Genetic targets for t cell-based immunotherapy

Abstract

Provided herein are genetically modified T cells that exhibit increased proliferation upon stimulation compared to wild-type T cells, methods of generating such T cells, and methods of using the T cells to treat diseases such as cancer.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 62 / 695,672, filed July 9, 2018, which is incorporated herein by reference. Background Technology

[0003] Cytotoxic T cells play a crucial role in immune-mediated tumor control and autoimmunity. Immunotherapy, such as checkpoint blockade or therapies based on engineered cells, is revolutionizing cancer treatment, achieving durable responses in some patients with other refractory malignancies. However, despite significant efficacy in some patients, the majority of patients do not respond to available immunotherapies.

[0004] Next-generation adoptive cell therapies are being developed using CRISPR-Cas9 genome engineering. Cas9 ribonucleoproteins can be delivered to primary human T cells to efficiently knock out checkpoint genes (Ren et al., 2017; Rupp et al., 2017; Schumann et al., 2015) or even rewrite endogenous genomic sequences (Roth et al.). While deletion of typical checkpoint genes encoding PD-1 can enhance responses to certain cancers (Ren et al., 2017; Rupp et al., 2017), an expanded set of targets will provide additional therapeutic opportunities. Advances in immunotherapy depend on further understanding of the genetic procedures by which T cells respond when they encounter their target antigens. Promising gene targets could enhance cell proliferation and effective effector responses after stimulation. Furthermore, immunosuppressive cells and soluble molecules such as cytokines and metabolites can accumulate within tumors and inhibit effective anti-tumor T cell responses. Gene targets affecting the ability of T cells to overcome the immunosuppressive tumor microenvironment could extend adoptive cell therapy to solid organ cancers.

[0005] Decades of animal model and cell line studies have identified regulators of T cell inhibition and activation, but a systematic strategy for comprehensively analyzing the function of genes regulating human T cell responses remains lacking. Gene knockdown using selected RNA interference libraries targets enhanced antigen-responsive T cell proliferation in mouse models (Zhou et al., 2014). Recently, CRISPR-Cas9 has ushered in a new era of functional genetics research (Doench, 2018). Large libraries of single-guide RNAs (sgRNAs) can be easily designed to target genomic sequences. Lentiviral transduction of cells encoding these sgRNAs generates cell pools with different genomic modifications, which can be traced through the sgRNA sequence integrated into the provirus. This method has been used in cell lines engineered with stable Cas9 expression and in Cas9 transgenic mouse models (Parnas et al., 2015; Shang et al., 2018). Pooling CRISPR screening has revealed gene targets in human cancer cells that regulate T-cell immunotherapy responses (Manguso et al., 2017; Pan et al., 2018; Patel et al., 2017). Summary of the Invention

[0006] This disclosure (in part) is based on a novel method, namely sgRNA lentiviral transfection with Cas9 protein electroporation (SLICE), to identify regulators of stimulus responses in primary human T cells. Genome-wide loss-of-function screening identified key T cell receptor signaling components and genes that negatively regulate proliferation after stimulation. Targeted ablation of single candidate genes validated hits and identified perturbations that enhance cancer cell killing. SLICE coupled with single-cell RNA-seq revealed characteristic stimulus-response gene programs altered by key genetic perturbations. Genome-wide SLICE screening is also applicable to identifying mediators of immunosuppression, revealing genes controlling adenosine signaling responses. Therefore, this document provides hematopoietic cells, such as stem cells and T cells, modified to suppress target gene expression. Thus, for example, the genetic modifications described in this disclosure can be used to regulate CD8+ T cell proliferation and function.

[0007] In one aspect, this document provides genetically modified hematopoietic cells comprising a genetic modification of a T-cell repressor gene that inhibits the expression or activity of a polypeptide product encoded by said T-cell repressor gene, wherein the expression or activity of said polypeptide product is inhibited by at least 60% compared to control wild-type hematopoietic cells. In some embodiments, the genetic modification of the T-cell repressor gene inactivates the gene. In some embodiments, the genetically modified hematopoietic cells are hematopoietic stem cells. In some embodiments, the genetically modified hematopoietic cells are hematopoietic T cells. In some embodiments, the T cells are CD8+ T cells or CD4+ T cells. In some embodiments, the T-cell repressor gene is inhibited using a clustered regularly spaced short palindromic repeat (CRISPR) system. Alternatively, the T-cell repressor gene can be inhibited using a transcription activator-like effector nuclease (TALEN) system, a zinc finger nuclease system, or a wide range of nuclease systems. In some embodiments, the T-cell repressor gene is inhibited using antisense RNA, siRNA, microRNA, or short hairpin RNA. In some embodiments, the modified T-cell repressor gene is RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM222. In some embodiments, the modified T-cell repressor gene is CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, or ARID1A. In some embodiments, the T-cell repressor gene is CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB2; or the T-cell repressor gene is SOCS1, TCEB2, RASA2, or CBLB. In some embodiments, the T-cell repressor gene is SOCS1, TCEB2, or RASA2. In some embodiments, the T-cell repressor gene is RASA2. In some embodiments, the T-cell repressor gene is TCEB2. In some embodiments, the T-cell repressor gene is SOCS1. In some embodiments, the T-cell repressor gene is CBLB. In some embodiments, the T-cell repressor gene is FAM105A. In some embodiments, the T-cell repressor gene is ARID1A. In some embodiments, the T-cell repressor gene is TMEM222. In some embodiments, the T-cell repressor gene is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB1. In another aspect, this document provides a cell population comprising hematopoietic cells, such as T cells, with the genetic modifications described herein (e.g., as described in this paragraph). In some embodiments, the hematopoietic cells (e.g., T cells) may comprise two or more of the genetic modifications described herein.

[0008] In another respect, this article provides a method for treating cancer, which includes administering a cell population containing genetically modified hematopoietic cells as described herein (e.g., as described in this paragraph).

[0009] In another respect, this article provides genetically modified T cells that possess regulated (e.g., reduced) immune function compared to control wild-type T cells, and which contain genetic modifications to suppress the expression of a polypeptide encoded by a T gene, wherein the expression of the polypeptide is suppressed by at least 60% compared to the control wild-type T cells; and the gene is selected from the group consisting of: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP21, EXOSC6, RPN1, VARS, CD3D, GRAP2, TRMT112, ALG8, VAV1, EXOSC6, SH2D1A, HSPA8, ZAP70, DDX54, CD247, ALDOA, ZNF1. 31. WDR36, AK2, LCP2, CD247, VHL, EIF2B2, PRELID1, GRPEL1, NAA10, ALDOA, ALG2, MARS, C4orf45, RAC2, LCK, SUPT4H1, SLC25A3, LUC7L3, C3orf17, RPP21 , HARS, ZNRD1, CCNH, MYC, CCDC25, EEF1G, CCND2, GCLC, TAF2, EIF6, SEC63, EXOSC6, RPS19BP1, SEC61B, VHL, DAD1, BEND6, FBL, VARS, EIF2B4, RAC2, PAGR1 , MYC, CD3E, LCP2, MYC, ENOSF1, POLR3H, NOP14, CLNS1A, POLR2L, ZPR1, CARD11, SLC35B1, TRMT112, FARSA, PRELID1, LARS, NOP16, POLR2L, HSPA8, CD247, GEMIN8, TTC27, PMPCA, PWP2, TAF1C, DDOST, ZNF654, FAU, EIF2B3, YARS, DDX20, DDX56, DDX49, UTP20, EPRS, RSL1D1, ATP1B3, EXOSC4, ARMC7, EIF2B4, AUP 1. VAV1, PAK1IP1, EIF6, FAM157A, HSPE1-MOB4, LAT, DCAF13, PPP1R10, EXOSC2, SRP9, POLR3K, TAF6, EIF3H, ABCF1, FLJ44635, PTP4A2, EIF3CL, ABCB7, GT F2H4, MARS, TAF4, RPL5, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3,EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND2, TTI2, THG1L, TAF1C, URI1, TRMT112, EIF3H, CCND2, G CLM, RBSN, QARS, POP7, TAF4, HUWE1, CARS, PTP4A2, PES1, ZNF785, WDR26, PRR20D, STK11, PIK3CD, YARS, STRAP, WDR77, NANS, TARS, HSPA8, TMEM1 27. FAM35A, ZBTB8OS, BPTF, INO80D, NOP14, KARS, SH2D1A, RHOH, DIMT1, CMPK1, TAF6, QTRT1, LCK, NOL10, MYBBP1A, NHP2, DDX54, LAT, TAF2, MBTP S1, GNL3, DEF6, BCL10, NFKBIA, PHB, CD3G, CD3D, QARS, EIF3C, GRPEL1, MBTPS2, ORAOV1, SLC4A2, GATA3, ODF3, SLC7A6OS, ORAOV1, ALG13 and TAF1B. In some implementations, the gene is one of the following genes: HSPA8, RPP21, EXOSC6, LCP2, MYC, CD247, NOP14, VAV1, RHOH, TAF1C, TRMT112, CCND2, SH2D1A, MARS, CD3D, NELFCD LCK, LUC7L3, EIF2B4, ORAOV1.VARS, NOL10, ZBTB8OS, SLC35B1, NAA10, EIF2B3, DHX37, LAT, EMG1, ALDOA, GRPEL1, ARMC7, POLR2L, NOP56, PSENEN, RELA, SUPT4H1, VHL, GFER, BPTF, RAC2, TSR2, TAF6, PMPCA, EIF6, STT3B, POP7, GMPPB, TP53RK, CCNH, TEX10 , DHX33, QARS, EID2, IRF4, TAF2, IARS, GTF3A, NOP2, IMP3, RPL28, UTP3.EIF4G1, GPN1, UTP6, DAD1, ALG2, CDK6, MED19, RASGRP 1. PHB2, NFS1, POLR2E, CDIPT, POLR3H, HARS, SEH1L, EIF2B5, TTC27, RRP12, JUNB, HSPE1, GMPS, EIF2S3, SRP14, FAM96B, RPL8,RRP36, MED11, ISG20L2, ROMO1, ATP6V1B2, RPN2, or WASH1. In some embodiments, the gene is inactivated. In some embodiments, a CRISPR system, TALEN system, zinc finger nuclease system, large-scale nuclease system, siRNA, antisense RNA, microRNA, or hairpin RNA is used to repress the gene. In another aspect, the present invention provides cell cultures comprising genetically modified T cells, such as those described in this paragraph.

[0010] In another respect, this article provides methods for treating autoimmune diseases or for treating or preventing graft rejection, the methods comprising administering to subjects suffering from autoimmune diseases or undergoing tissue transplantation a population of T cells, such as CD8+ or CD4+ T cells, as described in the preceding paragraphs.

[0011] In other aspects, this document provides a method for generating a genetically modified cell population for treating a subject with cancer, the method comprising: obtaining hematopoietic cells from a patient; inhibiting the expression of a T-cell repressive gene selected from the group consisting of RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, and TMEM222; selecting hematopoietic cells in which the T-cell repressive gene is inhibited; and in vitro amplifying the selected hematopoietic cell population. In some embodiments, the hematopoietic cells are hematopoietic stem cells. In some embodiments, the hematopoietic cells are T cells, such as CD8+ or CD4+ T cells. In some embodiments, the T-cell repressive gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a large-scale nuclease system, siRNA, antisense RNA, microRNA, or short hairpin RNA.

[0012] In other aspects, this document provides a method for generating a genetically modified cell population for treating a subject with cancer, the method comprising: obtaining hematopoietic cells from a patient; inhibiting the expression of a T-cell repressive gene selected from the group consisting of: CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, and ARID1A; selecting hematopoietic cells in which the T-cell repressive gene is inhibited; and in vitro amplification of the selected hematopoietic cell population. In some embodiments, the hematopoietic cells are hematopoietic stem cells. In some embodiments, the hematopoietic cells are T cells, such as CD8+ or CD4+ T cells. In some embodiments, the T-cell repressive gene is inhibited using a CRISPR system, a TALEN system, a zinc finger nuclease system, a large-scale nuclease system, siRNA, antisense RNA, microRNA, or short hairpin RNA.

[0013] definition

[0014] As used herein, unless otherwise expressly stated in the text, the singular forms “a,” “an,” and “the / that” are also intended to refer to the plural forms.

[0015] The terms "polynucleotide" and "nucleic acid" are used interchangeably and refer to a polymeric form of nucleotides of any length, namely deoxyribonucleotides or ribonucleotides. The term includes RNA, DNA, and synthetic forms, as well as polymers of mixtures thereof. In a particular embodiment, a nucleotide refers to a ribonucleotide, a deoxyribonucleotide, or any type of modified form of nucleotide or the like, and combinations thereof. Furthermore, a polynucleotide may include one or both of naturally occurring and modified nucleotides linked together by naturally occurring and / or non-naturally occurring nucleotide bonds. Nucleic acid molecules may be chemically or biochemically modified, or may contain non-natural or derived nucleotide bases. Such modifications include, for example, markers, methylation, substitution of one or more naturally occurring nucleotides with analogs, internucleotide modifications such as uncharged linkages (e.g., methylphosphonates, triphosphates, aminophosphates, carbamates, etc.), charged linkages (e.g., thiophosphates, dithiophosphates, etc.), side moieties (e.g., polypeptides), intercalating agents (e.g., acridine, psoralen, etc.), chelating agents, alkylating agents, and modified linkages (e.g., α-anomeric nucleic acids, etc.). "Polynucleotide" and "nucleic acid" are also intended to include any topological conformation, including single-stranded, double-stranded, partially double-stranded, triple-stranded, hairpin, circular, and padlock conformations. Unless otherwise explicitly stated, references to nucleic acid sequences include their complements. Therefore, references to nucleic acid molecules having a specific sequence should be understood to encompass their complementary strand and its complementary sequence. References to "polynucleotide" or "nucleic acid" encoding polypeptide sequences also include nucleic acids containing alternative codons encoding the same polypeptide sequence and codon-optimized nucleic acids.

[0016] In this article, "complementarity" or "complementarity" refers to specific base pairings between nucleotides or nucleic acids. Base pairings can be completely complementary or partially complementary.

[0017] The term "gene" can refer to a segment of DNA that is involved in the production or encoding of a polypeptide chain. It can include regions before and after the coding region (leader and tail regions) as well as intercalation sequences (introns) between individual coding segments (exons). Genes are defined by a notation and nomenclature system for human genes, as specified by the HUGO Gene Nomenclature Committee.

[0018] A “promoter” is defined as one or more nucleic acid control sequences that direct the transcription of nucleic acids. As used herein, a promoter comprises an essential nucleic acid sequence located near the transcription start site. A promoter may also optionally include a distal enhancer or repressor element, which may be located up to several thousand base pairs from the transcription start site.

[0019] The term "suppressed expression" refers to the suppression or reduction of gene or protein expression. To suppress or reduce the expression of a gene (i.e., a gene encoding a transcription factor or a gene regulated by a transcription factor), the sequence and / or structure of the gene can be modified such that the gene is not transcribed (for DNA) or translated (for RNA), or is not transcribed or translated to produce a functional protein (e.g., a transcription factor). Various methods for suppressing or reducing gene expression are described in further detail herein. Some methods may introduce nucleic acid substitutions, additions, and / or deletions into wild-type genes. Some methods may also introduce single-strand or double-strand breaks into the gene. To suppress or reduce the expression of a protein (e.g., a T-cell repressive protein), the expression of the gene or polynucleotide encoding the protein can be suppressed or reduced, as described above. In other embodiments, proteins can be directly targeted using, for example, antibodies or proteases to suppress or reduce protein expression. “Inhibition” expression refers to a reduction of at least 10% compared to a reference control level, such as a reduction of at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or higher, and includes a reduction of 100% (i.e., a level not present compared to the reference sample). As used herein, the term “inactivated” means preventing the expression of the polypeptide product encoded by the gene. Inactivation can occur at any stage or process of gene expression, including but not limited to transcription, translation, and protein expression, and inactivation can affect any gene or gene product, including but not limited to DNA, RNA, such as mRNA, and polypeptides. In some implementations, “inhibited expression” reflects a certain percentage of inactivation in the modified cells, for example, also including at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90% or more of the cells in a population where the target gene is not inactivated.

[0020] As used in this article, the term "modification" in the context of modifying a cell's genome refers to inducing structural changes in a genomic sequence at a target genomic region. For example, modification can take the form of inserting a nucleotide sequence into the cellular genome. For instance, a nucleotide sequence encoding a polypeptide can be inserted into a genomic sequence encoding an endogenous cell surface protein in T cells. The nucleotide sequence can encode a functional domain or a functional fragment thereof. Such modifications can be performed, for example, by inducing double-strand breaks within a target genomic region, or by single-strand nicks located on opposite strands and flanking the target genomic region. Methods for inducing single-strand or double-strand breaks at or within a target genomic region include using nuclease domains (e.g., Cas9) or derivatives thereof, and guides (e.g., guide RNA) targeting the target genomic region.

[0021] The terms “patient,” “object,” “individual,” etc., are used interchangeably herein and refer to any animal, such as a mammal, like a primate. In some non-limiting embodiments, the patient, object, or individual is a person.

[0022] As used herein, the terms “treatment,” “management,” etc., generally refer to achieving the desired pharmacological and / or physiological effect. Such an effect may be preventative in terms of completely or partially preventing a disease, condition, or its symptoms, and / or therapeutic in terms of partially or completely curing a disease or condition and / or adverse effects such as symptoms caused by that disease or condition. As used herein, the term “treatment / management” includes any treatment / management of a disease or condition, and includes: (a) preventing the occurrence of a disease or condition in subjects who may be susceptible to it but have not yet been diagnosed with it; (b) inhibiting the disease or condition (e.g., blocking its development); or (c) alleviating the disease or condition (e.g., causing the disease or condition to subside, providing improvement in one or more symptoms). Attached Figure Description

[0023] Figure 1A -C. Framework for unbiased discovery of T cell proliferation regulators using pooled CRISPR screening. Part A provides diagrams of the sgRNA lentiviral infection and Cas9 electroporation (SLICE) hybridization system, enabling pooled CRISPR screening in primary T cells. Part B provides illustrative data showing that editing the CD8A gene with SLICE produces highly efficient protein knockdown in two independent donors. Part C describes illustrative data from targeted screening (approximately 5,000 guides) showing that sgRNAs targeting CBLB and CD5 are enriched in proliferating T cells, while LCP2 and CD3D are depleted. Non-targeted sgRNAs are evenly distributed across the population.

[0024] Figure 2A-FA Section: Top: Distribution of log2 fold change (LFC) values ​​for dividing cells relative to non-dividing cells in a genome-wide (GW) library with >75,000 guides. Bottom: LFC values ​​for all four sgRNAs targeting three enriched genes (CBLB, CD5, UBASH3A) and three depleted genes (VAV1, CD3D, LCP2), overlaid on a gray gradient depicting the overall distribution. Values ​​are the average of two donors. B Section: Volcano plot of hits from primary GW screening. The X-axis shows the Z-score (ZS) of gene-level LFC (median LFC of all sgRNAs for each gene). The Y-axis shows the p-value calculated by MAGeCK. Genes labeled with an LFC-Z score less than “0” are negative hits (depleted in dividing cells, FDR < 0.2 and |ZS| > 2), which annotates the TCR signaling pathway via Gene Ontology (GO). Genes with LFC-Z scores greater than “0” are considered positive hits (rank <20 and |ZS|>2). All values ​​from both donors were calculated as biological replicates. Part C: Gene hits from secondary GW screening from two independent donors are positively correlated with primary screening. Overlapping hits of the top 25 targets in both positive and negative directions are shown. Part D: Box plots of scaled LFCs for the top 100 hits in each direction for three GW screenings with increased TCR stimulation titer (1X = data in (B)). For both sets of plots, LFC values ​​tend to 0, indicating that selection pressure decreases with increasing TCR signaling. Horizontal lines are medians, and vertical lines are data ranges. Part E: Gene set enrichment analysis shows a significant skew in the LFC ranking of screening hits in the two selected gene lists: (top set) hits previously identified by screening via shRNA in a melanoma mouse model and (bottom set) TCR signaling pathways via KEGG. The top 8 gene members at the enrichment front margin of each set are shown in the text box on the right. The vertical line on the x-axis represents the members of the gene set, sorted by their LFC ranking in the GW screening. FDR = False Discovery Rate, permutation test. F section: Regulators of TCR signaling and T cell activation detected in the GW screening. The left side shows the positive regulators of the TCR pathway (FDR < 0.25) found in our GW screening. TCR pathways are based on Wikipathways WP69 and literature reviews. The right side shows the negative regulatory genes (known and unknown) found in the GW screening (FDR < 0.25) and represents candidate targets that promote T cell proliferation. Cell localization and interaction margins are based on literature reviews. Gene nodes are represented by their LFC in the GW screening (red indicates positive LFC values, and blue indicates negative LFC values).

[0025] Figure 3A-E. Validation of gene targets regulating T cell proliferation using RNP arrays. Part A: Overview of the orthogonal validation strategy using Cas9 RNP electroporation. Part B: Proliferation assays of CD8 T cells using CFSE staining. Each set of figures shows CFSE signals from TCR-stimulated (left peak) or unstimulated (rightmost peak) human CD8 T cells. Data for two guides, CBLB and CD5, for two positive hits compared to NT CTRL and negative hit LCP2 are shown. Part C: Summary of data from Part B: Gene targets (y-axis) are sorted by their ranking in the GW pooled screening. The x-axis is the proliferation index (experimental procedure) calculated relative to NT CTRL in each donor (log2 transformation). Bands show the mean of two independent experiments with two donors in each experiment. Error bars are SEM. Part D: Early activation markers measured by flow cytometry 6 hours post-stimulation. Representative distributions of two guides for each target gene (y-axis) against CD154 (left) and CD69 (right) are shown. Part E: Summary of data for all tested gene targets in (D) (y-axis). The x-axis represents the fold increase in the marker-positive (CD69 / CD154) population relative to NTCTRL. The vertical line is the mean, and the error bar is the SEM; two guides for each gene for four donors.

[0026] Figure 4A -F. SLICE was paired with single-cell RNA-Seq for high-dimensional molecular phenotypic analysis of gene knockout in primary cells; Part A: UMAP plot of all single cells with identified sgRNAs from stimulated and unstimulated T cells from two donors. Unstimulated cells are on the left; stimulated cells are on the right. Part B: UMAP with gene expression scaling for four genes, showing clusters associated with activation state (IL7R, CCR7), cell cycle (MKI67), and effector function (GZMB). Part C: Unsupervised clustering of single cells based on gene expression; 13 clusters were identified as labeled. Part D: Clustering of cells expressing sgRNAs targeting CBLB, CD5, LCP, and NT CTRL on the UMAP representation. Part E: Y-axis showing over-representation or under-representation of cells expressing sgRNAs among clusters (group plots), determined by a chi-square test. Part F: The heatmap shows the average gene expression (y-axis) among cells (x-axis) with different sgRNA targets. Data represents one donor.

[0027] Figure 5A-D. Optimal screening hits for engineered human T cells promoting in vitro tumor killing. Part A: Graphs of high-throughput experimental strategies testing gene targets that promote in vitro tumor killing. Part B: Representative images taken 36 hours after co-culturing human CD8 T cells and A375 tumor cells. Red fluorescent channels representing wells are shown, with annotations in the lower left of each set of images. Scale bar is 500 μm. Part C: A375 cell counts after 36 hours by antigen-specific CD8 T cells. Values ​​are normalized to A375 cell counts in wells without T cells. Two guides for each gene target in four donors and two technical replicates; the horizontal line is the mean, and the error bar is SEM. *** indicates p < 0.001, Wilcoxon rank-sum test. Part D: Time trajectory of A375 cell counts, measured using IncuCyte software for selected hits. The line is the mean for four donors, with two guides for each target gene. Error bar is SEM.

[0028] Figure 6A -C. Altering SLICE to reveal resistance to immunosuppressive signals in primary T cells. Part A: Scatter plot of z-scores for genome-wide screening of resistance to the adenosine A2A selective agonist CGS-21680. Part B: Top: Distribution of log2 fold changes of all sgRNAs in the GW library of CGS-21680-treated T cells. Bottom: LFC of individual sgRNAs in the stimulation-only (loador) screening (green) sgRNAs compared to CGS-21680 resistance screening (red). Part C: Validation of gene targets from adenosine resistance screening using RNP-edited T cells. Knockout of ADORA2A or FAM105A allowed cells to proliferate in the presence of adenosine agonists compared to NT CTRL. Each set of figures shows two independent sgRNAs from two donors.

[0029] Figure 7A -J. SLICE establishment in primary human T cells, as shown in Figure 1. Part A: To screen and optimize SLICE on a large scale, we investigated whether increasing the infection rate (mCherry reporter) with polybrene resulted in a higher absolute number of viable edited cells. While the addition of polybrene appropriately increased transduction efficiency (bottom label) – ignoring the improvement in viability – it resulted in a higher total number of transduced cells (top label). Part B: FACS plot showing antibody staining (BV-570, y-axis) from transduced primary human CD8A cells. +Expression of fluorescent reporter on T cells (x-axis). After electroporation of Cas9 protein (3 μL in 20 μL cell suspension, 20 μM reservoir), most cells expressing sgRNA were CD8 negative. Part C: Editing of CD8A protein from cells in (A) (y-axis), electroporated with different reservoir concentrations of Cas9 protein. Editing was calculated as the ratio of CD8 negative to CD8 positive cells in successfully transduced cells. Titer showed the dependence of editing efficiency on Cas9 concentration. Data showed two human donors. Part D: The proliferation dye VPD450 was used to measure the response to a second stimulation (day 9) via the anti-CD3 and anti-CD28 complex according to the first stimulation (day 0) in the SLICE system. Stimulation with anti-CD3 / CD28 beads (bottom panel) at a 1:1 bead-to-T-cell ratio impaired the ability of CD8 T cells to respond to restimulation, likely due to activation-induced depletion and cell death. The first stimulation using antibodies bound to the plate (top panel) maintained the ability of cells to respond, allowing for TCR-dependent proliferation screening. Part E: Figure 1B Partially shown cell gating strategies. F section: SLICE effectively knocks out primary human CD8. + and CD4 + Candidate target (CD45) in T cells. No target control sgRNA (NT.CTRL, gray) indicates knockout is specific for cells transduced with the CD45 target guide (blue). G section: CFSE signals of pre-sorted cells in the pilot selection, such as... Figure 1C H part: Distribution of sgRNA read counts after deep sequencing of sorted cell populations from the two donors in the pilot selection. I part: Absolute fold change in abundance between the first two guides, for Figure 1C Hit rate in the diagram. Each line represents a unique sgRNA targeting a given gene, labeled at the top of each set of maps. Among the two guides and two donors, the guide enrichment and depletion patterns were consistent. J section: Ranking comparison of log fold changes from parallel screenings, which used... Figure 1C The same experimental timeline and sgRNA library were used, but CFSE-based enrichment sorting was not employed. This growth-based screening had a low signal-to-noise ratio. Figure 1C The guides related to TCR are indicated near the diagonal line. Gray dots represent individual targeting guides, while black dots represent non-targeted control (NT.CTRL) guides.

[0030] Figure 8A-F. Genome-wide pooled CRISPR screening in primary T cells, as shown in Figure 2. Part A: Gating strategy used to estimate transduction efficiency in primary GW screening by puromycin titration. Part B: Cells transduced in Part A cultured for 2 days at various seeding densities with or without puromycin. The total number of viable cells was compared between the two conditions as described in Part A at various seeding dilutions. The fitted line was a linear regression with a slope of 0.51 (R²). 2 =0.99), indicating efficient transduction. Part C: Gating and sorting strategies for representative samples used for GW screening. We sorted the population as shown in the CFSE group, making the separation between sorting bins clearly visible. Part D: Comparison of hits found in primary GW screening using two donors (green bars) and pooled data from repeated screening using two donors (orange bars). Hit was defined as FDR < 0.25 across all aspects, using the MAGeCK RRA algorithm. The labels at the top of the bars are numerical values ​​of the number of hits in each group. Part E: The effect of TCR stimulation titer (x-axis) on the fold change at the gene level on the three positive and negative regulators of T cell stimulation. Part F: Enrichment of the KEGG pathway at the gene level leading to FDR < 0.01 via GSEA. In addition, a gene set from publicly available T cell tumor-infiltrating shRNAs is included. Each point represents an annotated set of genes (y-axis), and the x-axis displays the normalized enrichment score (NES) compared to the mean of a randomized set of genes of the same size. The size of each point is inversely proportional to the p-value of each enrichment.

[0031] Figure 9A -D. RNP array validation of hits from genome-wide screening, as shown in Figure 3. Part A: CFSE trajectories hit in Part C of Figure 3. Each set of plots shows two unique wizards for each gene target in two technical replicates. Data represents one donor. Part B: Curve fitting of representative CFSE trajectories, as shown in Part C of Figure 3. The set of plots shows the fitted Gaussian of the CFSE peaks, determined using the flowFit R package. The extracted parameter is the proliferation index, defined as the total cell count across all generations divided by the number calculated from the original parental cells. Part C: Distribution of the activation marker CD154 across all targets tested. The lines show the measured expression in stimulated (red) and unstimulated (blue) cells, edited using two wizards targeting the genes marked at the top of each set of plots. Data represents one donor. Part D: Same as Part B for CD69.

[0032] Figure 10A -F. SLICE coupled with single-cell RNA-Seq, as shown in Figure 4. Part A: Transduction efficiency of CropSeq experiments measured by puromycin selection. We estimated the infection rate to be 13.9% (R0) using linear regression.2 =0.76, blue line, shaded area is 95% CI). This intentionally low transduction rate is expected to enrich cells with only a single integrated sgRNA cassette. Part B: Volcano plot of differential gene expression by DESeq2 in synthetic batch RNA-Seq profiles (UMI counts of collapsed samples) of stimulated cells versus unstimulated cells. Points are genes, the y-axis represents the significance of enrichment, and the x-axis represents the magnitude and direction of log2 fold change (LFC) in transcript abundance. Points with |LFC|>2.5 and adjusted p-value <1e-18 are colored, with blue indicating upregulated genes in stimulated cells and red indicating downregulated genes. Part C: Enrichment of gene annotations in clusters based on single-cell RNA expression profiles. Each point represents the enrichment of REACTOME annotations (y-axis) in each cluster (x-axis). Genes associated with each cluster were identified by differential gene expression assays, comparing all cells in the cluster with all other cells. The size of each point is proportional to the significance of enrichment, and the color indicates whether the average expression level of the corresponding gene in each cluster is upregulated or downregulated (red and blue, respectively). Part D: Gene editing at the single-cell level in CROP-Seq experiments. Each set of figures shows the mean and standard error (SEM) of the counts of unique molecular markers (UMIs) of the targeted genes in the library. Transcripts of each target were calculated for all cells expressing either the target sgRNA (target) or the non-target control guide (NT.CTRL). The mean was calculated for four samples, two guides for each gene. Compared to the control guide, most gene targets showed lower levels of expression of the transcripts of the cells using the targeted guide, indicating successful knockout. We note that some gene targets are expressed at low levels, so the editing may be difficult to detect at the single-cell level. Part E: Gene expression profile of the second donor-selected sgRNA, same as Part F of Figure 4. The genes shown are enriched in clusters 8-12 associated with the stimulated cells (|logFC|>1 and adjusted p<0.05). The phylogenetic tree is based on Euclidean distance as implemented in hclust in R, using the Ward D2 algorithm. Part F: Similarity in cluster correlations of cells expressing the targeting sgRNA. The Pearson correlation coefficients of the chi-square residuals across all clusters are shown. The phylogenetic tree is calculated in three levels as described in Part E.

[0033] Figure 11A -D. In vitro tumor-killing activity of engineered human T cells, as shown in Figure 5. Part A: T cells transduced with 1G4TCR lentivirus showed high transduction rates based on HLA-A2+ restricted NY-ESO-1 peptidyl-PE staining. Cells were sorted to obtain tetramer-positive tumor-specific CD8+. +Pure population of T cells. Part B: Caspase levels measured on IncuCyte demonstrate that T cells induce apoptosis in A375-expressing melanoma cells (top panel) expressing the target NY-ESO in titrations with increasing T cell to tumor cell ratios (top panel). For the same T cell to tumor cell ratio, tumor cell killing corresponds to the time-varying nuclear count of A375 RFP markers (bottom panel). Part C: A375 clearance for all gene targets tested. The Y-axis represents A375 counts via IncuCyte software, normalized by counts in the NT.CTRL wells for each donor, time point, and gene target. The line shows the mean for each donor in two wizards and two technical replicates (n=4). Error bars are from SEM. Part D: Quantification of data in the two CD8 to A375 ratio pairs (B) at 36 hours. 1:4 is Figure 5C The data is summarized in the middle. A point represents a single hole in the array of all donors (n=4), two guides and two repetitions.

[0034] Figure 12A -E. SLICE screening of primary T cells for resistance to immunosuppressive signals, as shown in Figure 6. Part A: Dosage titration to determine the optimal inhibitory effect of the adenosine receptor agonist CGS-21680 on T cell proliferation; 20 μM was deemed sufficiently inhibitory. Representative CFSE curves for different concentrations of CGS-21680 (as labeled at the top of each group of figures) are shown. Data are for CFSE-labeled CD8 from two donors. + T cells. Part B: Figure 6AThe sorting gating and CFSE profiles for genome-wide screening are shown in Section C. Section C: Comparison of the top 25 hits from the two overlapping screening conditions (CGS-21680 and the loading agent). We note that while many hits from the loading agent screening also promoted proliferation (diagonally) in the presence of an immunosuppressive adenosine agonist, ADORA2A and FAM105A sgRNAs were selectively enriched in the adenosine agonist environment. ADORA2A and FAM105A were the only hits in the top 0.1% LFC under the CGS-21680 treatment condition and in the top 0.1% LFC difference between the CGS-21680 and loading agent GW screening conditions. Section D: Paralogous genes FAM105A and OTULIN (FAM105B) share 40% homology and are encoded at adjacent sites on chromosome 5. They share an OTU deubiquitinase domain, which is predicted to be catalytically inactive in FAM105A. Genome-wide association studies have linked non-synonymous single nucleotide polymorphisms (SNPs) in FAM105A to allergic diseases (Ferreira et al., 2017). Rare mutations in OTULIN are associated with monogenic severe inflammatory diseases (Damgaard et al., 2016). Part E: Clearance of A375 cells labeled with RFP by antigen-specific CD8T cells edited with sgRNAs targeting ADORA2A and FAM105A and untargeted controls at increased CGS-21680 doses. The Y-axis shows the relative clearance levels at each CGS-21680 concentration compared to the carrier. The X-axis shows the different targeted sgRNAs. The horizontal line is the median, and the whisker lines are the data boundaries, which are in two guides and four donors for each target.

[0035] Figure 13A -B. SLICE for in vivo pooling screening of immunotherapies in a humanized mouse model. Part A: Principal-competent analysis of guide counts for T cells collected from spleens (leftmost four points) and tumors (rightmost four points) 7 days post-metastasis. Each point represents one sample (2 donors from 4 mice). Clear separation was evident for guides enriched in each tissue. Part B: Log-fold change of in vivo SLICE experiments. Vertical lines represent the mean, and horizontal lines represent SEM (n=2 guides per tissue, n=2 guides per gene target). Detailed Implementation

[0036] In one aspect, this disclosure provides engineered T cells that exhibit enhanced cytotoxicity to targets of interest, such as tumor cells. These T cells are modified to suppress the expression of T cell repressive genes. As used herein, a “T cell repressive” gene refers to a gene that negatively affects proliferation upon stimulation. Modifications to T cell repressive genes according to the invention need not be limited to T cells. While modifications are T cell receptor (TCR) dependent, they can be applied to other hematopoietic cells. Thus, in some embodiments, the cells modified according to the invention are T cells, such as CD8+ T cells. In some embodiments, the cells are hematopoietic stem cells. In other embodiments, the T cells are stem memory T cells, effector memory T cells, central memory T cells, or primordial T cells. In some embodiments, the modifications according to the invention are performed on CD4+ T cells, NK cells, or γΔ T cells. Reviews of T cell subsets are available, for example, in Sallusto et al., Annual Rev. Immunol. 22745-763, 2004; Mueller et al., Annual Rev. Immunol 31:137-161, 2013; and a review of memory stem T cells, in Gattinoni et al., Nature Med. 23:18-27, 2018. Descriptions of subsets via markers can be found in the OMIPWiley online library (see, for example, Wingender and Kronenberg, OMIP-030: Characterization of human T cell subsets via surface markers, Cytometry Part A 87A:1067-1069, 2015).

[0037] The expression of the target gene can be suppressed, or in some embodiments, inactivated, thereby preventing the gene from expressing an active protein product. In some embodiments, a cell population can be enriched targeting cells in which the gene is inactivated.

[0038] In some embodiments, the T-cell repressor genes modified to suppress expression are CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2 (also known as ELOB, extended protein B, in HUGO nomenclature), UBASH3A, or ARID1A. In some embodiments, the T-cell repressor genes are CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB2. In some embodiments, the T-cell repressor genes are SOCS1, TCEB2, RASA2, or CBLB. In some embodiments, the T-cell repressor genes are SOCS1, TCEB2, or RASA2. In some embodiments, the T-cell repressor genes modified to suppress expression are RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM222. In some embodiments, the T-cell repressive gene modified to suppress expression is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB1. In some embodiments, the hematopoietic cells (e.g., T cells) also contain a second modification that suppresses the expression of the T-cell repressive gene.

[0039] Any number of assays can be used to evaluate function. Exemplary assays measure T cell proliferative responses, such as responses to T cell receptor (TCR) stimulation. Exemplary assays are described in the Examples section. Assays include, but are not limited to: CFSE (or other similar dye) dilutions, growth-based assays, in vivo expansion at specific sites, or sorting of other markers of activation or effector functions (e.g., cytokine production, induction of cell surface markers, or granzyme production).

[0040] In another aspect, this disclosure provides engineered T cells that are modified to suppress T cell gene expression to inhibit T cell function, for example, by targeting genes used to treat autoimmune diseases or other diseases for which it is desired to suppress T cell function (e.g., graft rejection). In some embodiments, the T cell genes to be modified are: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP21, EXOSC6, RPN1, VARS, CD3D, GRAP2, TRMT112, ALG8, VAV1, EXOSC6, SH2D1A, ZAP70, DDX54, CD247, ALDOA, ZNF131, WDR36, AK2, LCP2, CD247, VHL, EIF2B2, PRELID1, GRPEL1, NAA10, ALDOA, ALG2, MARS, C4orf45, R AC2, LCK, SUPT4H1, SLC25A3, LUC7L3, C3orf17, RPP21, HARS, ZNRD1, CCNH, MYC, CCDC25, EEF1G, CCND2, GCLC, TAF2, EIF6, SEC63, EXOSC6, R PS19BP1, SEC61B, VHL, DAD1, BEND6, FBL, VARS, EIF2B4, RAC2, PAGR1, MYC, CD3E, LCP2, MYC, ENOSF1, POLR3H, NOP14, CLNS1A, POLR2L, ZPR1 , CARD11, SLC35B1, TRMT112, FARSA, PRELID1, LARS, NOP16, POLR2L, CD247, GEMIN8, TTC27, PMPCA, PWP2, TAF1C, DDOST, ZNF654, FAU, EIF 2B3, YARS, DDX20, DDX56, DDX49, UTP20, EPRS, RSL1D1, ATP1B3, EXOSC4, ARMC7, EIF2B4, AUP1, VAV1, PAK1IP1, EIF6, FAM157A, HSPE1-MOB4 , LAT, DCAF13, PPP1R10, EXOSC2, SRP9, POLR3K, TAF6, EIF3H, ABCF1, FLJ44635, PTP4A2, EIF3CL, ABCB7, GTF2H4, MARS, TAF4, RPL5, FTSJ3, CD28, ALG13, CARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1,THAP11, IMP4, EXOSC7, NOB1, EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND2, TTI2, THG1L, TAF1C, URI1, TRMT112, EIF3H, CCND2, GCLM, RBSN, QARS, POP7, TAF4, HUWE1, CARS, PTP4A2, PES1, ZNF785, WDR26, PRR20D, STK11, PIK3CD, YARS, STRAP, WDR77, NANS, TARS, TMEM127, FAM35A, ZBTB8OS, BPTF, INO80D, NOP14, KARS, SH2D1A, RHOH, DIMT1, CMPK1, TAF6, QTRT1, LCK, NOL10, MYBBP1A, NHP2, DDX54, LAT, TAF2, MBTPS1, GN L3, DEF6, BCL10, NFKBIA, PHB, CD3G, CD3D, QARS, EIF3C, GRPEL1, MBTPS2, ORAOV1, SLC4A2, GATA3, ODF3, SLC7A6OS, ORAOV1, ALG13 and TAF1B. In some implementations, the gene is any one of the following genes: HSPA8, RPP21, EXOSC6, LCP2, MYC, CD247, NOP14, VAV1, RHOH, TAF1C, TRMT112, CCND2, SH2D1A, MARS, CD3D, NELFCD LCK, LUC7L3, EIF2B4, ORAOV1.VARS, NOL10, ZBTB8OS, SLC35B1, NAA10, EIF2B3, DHX37, LAT, EMG1, ALDOA, GRPEL1, ARMC7, POLR2L, NOP56, PSENEN, RELA, SUPT4H1, VHL, GFER, BPTF, RAC2, TSR2, TAF6, PMPCA, EIF6, STT3B, POP7, GMPPB, TP53RK, CCNH, TEX10, DHX33, QAR S, EID2, IRF4, TAF2, IARS, GTF3A, NOP2, IMP3, RPL28, UTP3.EIF4G1, GPN1, UTP6, DAD1, ALG2, CDK6, MED19, RASGRP1, PHB2, NFS1, POL R2E, CDIPT, POLR3H, HARS, SEH1L, EIF2B5, TTC27, RRP12, JUNB, HSPE1, GMPS, EIF2S3, SRP14, FAM96B, RPL8, RRP36, MED11, ISG20L2,ROMO1, ATP6V1B2, RPN2, or WASH1.

[0041] In another embodiment, T cells are modified to suppress the expression of genes associated with resistance to immunosuppressants. In some implementations, the gene is FAM105A (also known in HUGO nomenclature as OTULINL (an OTU deubiquitinase with linear linkage specificity)).

[0042] In some embodiments, T-cell repressive genes are inactivated by gene deletion. As used herein, "gene deletion" means the removal of at least a portion of the DNA sequence of the gene or a region adjacent to the gene. In some embodiments, the sequence undergoing gene deletion comprises the exon sequence of the gene. In some embodiments, the sequence undergoing gene deletion comprises the promoter sequence of the gene. In some embodiments, the sequence undergoing gene deletion comprises the side sequence of the gene. In some embodiments, a portion of the gene sequence is removed from the gene. In some embodiments, the complete gene sequence is removed from the chromosome. In some embodiments, the host cell contains the gene deletion, as described in any of the embodiments herein. In some embodiments, the gene is inactivated by deleting at least one nucleotide or nucleotide base pair in the gene sequence, producing a nonfunctional gene product. In some embodiments, the gene is inactivated by gene deletion, wherein the deletion of at least one nucleotide in the gene sequence produces a gene product that no longer has the function or activity of the original gene product; or the gene is a dysfunctional gene product. In some embodiments, the gene is inactivated by gene addition or substitution, wherein the addition or substitution of at least one nucleotide or nucleotide base pair in the gene sequence produces a nonfunctional gene product. In some embodiments, the gene is inactivated by gene inactivation, wherein the inclusion or substitution of at least one nucleotide in the gene sequence produces a gene product that no longer has the function or activity of the original gene product; or the gene is a dysfunctional gene product. In some embodiments, the gene is inactivated by gene addition or substitution, wherein the inclusion or substitution of at least one nucleotide in the gene sequence produces a dysfunctional gene product. In some embodiments, the host cell contains a gene deletion, as described in any of the embodiments herein.

[0043] Methods and techniques for inactivating T cell repressive genes in host cells or for inactivating target genes that inhibit T cell function as described herein include, but are not limited to: small interfering RNA (siRNA), small hairpin RNA (shRNA; also known as short hairpin RNA), clustered regular spaced short palindromic repeats (CRISPR), transcription activator-like effector nucleases (TALENs), zinc finger nucleases (ZFNs), homologous recombination, non-homologous end joining, and large-scale nucleases. See, for example, O'Keefe, Mater Methods, 3, 2013; Doench et al., Nat Biotechnol, 32, 2014; Gaj et al., Trends Biotechnol, 31, 2014; and Silva et al., Curr Gene Ther, 11, 2011.

[0044] Repressive RNA

[0045] In some implementations, T-cell repressive genes, or genes to be modified to suppress T-cell function, are inactivated using a small interfering RNA (siRNA) system. The siRNA sequence for inactivating the target gene can be identified using considerations such as: siRNA length, e.g., 21-23 nucleotides or less; avoiding regions with start and stop codons of 50-100 nucleotides, avoiding intronic regions; avoiding segments with four or more identical nucleotides; avoiding regions with GC content less than 30% or greater than 60%; avoiding repetitive and low sequence complexity regions; avoiding single nucleotide polymorphism sites; and avoiding sequences complementary to sequences in other off-target genes (see, for example, Rules of siRNA design for RNA interference, Protocol Online, May 29, 2004; and Reynolds et al., Nat Biotechnol, 22:3236-330 2004).

[0046] In some embodiments, the siRNA system comprises an siRNA nucleotide sequence of about 10-200 nucleotides in length, or about 10-100 nucleotides in length, or about 15-100 nucleotides in length, or about 10-60 nucleotides in length, or about 15-60 nucleotides in length, or about 10-50 nucleotides in length, or about 10-30 nucleotides in length, or about 15-30 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is about 10-25 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is about 15-25 nucleotides in length. In some embodiments, the siRNA nucleotide sequence is at least about 10, at least about 15, at least about 20, or at least about 25 nucleotides in length. In some embodiments, the siRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to the target mRNA molecule. In some embodiments, the siRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a target pre-mRNA (pro-mRNA) molecule. In some embodiments, the siRNA system comprises a double-stranded RNA molecule. In some embodiments, the siRNA system comprises a single-stranded RNA molecule. In some embodiments, the host cell comprises the siRNA system as described in any of the embodiments herein. In some embodiments, the host cell comprises a pre-siRNA nucleotide sequence that has been processed into an active siRNA molecule, as described in any of the embodiments herein. In some embodiments, the host cell comprises an siRNA nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a target mRNA molecule. In some embodiments, the host cell comprises an expression vector encoding an siRNA molecule, as described in any of the embodiments herein. In some embodiments, the host cell comprises an expression vector encoding a pre-siRNA molecule, as described in any of the embodiments herein.

[0047] In some embodiments, the siRNA system includes a delivery vector. In some embodiments, the host cell contains the delivery vector. In some embodiments, the delivery vector contains pre-siRNA and / or siRNA molecules.

[0048] In some embodiments, T-cell repressive genes are inactivated via a small hairpin RNA (shRNA; also known as short hairpin RNA) system. Gene inactivation is possible via the shRNA system. In some embodiments, the shRNA system comprises a nucleotide sequence of about 10-200 nucleotides in length, or about 10-100 nucleotides in length, or about 15-100 nucleotides in length, or about 10-60 nucleotides in length, or about 15-60 nucleotides in length, or about 10-50 nucleotides in length, or about 15-50 nucleotides in length, or about 10-30 nucleotides in length, or about 15-30 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is about 10-25 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is about 15-25 nucleotides in length. In some embodiments, the shRNA nucleotide sequence is at least about 10, at least about 15, at least about 20, or at least about 25 nucleotides in length. In some embodiments, the shRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a region of a T-cell repressive nucleic acid mRNA molecule. In some embodiments, the shRNA system comprises a nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a pre-mRNA molecule. In some embodiments, the shRNA system comprises a double-stranded RNA molecule. In some embodiments, the shRNA system comprises a single-stranded RNA molecule. In some embodiments, the host cell contains the shRNA system as described in any of the embodiments herein. In some embodiments, the host cell contains a pre-shRNA nucleotide sequence that has been processed into an active shRNA nucleotide sequence as described in any of the embodiments herein. In some embodiments, the pre-shRNA molecule is composed of DNA. In some embodiments, the pre-shRNA molecule is a DNA construct. In some embodiments, the host cell contains an shRNA nucleotide sequence that is at least about 80%, at least about 85%, at least about 90%, at least about 95%, or 100% complementary to a T-cell repressive gene mRNA molecule. In some embodiments, the host cell contains an expression vector encoding an shRNA molecule, as described in any of the embodiments described herein. In some embodiments, the host cell contains an expression vector encoding a pre-shRNA molecule, as described in any of the embodiments described herein.

[0049] In some embodiments, the shRNA system includes a delivery vector. In some embodiments, the host includes the delivery vector. In some embodiments, the delivery vector includes pre-shRNA and / or shRNA molecules. In some embodiments, the delivery vector is a viral vector. In some embodiments, the delivery vector is a lentivirus. In some embodiments, the delivery vector is an adenovirus. In some embodiments, the vector includes a promoter.

[0050] CRISPR

[0051] In some embodiments, the suppression of T-cell repressive gene expression is achieved using a CRISPR / Cas method. Illustrative methods for reducing gene expression using a CRISPR / Cas system are described in numerous publications, for example, U.S. Patent Application Publication No. 2014 / 0170753. A CRISPR / Cas system includes a Cas protein and at least one or two ribonucleic acids that hybridize to a target motif in a T-cell repressive gene and direct the Cas protein to the target motif. Any CRISPR / Cas system capable of altering the target polynucleotide sequence in the cell can be used. In some embodiments, the CRISPR / Cas system is a type I CRISPR system; in some embodiments, the CRISPR / Cas system is a type II CRISPR system; and in some embodiments, the CRISPR / Cas system is a type V CRISPR system.

[0052] The Cas protein used in this invention is a naturally occurring Cas protein or a functional derivative thereof. "Functional derivative" includes, but is not limited to, fragments of the natural sequence and derivatives and fragments of the natural sequence polypeptide, provided that they share a common biological activity with the corresponding natural sequence polypeptide. The biological activity contemplated herein is the ability of the functional derivative to hydrolyze DNA substrates into fragments. The term "derivative" includes amino acid sequence variants of polypeptides, covalent modifications, and fusions thereof, such as derived Cas proteins. Suitable derivatives of Cas polypeptides or fragments thereof include, but are not limited to, mutants, fusions, covalently modified forms, or fragments thereof of Cas proteins.

[0053] There are three main types of Cas nucleases (types I, II, and III) and ten subtypes, including five type I proteins, three type II proteins, and two type III proteins (see, for example, Hochstrasser and Doudna, Trends Biochem Sci, 2015:40(1):58-66). Type II Cas nucleases include Cas1, Cas2, Csn2, and Cas9. These Cas nucleases are well known to those skilled in the art. For example, the amino acid sequence of the *Streptococcus pyogenes* wild-type Cas9 polypeptide is shown, for example, NBCI sequence number NP_269215, and the amino acid sequence of the *Streptococcus thermophilus* wild-type Cas9 polypeptide is shown, for example, NBCI sequence number WP_011681470. Some CRISPR-related endonucleases that can be used in the methods described herein are disclosed, for example, U.S. Patent Application Publication Nos. 2014 / 0068797, 2014 / 0302563, and 2014 / 0356959. Non-restrictive examples of Cas nucleases include: Cas1, Cas1B, Cas2, Cas3, Cas4, Cas5, Cas6, Cas7, Cas8, Cas9 (also known as Csn1 and Csx12), Cas10, Csy1, Csy2, Csy3, Cse1, Cse2, Csc1, Csc2, Csa5, Csn2, Csm2, Csm3, Csm4, Csm5, Csm6, Cmr1, Cmr3, Cmr4, Cmr5, Cmr6, Csb1, Csb2, Csb3, Csx17, Csx14, Csx10, Csx16, CsaX, Csx3, Csx1, Csx15, Csf1, Csf2, Csf3, Csf4, their homologs, their variants, their mutants, and their derivatives.

[0054] Cas9 homologs are found in a variety of eubacteria, including but not limited to the following taxa: Actinobacteria, Aquificae, Bacteroidetes-Chlorobi, Chlamydiae-Verrucomicrobia, Chlroflexi, Cyanobacteria, Firmicutes, Proteobacteria, Spirochaetes, and Thermotogae. An exemplary Cas9 protein is the Cas9 protein of Streptococcus pyogenes. Other Cas9 proteins and their homologs are described, for example, Chylinksi, et al., RNA Biol. May 1, 2013; 10(5):726–737; Nat. Rev. Microbiol. June 2011; 9(6):467–477; Hou, et al., Proc Natl Acad Sci US A. September 24, 2013; 110(39):15644–9; Sampson, et al., Nature. May 9, 2013; 497(7448):254–7; and Jinek, et al., Science. August 17, 2012; 337(6096):816–21. Any variants of the Cas9 nucleases presented herein can be optimized to provide high activity or enhanced stability in host cells. Therefore, engineered Cas9 nucleases are also considered. Cas9 from *Streptococcus pyogenes* contains two endonuclease domains: a RuvC-like domain that cleaves target DNA that is not complementary to crRNA, and an HNH-like domain that cleaves target DNA that is complementary to crRNA. The double-stranded endonuclease activity of Cas9 also involves a short, conserved sequence (2-5 nucleotides) called the protospacer-associated motif (PAM), which follows the target motif at the 3' end.

[0055] Furthermore, Cas nucleases, such as the Cas9 polypeptide, can be derived from a variety of bacterial species, including but not limited to: Veillonella atypical, Fusobacterium nucleatum, Filifactor alocis, Solobacterium moorei, Coprococcus catus, Treponema denticola, Peptoniphilus duerdenii, Catenibacterium mitsuokai, Streptococcus mutans, Listeria innocua, Staphylococcus pseudintermedius, Acidaminococcus intestine, Olsenella uli, Oenococcus kitaharae, and Bifidobacterium. Lactobacillus bifidum, Lactobacillus rhamnosus, Lactobacillus gasseri, Finegoldia magna, