Compositions and methods for improving T cell persistence and function

Abstract

Provided herein are compositions comprising T cells modified to remove genes that play a role in T cell exhaustion, as well as compositions and methods thereof. Engineered T cells are provided that lack at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. Engineered T cells are also provided that lack at least one gene encoding a chromatin remodeling protein, including INO80 nucleosome positioning complex protein and SWI / SNF family member (e.g., BAF complex protein). Methods of treating a disease or disorder in a subject are provided, comprising administering the engineered T cells. Methods for preventing exhaustion of engineered T cells, methods of making therapeutic T cells, and methods of identifying genes that play a role in T cell exhaustion are also provided.

Description

[Technical field]

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 226,559, filed July 28, 2021, the contents of which are incorporated herein by reference in their entirety.

[0002] The present disclosure relates to engineered T cells and compositions and methods of use thereof.

[0003] Sequence Listing Statement The contents of the Electronic Sequence Listing entitled (STDU2-39684-601.xml; size: 7,214 bytes; and creation date: July 28, 2022) are incorporated herein by reference in their entirety.

[0004] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with Government support under Grant No. CA230188 awarded by the National Institutes of Health. The Government has certain rights in this invention. [Background technology]

[0005] The development of T cell exhaustion is a major barrier to durable and effective CAR-T cell therapy, especially for solid tumors. Once cancer is recognized, chronic T cell activation by tumor cells leads to T cell exhaustion, which impairs proliferation, cytotoxicity, and effector function, thereby limiting T cell killing of cancer cells. Exhaustion is often targeted using checkpoint inhibitors. However, the majority of patients do not respond to these drugs, and they have not shown any efficacy in combination with CAR T cells in clinical trials. Summary of the Invention [Means for solving the problem]

[0006] (Abstract) Provided herein are engineered T cells that lack at least one gene that promotes or supports T cell persistence and functionality, in some embodiments, the engineered T cells maintain functionality under conditions in which non-engineered T cells exhibit exhaustion.

[0007] In some embodiments, the engineered T cells lack at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. In some embodiments, the engineered T cells lack two or more genes selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

[0008] In some embodiments, the engineered T cells lack at least one chromatin remodeling protein or gene encoding same. In some embodiments, the engineered T cells lack two or more chromatin remodeling proteins or genes encoding same. In some embodiments, the at least one chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF (canonical BRG1 / BRM associated factor) complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof. In some embodiments, the engineered T cells further lack at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1 and ELMSAN1.

[0009] The engineered T cells may further comprise an exogenous receptor or a nucleic acid encoding the same. In some embodiments, the exogenous receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR). In some embodiments, the exogenous receptor is specific for a tumor antigen.

[0010] In some embodiments, the T cells are derived from a biological sample from a subject. In some embodiments, the T cells are isolated from a tumor sample. In some embodiments, the T cells are expanded ex vivo.

[0011] Also provided herein is a composition comprising the population of engineered T cells described herein.The composition can further comprise at least one therapeutic agent.In some embodiments, the at least one therapeutic agent is selected from the group consisting of: an agent for treating T cell exhaustion; an antiviral agent; an antibiotic; an antibacterial agent; a chemotherapeutic agent; or a combination thereof.

[0012] Further provided herein are methods of generating therapeutic T cells. In some embodiments, the methods include obtaining a sample comprising T cells; altering the DNA of the T cells to knock out or disrupt at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8; and engineering the T cells to express an exogenous receptor.

[0013] In some embodiments, the method includes obtaining a sample comprising a T cell; altering the DNA of the T cell to knock out or disrupt at least one gene encoding a chromatin remodeling protein; and engineering the T cell to express an exogenous receptor. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof. In some embodiments, the method further includes altering the DNA of the T cell to knock out or disrupt at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, and ELMSAN1.

[0014] In some embodiments, altering the DNA prevents or reduces T cell exhaustion.

[0015] In some embodiments, the T cells are derived from a biological sample from a subject. In some embodiments, the T cells are isolated from a tumor sample. In some embodiments, the T cells are expanded ex vivo.

[0016] In some embodiments, the foreign receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR). In some embodiments, the foreign receptor is specific for a tumor antigen.

[0017] Further provided is a method for treating a disease or disorder in a subject, comprising administering to the subject an effective amount of an engineered T cell or composition thereof described herein. In some embodiments, the disease or disorder comprises an infectious disease or cancer. In some embodiments, the cancer comprises a tumor.

[0018] In some embodiments, administration reduces the number of cancer cells in the subject, reduces and / or eliminates tumor burden in the subject, and / or exhibits enhanced cancer treatment compared to administration of unmodified T cells.

[0019] In some embodiments, the method further comprises administering at least one additional therapeutic agent. The at least one therapeutic agent may be selected from the group consisting of an agent for treating T cell exhaustion; an antiviral agent: an antibiotic; an antibacterial agent; a chemotherapeutic agent; or a combination thereof.

[0020] In some embodiments, the T cells are autologous to the subject.

[0021] In some embodiments, the T cells maintain functionality under conditions in which non-engineered T cells exhibit exhaustion and / or have improved persistence and function compared to non-engineered T cells.

[0022] Provided herein are methods for preventing T cell exhaustion. In some embodiments, the methods include genetically modifying T cells to lack at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

[0023] In some embodiments, the method comprises genetically modifying the T cell to lack at least one gene encoding a chromatin remodeling protein. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof. In some embodiments, the method further comprises genetically modifying the T cell to lack at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, and ELMSAN1.

[0024] In some embodiments, the T cells have increased survival in the presence of a chronic antigen.

[0025] The engineered T cells may further comprise an exogenous receptor or a nucleic acid encoding the same. In some embodiments, the exogenous receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR). In some embodiments, the exogenous receptor is specific for a tumor antigen.

[0026] The method may further include administering the T cells to a subject in need thereof. In some embodiments, the subject has cancer or an infectious disease.

[0027] Provided herein is a method of screening for genes that promote T cell exhaustion, the method comprising: culturing T cells under conditions of chronic or acute stimulation for at least 6 days, wherein each of the T cells comprises at least one gene knockout or knockdown; isolating T cells that do not display an exhausted T cell surface phenotype; and identifying the at least one gene knockout or knockdown.

[0028] In some embodiments, the T cells are a T cell library, the T cell library comprising at least one T cell with a knockout or knockdown for each gene in the genome of the T cell. In some embodiments, the T cells are CD8+ T cells. In some embodiments, the T cells are isolated from a subject.

[0029] In some embodiments, T cells are generated using a CRISPR-Cas system, where each cell contains at least one guide RNA directed to a gene of interest.

[0030] In some embodiments, the chronic stimulation conditions include culturing the T cells using anti-CD3 coated plates. In some embodiments, the chronic stimulation conditions further include culturing the T cells with IL-2. In some embodiments, the acute stimulation conditions include culturing the T cells with IL-2. In some embodiments, the culture continues for 6-10 days.

[0031] Also provided herein are systems or kits comprising the engineered T cells described herein or systems for engineering T cells. A system for engineering T cells may comprise a clustered interspersed short palindromic repeats (CRISPR) / CRISPR-associated protein (Cas) system described herein or a nucleic acid(s) encoding same. In certain embodiments, a system for engineering T cells comprises Cas9 (e.g., dCas9) or a nucleic acid encoding Cas9, and a gRNA or a nucleic acid encoding a gRNA directed to at least one gene that promotes T cell exhaustion.

[0032] In some embodiments, the at least one gene that promotes T cell exhaustion may be selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

[0033] In some embodiments, the at least one gene that promotes T cell exhaustion is a gene encoding a chromatin remodeling protein. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof.

[0034] In some embodiments, the system or kit further comprises an exogenous receptor or a nucleic acid encoding same.

[0035] In some embodiments, the system or kit further comprises at least one additional therapeutic agent. The at least one therapeutic agent may be selected from the group consisting of: an agent for treating T cell exhaustion; an antiviral agent: an antibiotic; an antibacterial agent; a chemotherapeutic agent; or a combination thereof.

[0036] Other aspects and embodiments of the present disclosure will become apparent in light of the following detailed description. [Brief description of the drawings]

