a strong role in inducing an activated cell state in trans-biological repeats, although it appears that there are more cells strongly stimulated in donor 1 than in donor 2. To systematically estimate the cell status, we clustered single cells by gene expression based on their common nearest neighbors (fig. 4C). Stimulated cells were enriched in clusters 9-12 and characterized by preferential expression of the mitotic cell cycle and T cell activating cell program (fig. 10C). This analysis of the single-cell transcriptome revealed the cell status characteristics of human T cells before and after restimulation.

We next evaluated the effect of CRISPR-mediated genetic perturbation on the cell state. Efficient editing of most gene targets was verified by reducing expression of sgRNA target transcripts compared to levels in cells with non-targeted control sgrnas (fig. 10D). We tested whether gene perturbation results in genetic program changes. Cells with non-targeted control sgrnas were distributed relatively evenly between clusters. In contrast, cells with CBLB and CD5 sgRNA were enriched in clusters associated with proliferation and activation, whereas cells with LCP2 sgRNA were mostly present in clusters characterized by a resting profile (fig. 4D). Then, we quantified which sgRNA targets pushed the cells to different clusters of cell states according to their transcriptional profile (fig. 4E). Targeting multiple negative regulators identified in GW screens, such as CD5, RASA2, SOCS1, and CBLB, facilitated the cluster 10-12 program. Perturbation of negative regulators induced markers of activation status (IL2RA, TNFRSF18/GITR), cell cycle genes (MKI67, UBE2S, CENPF, and TOP2A), and effector molecules (GZMB, XCL1) (fig. 4F and fig. 10E). In contrast, sgrnas targeting CD3D or LCP2 inhibited cluster 10 activation programs and facilitated cluster 1-2 programs. SLICE paired with single-cell RNA-Seq revealed how manipulation of target genes modeled stimulus-dependent cellular states.

Targeting different negative regulators of proliferation results in different transcription outcomes. Knock-out of CBLB tended to induce cell state features similar to those targeting the known checkpoint genes BTLA or LAG3, as evidenced by similarity in cluster representation (representation) (fig. 10F). Because CD5, TCEB2, RASA 2or CDKN1B were targeted, different shared activation programs were observed. CRISPR screening of slide pools and integration of single-cell RNA-Seq provides a powerful method for discovering and characterizing key genetic pathways in human primary cells. These data also indicate that targeted negative modulators of proliferation may also induce specialized stimulus-dependent effector gene programs that may enhance T cell potency.

Screening hit engineered human T cells to promote in vitro tumor killing

Cells engineered to enhance proliferative responses and promote enhanced effector gene programs in response to TCR stimulation are expected to hold promise for cancer immunotherapy. We tested the effect of target gene knockdown in an antigen-specific in vitro tumor killing system (fig. 5A). Specifically, we used the RFP-expressing A375 melanoma cell line, which expresses the tumor antigen NY-ESO, as the target cell (Robbins et al, 2008). Reactivity α 95 by using NY-ESO 1: transduction of LY TCRs produced antigen-specific T cells (Wargo et al, 2009) (fig. 11A). These transduced T cells were able to induce caspase-mediated cell death in target a375 cells, which was shown by increased caspase levels and decreased RFP-labeled a375 nuclear levels over time (fig. 11B). NY-ESO TCR + T cells were generated from 4 donors using lentiviral transduction and then compiled with RNP in an array of 24 guides targeting 11 genes, including non-targeted controls (methods). Antigen-specific T cells with or without gene deletion were then co-cultured with a375 cells and lethality was assessed by quantitating RFP-labeled a375 cells by real-time delayed microscopy over a four day period.

We compared the kinetics of tumor killing between gene edited and control NY-ESO TCR + T cells. NY-ESO specific T cells began to accumulate around RFP + tumor cells at 12 hours, and tumor clearance was higher for certain sgRNA targets at 36 hours compared to non-targeted controls (fig. 5B). As expected, knock-out of LCP2 (identified in our screen as essential for strong TCR stimulation response) severely abolished T-cell killing of a375 cells. In contrast, CRISPR ablation of the negative regulators SOCS1, TCEB2, RASA2 and CBLB significantly increased the clearance of tumor cells compared to control T cells that were not targeted for guide RNA electroporation (fig. 5C). Targeted deletion of these four genes resulted in improved tumor clearance kinetics in our trial compared to non-targeted control conditions (fig. 5D and 11C-D). Among them, CBLB has been best studied as an intracellular immune checkpoint that can be targeted in T cells to improve tumor control in mouse models (Peer et al, 2017). The negative regulator SOCS1 targeting JAK/STAT signaling in T cells showed enhanced T cell clearance compared to CBLB (Liau et al, 2018). Ablation of TCEB 2(a binding partner of SOCS1) also provided advantages in tumor clearance for T cells, indicating that the SOCS1/TCEB2 complex inhibits T cell responses and is a potential target for immunotherapy (Ilangumaran et al, 2017; Kamizono et al, 2001; Liau et al, 2018). RASA2 (gtpase activating protein that stimulates gtpase activity of wild-type RAS (Maertens and Cichowski, 2014)) has not been well studied in primary T cells and the immune system, but our findings suggest that it may be a modulator of TCR signaling and anti-tumor immunity. Surprisingly, TCEB2, SOCS1, CBLB and RASA2 gene ablated cells activated key genes, including granzyme b (gzmb) and interleukin-2 receptor alpha (IL2RA), more strongly than control cells (fig. 4F). In summary, several gene targets identified for stimulated proliferative responses in whole genome screens also enhanced tumor killing activity in vitro.

SLICE Screen for resistance to immunosuppressive adenosine Signaling

Adoptive cell therapy to effectively treat solid organ tumors would require cells that produce robust responses to tumor antigens even in immunosuppressive tumor microenvironments. Through genome editing, T cells can be resistant to specific immunosuppressive signals (cue), and it will be critical to identify relevant T cell modification pathways. We believe that our SLICE screening platform can also be used to identify gene deletions that allow T cells to escape various forms of suppression. We focused on adenosine, a key immunosuppressive factor in the tumor microenvironment (Allard et al, 2017). We performed a four day genome-wide proliferation screen by stimulating T cells in the presence of an inhibitory dose of 20uM adenosine receptor 2(A2A) agonist (CGS-21680) relative to vehicle controls. We sought sgrnas enriched in proliferating cell populations (CFSE low) compared to vehicle under A2A treatment conditions (fig. 12B).

Although many genetic modifications promoted a TCR proliferative response to stimulation in the presence or absence of adenosine receptor agonists, we identified multiple sgrnas that were enriched in dividing cells only in the presence of CGS-21680 (fig. 6A). These gene targets appear to play a selective role in adenosine receptor-mediated T cell suppression. Importantly, ADORA2A (encoding CGS-21680-specific targeted receptor) showed a higher ranking difference between the two treatment conditions (rank 19 in CGS-21680 versus rank 7399 in vehicle control), suggesting that its knockdown provided a specific escape (escape) for CGS-21680 (fig. 6A and 12C). In contrast, ADORA2B did not show any proliferative advantage when exposed to the selective A2A agonist CGS-21680 (fig. 6A). These findings have encouraged us to investigate other gene targets with a similar pattern of ADORA2A selective resistance to CGS-21680. Several guanine nucleotide binding proteins with potential roles in adenosine-responsive signaling have a high positive ranking in the adenosine agonist GW screen, including GCGR (rank 35 versus 1149), GNG3 (rank 199 versus 12976), and GNAS (rank 836 versus 2803). Surprisingly, we found that multiple guides targeting previously uncharacterized gene FAM105A (rank 15 in CGS-21680 versus rank 13390 in vehicle control) were specifically enriched to almost the same extent as ADORA2A (fig. 6B). Although little is known about FAM105A function, the GWAS of allergic disease suggests the existence of a plausible missense risk variant for this gene (Ferreira et al, 2017). The proximal paralogous gene Otulin (FAM105B) encodes a deubiquitinating enzyme (Damgaard et al, 2016) that plays an important role in immune regulation (FIG. 12D). This screening result indicates that FAM105A plays a key role in mediating adenosine immunosuppressive signaling in T cells.

To validate our findings, we used the arrayed RNP platform to compile ADORA2A and fam105a with CFSE proliferation reads in both donors we found that targeting each of these genes with two different sgrnas resulted in resistance to CGS-21680 inhibition as predicted by our screening (fig. 6C). Importantly, these edits did not result in increased T cell proliferation in the absence of TCR stimulation, suggesting that they selectively overcome CGS-21680 inhibition by TCR stimulation. Finally, we demonstrated that T cells targeting ADORA2A and FAM105A were resistant to CGS-21680 inhibition in an in vitro cancer cell killing assay (FIG. 12E). We have therefore identified extracellular and intracellular targets that can be modified to produce T cells that are resistant to adenosine inhibition. Taken together, these findings suggest that slide is able to identify known and novel components of pathways required for primary cells to respond to specific extracellular signals. This exemplifies the potential of using this platform for the discovery of gene targets that can enhance specific T cell functions. In vivo convergent screening of SLICEs to reveal modulators of T cell tumor infiltration

Primary human CD8+ T cells from 2 donors were isolated and stimulated with anti-CD 3/CD28 beads. Cells were then transduced with concentrated frozen lentivirus encoding the NY-ESO 1-reactive alpha 95: LY TCR and lentivirus containing the CROPseq plasmid library previously described (experimental procedure). 48 hours after transduction, cells were electroporated as previously described (experimental procedure). 48 hours after electroporation, cells were exposed to 2.5ug/ml puromycin to select cells transduced with the CROPseq library (20 gene targets (2 guides for each gene) and 8 non-targeted control guides for a total of 48 guides). The cells were then expanded in XVivo medium with IL-2 at 50U/mL at 1E6 cells/mL for a total of 14 days after isolation. 7 days after initial T cell isolation, tumors were inoculated by injecting 100 ten thousand A375 human melanoma cells, 8-12 week old NOD/SCID/IL-2R γ -Null (NSG) male mice (Jackson Laboratory) subcutaneously on one side. At day 7 post tumor inoculation, in two mice from each donor, T cells transduced with the NY-ESO specific CROPseq library described above (now day 14) were resuspended as 100 million cells in 100. mu.l serum-free RPMI and injected retroorbitally into mice. 7 days after T cell transfer, tumors and spleens were collected from each of 4 mice, and T cells were isolated by FACS. Genomic DNA was isolated from these cells and PCR amplified and barcoded as previously described (experimental procedure). The samples were then sequenced on HiSeq4000 (Illumina) and the guide frequency between the tumor and spleen was compared for each mouse and each donor. In this preliminary study with a limited number of mice, we found that guides targeting ARID1A were abundantly enriched in tumors compared to spleens from two donors. We further found that guides targeting CD5 also showed a tendency to enrich the tumor relative to the spleen. After demonstrating the technical feasibility of this experiment, we expanded the library size and planned in vivo pool screens in 5 mice per donor out of 2 donors.

Tables 1-4 list the gene targets identified by our SLICE screening platform.

Discussion of the related Art

SLICE provides a new platform for genome-wide CRISPR loss of function screening in primary human T cells, a cell type that has revolutionized cancer immunotherapy. SLICE screening can be routinely performed on a large scale in primary cells from multiple human donors, ensuring biologically reproducible findings. Here we chose genetic agitation that enhanced the proliferation of the stimulated responsive T cells. Proliferation is a broad spectrum (broad) phenotype regulated by complex genetics. Array editing using Cas9 RNP enabled us to further characterize the effects of single perturbations using multiplex proteomics measured by flow cytometry. Finally, the use of SLICE in conjunction with single cell transcriptomics enables a more comprehensive assessment of the functional consequences of perturbed hits from whole genome screening. Integration of these CRISPR-based functional genetic studies rapidly identified genes in human T cells that could be targeted to enhance stimulus-dependent proliferation, activation responses, effector programs and in vitro cancer cell killing.

The potential for a loss of function screen for human primary T cells was occasionally demonstrated in patients receiving CAR T cell therapy against Chronic Lymphocytic Leukemia (CLL) (Fraietta et al, 2018). Non-targeted integration of a lentiviral provirus encoding a CAR construct can disrupt the endogenous gene. Lentiviral integration that disrupts TET2 in patient T cells showed a preferred and near clonal large scale expansion at the peak of the response and may contribute to complete remission in patients. This patient had a pre-existing subtype mutation in exactly the second allele of TET 2. SLICE now provides the opportunity to more systematically search for genetic perturbations that enhance cell expansion and effector function of adoptive T cell therapies.

We found that ablating at least four targets (SOCS1, TCEB2, RASA2, and CBLB) in human T cells enhances proliferation and anti-cancer functions. Among these, CBLB has been studied in mouse models as an intracellular checkpoint that can be targeted to enhance control of tumors by CD8T cells. Our data indicate that RASA2 and SOCS1/TCEB2 complex members may also be effective targets for modulation in adoptive T cell cancer immunotherapy. These studies demonstrate the potential of SLICE as a tool in human primary CD8T cells that will quickly discover and validate relevant candidate targets for the development of novel immunotherapies. Looking into the future, SLICE convergent screens may be useful for selecting perturbations that confer more complex phenotypes on human T cells, including in vivo functions associated with T cell therapy.

The SLICE convergent screening method is flexible because it can be adapted to explore a variety of genetic programs that regulate primary T cell biology. Primary T cell screening can be performed with various extracellular selective pressure and/or FACS-based phenotypic selection. We focused on CD8+ T cells, but showed that SLICE could also be used for CD4+ T cells, and could be generalized to many other primary cells. We demonstrate that it is possible to add inhibitory pressure, in our case adenosine agonists, to the screen to identify gene perturbations conferring resistance. Future screens may be designed to overcome other key inhibitory forces in the tumor microenvironment, such as inhibitory cytokines, metabolites, nutrient depletion or inhibitory cell types, including regulatory T cells or myeloid derived inhibitory cells. In summary, we developed a novel convergent CRISPR screening technique with the potential to explore almost limitless for unknown biology of human primary cells.

The experimental scheme is as follows: methods adopted in the examples

Isolation and culture of human CD8T cells

Primary human T cells from all experiments were from one of two sources: (1) the residue of the leukoreduction (leukoredation) chamber after Trima Apheresis (pacific blood center), or (2) a fresh whole blood sample collected according to a protocol approved by the UCSF human research council (CHR # 13-11950). Peripheral Blood Mononuclear Cells (PBMCs) were isolated from the samples by Lymphoprep centrifugation (stem cells, catalog # 07861) using SepMate tubes (stem cells, STEMCELL, catalog # 85460). CD8T cells were isolated from PBMC by magnetic negative selection using the EasySep human CD8+ T cell isolation kit (stem cells, catalog No. 17953) and used as received. When using frozen cells (IncuCyte experiments), previously isolated PBMCs frozen in Bambanker freezing medium (Bulldog Bio, catalog No. BB01) were thawed, CD8T cells were isolated using the EasySep isolation kit described previously, and cells were allowed to sit in unstimulated medium for one day prior to stimulation. The cells were cultured in X-Vivo medium (Longza, Cat. No. 04-418Q) consisting of X-Vivo15 medium with 5% fetal bovine serum, 50mM 2-mercaptoethanol and 10mM N-acetyl group L-cysteine. After isolation, cells were stimulated at 1e6 cells/mL using plate-bound 10. mu.g/mL anti-human CD3 (Cat. No. 40-0038, clone UCHT1) and 5. mu.g/mL CD28 (clone CD28.2) (Tonbo Co., Cat. No. 40-0289) or ImmunoCult human CD3/CD28/CD 2T cell activator (Stem cell Co., Cat. No. 10970), and 50U/mL IL-2. Plate-bound (plateboud) stimulation was used for the first T-cell stimulation, while ImmunoCult was used for the second T-cell stimulation. Immunocult was used with cells at the manufacturer's recommended doses of 1/16, 1/8, and 1/2, i.e., 25. mu.L/mL.

Lentiviral production

HEK 293T cells were seeded at 1800 ten thousand cells in 15cm poly-L-lysine coated dishes and cultured in DMEM + 5% FBS + 1% pen/strep 16 hours prior to transfection. Cells were transfected with sgRNA transfer plasmid and the second generation lentiviral packaging plasmid pmd2.g (addge, cat # 12259) and psPAX2 (adge, cat # 12260) using lipofectamine 3000 transfection reagent following the manufacturer's protocol (cat # L3000001). The following day, the medium was changed by adding 500x virus facilitating reagent according to the manufacturer's protocol (catalog number VB100 by Alstem). Viral supernatants were collected 48 hours post transfection and centrifuged at 300g for 10 minutes to remove cell debris. To concentrate the lentiviral particles, an Alstem precipitation solution (catalog number VC100 from Alstem) was added, mixed, and frozen at 4 ℃ for 4 hours. The virus was then concentrated by centrifugation at 1500g for 30 minutes at 4 ℃. Finally, the lentivirus pellet was resuspended in cold PBS at 100 times the original volume and stored at-80 ℃ until use.

Lentiviral transduction and Cas9 electroporation

Lentivirus was added directly to cultured T cells at a ratio of 1:300v/v 24 hours after stimulation and gently mixed by tilting. After 24 hours, the cells were collected, pelleted and resuspended at 20e6 cells/100 μ l in Longsa electroporation buffer P3 (Longsa, Cat. No. V4 XP-3032). Cas9 protein (MacroLab, Berkeley, 40. mu.M stock) was then added to the cell suspension at a ratio of 1:10 v/v. Cells were electroporated with 20e6 cells per cuvette using pulse code EH115 (Longsa, catalog number VVPA-1002). The total number of cells used for electroporation was scaled as needed. Immediately after electroporation, 1mL of pre-warmed medium was added to each cuvette and the cuvette was placed at 37 degrees for 20 minutes. The cells were then transferred to a culture vessel in X-Vivo medium containing 50U/ml IL-2 at 1e6 cells/ml in an appropriate tissue culture vessel. Cells were expanded every two days, fresh medium with 50U/ml IL-2 was added, and the cell density was maintained at 1e6 cells/ml.

CFSE staining

Cultured cells were collected, spun, washed with PBS, and then resuspended in PBS at 100-1000 million cells/ml. CFSE (Biolegent, Cat. 423801) was prepared according to the manufacturer's protocol to prepare 5mM stock solutions in DMSO. The stock solution was diluted 1:1000 in PBS to give 5. mu.M working solution, which was then added to the cell suspension at a ratio of 1:1 v/v. After mixing, the cells were incubated for 5 minutes at room temperature in the dark. The staining is then quenched with at least 5 times the staining volume of medium (e.g., 2ml +10ml) and incubated in the dark at room temperature for 1 minute. The cells were then centrifuged and resuspended in culture medium and then restimulated.

Screening line (Pipeline)

PBMCs from various human healthy human donors were isolated from the TRIMA residue (see methods). TRIMA residues were purchased from Pacific Blood Centers (Blood Centers of the Pacific). After CD8T cells were isolated as described above (day 0), the cells were allowed to stand overnight in X-Vivo medium and then stimulated the following day (day 1) with plate-bound anti-human CD3/CD28 and IL-2 (50U/mL). 24 hours after stimulation (day 2), cells were transduced with concentrated lentiviruses encoding the pooled sgRNA library (see methods). 48 hours after transduction (day 3), cells were electroporated with CAS9 protein (method). The cells were then cultured and expanded in medium with 50U/mL IL-2, maintaining the medium target density at 1e6 cells/mL. On day 14, cells were CFSE stained (see methods), divided into relevant fractions for screening, and then restimulated with immunocult (methods). After 4 days, the cells were FACS sorted according to CFSE levels. Specifically, we defined non-proliferating cells as the cells with the highest CFSE peak, and highly proliferating cells as cells at and below the 3 rd high CFSE peak. Genomic DNA was isolated from the sorted cell pellet and then prepared for next generation sequencing. The barcoded amplified PCR products were sequenced on HiSeq 4000. Data were analyzed in pipeline using MAGeCK software.

Arrayed Cas9 ribonucleotide protein (RNP) preparation and electroporation

Lyophilized crRNA and tracrRNA (Dharmacon) were resuspended in 10mM Tris-HCl (7.4pH) with 150mM KCl at a stock concentration of 160. mu.M and stored at-80 ℃ until use. To prepare Cas 9-RNPs, crRNA and tracrRNA were first thawed, mixed at a ratio of 1:1v/v, and incubated at 37 ℃ for 30 minutes to form a complexed gRNA. Cas9 protein (stock solution 40. mu.M) was added at a 1:1v/v ratio and incubated at 37 ℃ for 15 min. The assembled RNPs were dispensed into 96W V format plates at 3. mu.L/well. Cells were centrifuged, resuspended in Longsa P3 buffer at 1e6 cells/20 μ L, and added to a V-plate with RNP. The cell and RNP mixture was transferred to a 96-well electroporation cuvette plate (torsa, catalog number VVPA-1002) for nuclear transfection using the pulse code EH 115. Immediately after electroporation 80uL of pre-warmed medium was added to each well and incubated at 37 ℃ for 20 minutes. The cells were then transferred to a culture vessel containing 50U/ml IL-2 at 1e6 cells/ml in an appropriate tissue culture vessel.

Arrayed CFSE staining

To perform CFSE staining of arrayed cells edited with RNPs, cells were collected from multiple replicate plates and pooled into 96-well deep-well plates. Cells were centrifuged in a deep well plate and after decanting the medium, the cells were resuspended in 1mL PBS per well using a manual multichannel pipettor. CFSE was prepared to prepare 5uM of working solution in PBS according to the manufacturer's protocol above. Then, 1mL of 5. mu.M CFSE was added to the cells of each well at a ratio of 1:1v/v using a multichannel pipettor. After mixing, the cells were incubated for 5 minutes at room temperature in the dark. The staining was then quenched with 2ml of X-Vivo media using a multichannel pipettor and incubated in the dark for 1 minute at room temperature. The cells were then centrifuged in a deep well plate, CFSE was decanted, and the cells were then resuspended in X-Vivo media and then restimulated.

Pooled sgRNA library construction

For cloning of cell surface sub-pools, we followed a custom sgRNA library cloning protocol as described by Joung et al (Joung et al, 2016). We utilized the pgRNA humanized backbone (Ed Gene, plasmid # 44248). To optimize this plasmid for cloning the library, first, we replaced the sgRNA with a 1.9kb stuffer sequence derived from the lentiGuide-Puro plasmid (alder gene, plasmid # 52963). The stuffer sequence was cut out using BfuAI restriction enzyme and the scaffold was gel purified. We selected a cell surface library that included 1211 gene targets (4 guides for each gene), and a total of 5000 guides, which included non-targeted controls. Wizards were derived from Brunello sgRNA libraries, pooled oligonucleotide libraries were purchased from Twist Bioscience. Oligonucleotides were amplified by PCR and cloned into pgRNA humanized backbone by Gibson assembly as described by Joung et al (Joung et al, 2016). For genome-wide screening, a Brunello plasmid library (Ed. Gene, Cat. No. 73178) in the lentiGuide-Puro backbone was purchased from Ed. Gene. Endura ElectroCompetent cells were used to expand the library according to the manufacturer's protocol (Endura, Cat. No. 60242-1).

Preparation of gDNA for next generation sequencing

After cell sorting and collection, genomic DNA was isolated from the cell pellet using a genomic DNA isolation kit (Machery-Nagel Co., Cat. No. 740954.20). Amplification and barcoding of sgrnas were performed on cell surface sub-libraries as described by Gilbert et al (Gilbert et al, 2014). For whole genome screening, after isolation of gDNA, sgRNA was amplified and barcoded using a two-step PCR protocol. Each sample was first divided into 100uL reactions, each with 4. mu.g of gDNA. The individual reactions were composed as follows: 50uL of NEBNext 2 XFidelity PCR master mix (NEB, Cat. No. M0541L), 4. mu.g of gDNA, 2.5. mu.L of each of 10. mu.M read1-stagger-u6 and concentrator-read 2 primers, and water to a total volume of 100. mu.L. The PCR cycling conditions were: 20 cycles at 98 ℃ for 3 minutes, then 98 ℃ for 10 seconds, 62 ℃ for 10 seconds, 72 ℃ for 25 seconds; finally, extension was carried out at 72 ℃ for 2 minutes. After PCR, all reactions for each sample were pooled and then purified using Agencour AMPure XP SPRI beads (Beckman Coulter, catalog number A63880) according to the manufacturer's protocol. Then, 5. mu.L of each purified PCR product was extracted for the second PCR for indexing. Each reaction comprises: mu.L of PCR product, 25. mu.L of NEBNext 2x master mix (NEB, Cat. No. M0541L), 1.25. mu.L each of 10. mu. M p5-i5-read1 and read2-i7-p7 index primers, and water was added to a total volume of 50. mu.L for each reaction. The PCR cycling conditions for the indexing PCR were: 10 cycles at 98 ℃ for 3 minutes, then 98 ℃ for 10 seconds, 62 ℃ for 10 seconds, 72 ℃ for 25 seconds; finally, extension was carried out at 72 ℃ for 2 minutes. After PCR, the samples were SPRI purified, quantified using a Qubit ssDNA high sensitivity assay kit (Thermo Fisher Scientific, Cat. No. Q32854), and then analyzed on a 2100 bioanalyzer. The samples were then sequenced on a HiSeq4000 (camida).

A375 and T cell in vitro Co-culture assay

A375 melanoma cells were transduced with lentiviruses to establish RFP-nuclear signatures (IncuCyte, catalog No. 4478) for optimal imaging on the IncuCyte imaging system. One day after stimulation, CD8T cells from healthy donors were transduced with virus containing the NY-ESO 1-reactive α 95: LY TCR construct. Five days after transduction, cells were FACS sorted against a pure cell population of the expression construct using HLA-A2+ restriction NY-ESO-1 peptide (SLLMWITQC) dextran-PE (Immundex, Cat. No. WB 2696). Then, after the initial stimulation, the cells were expanded in X-Vivo medium containing 50U/ml of IL-2 for 14 days. The day before co-culture, a375 cells were seeded at 5,000 cells/well into 100 μ L of complete RPMI medium in 96W plates. Complete RPMI medium includes: RPMI (Gibbo, Cat. No. 11875093), 10% fetal bovine serum, 1% L-glutamine, 1% NEAA, 1% HEPES, 1% pen/strep, 50mM 2-mercaptoethanol and 10mM N-acetyl L-cysteine. The following day, 14-day-old NY-ESO-1 specific T cells were added to the top of 5,000 a375 cells per well, in the indicated T cell to cancer cell ratio: 1:2, 1:4 and 1: 8. T cells were added to 50. mu.L of complete RPMI with IL-2 at 150U/ml and glucose at 6 g/dL. The plates were then imaged using the IncuCyte live cell imaging system, where the number of a375 RFP positive nuclei was counted over time. To initially optimize the system, a375 cells were seeded at 24,000 cells/well and T cells from two donors transduced with NY-ESO specific TCRs were added at the following T cell to tumor cell ratios (8:1, 4:1, 2:1, 1: 1). The IncuCyte caspase-3/7 red apoptosis reagent (IncuCyte, cat. No. 4704) was added to each well according to the manufacturer's instructions and imaged every 4 hours on the IncuCyte viable cell imaging system. In parallel, a375 cells with RFP nuclear tag were seeded at 4,000 cells/well and the same T cells from two donors transduced with NY-ESO specific TCR were added in the same ratio as the caspase experiment described above and imaged in parallel.

CROPseq library Generation

The backbone plasmid used to clone the CROPseq library was CROPseq-Guide-Puro (Ed Gene, plasmid #86708) from Ed Gene. The library contained 20 gene targets (2 guides for each gene, selected from the Brunello library (Doench et al, 2016)) and 8 non-targeted control guides, for a total of 48 guides. These library-guided oligonucleotides were purchased from Integrated DNA Technologies, IDT, and cloned into the CROPseq-Guide-Puro plasmid backbone using the methods described by Datlinger et al for cloning pooled gRNA libraries into CROPseq-Guide-Puro plasmids without amplification. Lentiviruses were generated from this pooled plasmid library and used to transduce CD8T cells from two healthy donors as described previously. 48 hours after transduction, cells were treated with 1uM puromycin for 3 days, and then viable cells were sorted using Ghost Dye 710(Tonbo biosciences, Cat. No. 13-0871). Cells were then collected, counted, and loaded into a 10X chromosome single cell sequencing system with v2 chemistry (chemistry).

CROPseq guide re-amplification

For guided re-amplification, the samples were amplified and barcoded using a two-step PCR protocol. Each sample was first divided into 8 PCR reactions, each with.1 ng of cDNA template. The reaction for each 25uL consisted of: 1.25uL P5 forward primer 1.25uL Nextera Read2 reverse primer, priming the U6 promoter to enrich the guide, 12.5uL NEBNext Ultra II Q5 master mix (NEB, Cat. No. M0544L),. 1ng template and water to 25 uL. The PCR cycling conditions were: 10 cycles at 98 ℃ for 3 minutes, then 98 ℃ for 10 seconds, 62 ℃ for 10 seconds, 72 ℃ for 25 seconds; finally, extension was carried out at 72 ℃ for 2 minutes. After PCR, all reactions for each sample were pooled and purified using Agencourt AMPure XP SPR beads according to the manufacturer's protocol. Then, 1uL was extracted from each purified PCR product to perform a second PCR for indexing. Each reaction comprises the following steps: 1uL of PCR product, 12.5uL of NEBNext Ultra II Q5 master mix (NEB, Cat. No. M0544L), 1.25uL of P5 forward primer, 1.25uL of Yimingda i7 primer, and water to 25 uL. The PCR cycling conditions were: 10 cycles at 98 ℃ for 3 minutes, then 98 ℃ for 10 seconds, 62 ℃ for 10 seconds, 72 ℃ for 25 seconds, and finally extension at 72 ℃ for 2 minutes. After PCR, SPRI purification and quantification were performed for all reactions using the Qubit dsDNA high sensitivity assay kit (seemer feishell technologies, catalog No. Q32854) and run on a gel to confirm size. The samples were then sequenced on a MiniSeq (hungry company).

Arrayed validated flow cytometry

All array-based validation studies were performed on a 96-well round bottom plate and read on an Attune NxT flow cytometer with a 96-well plate reader. For RNP-based proliferation validation assays against top-ranked targets from whole genome screens, cells were CFSE stained in 96-well format prior to restimulation, as described above. To assess the level of upper activation markers in arrayed RNP-edited cells, the following antibodies were used: CD69 (Bailejin, Cat. No. 310904), CD154 (Bailejin, Cat. No. 310806), PD-1 (Bailejin, Cat. No. 329908), TIM-3 (Bailejin, Cat. No. 345005), LAG-3 (Bailejin, Cat. No. 369308), and CD8a (Bailejin, Cat. No. 01038).

Quantitative and statistical analysis

Analysis of pooled CRISPR screens

To identify negative and positive hits in our screen, we used MAGeCK software to quantify and test wizard enrichment (Li et al, 2014). First, the abundance of the wizard is determined by using the MAGECK "count" module on the original fastq file. For the genome-wide Brunello library, the 5' trim (trim) length was set to eliminate the stagger offset introduced by library preparation by using the parameter "-trim-523, 24,25,26,28,29, 30". For the targeted library, constant 5' trim was automatically detected by MAGeCK. In more than 80% of the samples, we removed the guide with absolute counts below 50. To test robust wizards and enrichment at the gene level, the MAGeCK "test" module was used with default parameters. This step includes median ratio normalization to account for varying read depths. We used the non-targeted control guide to estimate the size factor for normalization and the mean variance model to perform zero-distribution for finding significant guide enrichment. All donors in each screen were repeatedly grouped for analysis of biological noise. MAGECK generates a guide level enrichment score in each direction (i.e., positive and negative) and then uses it for alpha-robust rank aggregation (alpha-robust rank aggregation) to obtain a gene level score. The p-value of each gene was determined by permutation test, the wizard assignments were randomized, and adjusted for false discovery rate by the Benjamini-Hochberg method. Log2 fold change (LFC) was calculated for each gene, defined as the median LFC of all the guides for each gene target throughout. Where indicated, LFCs were normalized to have a mean of 0and a standard deviation of 1 to obtain LFC Z scores.

Gene set enrichment analysis of screening hits

To find annotations of Enrichment in the screening clicks, we used a Gene Set Enrichment Analysis (Gene Set Enrichment Analysis), as implemented in the fgsea R software package. The input for enrichment consisted of LFC values for all genes tested in the screen. We used the KEGG pathway dataset as a reference gene annotation database, including only gene sets with more than 15 members and less than 500 members. For the external gene set of in vivo immunotherapy shown in fig. 2E, we used 43 genes identified by Zhou et al with 3or more shRNA guides with 4-fold enrichment. Normalized enrichment scores and p-values were determined by permutation tests with 10,000 iterations with the same size randomized gene set and adjusted by the FDR method.

Fitting CFSE distributions for use in arrayed validation screening

We extracted quantitative parameters from CFSE profiles of all samples using the FlowFit R software package. Because CFSE staining of the array was performed for each population of edited cells, the signal peaks of the parent population may shift slightly between wells. Thus, for each well, the stimulated well was compared to the same unstimulated well, expected to have a single peak at the end of the experiment. The FlowFit software package implements the Levenberg-Marquadt algorithm to estimate the size and position of the parent population peaks. We then fit the CFSE profiles of the respective stimulated cells using the fitting parameters from the unstimulated wells. These CFSE profiles were modeled as gaussian distributions with a log2 distance peak due to cell division and CFSE dilution. The fitted model is visually inspected and the fitting parameters are adjusted to minimize deviation from the original CFSE signal. The fitted model was used to calculate the proliferation index, defined as the total number of cells at the end of the experiment divided by the calculated starting number of parental cells. This parameter is robust to variations in the initial CFSE staining intensity. Analysis of SLICE paired with Single-cell RNA-Seq

The results of the Hamming sequencing generated from the 10 Xgenomics V2 library were pre-processed using CellRanger software version 2.1. This pipeline generates a sparse numerical matrix for each sample and has gene-level counts for unique molecular markers (UMIs) identified for all single cells by default quality control indicators. These gene expression matrices were processed using the Seurat R software package as described elsewhere (https:// satijalab. org/semuat/pbmc 3k _ tuple. html). Only cells with more than 500 identified genes were used for downstream analysis. Using sourtat, the counts were log normalized, the total UMI count for each cell and the percentage of mitochondrial genes detected for each cell were regressed out and scaled to obtain a z-score at the gene level. Then, we performed Principal Component Analysis (PCA) using 1,000 most variable genes in the cells. As shown in fig. 4A, the first 30 PCA components were used to construct a Unified Manifold Approximation and Projection (UMAP) to display single cells in a two-dimensional image. Gene expression of single cells as shown in fig. 4B was calculated as log10(UMI count +1) and scaled. The clustering in fig. 4C is performed on the shared nearest neighbor graph by the Louvain algorithm, implemented by findsclusters command in the sourat R package. For the synthetic large number of differential gene expressions in fig. S4B, the UMI counts for each gene were summed for all cells with non-targeted control guides in each sample and the differentially expressed genes were determined using the DESeq 2R software package.

To correlate the guide with the identified cell barcodes, we processed the fastq files from the 10X library and from the re-amplification PCR. The read2 file was matched to the wizard library using the matchPattern as implemented in the R ShortRead software package. The mode used was the U6 promoter sequence preceding the guide sequence, which was appended to a20 bp library guide sequence (e.g., TGGAAAGGACGAAACACCGNNNNNNNNNNNNNNNNNN, where N denotes the guide sequence), allowing a total of 4 mismatches. The paired Read1 pair with matching guide readings was used to determine cell barcode and UMI assignment. We filtered out reads that occurred less than twice and cells assigned more than one guide. The chi-square test was used to determine the overexpression of the down-guided cells with the same gene target in cell state driven clusters. The normalized residual of the chi-squared test is scaled and used to generate fig. 4E and 10F.

Data and software availability

The original sequencing file for all screens performed was available at PENDING. The original file of the single cell RNA-Seq experiment was saved to GEO PENDING. All code for analyzing data and generating graphics may be provided as required.

Brief description and examples of references by author and year

Adamson,B.,Norman,T.M.,Jost,M.,Cho,M.Y.,J.k., Chen, y., Villalta, j.e., Gilbert, l.a., Horlbeck, m.a., Hein, m.y., et al (2016.) the multiplex Single-Cell CRISPR Screening Platform is capable of systematically resolving Unfolded Protein responses (a Multiplexed Single-Cell CRISPR Screening Platform enzymes) Cell 167,1867-1882. e21.

Allird, d., turcote, m., and Stagg, j. (2017). a2 adenosine receptor is targeted in cancer (Targeting a2 adenosine receptors in cancer), immunol. cell biol.95, 333-339.

Arranz-Nicolás,J.,Ogando,J.J.,Soutar,D.,Arcos-Pérez,R.,Meraviglia-Crivelli,D.,S.,Mérida,I.,Aranz-Nicol, as, J., Ogando, J.J., et al (2018). Diacylglycerol kinase α inactivation is a component of the co-stimulatory pathway for amplifying TCR signals (Diacylglycerol kinase α inactivation an integral component of the synergistic pathway of the TCR signals). Cancer Immunotherol.67, 965-980.

Azzam, h.s., dejarnet, j.b., Huang, k., Emmons, r., Park, c.s., Sommers, c.l., El-Khoury, d., Shores, e.w., and Love, P.E (2001). Fine-tuning of TCR signals by CD5 (Fine tuning of TCR signaling by CD5).

Bartelt, r.r., and Houtman, j.c.d. adaptor protein LAT serve as an integration node for The signaling pathway driving T cell activation (The adaptor protein LAT as an integration node for signaling pathway T cell activation), Wiley interdiscip.rev.syst.biol.med.5, 101-110.

Chae, h. -d., Siefring, j.e., Hildeman, d.a., Gu, y., and Williams, D.A, (2010). RhoH regulates the subcellular localization of ZAP-70and Lck in T cell receptor signaling (RhoH modulators of ZAP-70and Lck in T cell receptor signaling). PLoS One 5, e13970.

Diacylglycerol Kinases (Diacylglycerol Kinases in T Cell Tolerance and Effector Function) front Cell dev. biol.4,130 in Chen, s.s., Hu, z., and Zhong, x. -p. (2016) T Cell Tolerance and Effector Function.

Damgaard, r.b., Walker, j.a., Marco-Casanova, p., Morgan, n.v., titeradge, h.l., Elliott, p.r., McHale, d., Maher, e.r., McKenzie, a.n.j., and Komander, D. (2016.) Deubiquitinase OTULIN Is an important Negative Regulator of Inflammation and Autoimmunity, Cell 166,1215-1230 e20.

Dateringer, p., rendirero, a.f., Schmidl, c., kraussgruber, t., Traxler, p., Klughammer, j., Schuster, l.c., Kuchler, a.a., Alpar, d., and Bock, c. (2017) compiled CRISPR screening and single cell transcriptome reading (Pooled CRISPR screening with single-cell transcriptional readout) nat.methods 14, 297-.

The thymic cell subpopulations of Dave, v.p., Keefe, r., Berger, m.a., Drbal, k., Punt, j.a., Wiest, d.l., Alarcon, b., and kappa, D.J, (1998) Altered functional responsiveness of a CD3 Δ deficient mouse to TCR-CD3 involvement (Altered functional responsiveness of thymocyte subsets from CD3 delta-specific mice to TCR-CD3 gag).

The mitotic CD28 signal requires the exchange factor Vav1 to enhance TCR signaling on SLP-76-Vav-Itk semaphores (Mitogenic CD28 signals required the exchange of the vector Vav1 to enhance TCR signaling at the SLP-76-Vav-Itk semaphores J.Immunol.178, 1363-1371.

Dixit, a., Parnas, o., Li, b., Chen, j., Fulco, c.p., jirby-Arnon, l., Marjanovic, n.d., Dionne, d., Burks, t., Raychowdhury, r., et al (2016). Molecular Circuits with expanded Single-Cell RNA Profiling of phased Genetic Screens were resolved with a Genetic screen of expanded Single-Cell RNA profiles (Perturb-Seq: separating Molecular Circuits with Scalable Single-Cell RNA Profiling of phased Genetic Screens) Cell 167,1853-1866. e17.

Doench, J.G, (2018)? User guide for genetic screening (Am I ready for CRISPER user's guide to genetic screens), nat. Rev. Genet.19, 67-80.

Doench, j.g., fusa, n., surender, m., Hegde, m., Vaimberg, e.w., Donovan, k.f., Smith, i., Tothova, z., Wilen, c., archer, r., et al (2016.) an Optimized sgRNA design to maximize CRISPR-Cas9 activity and minimize off-target effects (Optimized sgRNA design to minimize CRISPR-Cas activity and minim off-target effects of CRISPR-Cas9) nat. biotechnol.34, 184-191.

D ü wel, M., Welteke, V., Oeckinghaus, A., Balns, M., Kloo, B., Ferch, U.S., Darnay, B.G., Ruland, J., Marynen, P., and Krappmann, D. (2009). A20 negatively regulates T cell receptor signaling to NF-kappa B by cleaving the Malt1 ubiquitin chain (A20 novel regulation Malt cell 1 ubiuin chains). J.Immunol.182, 7718-7728.

The common genetic origin of Ferreira, m.a., Vonk, j.m., Baurecht, h., Marenholz, i., tianan, c., Hoffman, j.d., Helmer, q., Tillander, a., Ullemar, v., van Dongen, j., et al (2017) asthma, pollinosis, and eczema sets forth the biological characteristics of allergic diseases (Shared genetic origin of asthma, hay mover and emaciation diseases allergic disease biology) nat. natural. gene.49, 1752-1757.

Disruption of Fraietta, J.A., Nobles, C.L., Sammons, M.A., Lundh, S., Carty, S.A., Reich, T.J., Cogdill, A.P., Morrissette, J.J.D., DeNizio, J.E., Reddy, S., et al (2018) TET2 facilitates therapeutic efficacy of T cells targeting CD19 (discrimination of TET2 proteins the therapeutic efficacy of CD19-targeted T cells). Nature 558, 307-.

Ge, y., Paisie, t.k., Newman, j.r.b., McIntyre, l.m., and Concannon, P. (2017). UBASH3A mediate the Risk of Type 1Diabetes by inhibiting T Cell Receptor-Induced NF- κ B Signaling (UBASH3A media rib for Type 1Diabetes Through Inhibition of T-Cell Receptor-Induced NF- κ B Signaling). Diabetes 66, 2033-.

Gharbi, s.i., Rinc, n, e., Avila-Flores, a, Torres-Ayuso, p, Almena, M, Cobos, m.a., Albar, j.p., and murida, i. (2011) Diacylglycerol kinase ζ controls Diacylglycerol metabolism at the immunological synapse, mol.biol.cell 22, 4406-.

Gilbert, L.A., Horlbick, M.A., Adamson, B., Villalta, J.E., Chen, Y., Whitehead, E.H., Guimuraes, C., Panning, B., Ploegh, H.L., Bassik, M.C., et al (2014) Genome-Scale CRISPR-Mediated Control of Gene suppression and Activation (Genome-Scale CRISPR-media Control of Gene expression and Activation) Cell 159, 647-661.

Hultquist, J.F., Schumann, K., Woo, J.M., Manganaro, L., McGregor, M.J., Doudna, J., Simon, V., Krogan, N.J., and Marson, A. (2016) Cas9 Ribonucleoprotein Platform for Functional Genetic Studies of HIV-Host Interactions in Human Primary T Cells (A Cas9 ribosomal protein platforms for Functional Genetic Studies of HIV-Host Interactions in Primary man T Cells) Cell Rep.17, 1438-1452.

Ilangumaran, S., Bobbala, d., and ramatahan, S. (2017) SOCS1: modulators of T Cells in Autoimmunity and Cancer (SOCS1: Regulator of T Cells in Autoimmunity and Cancer), curr. Top. Microbiol. Immunol.410, 159-189.

Jaitin, d.a., Weiner, a., Yofe, i., Lara-asseso, d., Keren-Shaul, h., David, e., Salame, t.m., Tanay, a., van oudene, a., a, and Amit, i. (2016). by Linking the screening of CRISPR pools to unicellular RNA-Seq, the Immune circuit was resolved (separating Immune Circuits by Linking CRISPR-Pooled Screens with Single Cell RNA-Seq) Cell 167,1883-1896. e15.

Joung, j., Konermann, s., Gootenberg, j.s., Abudayyeh, o.o., Platt, r.j., Brigham, m.d., Sanjana, n.e., and Zhang, F. (2016) protocol: genome-scale CRISPR-Cas9 knockdown and Transcriptional Activation Screening (Protocol: Genome-scale CRISPR-Cas9 knock-out and Transcriptional Activation Screening) BioRxiv 12,059626.

June, c.h., O' Connor, r.s., Kawalekar, o.u., Ghassemi, s., and Milone, M.C, (2018).

The SOCS cassette of SOCS-1 promotes Ubiquitin-dependent Proteolysis of TEL-JAK2 (The SOCS Box of SOCS-1 Accelerates-dependent Proteolysis of TEL-JAK2), J.biol. chem.276, 12530-12538.

Kamura, T.A., Sato, S.A., Haque, D.A., Liu, L.A., Kaelin, W.G., Conaway, R.C., and Conaway, J.W. (1998) The extensin BC complex interacts with conserved SOCS-box motifs present in members of The SOCS, ras, WD-40 repeats and ankyrin repeat families (The Elongin BC complexes with The conserved SOCS-box motif in members of The SOCS, ras, WD-40repeat, and ankyrin repeat family) Genes Dev.12, 3872-3881.

Li, w., Xu, h., Xiao, t., Cong, l., Love, m.i., Zhang, f., irizary, r.a., Liu, j.s., Brown, m., and Liu, X.S. (2014.) MAGeCK can reliably identify essential genes from Genome-scale CRISPR/Cas9 knock-out screens (MAGeCK enabled robust identification of essential genes from Genome-scale CRISPR/Cas 9knockout sensors) Genome biol.15,554.

Liau, n.p.d., Laktyushin, a., Lucet, i.s., Murphy, j.m., Yao, s., Whitlock, e., Callaghan, k., Nicola, n.a., Kershaw, n.j., and Babon, J.J (2018), SOCS1 molecular basis for inhibiting JAK/TAT (The molecular basis of JAK/STAT inhibition by SOCS1), nat.commun.9,1558.

Lo pez-Cabrera, M., Santis, A.G., Fern 'S index-rule, E, Blache, R., Esch, F, S a nc-mathes, P, S a nc-Madrid, F, L \ a' op z-Cabrera, M, Santis, A.G., Fern \ a 'and a' index-rule, E, et al, (1993) Molecular cloning, expression and chromosomal localization of the human earliest lymphocyte activating antigen AIM/CD69 (a new member of the C-type lectin signaling receptor superfamily), (Molecular cloning, expression, and chromosomal localization of the human early lymphocyte activating antigen AIM/CD69 (a new member of the C-type lectin signaling receptor superfamily), expression of the antigen of the lymphocyte activating antigen of the human early lymphocyte activating antigen of the cell type, AIM/CD 69. sample of the C-design.

Maertens, O., and Cichowski, K. (2014). RAS GTP enzyme activator protein (RAS GAP) has An increasing role in cancer (An expansion role for RAS GTPase activating proteins (RAS GAPs) in cancer). Adv.biol.Regul.55, 1-14.

In vivo CRISPR screening identified Ptpn2 as a cancer immunotherapeutic target (In vivo CRISPR screening Ptpn2 as a cancer immunotherapy target) Nature 547, 413-418, r.t., Pope, h.w., Zimmer, m.d., Brown, f.d., Yates, k.b., Miller, b.c., Collins, n.b., Bi, k.d., LaFleur, m.w., Juneja, v.r., et al (2017).

McInnes, l., and Healy, j. (2018). Unified Manifold Approximation and Projection are used for Dimension Reduction (UMAP: unified managed Approximation and Projection for Dimension Reduction).

Naramura, m., Jang, i. -k., Kole, h., Huang, f., Haines, d., and Gu, h. (2002) c-Cbl and Cbl-b modulate T cell responsiveness by promoting ligand-induced TCR downregulation (c-Cbl and Cbl-b regulated T cell responsiveness by promoting movement of ligand-induced TCR down-modulation) nat.immun.3, 1192-1199.

Pan, d., Kobayashi, a., Jiang, p., de Andrade, l.f., Tay, r.e., lumma, a.m., Tsoucas, d., Qiu, x., Lim, k., Rao, p., et al (2018). a major chromatin modifier determines the resistance of tumor cells to T cell-mediated killing (a major chromatin regulator responses of tumor cells to T cell-mediated killing). sciences 359, 770-775.

Parnas, o., Jovanovic, m., Eisenhaure, t.m., Herbst, r.h., Dixit, a., Ye, c.j., przybybylski, d., Platt, r.j., Tirosh, i., Sanjana, n.e., et al (2015) Genome-wide CRISPR Screen in Primary Immune Cells to resolve Regulatory Networks (a Genome-wide CRISPR Screen in Primary Immune Cells regulation Networks) Cell 162, 675-686.

Patel, s.j., Sanjana, n.e., Kishton, r.j., Eidizadeh, a., Vodnala, s.k., Cam, m., Gartner, j.j., Jia, l., Steinberg, s.m., Yamamoto, t.n., et al (2017) Identification of genes essential for cancer immunotherapy (Identification of infectious genes for cancer immunotherapy) Nature 548,537 donova 542.

Peer, S., Baier, G., and Gruber, T. (2017). Cblb deficient T cells are less susceptible to PD-L1-mediated inhibition (Cblb-specific T cell individual to PD-L1-mediated inhibition). Oncotarget 8, 41841-41853.

Raab, M., da Silva, A.J., Findell, P.R., and Rudd, C.E, (1997) the modulation of Vav-SLP-76binding by ZAP-70and its correlation with TCR ζ/CD3 induced interleukin 2 (Regulation of Vav-SLP-76binding by ZAP-70and its dependence to TCR zeta/CD3 indication of interleukin-2) Immunity 6, 155-.

Reck,M.,Rodríguez-Abreu,D.,Robinson,A.G.,Hui,R.,T.,Comparison of (2016) Pembrolizumab with Chemotherapy treatment of PD-L1 Positive Non-Small Cell Lung Cancer (Pembrolizumab Chemotherapy for PD-L1-Positive Non-Small-Cell Cancer), N.Engl.J.Med.375, 1823-1833.

Ren, j., Liu, x., Fang, c., Jiang, s., June, c.h., and Zhao, Y. (2017) Multiplex Genome Editing to Generate Universal CAR T Cells Resistant to PD1 Inhibition (Multiplex Genome Editing to PD1 Inhibition) clin.cancer res.23, 2255-2266.

Single and double amino acid substitutions in the TCR CDRs of the T cell function enhancer can enhance antigen specific T cell function (Single and dual amino acid substitutions in TCR can be increased in CDRs of antigen-specific T cell functions) J.Immunol.180, 6116-6131.

Roederer, M. (2011.) interpretation of cell proliferation data: to avoid over optimism (Interpretation of cellular promotion data: influenced the pandosian).

Rupp, l.j., Schumann, k., Roybal, k.t., Gate, r.e., Ye, c.j., Lim, w.a., and Marson, a. (2017). CRISPR/Cas9-mediated PD-1disruption enhances the anti-tumor efficacy of human chimeric antigen receptor T cells (CRISPR/Cas9-media PD-1 differentiation opportunities anti-tumor cells) sci.rep.7,737.

de Saint Basile, g., Geissmann, f., Flori, e., ring-Lambert, b., soudeas, c., Cavazzana-Calvo, m., Durandy, a., Jabado, n., Fischer, a., and Le Deist, f. (2004) Severe combined immunodeficiency due to a deficiency of the Δ or ∈ subunits of CD3 (Severe combined immunological deficiency by side deficiency in the ethylene or epsilon subunit of CD3).

Schumann, k., Lin, s., Boyer, e., Simeonov, d.r., Subramaniam, m., Gate, r.e., Haliburton, g.e., Ye, c.j., Bluestone, j.a., Doudna, j.a., etc. (2015) use the generated knock-in primary human T cells (Generation of knock-in primary human T cells using).

Shalem, o., Sanjana, n.e., Hartenian, e., Shi, x., Scott, d.a., Mikkelson, t., Heckl, d.ebert, b.l., Root, d.e., Doench, j.g., et al (2014) Genome-scale CRISPR-Cas9 kckockout screening in human cells Science 343, 84-87.

Shang, w., Jiang, y., Boettcher, m., Ding, k., Mollenauer, m., Liu, z., Wen, x, Liu, c., Hao, p., Zhao, s., et al (2018) Genome-wide CRISPR screening determines FAM49B to be a key regulator of actin dynamics and T cell activation (Genome-wide CRISPR screening FAM49B as a key regulator of actin dynamics and T cell activation) proc.natl.ac.sci.u.s.a.115, E4051-E4060.

Sharma, p., Hu-Lieskovan, s., Wargo, j.a., and Ribas, a. (2017), Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy (Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy) Cell 168, 707-723.

The importance of Src homology 2domain-containing leukocyte phosphoproteins of The sterile alpha motif domain of Shen, S.A., Lau, J., Zhu, M.A., Zou, J.A., Fuller, D.A., Li, Q.A., and Zhang, W. (2009).76 kilodalton, in thymus selection and T-cell activation (The immunization of Src 2 domain-associating leukocyte protein of 76kilodalton, thymine-alpha-kinetic selection and T-cell activation). Blood 114, 74-84.

Shipkova, m., and Wieland, e. (2012.) Surface markers of lymphocyte activation and markers of cell proliferation (Surface markers of lymphocyte activation and markers of cell proliferation).

Tsuchihashi, n., Matsuda, s., Reinherz, e.l., and Koyasu, s. (2000). Two YxxL segments of a single immunoreceptor tyrosine-based activation motif in the CD3 ζ molecule differentially activate the calcium mobilization and mitogen-activated protein kinase family pathways (Two YxxL segments of a single immunoreceptor tyrosine-based activation motif in the CD3zeta molecule) r.j.immune system 30, 1785-1793.

Tybuliwicz, V.L.J. (2005). Vav-family proteins in T-cell signaling, curr.Opin. Immunol.17, 267-274.

Consensus analysis of CBL and CBLB signaling omics in primary T cells identified CD5 as a key regulator of TCR-induced ubiquitination (Co-conditioning analysis of the CBL and cbsigalonesomes in primary T cells CD 5a key regulator of TCR-induced ubiquitination) mol.s.system.12, 876.

Wang, h., kadleck, t.a., Au-Yeung, b.b., Goodfellow, h.e.s., Hsu, l. -y., Freedman, t.s., and Weiss, a. (2010). Key kinase in T cell signaling (ZAP-70: an addressing in T-cell signaling), Cold Spring Harb.Perspectrum.Biol.2, a002279.

Wang, t., Wei, j.j., Sabatini, d.m., and Lander, E.S, (2014) Genetic screening in human cells using the CRISPR-Cas9 system (Genetic screens in human cells using the CRISPR-Cas9 system) Science 343, 80-84.

Waggo, j.a., Robbins, p.f., Li, y, Zhao, y, El-Gamil, m., caragianu, d., Zheng, z, Hong, j.a., down, s., Schrump, d.s., et al (2009) enhanced the identification of NY-ESO-1+ tumor cells by engineered lymphocytes (registration of NY-ESO-1+ tumor cells by engineered lymphocytes enhanced expression of tumor cells by epigenetic modulation of tumor antigen expression. Cancer expression.58, 394.

Wolchok, J.D., Chiarion-Silen, V., Gonzalez, R., Rutkowski, P., Grob, J.J., Cowey, C.L., Lao, C.D., Wagstaff, J., Schadendorf, D., Ferrcu, P.F., et al (2017). Total Survival rate for combination of natemumab and Eijiu eggshell in Advanced Melanoma (overhall with Combined Nivolumab and Iipilimumab in Advanced Melanoma). N.Engl.J.Med.377, 1345-1356.

Zhou, p., Shaffer, d.r., Alvarez Arias, d.a., Nakazaki, y, Pos, w., Torres, a.j., Cremasco, v., Dougan, s.k., Cowley, g.s., Elpek, k., et al (2014) In vivo discovery of immunotherapeutic targets In the tumor microenvironment (In vivo discovery of immunotherapy microorganisms) Nature 506, 52-57.

It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety.

Table 1: positive hits from GW screening

Table 2: hits from in vitro and in vivo assays

In vitro tumor killing

Tumor infiltration in vivo

Gene sgRNA sequence

ARID1A CAGCAGAACTCTCACGACCA

SOCS1 CGGCGTGCGAACGGAATGTG

Table 3: adenosine resistance

Table 4: negative hit-GW screening