[0037] [Figure 1A] 1A-1F show exemplary in vitro assays summarizing the epigenetic signature of T cell exhaustion. Figure 1A is a diagram of an in vitro exhaustion assay. [Figure 1B] Figures 1A-1F show an exemplary in vitro assay summarizing the epigenetic signature of T cell exhaustion. Figure 1B shows the surface phenotype of CD8+ T cells at days 0 and 10 of a T cell exhaustion assay gated on live cells. [Figure 1C] 1A-1F show exemplary in vitro assays summarizing the epigenetic signature of T cell exhaustion. Figure 1C is a principal component analysis of ATAC-seq profiles of CD8+ T cells throughout the course of chronic stimulation. [Figure 1D] Figures 1A-1F show exemplary in vitro assays summarizing the epigenetic signature of T cell exhaustion. Figure 1D shows the Pdcd1 and Entpd1 loci for representative replicates of each time point in the in vitro exhaustion assay as well as previously published reference ATAC-seq profiles (Miller et al. (2019) Nat. Immunol. 20, 326-336) from T cells in tumors or LCMV. [Figure 1E] Figures 1A-1F show an exemplary in vitro assay summarizing the epigenetic signature of T cell exhaustion. Figure 1E is a heatmap showing the ATAC-seq coverage of each peak in the "terminal exhaustion peak set" at each time point in the in vitro exhaustion assay. Reference data from TILs are also included. Selected nearest neighbor genes are shown on the right. [Figure 1F] Figures 1A-1F show an exemplary in vitro assay summarizing the epigenetic signature of T cell exhaustion. Figure 1F is a chromVAR motif accessibility heat map for each ATAC-seq sample. Selected motifs are shown on the right. The top 100 most variable motifs are shown. [Figure 2A] Figures 2A-2F show genome-wide functional interrogation of T cell exhaustion. Figure 2A shows a diagram of the genome-wide T cell exhaustion screen. A histogram of all guide residuals is shown above, with 1000 randomly selected guides shown in grey in the background of each row for visual criteria. [Figure 2B] Figures 2A-2F show genome-wide functional interrogation of T cell exhaustion. Figure 2B shows the correlation between the replication screen and selected functional categories of genes with the indicated colors. Gene sets were based on GO terms and supplemented with manual annotations. Histograms for all guide residuals are shown above, with 1000 randomly selected guides shown in grey in the background of each column against visual criteria. [Figure 2C] Figures 2A-2F show genome-wide functional interrogation of T cell exhaustion. Figure 2C is a volcano plot with the top hits labeled. A histogram of all guide residuals is shown above, with 1000 randomly selected guides shown in grey in the background of each column for visual criteria. [Figure 2D] Figures 2A-2F show genome-wide functional interrogation of T cell exhaustion. Figure 2D shows individual sgRNA residuals for top hits in different functional categories: integrin or TCR signaling. Histograms for all guide residuals are shown above, with 1000 randomly selected guides shown in grey in the background of each row for visual criteria. [Figure 2E]Figures 2A-2F show genome-wide functional matching of T cell exhaustion. Figure 2E shows GO term analysis of the top 100 hits. A histogram of all guide residuals is shown above, with 1000 randomly selected guides shown in grey in the background of each column for visual criteria. [Figure 2F] Figures 2A-2F show genome-wide functional interrogation of T cell exhaustion. Figure 2F shows individual sgRNA residuals for top hits in different functional categories: chromatin (left), selected receptors and transcription factors (middle), or others (right). Histograms for all guide residuals are shown above, with 1000 randomly selected guides shown in grey in the background of each row for visual criteria. [Diagram 3] Figure 3 is a Cytoscape network display of the top hits. The top positive and negative hits from the genome-wide screen are shown. Each protein is represented by a node in the Cytoscape network and colored by its z-score in the genome-wide screen. Nodes are connected if there is a high-confidence protein-protein interaction. [Figure 4A] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4A is a diagram of an exemplary in vivo pooled screen. [Figure 4B] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4B is a volcano plot of genes enriched or depleted in MC-38 tumors. [Figure 4C] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4C shows correlation of tumor LFCz-score to spleen LFCz-score colored by functional category. [Figure 4D] Figures 4A-4G show targeted follow-up of the top hits in vivo, and Figure 4D shows the correlation of in vivo z-scores with in vitro (genome-wide) z-scores for genes in minipools. [Figure 4E] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4E is a cytoscape protein-protein interaction network colored by z-score in MC-38 tumors. [Figure 4F] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4F shows box plots of tumor vs. input log fold change for each sgRNA targeting the indicated gene, with the mean control log fold change subtracted. [Figure 4G] Figures 4A-4G show targeted follow-up of the top hits in vivo. Figure 4G shows the top 15 in vivo hits and z-scores for all tumors against control guides and Cd3d. [Figure 5A] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5A shows direct capture Perturb-seq of sorted TILs. [Figure 5B] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5B shows scRNA-seq profiles of TILs colored by cluster assignment. [Figure 5C] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5C, left, scRNA-seq profile of cells colored by perturbation detected in each cell. Cells in which no guide or multiple guides were detected per cell are shown in grey. In 5C, right, z-scores (MC-38 tumor z-scores) of each gene knockout in vitro and in vivo. [Figure 5D] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5D shows the correlation of each perturbation with the genome-wide differential gene expression of cells compared to control cells. Gene expression profiles were averaged across cells and then subtracted. [Figure 5E]Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5E is an upset plot of genes induced for different perturbations. [Figure 5F] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5F is an upset plot of repressed genes for different perturbations. [Figure 5G] Figures 5A-5G show in vivo Perturb-seq of tumor-infiltrating lymphocytes. Figure 5G shows the changes in expression compared to controls for selected genes and different perturbations, grouped by category. [Figure 6A] Figures 6A-6F show additional characterization of the in vitro assays: Figure 6A shows the surface phenotype of chronically stimulated T cells throughout the in vitro exhaustion assay. [Figure 6B] Figures 6A-6F show additional characterization of the in vitro assay: Figure 6B is a graph of proliferation of chronically and acutely stimulated T cells in vitro. [Figure 6C] Figures 6A-6F show further characterization of the in vitro assay. Figure 6C shows effector cytokine production of acutely (left) and chronically (right) stimulated T cells. Cells were restimulated with PMA and ionomycin 8 days after the initial stimulation. [Figure 6D] Figures 6A-6F show further characterization of the in vitro assay. Figure 6D is a graph of B16 cell survival after co-culture with acutely or chronically stimulated OT-1 T cells. Tumor cells were pulsed with the cognate peptide (SIINFEKL-SEQ ID NO: 1). [Figure 6E] Figures 6A-6F show additional characterization of the in vitro assays, and Figure 6E is a graph of B16-ovalbumin tumor growth in vivo after adoptive transfer of acutely or chronically stimulated T cells. [Figure 6F]Figures 6A-6F show additional characterization of the in vitro assay. Figure 6F is a heat map showing the ATAC-seq coverage of each peak in the "terminal exhaustion peak set" at each time point in the in vitro exhaustion assay. Reference data from T cells in LCMV are also included. [Figure 7A] Figures 7A-7D show quality control data for the in vitro genome-wide screen. Figure 7A shows the expression of BFP on day 2 of the screen. [Figure 7B] Figures 7A-7D show quality control data for the in vitro genome-wide screen. Figure 7B shows the surface phenotype of cells before gDNA extraction. [Figure 7C] Figures 7A-7D show quality control data for the in vitro genome-wide screen. Figure 7C shows the sgRNA representation of each sample. [Figure 7D] Figures 7A-7D show quality control data for the in vitro genome-wide screen. Figure 7D is a graph of guide number correlation (acute vs. chronic) for each replicate. The CD3 subunit is shown in red, all other guides in black. [Figure 8A] Figures 8A-8D show LCMV clone 13 expression analysis of the top hits. Figure 8A is a graph of cell types identified in the scRNA-seq data. [Figure 8B] Figures 8A-8D show expression analysis of the top hit LCMV clone 13. Figure 8B shows expression of Pdcd1, Havcr2, Tcf7 and Cx3cr1 in single cells. [Figure 8C] Figures 8A-8D show LCMV Clone 13 expression analysis of the top hits. Figure 8C is a violin plot of expression of gene modules containing the top 100 in vitro hits across the cluster. [Figure 8D] Figures 8A-8D show LCMV Clone 13 expression analysis of the top hits. Figure 8D is a cytoscape network of the top hits colored by average expression across all single cells. [Figure 9A]Figures 9A-9D show additional data for the targeted in vivo screen: Figure 9A is a graph of tumor size and T cell infusion timeline for each group in the minipool screen. [Figure 9B] Figures 9A-9D show additional data for the targeted in vivo screen. Figure 9B, top, is a box plot of spleen vs. input log fold change for each sgRNA targeting the indicated gene, with the average control log fold change subtracted. Figure 9B, bottom, is a heat map of the z-scores for the top 15 in vivo hits and all spleens for the control guide and Cd3d. [Figure 9C] Figures 9A-9D show additional data for the targeted in vivo screen. Figure 9C is a GO term analysis of the top 20 positive hits in tumors. [Figure 9D] Figures 9A-9D show additional data for the targeted in vivo screen: Figure 9D is an LCMV expression analysis of the nine top epigenetic hits. [Figure 10A] Figures 10A-10E show additional data for in vivo direct capture Perturb-seq: Figure 10A is a violin plot showing expression of Pdcd1, Havcr2 and Mki67 by cluster. [Figure 10B] Figures 10A-10E show additional data for in vivo direct capture Perturb-seq. Figure 10B is a volcano plot comparing cluster 1 (C1) and cluster 2 (C2). Selected differential genes are shown. [Figure 10C] Figures 10A-10E show additional data for in vivo direct capture Perturb-seq. Figure 10C shows the expression of selected transcription factors Tbx21, Tox, Eomes, and Tcf7. [Figure 10D]Figures 10A-10E show additional data for in vivo direct capture Perturb-seq. Figure 10D is a visualization of cells containing each gene knockout. Figure 10E, top, is a graph of the number of genes repressed or induced for each perturbation. [Figure 10E] Figures 10A-10E show additional data for in vivo direct capture Perturb-seq. Figure 10E, bottom, is a graph of the number of repressed or induced genes shared with Nr4a3-KO for each perturbation. [Figure 11A] FIG. 11A is a casTLE volcano plot of the chronic vs. acute irritation screen comparison, with the top hits labeled. [Figure 11B] FIG. 11B shows the correlation of acute vs. chronic z-scores in minipools vs. genome-wide screens. [Figure 11C] Figure 11C shows correlation of minipool chronic vs acute z-scores for acute vs input (left) or chronic vs input (right). Genes in (G) and (H) are colored by functional category: TCR signaling, integrin signaling (orange), chromatin (blue) or other (grey). Colored boxes in (H, left) represent enhanced, similar or decreased expansion growth (top to bottom) after acute stimulation. [Figure 12A] FIG. 12A shows the correlation between tumor LECz-scores and spleen LFCz-scores, colored by functional category. [Figure 12B] FIG. 12B shows the correlation between in vivo and in vitro z-scores for genes in CRISPR minipools. [Figure 12C] FIG. 12C shows the correlation of in vivo MC-38 and B16 tumor z-scores for genes in CRISPR minipools. [Figure 12D] FIG. 12D is a cytoscape protein-protein interaction network colored by z-score in MC-38 tumors. [Figure 12E]Figure 12E, top, is a box plot of tumor vs. input log fold change for each sgRNA targeting the indicated gene, with the mean control log fold change subtracted. Figure 12E, bottom, is a heat map showing the sgRNA averages of the indicated in vivo or in vitro screens for the same hits. [Figure 12F] Figure 12F is individual sgRNA residuals for the six top hits showing tumor vs. input comparisons (left), spleen vs. input (middle), and in vitro chronic vs. acute (right). [Figure 13A] Figures 13A-13F show SWI / SNF minipool CRISPR screen and functional studies showing that modulating cBAF activity can enhance anti-tumor immunity. Figure 13A shows an in vitro competition assay of Arid1a-sgRNA versus CTRL1 T cells. Left: Cells were mixed at the indicated ratios on day 4 and passaged for 6 days in a chronic stimulation assay. On day 10, proliferation and surface phenotype relative to CTRL1 T cells were assessed by flow cytometry. [Figure 13B] Figures 13A-13F show SWI / SNF minipool CRISPR screen and functional studies showing that modulating cBAF activity can enhance anti-tumor immunity. Figure 13B shows an in vivo competition assay of Arid1a-sgRNA vs CTRL1 T cells. Cells were mixed on day 6 (input) and then implanted into tumor-bearing mice. On day 15, relative tumor growth was assessed by flow cytometry. [Figure 13C] Figures 13A-13F show SWI / SNF minipool CRISPR screen and functional studies showing that modulating cBAF activity can enhance anti-tumor immunity. Figure 13C shows a graph of tumor size for each cohort. Statistical significance was assessed at day 15. [Figure 13D] Figures 13A-13F show SWI / SNF minipool CRISPR screen and functional studies demonstrating that modulating cBAF activity can enhance anti-tumor immunity. Figure 13D is a survival graph showing that Arid1a-sgRNA T cells significantly improve survival of tumor-bearing mice. [Figure 13E] Figures 13A-13F show SWI / SNF minipool CRISPR screens and functional studies demonstrating that modulating cBAF activity can enhance antitumor immunity. Figure 13E shows correlation of SWI / SNF CRISPR minipool tumor enrichment in MC-38 vs. B16 tumor models. [Figure 13F] Figures 13A-13F show SWI / SNF minipool CRISPR screen and functional studies showing that modulating cBAF activity can enhance antitumor immunity. Figure 13F is a painting of three BAF complexes colored by z-scores from SWI / SNF CRISPR minipool experiments in MC-38 tumors. BAF complex paintings were adapted from (Mashtalir et al., 2018). *p<0.05, ***p<0.001. [Figure 14A] Figures 14A-14D show the conserved function of ARID1A in human T cells in vitro and in vivo. Figure 14A is a graph of proliferation and viability of primary human T cells after electroporation with the indicated RNPs. Left: acutely stimulated T cells. Right: chronically stimulated T cells using anti-CD3 coated plates. Data shown are representative of three independent experiments and three donors. [Figure 14B] Figures 14A-14D show the conserved function of ARID1A in human T cells in vitro and in vivo. Figure 14B is a schematic diagram of a CRISPR minipool screen in primary human CD8+ T cells transduced with the NY-ESO-1 specific TCR, 1G4. [Figure 14C]Figures 14A-14D show the conserved function of ARID1A in human T cells in vitro and in vivo. Figure 14C shows the results of a human CRISPR minipool screen aggregated by gene. Figure 14D shows the results of a human CRISPR minipool screen with individual sgRNA replicates shown as dots. Genes are ordered from highest to lowest average LFC. Results shown in (Figures 14C and 14D) are combined from two independent donors, 2 mice per donor, and 2 sgRNAs per target gene, for a total of n=8 sgRNA replicates per target gene. In (Figures 14C and 14D), orange indicates inhibitory receptors, red indicates TCR signaling pathway genes, blue indicates chromatin remodelers, and grey indicates negative control. [Figure 14D] Figures 14A-14D show the conserved function of ARID1A in human T cells in vitro and in vivo. Figure 14C shows the results of a human CRISPR minipool screen aggregated by gene. Figure 14D shows the results of a human CRISPR minipool screen with individual sgRNA replicates shown as dots. Genes are ordered from highest to lowest average LFC. Results shown in (Figures 14C and 14D) are combined from two independent donors, 2 mice per donor, and 2 sgRNAs per target gene, for a total of n=8 sgRNA replicates per target gene. In (Figures 14C and 14D), orange indicates inhibitory receptors, red indicates TCR signaling pathway genes, blue indicates chromatin remodelers, and grey indicates negative control. [Figure 15A] Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15A is a schematic of direct capture Perturb-seq of sorted TILs. [Figure 15B] Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15B shows scRNA-seq profiles of TILs colored by cluster assignment. [Figure 15C]Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15C shows scRNA-seq profiles of cells colored by perturbations detected in each cell. Cells where no guide or multiple guides were detected are shown in grey. [Figure 15D] Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15D shows the expression of selected marker genes in respective single cells. [Figure 15E] Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15E shows the analysis of LCMV signature gene sets for each cluster. Gene set enrichment scores were calculated for each single cell and cell values ​​were averaged by cluster and z-scored. [Figure 15F] Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles of cBAF and INO80 complexes in TILs. Figure 15F shows a histogram of Pearson correlation of differential gene expression for pairs of sgRNAs. Top: Pairs targeting the same gene are shown in blue, other pairs in grey. Bottom: Pairs targeting the same protein complex are shown in red, other pairs in grey. Complexes considered in the analysis are cBAF (Arid1a, Arid1b, Smarcd2 and Smarcc1) and INO80 (Ino80c and Actr5). Pairs of sgRNAs targeting the same gene are excluded. [Figure 15G]Figures 15A-15G show in vivo Perturb-seq revealing distinct transcriptional roles for cBAF and INO80 complexes in TILs. Figure 15G, left: Heatmap of differential gene expression correlation for each pair of sgRNAs. Figure 15G, center (left to right): Representation of each sgRNA in pre-implant samples, cell counts for each sgRNA in the Perturb-seq dataset, and estimated fold change for each sgRNA compared to control. Figure 15G, right: Percentage of cells in each cluster for each sgRNA. [Figure 16A] Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16A is a volcano plot comparing aggregate cells with the indicated perturbations versus CTRL1 cells. [Figure 16B] Figures 16A-16H show that cBAF-depleted T cells display enhanced effector gene signatures and reduced terminal exhaustion. Figure 16B shows pairwise correlations of differential gene expression induced by each perturbation. [Figure 16C-1] Figures 16A-16H show that cBAF-depleted T cells display enhanced effector gene signatures and reduced terminal exhaustion. Figure 16C is a heatmap of all upregulated (top) or downregulated (bottom) genes in at least one perturbation, grouped by which perturbation has the strongest effect on expression. Selected genes in each block are labeled. [Figure 16C-2] Figures 16A-16H show that cBAF-depleted T cells display enhanced effector gene signatures and reduced terminal exhaustion. Figure 16C is a heatmap of all upregulated (top) or downregulated (bottom) genes in at least one perturbation, grouped by which perturbation has the strongest effect on expression. Selected genes in each block are labeled. [Figure 16D]Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16D shows a comparison of gene sets up- or down-regulated by perturbation of the cBAF subunits, Arid1a, Smarcd2, and Smarcc1. [Figure 16E] Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16E shows a comparison of gene sets up- or down-regulated by perturbation of INO80 subunits, Actr5, or Ino80c. [Figure 16F] Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16F shows a comparison of gene sets upregulated by perturbation of cBAF subunits, INO80 subunits, or Pdcd1, Gata3, or Arid2. [Figure 16G] Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16G shows enrichment of up- and down-regulated gene sets in LCMV expression data (Daniel et al., 2021). Module scores for each gene set were calculated for each single cell in the LCMV dataset, averaged by cluster, and then z-scored to obtain the indicated enrichment z-score. [Figure 16H] Figures 16A-16H show that cBAF-depleted T cells exhibit enhanced effector gene signatures and reduced terminal exhaustion. Figure 16H shows selected GO terms for the indicated gene sets. [Figure 17A]Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 17A shows principal component analysis of ATAC-seq profiles of Arid1a-sgRNA and CTRL1 cells in an in vitro exhaustion competition assay. Unperturbed naive and activated samples (day 0 and day 2) are included for reference. [Figure 17B] Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 17B shows a comparison of "open" and "closed" ATAC-seq peak sets from days 6 to 10 for each genotype. [Figure 17C] Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 16C is a visualization of "open" and "closed" ATAC-seq peak sets, with selected nearest neighbor genes labeled. [Figure 17D] Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 16D shows ATAC-seq signal tracks of selected loci. Representative replicates are shown for each condition. [Figure 17E] Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 16E is a heat map showing the ATAC-seq coverage of each peak in the "terminal exhaustion peak set" for Arid1a-sgRNA and CTRL1 cells at days 6 and 10 in an in vitro exhaustion assay. Reference data from TILs, as well as reference naive and activated cell profiles, are also included. [Figure 17F] Figures 17A-17F show that Arid1a promotes the acquisition of an exhausted T cell chromatin state. Figure 16F is a chromVAR motif accessibility heatmap for Arid1a-sgRNA and CTRL1 ATAC-seq samples. Selected motifs are shown on the right. The top 100 variable motifs are shown. [Figure 18A]Figure 18A shows effector cytokine production of acutely (left) and chronically (right) stimulated T cells after 6 days of chronic stimulation (day 8 after isolation). Cells were restimulated with PMA and ionomycin 8 days after the initial stimulation. [Figure 18B] Figure 18B is a graph of the empirical cumulative distribution of peak reachability for peaks in the Term.TEX Peak Set (top) and Prog.TEX Peak Set (bottom) at the indicated time points in vitro. Reference profiles from TILs are included as indicated. [Figure 18C] Figure 18C shows box plots for the indicated peak sets and a reference TIL sample in an in vitro exhaustion assay. Each point represents one peak. [Figure 19A] Figures 19A and 19B are a comparison of cytokine production following acute stimulation, chronic stimulation (anti-CD3 stimulation for 6 days) (Figure 19A) or a modified chronic stimulation protocol (anti-CD3 stimulation for 6 days followed by a 48 hour rest) (Figure 19B). [Figure 19B] Figures 19A and 19B are a comparison of cytokine production following acute stimulation, chronic stimulation (anti-CD3 stimulation for 6 days) (Figure 19A) or a modified chronic stimulation protocol (anti-CD3 stimulation for 6 days followed by a 48 hour rest) (Figure 19B). [Figure 19C] FIG. 19C shows the Gini index and empirical cumulative distribution function for each sample in the genome-wide screen. [Figure 20A-1] Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20A is a Volcano plot of genome-wide CRISPR screen results using casTLE (top left), MAGeCK (top right), and the closed pipeline (bottom). [Figure 20A-2] Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20A is a Volcano plot of genome-wide CRISPR screen results using casTLE (top left), MAGeCK (top right), and the closed pipeline (bottom). [Figure 20B]Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20B shows a comparison of the hit lists for each of the three pipelines. [Figure 20C] Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20C shows a comparison of LFC difference calculated by the closed pipeline vs. casTLE effect (left) and MAGeCK LFC (right). [Figure 20D] Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20D is a count table for Rpl13a. [Figure 20E] Figures 20A-20E show a comparison of CRISPR analysis strategies. Figure 20E shows genome-wide screen results when z-scores are calculated relative to all sgRNAs or the set of olfactory receptors (Vmnr* genes). [Figure 21A] Figures 21A-21E show data for the targeted in vitro screen: Figure 21A shows the sgRNA representation of each sample in the in vitro minipool screen. [Figure 21B] Figures 21A-21E show data for the targeted in vitro screen. Figure 21B shows the correlation of sgRNA counts for each sample in the minipool screen. [Figure 21C] Figures 21A-21E show data for the targeted in vitro screen, and Figure 21C shows the correlation of chronic vs. acute replication z-scores. [Figure 21D] Figures 21A-21E show data for the targeted in vitro screen. Figure 21D is a Cytoscape interaction network in which genes are colored by their z-score in the chronic vs. acute minipool screen. [Figure 21E] Figures 21A-21E show data for the targeted in vitro screen. Figure 21E is a Cytoscape interaction network in which genes are colored by their fitness category classification upon acute stimulation. [Figure 22A-1]Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a-targeting sgRNAs. Figure 22A shows sgRNA pool coverage for each sample in the in vivo minipool screen. [Figure 22A-2] Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a-targeting sgRNAs. Figure 22A shows sgRNA pool coverage for each sample in the in vivo minipool screen. [Figure 22B] Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a targeting sgRNAs. Figure 22B shows tumor, spleen and in vitro minipool chronic vs acute sgRNA residuals for selected genes in the "TCR signaling" and "integrin signaling" categories. [Figure 22C] Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a targeting sgRNAs. Figure 22C shows box plots of spleen vs. input and acute vs. chronic log fold changes for each sgRNA targeting the indicated gene, with the mean control log fold change subtracted. [Figure 22D] Figures 22A-22F show data on targeted in vivo screening and validation of Arid1a targeting sgRNAs. Figure 22D shows Sanger sequencing (TIDE) of the editing efficiency of Arid1a sgRNAs. [Figure 22E] Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a targeting sgRNAs. Figure 22E shows Western blot analysis of Arid1a sgRNAs and protein knockdown of Arid1b and Smarca4 expression. [Figure 22F]Figures 22A-22F show data for targeted in vivo screening and validation of Arid1a targeting sgRNAs. Figure 22F shows quantification of protein knockdown for each identified isoform of Arid1a (panel C3 band). *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. [Figure 23A] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23A shows scRNA-seq profiles of TILs colored by each independent experiment. [Figure 23B] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23B shows scRNA-seq profiles of TILs colored by each sample. [Figure 23C] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23C shows scRNA-seq profiles of TILs colored by expected stage of the cell cycle. [Figure 23D] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23D shows additional marker genes shown for each cluster. [Figure 23E] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23E shows an expanded reference LCMV dataset with single cell profiles colored by LCMV cluster. [Figure 23F] Figures 23A-23G show data for in vivo Perturb-seq. Figure 23F shows an expanded LCMV dataset in which single cell profiles are colored by LCMV infection (acute corresponds to Armstrong infection, chronic corresponds to clone 13) and time point (8 or 21 days post-infection). [Figure 23G]Figures 23A-23G show data for in vivo Perturb-seq. Figure 23G is a heat map of correlations of differential gene expression subsetted onto each cluster. Indicated gene knockdown was compared to CTRL1 cells within each cluster. Comparisons with <150 cells in the comparison group are excluded due to lack of power. [Figure 24A] Figures 24A-24G show data on up- and down-regulated gene sets and ATAC-seq data. Figure 24A shows a comparison of gene sets down-regulated by perturbation of cBAF subunits, INO80 subunits, or Pdcd1-sgRNA, Gata3-sgRNA, or Arid2-sgRNA. [Figure 24B-1] Figures 24A-24G show data on up- and down-regulated gene sets and ATAC-seq data. Figure 24B shows the module scores of the indicated gene sets calculated for each cell in the extended LCMV reference dataset. [Figure 24B-2] Figures 24A-24G show data on up- and down-regulated gene sets and ATAC-seq data. Figure 24B shows the module scores of the indicated gene sets calculated for each cell in the extended LCMV reference dataset. [Figure 24C] Figures 24A-24G show data for up- and down-regulated gene sets and ATAC-seq data. Figure 24C shows box plots for the indicated peak sets in in vitro exhaustion assays and reference TIL samples. Each point represents one peak. [Figure 24D] Figures 24A-24G show data for up- and down-regulated gene sets and ATAC-seq data. Figure 24D is a graph of the empirical cumulative distribution of peak reachability for peaks in the Term.TEX Peak Set (top) and Prog.TEX Peak Set (bottom) for the indicated samples in vitro. Reference profiles from TILs are included as indicated. [Figure 24E]Figures 24A-24G show data for up- and down-regulated gene sets as well as ATAC-seq data. Figure 24E is a principal component analysis of ATAC-seq data of primary human T cells chronically stimulated for 6 days. Results in (Figures 24E-24G) are pooled from three different human donors in two independent experiments using two different ARID1A targeting sgRNAs per donor. [Figure 24F] Figures 24A-24G show data for up- and down-regulated gene sets as well as ATAC-seq data. Figure 24F shows differential peaks between ARID1A-sgRNA and AAVS primary human T cells. Results in (Figures 24E-24G) are combined from three different human donors in two independent experiments using two different ARID1A-targeting sgRNAs per donor. [Figure 24G] Figures 24A-24G show data for up- and down-regulated gene sets as well as ATAC-seq data. Figure 24G is a HOMER analysis of TF motifs enriched in AAVS "up" peaks. Selected highly ranked motifs are shown. Results in (Figures 24E-24G) are combined from three different human donors in two independent experiments with two different ARID1A targeting sgRNAs per donor. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0038] Herein, an in vitro T cell exhaustion model allowed genome-wide screening for genes that affect T cell function. Using this model and a genome-wide CRISPR screen, several gene targets were identified whose deletion prevented CAR-T cell exhaustion, improved T cell survival in the presence of chronic antigen in vitro, and improved T cell persistence and function in tumor models in vivo. These included several genes involved in gene regulation and epigenetic modification, including ARID1A, WDR82, INO80, HDAC1, and ZFP219. Cell therapy that deletes each of these genes has applications in improved CAR-T or other adoptive T cell-based therapies.

[0039] The section headings used in this section and throughout the disclosure herein are for organizational purposes only and are not intended to be limiting.

[0040] 1.Definition The terms "comprise(s)", "include(s)", "having", "has", "can", "contain(s)" and variations thereof, as used herein, are intended to be open-ended transitions, terms, or words that do not exclude the possibility of additional acts or constructs. The singular forms "a", "and" and "the" include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments that "comprise", "consist of", and "consist essentially of" the embodiments or elements presented herein, whether expressly stated or not.

[0041] In recitation of numerical ranges herein, each intervening number therebetween is expressly contemplated with the same precision, for example, in the range 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and in the range 6.0 to 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly contemplated.

[0042] Unless otherwise defined herein, scientific and technical terms used in connection with this disclosure shall have the meanings commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear, but in the event of any potential ambiguity, the definitions provided herein shall take precedence over any dictionary or extrinsic definitions. Further, unless otherwise required by context, singular terms shall include the plural and plural terms shall include the singular.

[0043] The terms "engineered," "non-naturally occurring," "modified," and "synthetic" are used interchangeably and indicate the involvement of the hand of man. These terms, when referring to a cell or nucleic acid, mean that the nucleic acid or cell is at least substantially free of at least one other component with which it is naturally associated and found in nature.

[0044] As used herein, a "nucleic acid" or "nucleic acid sequence" refers to a polymer or oligomer of pyrimidine and / or purine bases, preferably cytosine, thymine, and uracil, and adenine and guanine, respectively (see Albert L. Lehninger, Principles of Biochemistry, at pp. 793-800 (Worth Pub. 1982)). The present technology contemplates any deoxyribonucleotide, ribonucleotide, or peptide nucleic acid component, and any chemical variants thereof, such as methylated, hydroxymethylated, or glycosylated forms of these bases, and the like. The polymers or oligomers may be heterogeneous or homogeneous in composition, and may be isolated from naturally occurring sources or artificially or synthetically produced. Furthermore, the nucleic acid may be DNA or RNA, or a mixture thereof, and may exist persistently or transiently in single-stranded or double-stranded form, including homoduplexes, heteroduplexes, and hybrid states. The term "nucleic acid" or "nucleic acid sequence" may also encompass strands that contain non-naturally occurring nucleotides, modified nucleotides, and / or non-nucleotide components (e.g., "nucleotide analogs") that can perform the same function as naturally occurring nucleotides; furthermore, the term "nucleic acid sequence" as used herein refers to oligonucleotides, nucleotides or polynucleotides, and fragments or portions thereof, as well as DNA or RNA of genomic or synthetic origin, which may be single- or double-stranded and may represent sense or antisense strands. The terms "nucleic acid", "polynucleotide", "nucleotide sequence" and "oligonucleotide" are used interchangeably. These terms refer to polymeric forms of nucleotides of any length, deoxyribonucleotides or ribonucleotides, or analogs thereof.

[0045] As used herein, the terms "providing," "administering," and "introducing" are used interchangeably herein and refer to placing a composition of the present disclosure into a subject by a method or route that results in at least partial localization of the composition to a desired site. The composition can be administered by any suitable route that delivers it to the desired location in the subject.

[0046] A "subject" or "patient" may be human or non-human, and may include animal strains or species, e.g., mouse models as described herein, used as "model systems" for research purposes. Similarly, a subject may include an adult or adolescent (e.g., a child). Furthermore, a subject may refer to any living organism, preferably a mammal (e.g., human and non-human), that may benefit from administration of the compositions contemplated herein. Examples of mammals include, but are not limited to, the class Mammalia: humans, non-human primates such as chimpanzees and other apes, and monkey species; farm animals such as cows, horses, sheep, goats, pigs; domestic animals such as rabbits, dogs, and cats; any member of the laboratory animal class, including rodents such as rats, mice, and guinea pigs, and the like. Examples of non-mammals include, but are not limited to, birds, fish, and the like. In one embodiment, the mammal is a human.

[0047] As used herein, "treat," "treating," and the like, refer to slowing, halting, or reversing the progression of a disease or injury when the engineered T cells or compositions described herein are given to a suitable control subject. The term also refers to reversing the progression of such disease or injury to the point of eliminating or significantly reducing its symptoms. Thus, "treat" refers to the application or administration of the engineered T cells or compositions described herein to a subject, the subject having a disease or symptoms of a disease, with the purpose of curing, curing, alleviating, mitigating, altering, treating, ameliorating, improving, or affecting the disease or symptoms of the disease.

[0048] 2. Engineered T Cells Provided herein are engineered T cells that lack at least one gene that promotes or supports T cell persistence and functionality, which gene may play a role in chromatin formation, chromatin remodeling (e.g., ATP-dependent chromatin remodeling), T cell receptor signaling pathway, immune response activation signaling, immune response activation cell surface receptor signaling pathway, nucleosome degradation, and / or Fc receptor signaling pathway. In some embodiments, the gene comprises a chromatin remodeling and transcription factor.

[0049] In some embodiments, the at least one gene is selected from the genes included in Figures 3, 5C, and 9B. In certain embodiments, the at least one gene is INO80C (INO80 complex subunit C), GATA3 (GATA binding protein 3), ARID1A (AT-rich interaction domain 1A), WDR82 (WD repeat domain 82), TRP53 (tumor protein P53), GPR137C (G protein-coupled receptor 137C), ZFP219 (zinc finger protein 219), HDAC1 (histone deacetylase 1), ELMSAN1 (ELM2 and Myb / SANT-like domain containing 1), or ACTR8 (actin-related protein 8). In some embodiments, the engineered T cells lack two or more of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. In some embodiments, the engineered T cells further lack NR4A3 (nuclear receptor subfamily 4 group A member 3).

[0050] In some embodiments, the engineered T cells lack at least one chromatin remodeling protein or gene encoding same. In some embodiments, the engineered T cells lack two or more chromatin remodeling proteins or genes encoding same. In some embodiments, the at least one chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF (SWItch / sucrose non-fermenting) family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5 (actin-associated protein 5), Ino80 (INO80 complex ATPase subunit), Ino80c (INO80 complex subunit C), Ino80b (INO80 complex subunit B), Actr8 (actin-associated protein 8), or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a (AT-rich interaction domain 1A), Arid2 (AT-rich interaction domain 2), Arid1b (AT-rich interaction domain 1B), Smarcb1 (SWI / SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily b, member 1), Smarcd2 (SWI / SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily d, member 2), Smarca4 (SWI / SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 4), Smarcc1 (SWI / SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily c, member 1), or a combination thereof. In some embodiments, the engineered T cells further lack at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, and ELMSAN1.

[0051] "Lack of a gene" can refer to a complete or partial deletion, mutation, or other disruption in which a functional gene product is not expressed or is targeted for degradation upon expression. Thus, lack of a gene can result from any disruption to the genetic code in which a portion of the gene is altered, thereby affecting transcription and / or translation, for example, rendering the gene unreadable through knock-out techniques or by inserting additional genes for a desired protein or by inserting regulatory sequences that regulate the transcription of an existing sequence. In certain embodiments, a gene or a portion thereof is deleted, commonly referred to as a gene knock-out.

[0052] Any method known in the art for genetic engineering may be used to generate the engineered T cells described herein, including, but not limited to, the use of clustered interspersed short palindromic repeats (CRISPR) / CRISPR-associated protein (Cas) systems, meganucleases, transcription activator-like effector nucleases (TALENs), or zinc finger nucleases (ZFNs).

[0053] In some embodiments, the T cells maintain functionality under conditions in which unmodified T cells, T cells that are not deficient in at least one gene that promotes or supports T cell persistence and functionality, exhibit exhaustion (e.g., maintain functionality of T cells exposed to excess antigen). "T cell exhaustion" refers to the loss of T cell function, which can occur as a result of infection (e.g., chronic infection) or disease. T cell exhaustion is associated with increased expression of exhaustion markers and inhibitory receptors (e.g., PD-1, TIM-3, and LAG-3), apoptosis, and decreased cytokine secretion.

[0054] The present invention is not limited by the type of T cell that is engineered to lack at least one gene that promotes or supports T cell persistence and functionality. The T cell may be selected from CD3+ T cells (e.g., a combination of CD4+ and CD8+ T cells), CD8+ T cells, CD4+ T cells, natural killer (NK) T cells, alpha beta T cells, gamma delta T cells, or any combination thereof. In some embodiments, the T cell is a memory T cell (e.g., a central memory T cell or an effector memory T cell). In some embodiments, the T cell is a tumor infiltrating lymphocyte. In some embodiments, the T cell is a cytokine-induced killer cell. In selected embodiments, the T cell is a CD8+ T cell.

[0055] In some embodiments, the T cells are naturally occurring T cells. For example, the T cells may be isolated from a subject sample. In some embodiments, the T cells are anti-tumor T cells (e.g., T cells that have activity against a tumor (e.g., an autologous tumor) and are activated and expanded in response to an antigen). Anti-tumor T cells include, but are not limited to, T cells obtained from a resected tumor or tumor biopsy (e.g., tumor infiltrating lymphocytes (TILs)) and polyclonal or monoclonal tumor-reactive T cells (e.g., obtained by apheresis and expanded ex vivo against tumor antigens presented by autologous or artificial antigen-presenting cells). In some embodiments, the T cells are expanded ex vivo.

[0056] In some embodiments, the T cell further comprises a foreign receptor or a nucleic acid encoding a foreign receptor. In some embodiments, the foreign receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR).

[0057] An exogenous receptor is not limited by its specificity in recognizing and responding to any particular antigen or protein. Such receptors are generally composed of an extracellular domain containing a specific antigen-binding motif (e.g., a single chain antibody (scFv)) linked to an intracellular T cell signaling motif.

[0058] In certain embodiments, T cells are genetically modified with exogenous receptors that recognize and respond to antigens for infectious disease and / or autoimmunity (e.g., Aspergillus carbohydrate β-glucan, Hepatitis C virus E2 glycoprotein, HIV envelope glycoprotein gp120).

[0059] In certain embodiments, T cells are genetically modified with an exogenous receptor that recognizes and responds to tumor antigens. The present invention is not limited by the type of tumor antigen so recognized. The term "tumor antigen" as used herein refers to any molecule (e.g., protein, peptide, lipid, carbohydrate, etc.) that is expressed or overexpressed solely or primarily by tumor or cancer cells, such that the antigen is associated with a tumor or cancer. A tumor antigen can also be expressed by normal, non-tumor, or non-cancerous cells. However, in such cases, the expression of the tumor antigen by normal, non-tumor, or non-cancerous cells is not as robust as the expression by tumor or cancer cells. In this regard, a tumor or cancer cell can overexpress the antigen or express the antigen at a significantly higher level than the expression of the antigen by normal, non-tumor, or non-cancerous cells. Moreover, a cancer antigen can also be expressed by cells of different states of development or maturation. For example, a tumor antigen can also be expressed by cells of the embryonic or fetal stage, which are not normally found in adults. Alternatively, tumor antigens may be expressed primarily by stem or progenitor cells, cells that are not normally found in adults.

[0060] A tumor antigen can be an antigen expressed by any cell of any cancer or tumor. A tumor antigen can be a tumor antigen of only one cancer or tumor, such that the tumor antigen is associated with or characterized by only one cancer or tumor. Alternatively, a tumor antigen can be a tumor antigen of more than one cancer or tumor (e.g., characterized by more than one cancer or tumor). For example, a tumor antigen can be expressed by both breast cancer cells and prostate cancer cells, but not expressed at all by normal, non-tumor or non-cancerous cells.

[0061] Exemplary tumor antigens include glycoprotein 100 (gp100), melanoma antigen recognized by T cells 1 (MART-1), melanoma antigen gene (MAGE) family members (e.g., MAGE-A1, MAGE-A2, MAGE-A3, MAGE-A4, MAGE-A5, MAGE-A6, MAGE-A7, MAGE-A8, MAGE-A9, MAGE-A10, MAGE-A11, MAGE-A12), New York esophageal squamous cell carcinoma 1 (NY-ESO-1), vascular endothelial growth factor receptor-2 (VEGF-1), and / or VEGF-2. GFR-2), glioma associated antigen, carcinoembryonic antigen (CEA), beta-human chorionic gonadotropin, alpha fetoprotein (AFT), lectin-reactive AFP, thyroglobulin, human telomerase reverse transcriptase, prostate specific antigen (PSA), prostate cancer tumor antigen-1 (PCTA-1), insulin growth factor (IGF)-I, IGF-II, IGF-I receptor, intestinal carboxylesterase, human epidermal growth factor receptor 2 (HER-2), mesothelin, and epidermal growth factor receptor variant III (EGFR III).

[0062] Any T cell that contains a receptor that recognizes a tumor antigen finds use in the T cells, compositions and methods of the invention. Examples include CD19, CD20, CD22, receptor tyrosine kinase-like orphan receptor 1 (ROR1), disialoganglioside 2 (GD2), Epstein-Barr virus (EBV) proteins or antigens, folate receptor, mesothelin, human carcinoembryonic antigen (CEA), prostatic acid phosphatase (PAP), CD33 / IL3R, tyrosine protein kinase Met (c-Met) or hepatocyte growth factor receptor (HGFR), prostate specific membrane antigen (PSMA), glycolipid F77, epidermal growth factor receptor mutants, and the like. These include, but are not limited to, T cells that express a receptor (e.g., a native or naturally occurring receptor, or a receptor engineered to express a synthetic receptor, such as an engineered TCR or CAR) that recognizes an antigen selected from epidermal growth factor receptor III (EGFRvIII), NY-ESO-1, melanoma antigen gene (MAGE) family member A3 (MAGE-A3), melanoma antigen recognized by T cell 1 (MART-1), GP1000, p53, or other tumor antigens described herein.

[0063] In some embodiments, the T cells are engineered to express a chimeric antigen receptor (CAR). Any CAR that specifically binds to a desired antigen (e.g., a tumor antigen) may be utilized in the present invention. In certain embodiments, the CAR comprises an antigen-binding domain. In certain embodiments, the antigen-binding domain is a single-chain variable fragment (scFv) that comprises heavy and light chain variable regions that specifically bind to a desired antigen. In some embodiments, the CAR further comprises a transmembrane domain (e.g., a T cell transmembrane domain (e.g., a CD28 transmembrane domain)) and a signaling domain (e.g., a T cell receptor signaling domain (e.g., a TCR zeta chain)) that comprises one or more immunoreceptor tyrosine-based activation motifs (ITAMs). In some embodiments, the CAR comprises one or more costimulatory domains (e.g., a domain that provides a second signal that stimulates T cell activation). The present invention is not limited by the type of costimulatory domain. Indeed, any costimulatory domain known in the art may be used, including, but not limited to, CD28, OX40 / CD134, 4-1BB / CD137 / TNFRSF9, high affinity immunoglobulin E receptor gamma subunit, FcERIγ, ICOS / CD278, interleukin 2 subunit beta (ILRβ) or CD122, cytokine receptor common subunit gamma (IL-2Rγ) or CD132, and CD40. In some embodiments, the costimulatory domain is 4-1BB. In some embodiments, the costimulatory domain is CD28.

[0064] CARs may contain target-specific binding elements, also called antigen-binding moieties. The choice of moiety depends on the type and number of ligands that define the surface of the target cell. For example, antigen-binding domains may be selected to recognize ligands that act as cell surface markers on target cells associated with a particular disease state. Examples of cell surface markers that may act as ligands for the antigen moiety domains in the CARs of the present invention include cell surface markers associated with viral, bacterial and parasitic infections, autoimmune diseases, and cancer cells as described above.

[0065] Depending on the desired antigen to be targeted, the CAR can be engineered to contain an appropriate antigen-binding moiety specific for the desired antigen target. For example, if CD19 is the desired antigen to be targeted, an antibody against CD19 can be used as an antigen-binding moiety for incorporation into the CAR of the present invention.

[0066] The nucleic acid encoding the exogenous receptor may comprise DNA or RNA (e.g., mRNA). In some embodiments, the nucleic acid comprises a vector.

[0067] The nucleic acid may comprise a constitutive, regulatable or inducible, cell type-specific, tissue-specific, or species-specific promoter. In addition to sequences sufficient to direct transcription, a promoter may also comprise sequences of other control elements involved in regulating transcription (e.g., enhancers, Kozak sequences, and introns). Many promoters / regulatory sequences useful for driving constitutive expression are available in the art, including, but not limited to, CMV (cytomegalovirus promoter), EF1a (human elongation factor 1 alpha promoter), SV40 (simian vacuolar virus 40 promoter), PGK (mammalian phosphoglycerate kinase promoter), Ubc (human ubiquitin C promoter), human beta-actin promoter, rodent beta-actin promoter, CBh (chicken beta actin promoter), CAG (hybrid promoter contains a CMV enhancer, chicken beta actin promoter, and rabbit beta-globulin splice acceptor), TRE (tetracycline response element promoter), H1 (human polymerase III RNA promoter), U6 (human U6 small nuclear promoter), and the like. Additional promoters that can be used for expression include, but are not limited to, cytomegalovirus (CMV) intermediate early promoter, Rous sarcoma virus LTR, HIV-LTR, HTLV-1 LTR, Moloney murine leukemia virus (MMLV) LTR, myeloproliferative sarcoma virus (MPSV) LTR, spleen focus forming virus (SFFV) LTR, and other viral LTRs, simian virus 40 (SV40) early promoter, herpes simplex tk virus promoter, and elongation factor 1-alpha (EF1-α) promoter with or without the EF1-α intron. Additional promoters include any constitutively active promoter. Alternatively, any regulatable promoter may be used, such that its expression can be regulated inside the cell.

[0068] Furthermore, inducible expression can be achieved by placing the nucleic acid encoding such a molecule under the control of an inducible promoter / regulatory sequence. Promoters known in the art that can be induced in response to inducers such as metals, glucocorticoids, tetracycline, hormones, and the like are also contemplated for use in the present invention. Thus, it is recognized that the present disclosure includes the use of any promoter / regulatory sequence known in the art that can drive expression of a desired protein operably linked thereto.

[0069] The disclosure also provides vectors containing the nucleic acids and cells containing the nucleic acids or vectors.

[0070] In certain embodiments, the vectors of the present disclosure can be used to drive expression of one or more sequences in mammalian cells using mammalian expression vectors. Examples of mammalian expression vectors include pCDM8 (Seed, Nature (1987) 329:840, incorporated herein by reference) and pMT2PC (Kaufman et al., EMBO J. (1987) 6:187, incorporated herein by reference). When used in mammalian cells, the control functions of the expression vector are typically provided by one or more regulatory elements. For example, commonly used promoters are derived from polyoma, adenovirus 2, cytomegalovirus, simian virus 40, and others disclosed herein and known in the art. For other suitable expression systems, see, for example, chapters 16 and 17 of Sambrook et al., MOLECULAR CLONING: A LABORATORY MANUAL. 2nd ed., Cold Spring Harbor Laboratory, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, 1989. The above documents are incorporated herein by reference.

[0071] Additionally, vectors may contain, for example, some or all of the following: selectable marker genes for selection of stable or transient transfectants in host cells; transcription termination and RNA processing signals; 5'- and 3'-untranslated regions; internal ribosome binding sites (IRES), versatile multiple cloning sites; and reporter genes for evaluating expression of chimeric receptors. Methods for making suitable vectors and vectors containing transgenes are well known and available in the art. Selectable markers include chloramphenicol resistance, tetracycline resistance, spectinomycin resistance, neomycin, streptomycin resistance, erythromycin resistance, rifampicin resistance, bleomycin resistance, heat-adapted kanamycin resistance, gentamicin resistance, hygromycin resistance, trimethoprim resistance, dihydrofolate reductase (DHFR), GPT; URA3, HIS4, LEU2 and TRP1 genes of Saccharomyces cerevisiae.

[0072] Once introduced into a cell, the vector may be maintained as an autonomously replicating sequence or an extrachromosomal genetic element, or may be integrated into the host DNA. The nucleic acid may be delivered to the cell by any appropriate means.

[0073] Viral and non-viral based gene transfer methods can be used to introduce nucleic acid into cells. Such methods can be used to administer nucleic acid to cells in culture or to a host organism. Non-viral vector delivery systems include DNA plasmids, cosmids, RNA (e.g., transcripts of vectors described herein), nucleic acid, and nucleic acid complexed with a delivery vehicle.

[0074] Viral vector delivery systems include DNA and RNA viruses, which have episomal or integrated genomes after delivery to cells. Various viral constructs can be used to deliver the nucleic acid to cells. Viral vectors include, for example, retroviruses, lentiviruses, adenoviruses, adeno-associated viruses, and herpes simplex virus vectors. Non-limiting examples of such recombinant viruses include recombinant adeno-associated viruses (AVV), recombinant adenoviruses, recombinant lentiviruses, recombinant retroviruses, recombinant herpes simplex viruses, recombinant poxviruses, phages, and the like. The present disclosure provides vectors that can integrate into the host genome, such as retroviruses or lentiviruses. See, e.g., Ausubel et al., Current Protocols in Molecular Biology, John Wiley & Sons, New York, 1989; Kay, MA et al., 2001 Nat. Medic. 7(1):33-40; and Walther W. and Stein U. 2000 Drugs, 60(2):249-71, all of which are incorporated herein by reference.

[0075] A vector according to the present disclosure can be transformed, transfected, or otherwise introduced into a cell. Transfection refers to the uptake of a vector by a cell, whether or not any coding sequences are actually expressed. Numerous transfection methods are known to those of skill in the art, such as lipofectamine, calcium phosphate co-precipitation, electroporation, DEAE-dextran treatment, microinjection, viral infection, and other methods known in the art. Transduction refers to the entry of a virus into a cell and the expression (e.g., transcription and / or translation) of sequences delivered by the viral vector genome. In the case of recombinant vectors, "transduction" generally refers to the entry of a recombinant viral vector into a cell and the expression of a nucleic acid of interest delivered by the vector genome.

[0076] Methods of delivering vectors to cells are well known in the art and may include DNA or RNA electroporation, transfection reagents such as liposomes or nanoparticles to deliver DNA or RNA; delivery of DNA, RNA or proteins by mechanical deformation (e.g., Sharei et al., Proc. Natl. Acad. Sci. USA (2013) 110(6):2082-2087, which is incorporated herein by reference); or viral transduction. In some embodiments, vectors are delivered to cells by viral transduction. Nucleic acids can be delivered as part of a larger construct, such as a plasmid or viral vector, or directly, for example, by electroporation, lipid vehicles, viral transporters, microinjection, and biolistic bombardment (high velocity particle bombardment).

[0077] Additionally, delivery vehicles such as nanoparticle- and lipid-based delivery systems can be used. Further examples of delivery vehicles include lentiviral vectors, ribonucleoprotein (RNP) complexes, lipid-based delivery systems, gene guns, hydrodynamic, electroporation or nucleofection microinjection, and biolistics. Various gene delivery methods are discussed in detail in Nayerossadat et al., (Adv Biomed Res. 2012; 1:27) and Ibraheem et al., (Int J Pharm. 2014 Jan. 1; 459(1-2):70-83), which are incorporated herein by reference.

[0078] Compositions comprising the populations of engineered T cells described herein are also provided.

[0079] The composition may optionally include at least one additional therapeutic agent, such as another agent for treating T cell exhaustion (e.g., an anti-PD-1 checkpoint inhibitor such as nivolumab), or another drug used to treat a subject for an infection or disease associated with T cell exhaustion (e.g., an antiviral, antibiotic, antibacterial, or anticancer drug).

[0080] In some embodiments, the at least one additional therapeutic agent comprises at least one chemotherapeutic agent. As used herein, the terms "chemotherapeutic drug", "chemotherapeutic agent" or "anti-cancer drug" include any small molecule or other agent used in cancer treatment or prevention. Chemotherapy agents include, but are not limited to, cyclophosphamide, methotrexate, 5-fluorouracil, doxorubicin, docetaxel, daunorubicin, bleomycin, vinblastine, dacarbazine, cisplatin, paclitaxel, raloxifene hydrochloride, tamoxifen citrate, abemaciclib, Afinitor (everolimus), alpelisib, anastrozole, pamidronate, anastrozole, exemestane, capecitabine, epirubicin hydrochloride, eribulin mesylate, toremifene, fulvestrant, letrozole, gemcitabine, goserelin, ixabepilone, emtansine, lapatinib, olaparib, megestrol, neratinib, palbociclib, ribociclib, talazoparib, thiotepa, toremifene, methotrexate, and tucatinib. In selected embodiments, the chemotherapeutic agent comprises paclitaxel.

[0081] Compositions can include, for example, cytokines, chemokines and other biological signaling molecules, tumor-specific vaccines, cellular cancer vaccines (e.g., GM-CSF transduced cancer cells), tumor-specific monoclonal antibodies, autologous and allogeneic stem cell rescue (e.g., to increase the graft-versus-tumor effect), other therapeutic antibodies, molecular targeted therapies, anti-angiogenic therapies, infectious agents with therapeutic intent (such as tumor-localizing bacteria), and gene therapies.

[0082] The composition may include a pharma- ceutically acceptable carrier. The term "pharma- ceutically acceptable carrier" as used herein means a non-toxic, inert solid, semi-solid or liquid filler, diluent, encapsulating material, surfactant, cyclodextrin or any type of auxiliary formulation. The carrier may include a single component or a combination of two or more components. Some examples of materials that can act as pharma- ceutically acceptable carriers include sugars, such as but not limited to lactose, glucose and sucrose; starches, such as but not limited to corn starch and potato starch; cellulose and its derivatives, such as but not limited to sodium carboxymethylcellulose, ethylcellulose and cellulose acetate; powdered tragacanth; malt; gelatin; talc; excipients, such as but not limited to cocoa butter and suppository wax; oils, such as but not limited to peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, corn oil and soybean oil; and sorbitol EL, Cremophor RH 60, Solutol HS 15 and Polysorbate 80. any of a variety of surfactants, including but not limited to; cyclodextrins, such as but not limited to alpha-CD, beta-CD, gamma-CD, HP-beta-CD, SBE-beta-CD; glycols, such as propylene glycol; esters, such as but not limited to ethyl oleate and ethyl laurate; agar; buffers, such as but not limited to magnesium hydroxide and aluminum hydroxide; alginic acid; pyrogen-free water; isotonic saline; Ringer's solution; ethyl alcohol and phosphate buffers, as well as other non-toxic compatible lubricating oils, such as but not limited to sodium lauryl sulfate and magnesium stearate, and preservatives and antioxidants can also be present in the compositions according to the judgment of the formulator.

[0083] The route of administration and the form of the composition will dictate the type of carrier used. The composition may be in a variety of forms suitable for, for example, systemic administration (e.g., oral, rectal, nasal, sublingual, buccal, implant or parenteral injection) or local administration (e.g., transdermal, pulmonary, nasal, aural, ocular, liposomal delivery system, or iontophoresis).

[0084] 3. How to generate therapeutic T cells The present disclosure provides methods for generating therapeutic T cells.

[0085] In some embodiments, the method includes obtaining a sample of T cells, altering the DNA of the T cells to knock out or disrupt at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8, and engineering the T cells to express an exogenous receptor.

[0086] In some embodiments, the method includes obtaining a sample comprising a T cell, altering the DNA of the T cell to knock out or disrupt at least one gene encoding a chromatin remodeling protein, and engineering the T cell to express an exogenous receptor. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof. In some embodiments, the method further includes altering the DNA of the T cell to knock out or disrupt at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, and ELMSAN1.

[0087] The DNA modifications prevent or reduce T cell exhaustion compared to cells that do not contain the modifications, thus increasing T cell persistence and function, thereby improving T cells for therapeutic use.

[0088] The T cells may be selected from CD3+ T cells (e.g., a combination of CD4+ and CD8+ T cells), CD8+ T cells, CD4+ T cells, natural killer (NK) T cells, alpha beta T cells, gamma delta T cells, or any combination thereof. In some embodiments, the T cells are memory T cells (e.g., central memory T cells or effector memory T cells). In some embodiments, the T cells are tumor-infiltrating lymphocytes. In some embodiments, the T cells are cytokine-induced killer cells. In selected embodiments, the T cells are CD8+ T cells.

[0089] In some embodiments, the T cells are naturally occurring T cells. For example, the T cells may be isolated from a subject sample. In some embodiments, the T cells are anti-tumor T cells (e.g., T cells that have activity against a tumor (e.g., an autologous tumor) and are activated and expanded in response to an antigen). Anti-tumor T cells include, but are not limited to, T cells (e.g., tumor infiltrating lymphocytes (TILs)) obtained from a resected tumor or tumor biopsy and polyclonal or monoclonal tumor-reactive T cells (e.g., obtained by apheresis and expanded ex vivo against tumor antigens presented by autologous or artificial antigen-presenting cells). In some embodiments, the T cells are expanded ex vivo.

[0090] Altering the DNA of the T cells to knock out or disrupt at least one gene may use methods known in the art and described elsewhere herein.

[0091] The step of engineering the T cells to express the exogenous receptor may include transfecting, transforming, or otherwise introducing a nucleic acid into the cell that expresses the exogenous receptor. Nucleic acids and methods for transfecting, transforming, or otherwise introducing such nucleic acids into cells described elsewhere herein are suitable for the disclosed methods.

[0092] In some embodiments, the foreign receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR). The foreign receptor is not limited by its specificity of recognizing and responding to any particular antigen or protein. In certain embodiments, the T cell is genetically modified with a foreign receptor that recognizes and responds to an antigen of infectious disease and / or autoimmunity. In certain embodiments, the T cell is genetically modified with a foreign receptor that recognizes and responds to a tumor antigen.

[0093] 4.Treatment method The present disclosure also provides methods for treating a disease or injury.

[0094] In some embodiments, the methods include administering to a subject an effective amount of T cells that have been modified to lack at least one gene that promotes or supports T cell persistence and functionality.

[0095] In certain embodiments, the at least one gene is selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1 and ACTR8.

[0096] In certain embodiments, at least one gene encodes a chromatin remodeling protein. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof.

[0097] The present invention is not limited by the type of disease or condition being treated: any disease or condition treatable via administration of T cells can be treated in an improved and more effective manner using the T cells and compositions thereof described herein.

[0098] In some embodiments, the administration inhibits or reduces T cell exhaustion (e.g., compared to a subject receiving the same amount of T cells (e.g., CAR T cells or T cells comprising an exogenous TCR) that are not engineered to lack at least one gene). In some embodiments, the at least one gene is selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof.

[0099] In some embodiments, administration results in improved T cell survival in the presence of chronic antigen and / or improved T cell persistence and function compared to non-engineered T cells.

[0100] T cells may be isolated from a subject. In some embodiments, the T cells are allogeneic to the subject. In some embodiments, the T cells are autologous to the subject. Thus, T cells may be isolated from a sample from a subject, modified and expanded ex vivo, and then returned to the subject.

[0101] In some embodiments, the disease or disorder is cancer. In some embodiments, the disease or disorder is an infectious disease. The present invention is not limited by the type of cancer or by the type of infectious disease. Indeed, any cancer known in the art and for which T cell therapy is used for treatment may be treated using the compositions and methods of the present invention. Similarly, any infectious disease known in the art and for which T cell therapy is used for treatment may be treated using the compositions and methods of the present invention.

[0102] In certain embodiments, the present invention provides methods for treating or slowing the progression of cancer or infectious disease in an individual, comprising administering to the individual an effective amount of an engineered T cell or composition thereof described herein, In some embodiments, the treatment results in a sustained response in the individual after cessation of treatment.

[0103] The method can be used on any cancer cell or in a subject having any type of cancer, for example, a cancer listed by the National Cancer Institute. In some embodiments, the cancer may be a carcinoma, sarcoma, lymphoma, leukemia, melanoma, mesothelioma, multiple myeloma, or seminoma. The cancer may be a cancer of the bladder, blood, bone, brain, breast, cervix, colon / rectum, endometrium, head and neck, kidney, liver, lung, muscle tissue, ovary, pancreas, prostate, skin, spleen, stomach, testis, thyroid, or uterus. In some embodiments, the cancer comprises a solid tumor. In some embodiments, the cancer is a metastatic cancer.

[0104] The methods described herein may find use in treating conditions where enhanced immunogenicity is desirable, such as increasing tumor immunogenicity for the treatment of cancer. In some embodiments, the recombinant receptor (e.g., CAR and / or TCR) is specific to the cancer being treated. In some embodiments, the recombinant receptor (e.g., CAR and / or TCR) is general to all cancers.

[0105] In certain embodiments, the present invention demonstrates that treating a subject with cancer with a therapeutically effective amount of the disclosed compositions is superior to treating a subject with cancer with unmodified T cells. In some embodiments, treatment with a therapeutically effective amount of the disclosed T cells or compositions thereof inhibits the development or growth of cancer cells and / or renders the cancer cells as a population more sensitive to other treatments (e.g., the cell death-inducing activity of a cancer therapeutic or radiation therapy). Thus, the T cells, compositions and methods of the present invention may be used as a monotherapy (e.g., to kill cancer cells and / or reduce or inhibit cancer cell growth, induce apoptosis and / or cell cycle arrest in cancer cells) or, when administered in combination with one or more additional agent(s), such as other anti-cancer agents (e.g., cell death-inducing or cell cycle disrupting cancer therapeutic agents or radiation therapy), rendered a greater percentage of cancer cells sensitive to killing, inhibited cancer cell growth, induced apoptosis and / or cell cycle arrest compared to a corresponding percentage of cells in an animal treated with only the cancer therapeutic or radiation therapy alone.

[0106] In some embodiments, the individual has a cancer that is resistant (e.g., has documented resistance) to one or more other forms of anti-cancer treatment (e.g., chemotherapy, immunotherapy, etc.). In some embodiments, resistance includes recurrence of the cancer or refractory cancer. Recurrence may refer to the reappearance of the cancer at the original site or at a new site after treatment. In some embodiments, resistance includes the worsening of the cancer during treatment with chemotherapy. In some embodiments, resistance includes cancer that does not respond to traditional or conventional treatment with chemotherapeutic agents. The cancer may be resistant at the beginning of treatment or may become resistant during treatment. In some embodiments, the cancer is in an early stage or in a late stage.

[0107] In some embodiments, the engineered T cells and compositions thereof are used to treat, ameliorate, or prevent cancers characterized by resistance to one or more conventional cancer therapies (e.g., cancer cells that are chemoresistant, radiation resistant, hormone resistant, etc.). In some embodiments, the treatment may drastically inhibit the growth of resistant cancer cells and / or render such cells as a population more sensitive to cancer therapeutic agents or radiation therapy (e.g., to their cell death-inducing activity).

[0108] In certain embodiments, a therapeutically effective amount of the modified T cell composition reduces the number of cancer cells in a subject following such treatment, hi certain embodiments, a therapeutically effective amount of the modified T cell composition reduces and / or eliminates the tumor burden in a subject following such treatment.

[0109] A wide variety of second therapies may be used in conjunction with the methods of the present disclosure. The second therapy may be the administration of an additional therapeutic agent or may be a second therapy unrelated to the administration of another agent. Such second therapies include, but are not limited to, surgery, immunotherapy, radiation therapy, or an additional chemotherapeutic or anticancer agent.

[0110] The second therapy may be administered simultaneously with the first therapy, either as a single composition or in a separate composition that is administered substantially simultaneously with the first therapy, hi some embodiments, the second therapy may precede or follow the treatment of the first therapy by intervals ranging from a few hours to several months.

[0111] In certain embodiments, the method further comprises administering radiation therapy to the subject, hi certain embodiments, the radiation therapy is administered prior to, concurrently with, and / or after the subject receives the therapeutically effective amount of the engineered T cell composition.

[0112] In certain embodiments, the method further comprises administering one or more anti-cancer drugs and / or one or more chemotherapeutic agents to the subject. In certain embodiments, the one or more anti-cancer drugs and / or one or more chemotherapeutic agents are administered before, simultaneously with, and / or after the subject receives a therapeutically effective amount of the engineered T cells or compositions thereof. In certain embodiments, combination treatment of a subject with a therapeutically effective amount of the engineered T cells and a set of anti-cancer drugs produces greater tumor responses and clinical benefit in such subjects compared to subjects treated with the engineered T cells or anti-cancer drugs / radiation alone. Since the doses for all approved anti-cancer drugs and radiation therapy are known, the present invention contemplates various combinations of engineered T cells with anti-cancer drugs and radiation therapy.

[0113] In some embodiments, the second therapy includes administration of an antibody. The antibody may target an antigen specifically expressed by tumor cells or an antigen shared with normal cells. In some embodiments, the antibody may target, for example, CD20, CD33, CD52, CD30, HER (also called erbB or EGFR), VEGF, CTLA-4 (also called CD152), epithelial cell adhesion molecule (EpCAM, also called CD326), and PD-1 / PD-L1. Suitable antibodies include, but are not limited to, rituximab, blinatumomab, trastuzumab, gemtuzumab, alemtuzumab, ibritumomab, tositumomab, bevacizumab, cetuximab, panitumumab, ofatumumab, ipilimumab, brentuximab, pertuzumab, and the like. In some embodiments, the additional therapeutic agent may comprise an anti-PD-1 / PD-L1 antibody, including, but not limited to, pembrolizumab, nivolumab, cemiplimab, atezolizumab, avelumab, durvalumab, and ipilimumab. The antibody may also be linked to a chemotherapeutic agent. Thus, in some embodiments, the antibody is an antibody-drug conjugate.

[0114] Administration of the second therapy may be administered to a subject by a variety of methods. In any of the uses or methods described herein, administration may be by a variety of routes known to those of skill in the art, including, but not limited to, oral, inhaled, intravenous, intramuscular, topical, subcutaneous, systemic and / or intraperitoneal administration to a subject in need thereof.

[0115] 5. How to prevent T cell exhaustion The present disclosure also provides a method of preventing exhaustion of engineered T cells (e.g., maintaining functionality of T cells exposed to excess antigen). In some embodiments, the method comprises genetically modifying a T cell to lack at least one gene selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. In some embodiments, the method comprises genetically modifying a T cell to lack at least one gene encoding a chromatin remodeling protein. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof.

[0116] In some embodiments, the method further comprises administering the engineered T cells to a subject in need thereof.

[0117] "Preventing T cell exhaustion" refers to a maintained or restored functional state of T cells characterized by one or more of the following, as compared to cells in an exhausted state: decreased expression and / or levels of one or more of PD-1, TIM-3, and LAG-3; increased memory cell formation and / or maintenance of memory markers (e.g., CD62L); prevention of apoptosis; increased antigen-induced cytokine (e.g., IL-2) production and / or secretion; enhanced killing capacity; increased recognition of tumor targets with low surface antigens; enhanced proliferation in response to antigen; and lower expression of inhibitory receptors (e.g., programmed cell death 1 (PDCD1, also known as PD1) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4)).

[0118] Thus, engineered T cells may exhibit increased functionality and / or activity (e.g., increased antigen-induced cytokine production, enhanced killing capacity (e.g., increased recognition of tumor targets with low surface antigens), increased memory cell formation and / or enhanced proliferation in response to antigen) and / or reduced features of exhaustion (e.g., lower levels of markers indicative of exhaustion or inhibitory receptors (e.g., PD-1, TIM-3, LAG-3) and / or programmed cell death) compared to unmodified T cells. In the context of therapeutic applications, modified T cells may enhance the clinical efficacy of therapeutic agents (e.g., CAR T cells).

[0119] In some embodiments, the isolated T cells further comprise a nucleic acid encoding a foreign receptor. In some embodiments, the foreign receptor is a T cell receptor (TCR) or a chimeric antigen receptor (CAR). The description of the foreign receptor and nucleic acid and its target antigen, subjects, and diseases and disorders presented above in relation to the methods for modifying T cells, the disclosed T cells and compositions and methods thereof, can also be applied to the method of preventing exhaustion of the engineered T cells.

[0120] The effective amount of the engineered T cells or compositions disclosed herein can be determined based on the type of disease being treated, the type of engineered T cell, the severity and course of the disease, the individual's clinical condition, the individual's clinical history and response to treatment, and the discretion of the attending physician.

[0121] The efficacy of any of the methods described herein (e.g., treating a disease or disorder) may be tested in various models known in the art, such as clinical or preclinical models. The efficacy of treatment may be any one or more of the following: prolonging survival (including overall survival and progression-free survival); objective response (complete response or partial response); or ameliorating a sign or symptom of a disease or disorder (e.g., cancer or infectious disease).

[0122] In some embodiments, the sample is obtained prior to treatment with the T cells (e.g., alone or in combination with another therapy described herein) as a baseline for measuring response to treatment. In some embodiments, the sample is a tissue sample (e.g., formalin-fixed and paraffin-embedded (FFPE), archived, fresh, or frozen). In some embodiments, the sample is whole blood. In some embodiments, the whole blood contains immune cells, circulating tumor cells, and any combination thereof.

[0123] In any exemplary cancer model, after tumors develop, mice may be placed into treatment groups receiving treatment or control treatment. Tumor size (e.g., tumor burden) is measured over the course of treatment, and overall survival is also monitored.

[0124] In some embodiments, efficacy can be an improvement in one or more factors according to the published RECIST guidelines set for determining tumor status in cancer patients, e.g., responding, stable, or worsening. A responding subject can be a subject whose cancer(s) show improvement according to one or more factors, e.g., based on the RECIST criteria. A non-responding subject can be a subject whose cancer(s) do not show improvement according to one or more factors, e.g., based on the RECIST criteria.

[0125] Efficacy may refer to improvement in one or more immune-related response criteria (irRC). In some embodiments, for example, new lesions are added to the defined tumor burden and followed for radiation disease progression at subsequent evaluations. In some embodiments, the presence of non-target lesions is included in the assessment of complete response and not in the assessment of radiation disease progression. In some embodiments, radiation disease progression may be determined solely on the basis of measurable disease and / or may be confirmed by serial evaluations following a period of time (e.g., 4 weeks) from the date of initial documentation.

[0126] 6. Methods for screening for T cell exhaustion genes The present disclosure also provides for screening for genes that promote T cell exhaustion. The method includes culturing T cells under conditions of chronic or acute stimulation for at least 6 days, where the T cells comprise at least one gene knockout or knockdown; isolating T cells that do not display an exhausted T cell surface phenotype; and identifying the at least one gene knockout or knockdown. In some embodiments, the T cells are a T cell library, where the T cell library comprises at least one T cell for each gene in the genome of the T cells.

[0127] The T cells may be selected from CD3+ T cells (e.g., a combination of CD4+ and CD8+ T cells), CD8+ T cells, CD4+ T cells, natural killer (NK) T cells, alpha beta T cells, gamma delta T cells, or any combination thereof. In some embodiments, the T cells are memory T cells (e.g., central memory T cells or effector memory T cells). In some embodiments, the T cells are tumor-infiltrating lymphocytes. In some embodiments, the T cells are cytokine-induced killer cells. In select embodiments, the T cells are CD8+ T cells.

[0128] In some embodiments, the T cells are naturally occurring T cells. For example, the T cells may be isolated from a subject's sample.

[0129] The T cells or T cell library may be generated using methods known in the art for genetic screening, such as RNAi, complementary DNA (cDNA) libraries, or CRISPR / Cas9-based genome editing. The method may be designed to measure single gene knockdown or knockout separately. Alternatively, the method may be designed to measure combination knockdown or knockout.

[0130] In some embodiments, the T cells are generated by performing single or combinatorial CRISPR-Cas-based gene knockdown using a genome-wide library of guide RNAs. Thus, in certain embodiments, the T cells are generated using a CRISPR-Cas system, with each cell comprising at least one guide RNA. See, for example, US Patent Publication No. 20190085324, which is incorporated herein by reference in its entirety.

[0131] CRISPR-Cas system, for example, CRISPR-Cas9 system, as used herein, refers to a non-naturally occurring system derived from bacterial clustered regularly interspaced short palindromic repeats locus.These systems generally include an enzyme (Cas protein, such as Cas9 protein) and one or more guide RNAs.CRISPR-Cas system can be engineered, for example, for optimal use in mammalian cells, for optimal delivery therein, and for optimal activity in gene editing.

[0132] The guide RNA (gRNA) may be a crRNA, crRNA / tracrRNA (or single guide RNA, sgRNA). The terms "gRNA", "guide RNA" and "CRISPR guide sequence" are used interchangeably throughout and may refer to a nucleic acid that comprises a sequence that determines the binding specificity of the CRISPR-Cas system. The gRNA hybridizes to a (partially or completely complementary) target nucleic acid sequence (e.g., a gene in the genome of a cell).

[0133] Many computational tools have been developed to facilitate gRNA design (Prykhozhij et al., (PLoS ONE, 10(3):(2015)); Zhu et al., (PLoS ONE, 9(9)(2014)); Xiao et al., (Bioinformatics. Jan 21(2014)); Heigwer et al., (Nat Methods, 11(2):122-123(2014)). Methods and tools for guide RNA design are reviewed by Zhu (see Frontiers in Biology, 10(4)289-296(2015)), which are incorporated herein by reference. Additionally, Genscript Interactive CRISPR gRNA design tools, WU-CRISPR and Broad Institute GPP There are many published software tools, including but not limited to sgRNA Designer. There are also published pre-designed gRNA sequences, including but not limited to IDT DNA Predesigned Alt-R CRISPR-Cas9 guide RNAs, Addgene Validated gRNA target sequences, and the GenScript genome-wide gRNA database, that target many genes and locations within the genomes of many species (human, mouse, rat, zebrafish, C. elegans).

[0134] In the genome-wide approach, it is possible to design and construct a suitable gRNA library. Such gRNAs can be delivered to cells using vector delivery, such as viral vector delivery. Combinations of CRISPR-Cas-mediated perturbations can be achieved by delivering multiple gRNAs into a single cell.

[0135] T cells cultured under chronic or acute stimulation conditions can become exhausted. As described herein, exhausted T cell surface phenotypes include increased concentrations of PD-1, TIM-3, and LAG-3. Thus, any stimulation condition that results in T cell exhaustion may be used in the disclosed methods. In some embodiments, the chronic stimulation condition includes culturing the T cells using anti-CD3 coated plates. In some embodiments, the chronic stimulation condition further includes culturing in the presence of IL-2. In some embodiments, the acute stimulation condition includes culturing the T cells in the presence of IL-2.

[0136] The culture may continue for any period necessary to initiate T cell exhaustion. In some embodiments, the culture is for at least 6 days. In some embodiments, the duration of the culture is 6-10 days (e.g., 6 days, 7 days, 8 days, 9 days, or 10 days). In some embodiments, the culture continues for longer than 10 days.

[0137] The T cells may be in culture prior to being cultured under conditions of chronic or acute stimulation. For example, the T cells may be cultured under normal culture conditions for growth, renewal, or genetic manipulation prior to placing the T cells under conditions of chronic or acute stimulation.

[0138] Isolating T cells that do not display an exhausted T cell surface phenotype includes any method(s) that allow for the identification of exhausted T cells and / or the separation of identified cells. For example, FACS analysis of markers of T cell exhaustion allows for the identification and removal of non-exhausted T cells prior to the identification of at least one gene knockout or knockdown, as described elsewhere herein.

[0139] To identify genes that promote T cell exhaustion, T cells that do not exhibit an exhaustion phenotype are isolated and genomic DNA is extracted for analysis. Analysis may include sequencing the genome to determine the genes that have been knocked out. In the case of CRISPR-Cas screens, the gRNA coding regions are PCR amplified, sequenced, and mapped to a gRNA library. By comparing the gRNA profiles, the association between knockout and T cell exhaustion can be determined.

[0140] The present disclosure further provides systems or kits that contain one or more reagents or other components that are useful, necessary, or sufficient to carry out any of the methods described herein. The systems or kits may include exogenous receptor reagents (nucleic acids, vectors, compositions, etc.), transfection or administration reagents, negative and positive control samples (e.g., T cells or empty vector DNA), T cells, systems for genetically engineering T cells (e.g., Cas proteins, gRNAs, vectors thereof, etc.), additional therapeutic agents, containers (e.g., microcentrifuge tubes), detection and analysis equipment, software, instructions, etc. The descriptions of nucleic acids, vectors, compositions, T cells, additional therapeutic agents provided elsewhere herein are suitable for use with the disclosed systems or kits.

[0141] In some embodiments, the system or kit comprises a system for engineered T cells or engineered T cells as described herein. The system for engineered T cells may comprise a clustered interspersed short palindromic repeats (CRISPR) / CRISPR-associated protein (Cas) system as described herein. In certain embodiments, the system for engineered T cells comprises a Cas protein (e.g., Cas9, dCas9), or a nucleic acid encoding a Cas protein, and a gRNA, or a nucleic acid encoding a gRNA, directed to at least one gene that promotes T cell exhaustion. In some embodiments, the nucleic acid encoding the Cas protein (e.g., Cas9) and the gRNA are the same or different nucleic acids. For example, the gRNA and the Cas protein may be expressed from the same vector. The at least one gene that promotes T cell exhaustion may be selected from the group consisting of INO80C, GATA3, ARID1A, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8. At least one gene that promotes T cell exhaustion may encode a chromatin remodeling protein. In some embodiments, the chromatin remodeling protein is an INO80 nucleosome positioning complex protein or a SWI / SNF family member, or a combination thereof. In some embodiments, the INO80 nucleosome positioning complex protein is Actr5, Ino80, Ino80c, Ino80b, Actr8, or a combination thereof. In some embodiments, the SWI / SNF family member is a member of the cBAF complex. In some embodiments, the SWI / SNF family member is Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, or a combination thereof.

[0142] In some embodiments, the system or kit further comprises an exogenous receptor or a nucleic acid encoding same.

[0143] In some embodiments, the system or kit further comprises at least one additional therapeutic agent. The at least one therapeutic agent may be selected from the group consisting of an agent for treating T cell exhaustion; an antiviral agent; an antibiotic; an antibacterial agent; a chemotherapeutic agent; or a combination thereof.

[0144] In some embodiments, the system or kit further comprises instructions for using the components of the system or kit. The instructions are related materials or methodologies related to the system or kit. The materials may include any combination of the following: background information, a list of components and their inventory information (purchasing information, etc.), brief or detailed protocols for using the system or kit, troubleshooting, references, technical assistance, and any other related documentation. The instructions may be provided with the system or kit or as separate member components, in written form or in electronic form that may be provided on a computer readable storage device or downloaded from an internet website, or as recorded presentations. EXAMPLES

[0145] Materials and Methods Mice All mice were sourced from JAX. Wild-type mice were C57BL / 6J mice (JAX:000664). Cas9 knock-in mice were bred in-house (JAX:026179). OT-1 mice (JAX:003831) were crossed with Cas9 mice. Rag1 - / - Mice were bred in-house (JAX:002216).

[0146] Primary T cell isolation and culture Spleens were collected and mashed through a 70 μM filter. RBCs were lysed in ACK lysis buffer (Gibco) and incubated for 6 min before washing with PBS. Cells were counted and then resuspended in MACS buffer (PBS + 0.5% BSA + 2 μM EDTA) according to the Miltenyi protocol. CD8 T cells were enriched using a mouse CD8 T cell isolation kit from Miltenyi and then resuspended in RPMI supplemented with 10% FBS, 1% sodium pyruvate, 1% non-essential amino acids, 100U Pen / Strep, 50nM B-mercaptoethanol (cRPMI) and 10ng / ml mouse IL-2. Cells were seeded at a concentration of 1 million cells / ml on plates coated with 5 μg / ml anti-CD3 and 2 μg / ml anti-CD28. Cells were kept on these activation plates for 48 hours at the start of all experiments. CD8 + T cell purity was confirmed by flow cytometry. Cells were passaged every 2 days and maintained at 1 million cells per mL.

[0147] In vitro T cell exhaustion assay To induce T cell exhaustion, chronic stimulation was performed using anti-CD3 coated plates at 5 μg / mL (in the continuous presence of 10 ng / ml IL-2). Cells were passaged onto fresh coated plates every 2 days and analyzed on days 6, 8 or 10 as described in Results. In contrast, acutely stimulated cells were maintained in 10 ng / ml IL-2 alone, passaged every 2 days and analyzed on days 6, 8 or 10.

[0148] Measurement of cytokine production T cells were cultured with phorbol myristate acetate (Sigma, 50 ng ml -1 ) and ionomycin (Sigma, 500 ng ml -1) or 3 μg / ml plate-bound anti-CD3. After 90 min, cells were treated with Brefeldin A to block cytokine secretion. Then, after 3 h, cells were stained for surface markers for 20 min at 4°C and simultaneously labeled with Live / Dead Blue Viability Dye (Thermo Fisher). Cells were washed twice and fixed overnight using FoxP3 Fixation / Permeabilization Kit (Thermo Fisher). The following day, cells were washed and stained for intracellular cytokines for 1 h at room temperature. Cells were then washed three times and analyzed using an LSR Fortessa machine (Beckman Dickinson). Analysis of mean fluorescence intensity was performed using FlowJo v.10.0. All experiments were performed in at least two biological replicates. Antibodies used (1:100 unless otherwise stated) were: TNF-PE (BioLegend, MP6-XT22, 506306), PD-1-PECy7 (BioLegend, RMP1-30, 109110) IFN-γ-FITC (BioLegend, XMG1.2, 505806), CD4-BV711 (BioLegend, RM4-5, 100550) and CD8α-BV786 (BioLegend, 53-6.7, 100750).

[0149] Growth curves T cells were activated as described. Cells were subsequently resuspended in 10% FBS, 2 mM l-glutamine, 5 μM β-ME and 10 ng ml -1 5 x 10 cells in 1 ml RPMI-1640 medium containing IL-2 5 Cells were incubated with plate-bound anti-CD3 (3 μg ml -1 ) into 24-well plates. Every 2 days during the experimental period, cells were harvested and cell numbers were counted using a Beckmann Coulter Counter with a cell volume gate of 75–4,000 femtoliters. 50% of the cells were then replated in 1 ml of fresh T cell medium. All experiments were performed at least two independent times.

[0150] In vitro killing assay. B16 cells expressing a luciferase reporter were pulsed with SIINFEKL peptide (Invivogen) at the concentrations mentioned for 4 h at 37° C. Cells were then washed twice and incubated with 1×10 cells either acutely or chronically stimulated for 8 days as previously described. 5 4 × 10 per well with OT-1 transgenic T cells 4 After 24 h of co-culture, cells were lysed and luciferase activity was measured using a luciferase assay kit (Promega) according to the manufacturer's instructions. Luciferase activity was normalized to cells cultured without T cells.

[0151] B16-ovalbumin in vivo tumor model C57BL / 6 scid (Jackson 001913) mice were inoculated with 2 × 10 5 B16-OVA cells were injected subcutaneously. Five days later, 2 × 10 6 OT-1 cells were adoptively transferred into mice by retroorbital injection. Mice were monitored daily and sacrificed for signs of morbidity.

[0152] ATAC-seq sample processing and analysis ATAC-seq was performed using the Omni-ATAC protocol (Corces et al., 2017, Nat. Methods 14, 959-962). Briefly, 50,000 viable cells were purified by flow cytometry immediately prior to ATAC-seq. Lysis, nuclei isolation and translocation were performed according to the Omni-ATAC protocol. Libraries were prepared for sequencing and sequenced in 2x75 dual index format on an Illumina NovaSeq.

[0153] Fastq files were trimmed using fastp and aligned to the mm10 genome using hisat2. Reads were deduplicated and filtered to create bed files for each sample containing deduplicated ATAC-seq fragments. Peaks for each sample were called individually using MACS2 and then filtered into reproducible peaks based on peaks present in the majority of replicates for that sample. A union peak set for all samples was constructed by merging reproducible peaks for each sample into a set of high-confidence non-overlapping fixed-width (500 bp) peaks, which were used to create peaks by sample matrix for downstream analysis. Differential peaks were determined using DESeq2 (Love et al., 2014). Principal component analysis was performed on the peak matrix by first normalizing using 'DESeq2::varianceStabilizingTransformation' and then 'stats::prcomp'. Genome track files were created by loading fragments for each sample into R and exporting bigwig files normalized by read data at transcription start sites using 'rtracklayer::export'. Coverage files were visualized using the Integrative Genomics Viewer. For analysis of previously published ATAC-seq data (Miller et al., 2019, Nat. Immunol. 20, 326-336), fastq files were downloaded from accession GSE123236 and reprocessed using the published pipeline for consistency. EX ATAC-seq peaks correlate with terminal vs. progenitor T EXWhen comparing samples, Log2FC was calculated using DESeq2 with a cutoff of >= 1 and FDR <= 0.05 (TIL or LCMV samples, as indicated). For quantification of overlapping peaks between published and in vitro assay data, a union peak set encompassing all samples was created and reanalyzed. For HOMER motif enrichment analysis, the HOMER findMotifsGenome command line utility was used to identify motifs present in peaks in the indicated peak set compared to the background peak set, as shown in Figure 24. For the background peak set, a union peak set of the examined samples was used, such that enriched motifs correspond to motif peaks enriched in the differential peak set compared to the aggregated samples, rather than motifs enriched in human T cells compared to random genomic regions.

[0154] Genome-wide sgRNA library The retroviral mouse genome-wide CRISPR knockout library was a kind gift from Sarah Teichmann (Addgene #104861). The library was amplified by electroporation and verified by sequencing.

[0155] sgRNA pool design and cloning sgRNA minipools were designed using previously developed protocols for cloning into lentiviral backbones and then subcloned into the retroviral construct pMSCV (Flynn et al., 2021, Cell 184, 2394-2411). LentiCRISPR-v2 was a gift from Feng Zhang (Addgene plasmid #52961). pMSCV-U6sgRNA(BbsI)-PGKpuro2ABFP was a gift from Sarah Teichmann (Addgene plasmid #102796).

[0156] Briefly, six 20bp variable sgRNA sequences per target gene were obtained from the Broad Genetic Perturbation Platform (GPP) genome-wide design: sgRNA_design_10090_GRCm38_SpyoCas9_CRISPRko_NCBI_20200317(dot)txt(dot)gz, available online at portals(dot)broadinstitute(dot)org / gpp / public / dir?dirpath=sgrna_design. 100 non-targeting and 100 single-targeting negative control guides designed for the mouse genome, also from the Broad GPP web portal, were included. A "G" was added to the beginning of each 20bp sequence. This 21bp sequence was flanked by a BsmBI-v2 enzyme site and then two nested PCR handles. Pooled oligos were synthesized by Twist Bioscience. Oligos were amplified by two rounds of PCR and the lentiCRISPR-v2 backbone was digested with Esp3I overnight. One-step digestion / ligation of the amplified oligos into lentiCRISPR-v2 was performed in a 20 μL reaction with 1 μL T4 ligase, 1 μL Esp3I, 2 μL T4 ligase buffer, 200 ng digested backbone, and 50 ng amplified insert at 37°C for 1 hour. The reaction was heat inactivated at 65°C for 15 minutes and then 1 μL was electroporated using a BioRad MicroPulser with a 25 μL Lucigen Endura electrocompetent cell and a 0.1 cm gap cuvette. After 1 hour recovery in SOC, a 1000x dilution was plated on an agar plate to confirm library coverage. The remainder was grown overnight in a 150 mL liquid culture and then purified by maxiprep. Finally, the pool was subcloned into pMSCV and BbsI predigested pMSCV backbones by Gibson Assembly of PCR amplified sgRNA variable regions. Electroporation was repeated as above. Guide display was confirmed by sequencing.

[0157] sgRNA SWI / SNF minipools and minipools for perturb-seq were designed with 4 guides per gene as described above for minipools using the Broad GPP mouse genome-wide design. SWI / SNF minipools contained 50 single-targeting controls and Perturb-seq micropools contained 12 single-targeting controls. For cloning by annealing, two primers were required per designed guide. pMSCV vector was digested with BbsI. All primer pairs were annealed separately. Annealed products were pooled equally, diluted, and then ligated into pMSCV. Amplification was performed using Stbl3 chemically competent cells (ThermoFisher C737303) and library coverage was confirmed by colony counting followed by sequencing.

[0158] Retrovirus production and transduction. The pMSCV plasmid was transfected into GP2-293 cells (Takara, RetroPack) at approximately 80% confluency on 15 cm tissue culture plates coated with poly-D-lysine. TM The virus was transfected into 293T HEK cells (PT67 cell line) or 293T HEK cells. Viral supernatants were collected 48 and 72 hours post-transfection and filtered through 0.45 μm filtration units (Millipore). The filtered virus was concentrated using a LentiX concentrator (Takara) at 1500×g for 45 min. The concentrated supernatant was subsequently aliquoted, flash frozen, and stored at −80° C. until use.

[0159] CD8 T cells were transduced with concentrated retrovirus 24 hours after isolation. 4 μg / ml polybrene was added to each well. Plates were sealed and then spun at 1100×g for 90 minutes at 32° C. 24 hours after spinfection (e.g., starting on day 2) cells were examined for fluorescence by flow cytometry and 2 μg / ml puromycin was added to the medium.

[0160] sgRNA library preparation and sequencing For samples from in vitro long-term cultures, viable cells were first isolated by FACS. gDNA was extracted using a commercially available Zymo kit. sgRNA libraries were prepared for sequencing as previously described (Flynn et al., 2021, Cell 184, 2394-2411). Briefly, a standard three-step amplification protocol was used. First, sgRNA was amplified from gDNA with 22 cycles of PCR using primers specific for the pMSCV vector. 100 μL reactions with up to 4 μg gDNA per reaction were used, and the number of reactions was scaled up until all gDNA was used. For sequencing of plasmid pools, this first PCR was skipped. In the second PCR, a 0-7 bp offset was added to the front of the library using eight pooled stagger primers to increase library diversity. The PCR2 primer target sites were nested inside the PCR1 primer target sites to improve product specificity. Finally, in PCR3, an index sequence was added. Libraries were sequenced on an Illumina NextSeq or NovaSeq in dual-index 1x75bp or 1x150bp format.

[0161] Bulk sgRNA Screening Data Analysis sgRNA sequencing data was analyzed using a previously published pipeline (Flynn et al., 2021, Cell 184, 2394-2411). Briefly, fastq files were trimmed using 'fastp -f 10 --max_len1=50'. Trimmed reads were aligned to custom fasta files of relevant pools (genome-wide pools or minipools) constructed by taking the sgRNA variable sequence and flanking it with adjacent sequences in the pMSCV vector backbone. Alignment was performed using hisat2 with the --no-spliced-alignment option. Bam files were imported into R and converted to counts per guide using 'Rsamtools::scanBam'. A table of guides per sample was constructed in R and normalized by multiplying each count by 1e6, dividing by the total counts in that sample, adding 1, and then log2 normalizing. Log fold changes between two conditions (e.g., chronic vs. acute or tumor vs. input) were calculated and then z-scored by subtracting the reference LFC mean and dividing by the reference LFC standard deviation. In genome-wide screens, all guides were used as references (e.g., guides were z-scored relative to all other guides), and in minipool screens, control guides were used as references. p-values ​​were calculated from the z-scores using normal distribution, and then FDR was calculated by correcting for multiple hypothesis testing using 'p.adjust' in R. For Gini index analysis, the 'ineq' R package was used, as shown in Figure 19.

[0162] GO Term Analysis For the gene classification shown in Figure 6C and elsewhere, gene sets were defined as TCR-KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY, chromatin-GOCC_CHROMATIN, integrin-GOBP_INTEGRIN_ACTIVATION, inhibitory receptor-GOBP_NEGATIVE_REGULATION_OF_LYMPHOCYTE_ACTIVATION. Gene lists were manually populated with the following genes: chromatin-ZFP219, TBX21, KDM6A, ELMSAN1, DNTTIP1, SETD1B, TADA2B, ZFP217, EOMES; integrin-ITGB3, APBB1IP, ITGAV; inhibitory receptor-PDCD1. For gene set enrichment analysis, the indicated gene lists were uploaded to the online gProfiler tool (available at biit(dot)cs(dot)ut(dot)ee / gprofiler / gost).

[0163] Cytoscape interaction network The top 100 positive hits and the top 20 negative hits were imported into Cytoscape. Edges were created by using the stringApp Cytoscape plugin to import known protein-protein interactions collected from string-db (Szklarczyk et al., 2019, Nucleic Acids Res. 47, D607-D613). A cutoff of stringdb score ≥ 0.75 was used to filter these protein-protein interactions, which represents a conservative cutoff to identify only high-confidence interactions. Nodes were grouped based on GO term analysis, subcellular localization and / or manual collection. A small number of poorly characterized and / or truncated nodes were removed from the visualization.

[0164] Tumor inoculation and T cell adoptive transfer for in vivo CRISPR experiments MC-38 B16 cells ectopically expressing mCherry-ovalbumin fusion constructs were prepared for injection by resuspending in a 1:1 mixture of Matrigel and PBS. 1 × 10 per tumor 6Cells were transfected with Rag1 - / - Mice were injected subcutaneously into the flank (2 tumors per mouse). Tumors were measured every 3 days. Cas9-OT-1 CD8 + T cells were transduced with sgRNA pools or individual sgRNAs and selected with puromycin for 4 days as described above. T cells were then injected intravenously into tumor-bearing mice. For in vivo competition assays, cells were mixed immediately before injection. Nine days after T cell injection, spleens and tumors were harvested from each mouse.

[0165] Tissue processing and isolation of tumor-infiltrating lymphocytes. Tumors were weighed and then minced into small pieces. Tumors were transferred to gentleMACS C tubes and digested with the protocol-recommended enzyme mix using a gentleMACS octo dissociator using the included soft / medium tumor program. The tumor suspension was then filtered through a 70 μM filter and then subjected to RBC lysis. Spleens were mashed, filtered through a 70 μM strainer, and then treated with RBC lysis buffer. For large-scale sgRNA sequencing and perturb-seq, tumor-infiltrating lymphocytes or T cells were isolated from tumors or spleens by FACS. Samples were washed twice with MACS buffer and stained on ice for 30 min. CD8+BFP+ cells were isolated by flow cytometry.

[0166] Competitive assay for confirmation of individual sgRNA amplification The pMSCV retroviral vector was modified to replace the BFP-puromycin fusion with a VEX-puromycin fusion. Individual guides were cloned by annealing pairs of primers as described above. The Arid1a-1 sgRNA sequence used was GCAGCTGCGAAGATATCGGG (SEQ ID NO:2) and the Arid1a-2 sequence used was CAGCAGAACTCGCACGACCA (SEQ ID NO:3). The CTRL sgRNA sequence used was CTTACTCGACGAATGAGCCC (SEQ ID NO:4). Tumor treatment was performed as described above for in vivo confirmation.

[0167] Validation of Arid1a-targeting sgRNA Tracking of indels by degradation (TIDE): Genomic DNA was isolated from transduced cells using a commercial kit (Zymo Cat# D3025). PCR reactions were performed with primers surrounding the predicted editing site and 50ng of input DNA. PCR conditions were 98°C for 30 s, followed by 35 cycles of 98°C for 10 s, annealing at 60°C for 10 s, 72°C for 25 s, then 72°C for 2 min. PCR amplicons were purified using the commercial Zymo DNA cleanup kit and Sanger sequenced. Quantification of editing was performed using the online tool tide(dot)nki(dot)nl.

[0168] Western blot: Protein lysates were prepared from mouse T cells transduced with the indicated sgRNAs using the Radioimmunoprecipitation Assay (RIPA) Buffer System (Santa Cruz, sc-24948). Protein concentrations were quantified using a bicinchoninic acid (BCA) assay (Pierce, ThermoFisher 23225). 20 μg of protein per sample was loaded and run on a 4–12% Bis-Tris PAGE gel (NuPAGE 4–12% Bis-Tris Protein Gel, Invitrogen) and transferred onto a polyvinylidene fluoride (PVDF) membrane (Immobilon-FL, EMD Millipore). Membranes were blocked with 5% milk in PBST for 1 h at room temperature and incubated overnight at 4°C with primary antibodies against Arid1a (rabbit, 1:1000, Cell Signaling, 12354S:Lot 4), Arid1b (mouse, 1:1000, Abcam, ab57461:Lot GR3345290-4), Smarca4 (rabbit, 1:1000, Cell Signaling, 49360S:Lot 3) and Tbp (mouse, Abcam, ab51841:Lot GR3313213-3). Membranes were washed three times with PBST and then incubated with near-infrared fluorophore-conjugated species-specific secondary antibodies: goat anti-mouse IgG polyclonal antibody (IRDye 680RD, 1:10,000, LI-COR Biosciences, 926-68070) or goat anti-rabbit IgG polyclonal antibody (IRDye 800CW, 1:10,000, LI-COR Biosciences, 926-32211) for 1 hour at room temperature. Following secondary antibody application, membranes were washed three times with PBST and then imaged using a LI-COR Odyssey CLx imaging system (LI-COR). Protein band intensity was quantified using Image Studio Lite (LI-COR) with integrated background correction and normalization to Tbp control. Statistical analysis comparing Arid1a levels normalized to Tbp was performed using Dunnett's multiple comparison test on Prism (v9.2.0).

[0169] In vitro experiments with primary human T cells. T cell expansion and viability assay: T cells were activated with T cell versus CD3 / 28 Dynabeads (Invitrogen) in a 1:3 ratio for 4 days. T cell expansion assays were performed with IL-2 at 10 ng / mL in culture medium. Cell counts and viability measurements were obtained using a Cellaca Mx automated cell counter (Nexcelom). Cells were stained with acridine orange and propidium iodide to assess viability.

[0170] Targeted CRISPR gene editing: Ribonucleoproteins (RNPs) were prepared using synthetic sgRNA with 2'-O-methyl phosphorothioate modifications (Synthego) diluted in TE buffer at 100 μM. 5 μl of sgRNA was incubated with 2.5 μl of Duplex Buffer (IDT) and 2.5 μg of Alt-R SpCas9 Nuclease V3 (IDT) for 30 min at room temperature. A 100 μl reaction was assembled with 10,000,000 T cells, 90 μl of P3 buffer (Lonza) and 10 μl of RNP. Cells were pulsed with protocol EO115 using the P3 Primary Cell 4D-Nucleofactor Kit and 4D Nucleofactor System (Lonza). Cells were immediately allowed to recover in warm medium for 6 h. Guide sequences: AAVS1-sg1 5'GGGGCCACUAGGGACAGGAU 3' (SEQ ID NO:5), ARID1A-sg58 5'CCUGUUGACCAUACCCGCUG 3' (SEQ ID NO:6), ARID1A-sg60 5'UGUGGCUGCUGCUGAUACGA 3' (SEQ ID NO:7).

[0171] Assessment of targeted CRISPR gene editing: Four to seven days after editing, genomic DNA was extracted using QuickExtract DNA extraction solution (Lucigen) and ~500 bp regions flanking the cut sites were amplified using Phusion Hot Start Flex 2X Master Mix (New England Biolabs) according to the manufacturer's instructions. Sanger sequencing traces were analyzed by Inference of CRISPR Editing (ICE).

[0172] Pooled CRISPR screen in primary human T cells in vivo Activated human T cells from two donors were lentivirally transduced to express NY-ESO-specific TCRs in parallel with lentiviral transduction of an sgRNA library of two sgRNAs per target gene and eight negative controls. 24 hours after transduction, cells were electroporated with Cas9 protein as previously described (Shifrut et al., 2018, Cell 175, 1958-1971.e15). After electroporation, T cells were expanded in complete X-vivo 15 medium, split every 2 days, and supplemented with IL-2 at 50 U / ml. On day 7, 1 × 10 cells were injected into two NSG mice per donor as previously described (Roth et al., 2020, Cell 181, 728-744.e21). 6 A375 cells were injected subcutaneously at 1 × 10 6 TCR-positive T cells were transferred into mice 7 days later by retro-orbital injection. Tumors and spleens were harvested 7 days after T cell transfer and processed to single cell suspensions as previously described (Roth et al., 2020, Cell 181, 728-744.e21). T cells were sorted by CD45 staining and gDNA was extracted using a commercially available kit. Library preparation, next generation sequencing and analysis were performed as previously described (Shifrut et al., 2018, Cell 175, 1958-1971.e15). The amount of guide in the spleen and tumor of each mouse was used to calculate the log fold change of each guide, and MAGeCK scores were calculated using default parameters.

[0173] The 10x Chromium Next GEM Single Cell V(D)J Reagent Kit v1.1 5' scRNA with Direct Capture Perturb-seq Feature Barcoding Reagents and Protocol was adapted to be compatible with direct capture of sgRNA in single cells. Modifications to the protocol are summarized here. In step 1, GEM Generation and Barcoding, 5 pmol of primer KP_bead_sgRNA_RT was added to the reaction to allow capture of sgRNA in droplets and then reverse transcription of sgRNA. In step 3.2B, supernatant cleanup for cell surface protein library was performed to isolate the sgRNA library. Finally, 2 μL of the product from step 3.2B was amplified and labeled using 3 rounds of PCR. The 250 bp library was purified by agarose gel and sequenced together with the gene expression (GEX) library in 26x91 format following the 10x protocol guidelines. In the Perturb-seq replicate samples shown in Figure 23B, each replicate represents an individual tumor or two tumors from the same mouse combined into one sample. Tumors from the same mouse were combined if the cell yield was well below the 10x guideline for targeted recovery of 10,000 cells per capture. In certain cases, samples were split into multiple 10x captures to maximize cell yield if the cell yield was well above the amount required for recovery of 10,000 cells. Samples split into multiple captures were computationally merged and not counted as separate replicates.

[0174] Fastq files were processed using a 10x cellranger counting pipeline and feature barcode analysis can process GEX and sgRNA libraries together. The mm10 reference transcriptome was used for the GEX library. For the sgRNA library, a feature reference spreadsheet was constructed containing the variable sequence of each guide (converted to reverse complement since guides were sequenced as part of read 2), guide ID and target gene. Filtered matrices for both "Gene Expression" and "CRISPR Guide Capture" were loaded into Seurat for downstream analysis (Hao et al., 2021, Cell 184, 3573-3587). Samples from two independent experiments were merged using the Seurat "IntegrateData" utility.

[0175] To assign sgRNAs to cells, raw z-scores were calculated for the "CRISPR guide capture" matrix. X-scores were calculated to quantify how enriched each sgRNA was relative to other sgRNAs detected in the same cell. The z-score difference was also calculated between the most enriched and second most enriched sgRNA. Cells with maximum sgRNA z-score ≥ 5 and z-score difference ≥ 2 were determined to contain the guide with maximum z-score, cells with no sgRNA count were assigned as "no guide" and other cells were assigned as "multiple guides". Guide assignments were added to Seurat metadata for downstream processing. Seurat cell cycle scoring was used to predict the cell cycle stage of each single cell. Volcano plot analysis identified significantly differential genes with FDR < 0.05. For comparisons across perturbations of different gene sets, additional fold change cutoffs of mean log2FC > 0.1 or mean log2FC < -0.1 were applied. For categorization of shared "up" and "down" gene sets within the cBAF and INO80 complexes (analysis shown in Figures 16D-16E), the union of significantly differential genes within each complex was summed, and then "up" and "down" genes for each subunit were defined only as LFC>0 or LFC<0. This strategy was chosen to compare gene sets despite the different amounts of cells collected for each perturbation and thus the difference in power to reach the FDR<0.05 threshold. Seurat gene module scoring was used to convert the LCMV gene sets (consisting of the top 100 marker genes per LCMV cluster) into gene module scores for each cell in the perturb-seq database. Gene module scoring was used to convert the up- and down-regulated gene sets into module scores for each cell in the expanded LCMV dataset, as shown in Figure 24.

[0176] [Example 1] In vitro models of T cell exhaustion To develop an assay amenable to genome-wide CRISPR / Cas9 screening of T cell exhaustion, we used anti-CD3 antibodies to enhance clustering of the T cell coreceptor, CD3, thereby inducing chronic TCR signaling in an antigen-independent manner (Figure 1A). This model isolates the core determinants of T cell exhaustion - chronic stimulation through the TCR complex - and removes T cell localization and trafficking effects, as well as immune suppressive factors in the tumor microenvironment, which may be specific to a particular in vivo model. Importantly, this assay is scalable and can be used for 10 8 The 1000× coverage of genome-wide CRISPR sgRNA libraries allows for the culture of overexpressing cells. Over the course of 8 days of anti-CD3 stimulation (following 2 days of anti-CD3 anti-CD28 activation), we observed a progressive upregulation of the inhibitory receptors PD-1 and TIM3 as well as proliferation defects in chronically stimulated T cells compared to cells passaged without further stimulation after initial activation (acute stimulation; p<0.0001, unpaired t-test; Figures 1B and 6A-6B). Chronically stimulated T cells also showed defects in IFNγ and TNFα secretion after restimulation with phorbol myristate acetate and ionomycin compared to acutely stimulated cells (acute: 80% IFNγ+TNFα+, chronic: 1% IFNγ+TNFα+, Figures 6C and 18A). Co-culture of OT-1 T cells with B16 tumor cells expressing luciferase and pulsed with the cognate peptide antigen SIINFEKL (SEQ ID NO: 1) also demonstrated that chronically stimulated cells were impaired in tumor killing in vitro (Figure 6D). Finally, transplantation of chronically stimulated OT-1 T cells into mice bearing B16-OVA tumors showed reduced tumor control in vivo compared to transplantation of acutely stimulated T cells (mean tumor size 20 days after transplantation: 1,849.6 mm). 3 (Chronic) or 755.0 mm 3 (acute); p = 0.005, unpaired t test; Figure 6E).

[0177] Assays for transposase-accessible chromatin using sequencing (ATAC-seq) were performed every 2 days during chronic stimulation and global chromatin accessibility profiles were analyzed. Principal component analysis (PCA) of ATAC-seq profiles showed that PC1 separated naive cells (day 0) from all other samples, while PC2 captured the progressive epigenetic polarization of T cells during chronic stimulation (Figure 1C). Analysis of individual loci, including Pdcd1 and Entpd1, showed increased accessibility at known exhaustion-specific regulatory elements (Figure 1D). Global epigenetic similarity of in vitro stimulated cells to reference T cell exhaustion data from tumors and chronic infections was assessed. The "terminal exhaustion peak set" was defined as ATAC-seq peaks that were differentially active in terminally exhausted T cells compared to precursor exhausted T cells. 3,537 terminal exhaustion ATAC-seq peaks were identified in a B16 melanoma tumor model and 2,346 peaks were identified in a lymphocytic choriomeningitis virus (LCMV) chronic infection model (Log2 FC ≥ 1; FDR ≤ 0.05; Figures 1E and 6F). Comparison of terminal exhaustion peak accessibility in each model with in vitro exhaustion ATAC-seq data demonstrated that the in vitro assay closely recapitulates the global epigenomic changes observed in vivo, with 88.6% of ATAC-seq peaks in tumors and 70.1% of ATAC-seq peaks in chronic infection showing a shared increase in accessibility in the in vitro model at day 10 (Figures 1E, 6F and 18B-18C). In contrast, 2,926 precursor Ts identified in TILs were identified in the LCMV chronic infection model (Figures 1E, 6F and 18C). EXAnalysis of the peaks demonstrated that these sites showed decreased accessibility with repeated stimulation (Figures 6F, 18B-18C). Chromatin accessibility at transcription factor (TF) binding sites was assessed using chromVAR, which showed that TF motifs previously associated with terminal exhaustion, including Batf, Fos, Jun, and Nr4a, were highly accessible at day 10 in vitro. Furthermore, a progressive loss of accessibility at naive and progenitor exhaustion-associated Lef1 and Tcf7 motifs, as well as the initial dynamic accessibility of NF-κB and Nfat motifs, was observed, mirroring the progression of TF activity observed upon T cell exhaustion in vivo (Figure 1F).

[0178] [Example 2] Genome-wide screening of genes involved in T cell exhaustion The in vitro exhaustion assay was adapted to be compatible with CRISPR screening by using Rosa26-Cas9 knock-in mice, which constitutively express Cas9-P2A-EGFP (Figure 2A). 24 h after T cell isolation, Cas9+CD8+ T cells were transduced with a genome-wide retroviral sgRNA library containing 90,230 sgRNAs. A 48-h delay was introduced between activation and the start of chronic stimulation to allow time for sufficient gene editing and puromycin selection of transduced cells. This modified chronic stimulation protocol caused a similar defect in cytokine production after restimulation with anti-CD3 or PMA / IO (Figures 19A-19B). To identify genes that differentially regulated fitness in the presence of chronic stimulation, cells were split into acute (IL-2 only) and chronic (anti-CD3 and IL-2) stimulation conditions on day 4, and both pools were sequenced on day 10 (Figure 2A). A replicate screen was prepared to (1) transduce T cells at a low multiplicity of infection (MOI) to optimize single sgRNA targeting of cells (replicate 1: 16.9% sgRNA+ cells, MOI=0.18; replicate 2: 29.3% sgRNA+ cells, MOI=0.35; Figure 7A), and (2) confirm T cell exhaustion using cell surface phenotypic analysis at day 10 of chronic culture (Figure 7B). Guide expression in each condition was analyzed: of the 90,230 sgRNAs present in the plasmid library design, >99% were recovered in each acute sample (acute replicate 1: 89,324 (99.0%); acute replicate 2: 89,625 (99.3%); Gini index average: 0.39; Figures 7C and 19C). "Chronic" samples showed more evidence of selection pressure, with a more widespread range of guide numbers and a greater number of guides exiting the screen (Chronic replicate 1: 75,776 sgRNAs detected (83.9%); Chronic replicate 2: 87,524 sgRNAs detected (97.0%); Gini index average: 0.64; Figures 7C and 19C). Comparing the numbers observed for each sgRNA in acute and chronic conditions revealed a positive correlation with a small population of sgRNAs dramatically enriched in the chronic condition (Figure 7D).

[0179] Positive controls for the screen are components of the TCR signaling pathway, since knocking out these factors would prevent antigen-driven (or anti-CD3-driven) signaling and therefore prevent exhaustion. Therefore, enrichment of CD3 receptor subunits (Cd3e, Cd3d, Cd3g, Cd247, Figure 7D) was analyzed, and robust enrichment of guides targeting these genes was observed in both replicates. As previously described (Flynn et al., (2021) Cell 184, 2394-2411), count tables were normalized and z-scores were calculated for each sgRNA, and these sgRNA-level z-scores were merged into a z-score for each gene. Merging replicates gave an overall z-score and ranking for each gene ("hit" corresponds to FDR<0.001; Figures 2B and 2C). This approach was validated by comparing screen hits obtained from two additional widely adopted CRISPR sgRNA enrichment analysis methods, MAgeCK and casTLE, which showed high correlation between effect size estimates (casTLE effect size correlation: R = 0.66; MAgeCK log fold change correlation: R = 0.77; Figures 20A-20D). Comparison of genes classified as hits using each method revealed that the largest group of hits was shared by all three methods ("hit" corresponds to FDR < 0.05 for pipeline and MAgeCK or casTLE score > 10; Figure 20B). The retroviral library tool did not include a control sgRNA set, and the normalization strategy (compared to all sgRNAs in the pool) was compared to a strategy utilizing a set of sgRNAs targeting olfactory receptors that are not expressed or predicted to function in T cells (Gilbert et al., 2014, Cell 159, 647-661). Normalizing sgRNA enrichment to the olfactory receptor sgRNA set modestly boosted the power of the screen results but otherwise had minimal impact on the results (Figure 20E).

[0180] In addition to Cd3e, Cd3d, and Cd3g, the top hits in the screen included other known components of the TCR signaling pathway, such as Zap70, Lcp2, Lat, and Lck, as well as cell adhesion and integrin-related genes Fermt3, Tln1, Itgav, and Itgb3 (Figures 2B-2D). GO term analysis of the top 100 positive regulators of exhaustion revealed that "T cell receptor signaling pathway" terms were highly enriched (padj=7.30×10-6; Figure 2E). Strikingly, in addition to TCR-related GO terms, other top terms were related to epigenetics, including "chromatin remodeling" (padj=1.46×10-6), "chromatin organization" (padj=8.92×10-4), and "nucleosome disassembly" (padj=4.02×10-5; Figure 2E). Examination of additional top hits identified several chromatin-associated factors, including Wdr82, Actr8, Ino80, Actb, Elmsan1, Ino80b, Hdac1, and Arid1a (Figure 2F, left). The costimulatory and inhibitory receptors Icos, Pdcd1, Ctla4, Cd28, Havcr2, Lag3, and Tigit were not significantly enriched by the screen (Figure 2F, center). The TFs Irf4, Junb, Eomes, and Batf3 were depleted, and Tbx21 and Nr4a3 were moderately enriched, supporting previous evidence for their role in exhaustion (Figure 2F, center). In contrast, Tox and Tox2 were not significant hits in this screen, indicating that deletion of these factors may not improve T cell persistence in vivo, possibly due to activation-induced cell death (Figure 2F, center). Similarly, Jun and Batf were not hits, suggesting that overexpression of these factors improved T cell persistence, whereas deletion did not. Other top hits included genes such as Pggt1b, Spcs3, Sec63, Eif4g2, Sec62, and Fas (Figure 2F, right). The screen identified negative hits, which represent genes for persistence in the presence of chronic antigen, including Zfp217, Gcnt2, Usp22, Irf4, and Cblb (Figure 2C).

[0181] Cytoscape was used to visualize the protein-protein interaction network of the top positive and negative hits (Figure 3). This analysis identified a highly interconnected and enriched network of hits that directly associate with the TCR complex and downstream signaling components, as well as several other protein complexes and functional categories. These complexes included the Ino80 nucleosome remodeling complex (hits included Ino80, Ino80b, Actr5, and Actr8), the Set1C / COMPASS complex that regulates histone methylation (hits included Wdr82, Dpy30, and Setd1b), the SWI / SNF chromatin remodeling complex (hits included Arid1a, Smarcb1, Smarcd2, Smarca4, and Smarcc1), and the mitotic deacetylase (MiDAC) complex, which includes Hdac1, Dnttip1, and Elmsan1. Within the SWI / SNF family members, Smarcc1, Smarcd2, and Smarcb1 are part of the BAF core, which assembles together with Arid1a, Smarca4 (ATPase), Actb, and other components to form the BAF complex (Mashtalir et al., 2018). We also observed enrichment of several hits related to mRNA processing CSTF complex (Cstf1, Cstf2, and Cstf3), N6-methyladenosine (m6A) RNA modification-related genes (Zfp217, Rbm15, and Virma), as well as endoplasmic reticulum and protein secretion (Spcs2, Spcs3, Sec62, Sec63), lipid biosynthesis (Gpi1, Pigv, Dpm3), and mitochondrial complex V (Atp5b, Atp5d, Atp5a1).

[0182] Gene expression patterns in previously reported single-cell RNA-seq data of exhausted T cells were analyzed in chronic viral infection (Raju et al., (2021) J. Immunol. 206(12) pp. 2924-2936; Fig. 8A-8B). This dataset encompassed important subtypes of precursor, transient, and terminal exhausted T cells, and analysis of the top 100 positive hits showed that all factors were detectably expressed in T cells in chronic viral infection (Fig. 8C-8D). Furthermore, all but two of these genes, Tmem253 and Itgb3, were expressed early during exhaustion (98 of the top 100 hits were detectably expressed in precursor exhausted T cells) and remained relatively stable across exhausted subtypes, suggesting that disruption of epigenetic factors and other hits alters the molecular course of T cell exhaustion rather than reversing exhaustion only after terminal differentiation (Fig. 8C).

[0183] [Example 3] In vivo CRISPR screen identifies epigenetic factors that limit T cell persistence in tumors A custom pool of 2,000 sgRNAs was created, which included sgRNAs targeting the top 300 hits (6 sgRNAs per gene), as well as 100 non-targeting and 100 single-targeting controls. The sgRNA pool was introduced into Cas9 / OT-1 T cells to remove functional variability due to different TCR sequences. On day 0, bilateral MC-38 colon adenocarcinoma tumors that ectopically expressed ovalbumin overtook Rag1. - / - CD8+ T cells were isolated from Cas9 / OT-1 mice. On day 1, T cells were transduced with a custom minipool (Figure 4A). Three different T cell administration protocols were used to monitor protocol-specific knockout efficacy: Group 1, 1 × 10 per mouse 6 days after tumor seeding; 6 Group 2 received 5 x 10 T cells per mouse 6 days after tumor seeding. 5 Group 3 received 5 x 10 T cells per mouse 9 days after tumor seeding. 5All mice received 100-fold increased T cell counts (Figure 9A). A T cell dose-dependent reduction in tumor size was observed. Tumors and spleens of mice were harvested on day 15 (groups 1 and 2) or day 18 (group 3) and the sgRNAs present in each tissue were sequenced (Figures 9A and 22A). Finally, sgRNA enrichment was calculated and results were combined from all mice to generate aggregate tumor LFC z-scores and spleen LFC z-scores for each gene compared to the control distribution (Figures 4B and 12A-12C).

[0184] We analyzed sgRNAs targeting TCR complex and signaling genes because cells containing these guides have an impaired ability to recognize antigens and should therefore be depleted in tumors. Indeed, sgRNAs targeting nearly all previously identified TCR and integrin signaling-related hits were depleted in tumors compared to spleens (Figure 4C). Similarly, genes in several other functional categories were depleted in both tumors and spleens, likely indicating a general growth defect of these knockouts in vivo (Figures 12A and 12D).

[0185] However, in contrast, a select group of in vitro hits was strongly enriched in both tumors and spleen, consisting mostly of chromatin-associated factors (Figures 4B-4C and 12A-12C). Among the top hits enriched in tumors were the TFs Nr4a3 and Gata3. Functional roles for eight epigenetic regulators were identified, including the Ino80 complex factors Ino80, Actr5 and Actr8, the Set1C / COMPASS complex members Wdr82 and Setd1b, the BAF complex member Arid1a, and the MiDAC complex members Hdac1 and Elmsan1 (Figure 4B). Gata3 is a transcription factor previously demonstrated to regulate the development of T cell exhaustion, and importantly, deletion of this factor improves T cell function, persistence and tumor suppression in vivo. The remaining hits have not been studied in a T cell exhaustion or immunotherapy context. Visualization of tumor enrichment of each gene in the context of the cytoscape network revealed that many of the in vivo positive hits were epigenetic factors, including subunits of two chromatin remodeling complexes, the INO80 complex (subunits Ino80c and Actr5) and the BAF complex (subunits Arid1a, Smarcd2, and Smarcc1; Figure 12D). Other categories such as Gpr137c, a G protein-coupled receptor; B4galnt1, an enzyme involved in ganglioside biosynthesis; and Itk, an IL-2-inducible T cell kinase, were also represented. sgRNA enrichment of the top positive hits was calculated and compared to input controls. Respective gene knockdown improved T cell accumulation in tumors by 3.4-fold. In comparison, T cells lacking Cd3d were depleted 6.7-fold and T cells lacking Cd3e were depleted 3.3-fold, indicating that targeting the top hits substantially improved T cell persistence in the tumor (Figures 12E and 22C).Furthermore, the persistence advantage of each knockout was similar in tumors and spleens, and no perturbation except Trp53 showed substantially improved fitness in the absence of chronic TCR stimulation (during acute stimulation in vitro), again demonstrating the specificity of the CRISPR screen strategy to identify perturbations that improve T cell persistence only in the context of chronic antigen stimulation rather than improving overall T cell fitness (Figures 12E-12F and 22C).

[0186] A role for a previously uncharacterized TF, Zfp219, in T cell function in vivo was also identified. sgRNAs targeting these epigenetic factors were more highly enriched in tumors than sgRNAs targeting Nr4a3 and Gata3. GO term analysis and visualization of in vivo sgRNA z-scores in the context of the cytoscape network confirmed that functional categories were related to chromatin and nucleosome remodeling and organization, with histone modifications being the predominantly enriched group of genes (Figures 4E and 9C-9D). sgRNA enrichment for the top 15 in vivo hits in tumor and spleen samples was analyzed separately and across T cell dosing protocols. sgRNAs targeting each gene were reproducibly enriched across individual mice, organs, and tumor sizes (Figures 4F, 4G, 9B). Notably, deletion of each epigenetic factor improved T cell accumulation in tumors by approximately 3-5 fold compared to control sgRNA, comparable to the scale of T cell depletion observed for T cells lacking components of the CD3 co-receptor, Cd3d, indicating a significant improvement in T cell function mediated by deletion of these genes (Figure 4F).

[0187] [Example 4] In vivo Perturb-seq of T cell exhaustion factors in TILs Perturb-seq, which captures CRISPR perturbations and transcriptomes in single cells, was used to understand the molecular mechanisms driving improved T cell function in each knockout identified by the in vivo CRISPR screen. Specifically, direct capture Perturb-seq was used because it does not require vectors with barcode sequences separate from the sgRNA or other modifications to standard sgRNA vectors, and is therefore immediately compatible with retroviral reagents. A third custom sgRNA pool (micropool) of sgRNAs was designed by prioritizing genes that (1) preferentially persisted in in vitro assays, (2) preferentially proliferated and infiltrated tumors in vivo, and (3) were chromatin-associated proteins or TFs. Based on these criteria, nine genes were selected for Perturb-seq analysis: Wdr82, Setd1b, Arid1a, Actr8, Ino80, Hdac1, Elmsan1, Nr4a3, and Zfp219. The sgRNA pool contained two guides per gene, as well as two non-targeting and two single-targeting control guides, for a total of 22 sgRNAs. To ensure similar representation of all guides, pairs of primers containing the 20 bp variable sgRNA sequence were annealed individually, then pooled and cloned together into the retroviral vector pMSCV.

[0188] A similar in vivo T cell protocol was performed as previously described for the larger CRISPR screen, where CD8+ T cells were isolated from Cas9 / OT-1 mice, transduced with sgRNA micropools, and then transduced into Rag1+ / - bearing MC-38 ovalbumin tumors. - / -After 9 days, tumors were harvested, TILs were isolated, and direct capture Perturb-seq was used to simultaneously read out sgRNA identity and gene expression profile information using the 10× genomic 5' gene expression platform (Figure 5A). After quality control filtering, high-quality scRNA-seq profiles were obtained from 2,305 cells, and scRNA-seq clustering and dimensionality reduction identified 4 clusters (Figure 5B). All cell clusters contained cells with moderate Pdcd1 and Havcr2 expression, indicating that the cell clusters represent exhausted T cells in the tumor microenvironment (Figure 10A). Comparing the marker genes between cluster 1 and cluster 2 revealed that cluster 1 had higher expression of costimulatory molecules including Tnfrsf9 (encoding 4-1BB) and Tnfrsf4 (encoding OX40), cytotoxic molecules Gzmb and Prf1, and inhibitory receptors including Lag3, Havcr2, and Cd160 (Figure 10B). In contrast, cluster 2 had higher expression of progenitor exhaustion genes Tcf7, Ifngr1, and Ccl5, as well as several interferon-responsive genes, including Ifit1, Ifit3, Irf1, and Irf7 (Figures 10B-10C). Clusters 1 and 2 contained the majority of cells, cluster 3 contained a small population of cells with a higher percentage of mitochondrial readout, and cluster 4 contained a small number of proliferating cells characterized by Mki67 expression (Figure 10A).

[0189] High-confidence sgRNA identity was determined for each cell by examining the sgRNAs through a cell count matrix and calculating the raw (cell) z-score (Figures 5C and 10D). Any cell with a maximum sgRNA z-score >3 was determined to contain the guide with the maximum z-score, cells with no sgRNA counts were designated "no guide", and cells with a lower maximum z-score were designated "multiple guides". The average gene expression profile for cells with a given perturbation was calculated, the gene expression profile of the control cells was subtracted, and the differential gene expression profiles were correlated across the different perturbations (Figure 5D). Overall, cells with each gene KO showed large-scale changes in their transcriptional profile (1,474-2,533 induced genes LFC>0.1; 643-2,900 repressed genes LFC<-0.1; Figure 10E). Cells depleted of Elmsan1, Nr4a3, Zfp219, Arid1a, or Setd1b displayed highly correlated changes in gene expression (R>0.5 for all pairs of perturbations), indicating convergent phenotypes induced by knockout of these factors (Figure 5D). Sets of "induced" or "repressed" genes for each perturbation compared to control cells were defined, with all perturbations sharing roughly half of the genes induced or repressed by Nr4a3 (Figure S10E). Overlapping gene sets were visualized for five perturbations - Zfp219-KO, Nr4a3-KO, Arid1a-KO, Elmsan1-KO, and Setd1b-KO - and the largest group of overlapping genes was found to be shared across all perturbations (Figure 5E-5F). These genes included (1) cytotoxic molecules, cytokines and cytokine receptors, including upregulation of Tnf, Ifng and Il7r and downregulation of Gzmb, Gzmc and Gzmf; (2) exhaustion-related TFs, including upregulation of Batf, Irf4 and Klf2 and downregulation of Tbx21; and (3) amino acid transporters and other metabolic genes, including upregulation of Slc1a5 (ASCT2, glutamine import), Slc38a1 (SNAT1, neutral amino acid) and Slc38a2 (SNAT2, neutral amino acid).Although isolated changes in inhibitory receptor expression were observed, no consistent changes were observed across perturbations (Figure 5G).

[0190] [Example 5] A TIL minipool CRISPR screen identifies genetic regulators of T cell exhaustion in vitro To further confirm and characterize the top-ranked genome-wide screen factors, a custom minipool of 2,000 sgRNAs was created that contained sgRNAs targeting the 300 top-ranked genes (6 sgRNAs per gene), as well as 100 non-targeting and 100 single-targeting controls. The in vitro stimulation screen was repeated, with acute and chronic samples, as well as input samples, collected on day 4 (Figure 21A). High concordance between biological replicates was observed, and therefore replicates were combined to perform three comparisons: (1) chronic vs. acute, (2) acute vs. input, and (3) chronic vs. input (Figures 21B-21E). The chronic vs. acute comparison served as confirmation of the initial genome-wide screen, and of the 88 genes in the pool that were significant positive hits in the genome-wide screen, 52 (59.1%) were confirmed in the minipool (FDR<0.05; Figures 11B and 21C). Next, chronic vs. acute gene enrichment was compared to acute vs. input enrichment, measuring the fitness advantage or disadvantage of each gene knockdown in acutely stimulated proliferating T cells in culture (Figures 11C, left and 21E). Two hits, Trp53 and Brdl, were enriched in both comparisons, indicating that depletion of these factors confers an overall proliferation advantage to T cells in both acute and chronic stimulation conditions. In contrast, the majority of genes showed similar (233 / 300; 77.7%) or decreased (64 / 300; 21.3%) enrichment in acute stimulation compared to input, allowing the identification of sgRNAs that specifically improved T cell persistence in the presence of chronic antigen and maintained proliferation capacity after acute stimulation, rather than T cell proliferation in general (similar: -3.5≦z≦3, decreased z<-3.5, improved; z>3.5; Figures 11C, left and 21E). Finally, chronic vs. acute sgRNA enrichment was compared to chronic vs. input enrichment to identify sgRNAs with an overall persistence advantage following chronic antigen stimulation rather than only a comparative advantage over acute stimulation (Figure 11C, right). In summary, these minipool experiments confirmed the hits from the genome-wide CRISPR screen and identified genes that selectively limit T cell persistence in the context of chronic antigen stimulation.

[0191] [Example 6] Modulation of cBAF activity can enhance T cell persistence To confirm the persistence advantage of Arid1a-sgRNA cells (top hits in the screen) and to determine whether these cells retain effector function in vivo, we used a cellular competition assay in which a single targeting control (CTRL1) sgRNA was cloned into a retroviral vector expressing violet-excited fluorescent protein (VEX), while two Arid1a-sgRNA sgRNAs (Arid1a-1 and Arid1a-2) were cloned into vectors that were identical except for their replacement with blue fluorescent protein (BFP). The activity of both Arid1a-targeting sgRNAs was confirmed at the DNA and protein levels by Sanger sequencing and Western blot (Figures 22D-22F). Cells were transduced with either vector separately, selected with puromycin to enrich for transduced cells, and mixed together. The mixed cells were then loaded into an in vitro chronic stimulation assay (Figure 13A) or an in vivo MC-38 tumor model (Figure 13B). In vitro and in vivo, Arid1a-sgRNA cells showed significantly enhanced persistence compared to control cells, confirming the results of the pooled screen (Figures 13A-13B; mean normalized ratio of Arid1a-1 to CTRL1: in vitro day 10 = 4.03, p = 0.0059; in vivo day 15 = 2.46, p = 0.033; mean normalized ratio of Arid1a-2 to CTRL1: in vitro day 10 = 3.79, p = 0.012; in vivo day 15 = 2.72, p = 0.0088; Welch two-sample t-test). Furthermore, Arid1a-sgRNA cells exhibited lower levels of PD-1 and Tim3 after chronic stimulation in vitro (percentage double positive cells: 27.7% Arid1a-1 mean reduction, p=0.00099; 10.6% Arid1a-2 mean reduction, p=0.038; Welch two-sample t-test; Figure 13A). Finally, we assessed whether the observed enhanced persistence and altered differentiation trajectory of Arid1a-sgRNA cells would translate to improved anti-tumor responses in vivo. - / -Mice were inoculated with MC-38 tumors as previously described and transduced with CTRL1 retrovirus or Arid1a-sgRNA retrovirus on day 6 (5 × 10 5 Cas9 / OT-1 CD8+ T cells were implanted and tumor growth was monitored (Figure 13C). By day 15, transfer of Arid1a-sgRNA cells significantly improved tumor clearance compared to transfer of control cells (Arid1a-sgRNA vs. CTRL1 tumor size, day 15: p=5×10 -8 , Welch two-sample t-test). Importantly, survival of mice receiving Arid1a-sgRNA T cells was significantly extended compared to mice receiving CTRL1 T cells (median survival = 12 days (no transplant), 15 days (CTRL1), 25 days (Arid1a-sgRNA); Arid1a-sgRNA vs. CTRL1: p = 1.20 × 10 -8 , Figure 13C).

[0192] To provide a deeper putative mechanism for the role of BAF complex factors in T cell exhaustion, additional CRISPR minipool screens were designed targeting each of the 29 SWI / SNF complex subunit genes in B16 and MC-38 tumor models, and these results were interpreted in the structural context of SWI / SNF complex assembly. As observed in the previous in vivo screen, the three most significant hits were in the cBAF complex (Arid1a, Smarcc1, and Smarcd2), notably in locations of the complex that could be replaced by paralogs in other forms of the complex (Figures 13E-13F). In contrast, perturbation of non-replaceable subunits of the BAF core (e.g., Smarce1, Smarcb1) or ATPase module components was deleterious, resulting in depletion of these sgRNAs. Thus, a model is proposed in which modulating (reducing) the presence of cBAF on chromatin would be beneficial for T cell persistence. Previous mechanistic studies have demonstrated that ARID1A-deficient tumors exhibit reduced (but not ablated) levels of cBAF complexes on chromatin, which reduces access of key transcription factors, including AP-1 factors. In addition to cBAFs, we also observed positive enrichment of sgRNAs targeting PBAF complex member Arid2, and strong depletion of sgRNAs targeting ncBAF complex members Bicral, Bicra, and Brd9 (Figures 13E-13F). In summary, these results demonstrate that perturbation of cBAF complex subunit genes, including Arid1a, can improve T cell persistence and antitumor immunity in vivo.

[0193] [Example 7] Perturbation of ARID1A improves T cell persistence in primary human T cells To recapitulate the in vitro chronic stimulation assay using human T cells (Figure 14A), primary human T cells were transduced with CRISPR-Cas9 / sgRNA RNPs targeting ARID1A (two independent sgRNAs) or control RNPs. Cells were split into acute and chronic cultures, with chronic conditions stimulated with anti-CD3 coated plates for 6 days (similar to the mouse assay). In acute stimulated cultures, no differences were observed between phenotypes for proliferation or viability. However, in chronic stimulated cultures, ARID1A-sgRNA cells proliferated significantly more and maintained higher viability than CTRL T cells (ARID1A-sgRNA vs. CTRL1 cells: 22.75% mean increase in viability, p=1.70×10 -5 , and a mean increase in expansion proliferation of 5.25-fold, p = 0.013; Figure 14A).

[0194] To validate the ARID1A-sgRNA T cell persistence advantage in vivo and in the context of other genetic factors that have recently emerged from human T cell functional CRISPR screens, a CRISPR minipool was designed for in vivo human T cell experiments that encompassed 48 sgRNAs targeting 20 genes and included 8 negative control guides. Included were sgRNAs targeting ARID1A, as well as the inhibitory receptors PDCD1, LAG3, and HAVCR2, as well as other top-ranked genes from previous screens such as TMEM222, CBLB, TCEB2, and SOCS1. The screen was performed in the A375 human melanoma xenograft model, which expresses the NY-ESO-1 antigen that can be targeted using the 1G4 TCR. Cognate 1G4 TCRs were introduced together with sgRNAs into primary human T cells from two independent donors on day 1, and T cells were transplanted into NOD-SCID-IL2Rγ-null (NSG) tumor-bearing mice on day 14 (Figure 14B). Seven days later, T cells were sorted from tumor and spleen, and the sgRNAs present in each organ were sequenced and compared to the amount in the input sample before transplantation. No enrichment of control sgRNAs or sgRNAs targeting inhibitory receptors was observed, and depletion of sgRNAs targeting CD3D was observed (Figures 14C-14D). In contrast, consistent with results in mouse T cells, sgRNAs targeting ARID1A were significantly enriched in tumors compared to input samples in both donors, demonstrating that the function of cBAF in limiting T cell persistence is conserved in human T cells (seven of eight ARID1A-sgRNA replicates enriched in tumors vs. input occurred across two independent sgRNAs; ARID1A-sgRNA vs. CTRL LFC p=0.0010 by Wilcoxon test, Figures 14C-14D).

[0195] [Example 8] Transcriptional effects of chromatin remodeling complexes in TILs To understand the molecular mechanisms driving improved T cell function in hits identified by in vitro and in vivo CRISPR screens, we performed Perturb-seq to simultaneously capture CRISPR sgRNA and transcriptome in single cells. We designed a third custom sgRNA pool (micropool) to target INO80 and the BAF complex. Both complexes are ATP-dependent chromatin remodelers essential for many aspects of development. In SWI / SNF genes, Arid1a, Smarcc1, and Smarcd2 (top hits identified in vitro and in vivo), as well as Arid2 and Arid1b, two of which were enriched in the SWI / SNF-specific minipool screen, were targeted. Of these, Smarcc1 and Smarcd2 are in the BAF core, Arid1a and Arid1b are in the cBAF complex, and Arid2 is present only in the PBAF complex. From the INO80 complex, Actr5 and Ino80c were selected, which were enriched in both the in vitro and in vivo screens. Interestingly, Arp5 and Ies6, the yeast homologs of Actr5 and Ino80c, were found to physically associate with each other to form a subcomplex that is independent of the rest of the INO80 complex. The subcomplex can regulate the activity of the rest of the INO80 complex; in particular at metabolic genes, it interacts with chromatin in an INO80-dependent manner and repositions nucleosomes (especially +1 nucleosomes) to activate gene transcription. Finally, positive controls Pdcd1 and Gata3 were included, as well as 12 single-targeting negative controls, for a total of 48 sgRNAs targeting 9 genes. A similar in vivo T cell protocol was performed as described above for the larger CRISPR screen, and CD8 + T cells were isolated from Cas9 / OT-1 mice and transduced with sgRNA micropools to express Rag1 bearing MC-38 ovalbumin tumors. - / -T cells were then implanted into mice. As with the previous screen, input samples (collected on the day of implantation) were also collected to assess the persistence phenotype of each sgRNA. Nine days after T cell implantation, tumors were harvested, TILs were isolated, and direct capture Perturb-seq was used to simultaneously read out sgRNA identity and gene expression profile information using a 10x Genome 5' gene expression platform (Figure 15A). Cells from seven biological replicate Perturb-seq samples across two independent experiments were sequenced (Figures 23-23B).

[0196] After quality control filtering, high-quality scRNA-seq profiles were obtained from 70,646 cells, and scRNA-seq clustering and dimensionality reduction identified six clusters (Figure 15B). High-confidence sgRNA identity was identified for each cell by using a z-score to quantify the enrichment of each sgRNA relative to other sgRNAs detected in the same cell. A cell was assigned to a particular sgRNA if that sgRNA had a z-score of at least 5 and a z-score at least 2 units higher than the next most relevant sgRNA. Using this strategy, cells with multiple enriched sgRNAs due to retroviral infection doublets, single-cell capture doublets, and / or background reads were removed from further analysis, and 52,607 cells were confidently assigned to a single sgRNA (74.4%; Figure 15C). The cell type clusters expressed various levels of inhibitory receptors, effector cytokines, and key transcription factors, indicating that the clusters represent a mixture of exhausted and effector T cells in the TME (Figures 15D and 23C-23D). Cluster 1 cells expressed high levels of Klf2 and Slprl (T effector memory; T EM ), and cluster 2 expressed high levels of interferon-stimulated genes (ISGs), including Mxl (T ISG), cluster 3 expresses high levels of Tnfrsf9 (encoding 41BB) and Cdl60 (T-41BB), and cluster 4 expresses high levels of progenitor exhaustion genes including Pdcd1, Tcf7, and Slamf6 (T EX Prog), and cluster 5 expressed the highest levels of the inhibitory receptors Pdcd1, Lag3, and Havcr2 (T EX Term), cluster 6 was predominantly composed of cycling cells as characterized by Mki67 and confirmed by cell cycle analysis (T-cycling; Figures 23C-23D). To further define cluster identity, gene signatures were analyzed to identify CD8 + The top 100 marker genes were used for each LCMV T cell cluster to score each single cell in our Perturb-seq dataset according to the average expression of these signature gene sets. Visualization of the enrichment of these LCMV signatures in each cluster showed transcriptional similarity of some clusters to cell types in the reference dataset (Figure 15E). For example, cluster 1 was enriched for effector memory-related genes (T EM signature), cluster 2 is T EM Similar to the ISG signature, the precursor and terminal exhausted clusters (clusters 4 and 5) were enriched for the corresponding LCMV signature (Figure 15E).

[0197] Several sgRNA-level quality controls were performed to assess the reproducibility of the effects of independent sgRNAs (Figures 15F-15G). Differential gene expression was calculated between each sgRNA and all other cells in the dataset, confirming that independent sgRNAs targeting the same gene had highly correlated gene expression changes compared to pairs of sgRNAs targeting different genes, the latter of which, as expected, were centered around zero (Figure 15F, top). We assessed the correlation of pairs of sgRNAs targeting the same complex, grouping together guides targeting cBAF genes (Arid1a, Arid1b, Smarcc1, and Smarcd2) and guides targeting INO80 genes (Ino80c or Actr5). Strikingly, these sgRNA pairs were also significantly more correlated than all pairs of guides, indicating a common transcriptional effect of targeting different subunits within the same complex (Figure 15F, bottom). Gene expression correlations of all pairs of sgRNAs were visualized together (Figure 15G). Unbiased clustering organized sgRNAs into correlated groups, driven primarily by target gene and target complex identity. Interestingly, Arid2 clustered separately from the rest of the BAF-targeting sgRNAs, suggesting distinct roles for cBAF and the PBAF complex (Figure 15G). The input representation of each sgRNA, along with the number of cells detected with each sgRNA, was used to assess the T cell accumulation advantage between each sgRNA and a set of single-targeting negative controls (Figure 15G). This analysis showed that, consistent with the in vivo screen results, the majority of sgRNAs enhanced T cell accumulation in tumors compared to control sgRNAs. Notably, Arid1a-sgRNA cells were enriched on average 2.74-fold compared to controls, and Pdcd1-sgRNA cells were enriched on average 2.67-fold compared to controls (Figure 15G). Finally, the cell type cluster composition of cells containing each sgRNA was examined (Figure 15G, far right).Notably, all perturbations involved similar proportions of cells from each cluster, suggesting that depletion of each target gene does not affect whole-scale changes in cell type composition or trajectories, but rather may modulate gene expression in one or more clusters.

[0198] To further explore this possibility, cells containing sgRNAs targeting the same genes were assembled and differential gene expression was calculated for each perturbation and compared to CTRL1 cells (Figure 16A). Targeting the cBAF subunits Arid1a, Smarcd2, or Smarcc1 induced shared global changes in the transcriptional program of T cells, including upregulation of the effector molecules Gzmb and Ifng, the cell surface receptors Cxcr6 and Il7r, and the transcription factors Irf4 and Batf. Meanwhile, Pdcd1, Lag3, and Ccl5 were consistently downregulated by cBAF perturbation (Figure 16A). In contrast, Arid2 perturbation induced a distinct gene expression program despite some similarities, including downregulation of Pdcd1 and Lag3. Perturbations of Gata3 and Pdcd1 induced distinct gene expression changes from cBAF or Arid2 perturbations; for example, the most upregulated gene after Pdcd1 depletion was Tox, possibly consistent with the proposed effect of PD-1 deletion on accelerating differentiation to terminal exhaustion (Figure 16A). To quantify the collective similarity of gene expression changes induced by each perturbation, all pairs of perturbations were correlated with each other, and clustering was performed to group perturbations that were similar according to this metric (Figure 16B). This analysis quantitatively confirmed the observation that cBAF perturbations Arid1a, Smarcc1, and Smarcd2 induced similar programs, and that INO80 perturbations Ino80c and Actr5 also showed highly correlated changes (distinct from those induced by cBAF perturbations). In contrast, Pdcd1 and Gata3 perturbations clustered separately, but were moderately correlated with each other. Finally, when gene expression changes were examined for perturbed versus CTRL1 cells within each cluster, each perturbation induced highly concordant changes in gene expression independent of T cell subtype (Figure 23G).

[0199] All genes significantly differential between perturbed and CTRL1 cells were assembled, defining the core gene programs perturbed by depletion of cBAF and INO80 complexes (Figs. 16C-16E). The upregulated and downregulated gene sets were highly conserved within each complex (Figs. 16D-16F and 24A), with cBAF perturbation inducing genes such as Batf, Irf4, Il7r, and Ccr2, and suppressing genes such as Stat3, Nfkb1, Nr4a3, and Eomes. In contrast, INO80 perturbation substantially modulated metabolism-related genes (Fig. 16E). Projection of genes upregulated by cBAF depletion onto the canonical T cell state identified in chronic LCMV infection showed enrichment for effector T cell genes, and projection of downregulated genes showed enrichment for terminal exhaustion-related genes (Figs. 16G and 24B). GO term analysis of the upregulated gene set was performed. Genes upregulated in cBAF-deficient T cells were enriched for effector terms, including T cell activation, cell adhesion, cytokine production, and T cell proliferation, whereas genes upregulated in INO80-deficient T cells were enriched for metabolic terms, including oxidative phosphorylation and aerobic respiration (Figure S16H). In contrast, perturbation of Pdcdl induced cell signaling-related terms (Figure S16H). These data indicate that subunits of the cBAF and INO80 chromatin remodeling complexes have distinct roles in T cell exhaustion that are largely conserved within the same complex, with cBAF primarily regulating effector- and exhaustion-related genes and INO80 regulating metabolism. Furthermore, the transcriptional effects of targeting chromatin remodeling factors were minimally overlapping with those of previously known targets Pdcd1 and Gata3, suggesting the potential to synergistically target multiple pathways to improve T cell function (Figures S16F and S24A).

[0200] [Example 9] Terminal exhaustion-associated chromatin accessibility using Arid1a perturbation Competition assays were performed as described above, where CTRL1 and Arid1a-sgRNA cells were mixed at defined ratios and subjected to in vitro exhaustion. Two independent sgRNAs targeting Arid1a were used in duplicate, for a total of four replicate samples. On days 6 and 10, CTRL1 and Arid1a-sgRNA cells were isolated from the same cultures, and ATAC-seq was performed on each population. To analyze these results in the context of the initial assay characterization (Figure 1), profiles of naïve (day 0) and activated (day 2) WT T cells were included (Figure 17A). Chromatin state progression in CTRL1 cells proceeded similarly to the state previously observed in unperturbed cells. However, Arid1a-sgRNA cells proceeded along a distinct chromatin state trajectory, remaining closer to the naïve and activated samples than CTRL1 cells at both time points (Figure 17A).

[0201] Regulatory elements were defined as “open” peaks if increased accessibility was observed on day 10 compared to day 6, and as “closed” peaks if decreased accessibility was observed on day 10 compared to day 6 ( p adj <0.05, Log2FC>1). Analysis of these peak sets demonstrated substantially different chromatin reorganization changes in Arid1a-sgRNA T cells compared to CTRL1 T cells (Figures 17B-17C). First, Arid1a-sgRNA cells showed a marked overall reduction in the number of open peaks, likely representing a relative inability of cBAF-depleted cells to establish accessible chromatin (Arid1a-sgRNA: 1,419 peaks, CTRL1: 5,692 peaks; Figure 17B). Second, although Arid1a-sgRNA and CTRL1 cells closed chromatin to a similar extent, the majority of these regions were non-overlapping (Arid1a-sgRNA: 5,126 peaks, CTRL1: 4,558 peaks; Figure 17B). Examination of individual exhaustion-associated regulatory elements, including those surrounding the Pdcd1, Lag3, Entpd1, and Ifng loci, revealed a substantial loss of accessibility in Arid1a-sgRNA cells compared to CTRL1 cells (Figure S17D).EX Analysis of the specific peak sets (defined in Figure 1) showed that these sites were significantly less accessible in Arid1a-sgRNA cells than in CTRL1 cells at both time points (terminal T in Arid1a-sgRNA compared to CTRL1 cells). EX Mean decrease in peak attainability: 41.7% on day 6 (p<2.2×10 -16 , Wilcoxon test) and 40.8% on the 10th day (p<2.2×10 -16 , Wilcoxon test); Figures 17E and 24C-24D). Chromatin accessibility at TF binding sites was analyzed using chromVAR, which showed that terminal exhaustion-associated TF motifs, including Fos, Jun, and AP-1 motifs, were significantly less accessible in Arid1a-sgRNA cells compared to CTRL1 cells (Figure 17F). Conversely, several TF motifs associated with effector T cell function, including Ets, Klf, and Irf motifs, showed increased accessibility in Arid1a-sgRNA cells. Finally, ATAC-seq analysis of chronically stimulated ARID1A-sgRNA human T cells showed a similar loss of global chromatin accessibility at AP-1 motifs compared to control T cells, supporting a conserved epigenetic function of ARID1A in human T cells (Figures 24E-24G). These results suggested that depletion of cBAF subunits, including Arid1a, may improve T cell function by limiting the access of AP-1 TFs to chromatin, thereby preventing the acquisition of a terminal exhaustion-associated chromatin state.

[0202] All references cited in this specification, including publications, patent applications, and patents, are hereby incorporated by reference to the same extent as if each reference was individually and specifically indicated to be incorporated by reference and was set forth in its entirety herein.

[0203] Preferred embodiments of the invention are described herein, including the best mode known to the inventors for carrying out the invention. Variations of those preferred embodiments may become apparent to those skilled in the art upon reading the foregoing description. The inventors expect that such variations will be employed by those skilled in the art as appropriate, and the inventors intend that the invention be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

Claims

1. An engineered T cell lacking at least one gene selected from the group consisting of ARID1A, INO80C, GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

2. An engineered T cell lacking at least one chromatin remodeling protein or a gene encoding the same, wherein the at least one chromatin remodeling protein is an INO80 nucleosome positioning complex protein selected from Actr5, Ino80, Ino80c, Ino80b, Actr8, and combinations thereof, a SWI / SNF family member selected from Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, and combinations thereof, or a combination thereof is the engineered T cell.

3. The engineered T cell according to claim 2, further lacking at least one gene selected from the group consisting of GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, and ELMSAN1.

4. The engineered T cell according to claim 1 or 2, which maintains functionality under conditions where unengineered T cells exhibit exhaustion and / or has improved persistence and functionality compared to unmodified T cells.

5. The engineered T cell according to claim 1 or 2, further comprising an exogenous T cell receptor (TCR) or a chimeric antigen receptor (CAR) or a nucleic acid encoding the same.

6. The engineered T cell according to claim 5, wherein the exogenous T cell receptor (TCR) or chimeric antigen receptor (CAR) is specific for a tumor antigen.

7. The engineered T cell according to claim 1 or 2, derived from a biological sample from a subject.

8. The engineered T cell according to claim 1 or 2, expanded ex vivo.

9. A composition comprising a population of the engineered T cells according to claim 1 or 2, and optionally at least one therapeutic agent.

10. A method of producing a therapeutic T cell, comprising altering the DNA of T cells isolated from a sample to knockout or disrupt at least one gene selected from the group consisting of ARID1A, INO80C, GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8; and A step of manipulating T cells to express an exogenous T cell receptor (TCR) or a chimeric antigen receptor (CAR) A method comprising the same.

11. A method for producing therapeutic T cells, comprising: A step of changing the DNA of T cells isolated from a sample to knockout or disrupt at least one gene encoding a chromatin remodeling protein; and A step of manipulating T cells to express an exogenous T cell receptor (TCR) or a chimeric antigen receptor (CAR) A method comprising the same.

12. The method according to claim 10 or 11, wherein the T cells are derived from a subject.

13. The method according to claim 10 or 11, wherein the exogenous T cell receptor (TCR) or chimeric antigen receptor (CAR) is specific for a tumor antigen.

14. The method according to claim 10 or 11, wherein exhaustion of T cells is prevented or reduced by changing the DNA.

15. A method for preventing T cell exhaustion, comprising genetically modifying T cells to lack at least one gene selected from the group consisting of ARID1A, INO80C, GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

16. A method for preventing T cell exhaustion, comprising genetically modifying T cells to lack at least one chromatin remodeling protein or a gene encoding the same.

17. The method according to claim 15 or 16, wherein the T cells further comprise an exogenous T cell receptor (TCR) or chimeric antigen receptor (CAR) or a nucleic acid encoding the same.

18. The method according to claim 15 or 16, wherein the T cells have increased survival in the presence of chronic antigen.

19. A pharmaceutical composition for use in a method for treating a disease or disorder in a subject, the pharmaceutical composition comprising the engineered T cells according to claim 1 or 2, the method comprising administering the pharmaceutical composition to the subject, the pharmaceutical composition.

20. The pharmaceutical composition according to claim 19, wherein the disease or disorder comprises an infectious disease or cancer.

21. By the method, the number of cancer cells in the subject is reduced; By the method, the tumor burden in the subject is reduced and / or removed; and / or The method provides enhanced cancer treatment compared to the use of non-engineered T cells, The pharmaceutical composition according to claim 20.

22. A system for genetically engineering T cells to produce the engineered T cells according to Claim 1 or 2.

23. The system according to Claim 22, comprising a clustered regularly interspaced short palindromic repeats (CRISPR) / CRISPR-associated protein (Cas) system, and a guide RNA (gRNA) or a nucleic acid encoding the gRNA directed to at least one gene that promotes T cell exhaustion.

24. The system according to Claim 23, wherein the at least one gene is selected from the group consisting of ARID1A, INO80C, GATA3, WDR82, TRP53, GPR137C, ZFP219, HDAC1, ELMSAN1, and ACTR8.

25. The system according to Claim 24, wherein the at least one gene encodes a chromatin remodeling protein.

26. The at least one chromatin remodeling protein is an INO80 nucleosome positioning complex protein selected from Actr5, Ino80, Ino80c, Ino80b, Actr8, and combinations thereof, a SWI / SNF family member selected from Arid1a, Arid2, Arid1b, Smarcb1, Smarcd2, Smarca4, Smarcc1, and combinations thereof, or a combination thereof The system according to Claim 25.

27. The system according to Claim 23, further comprising an exogenous receptor or a nucleic acid encoding the same, and / or at least one additional therapeutic agent.

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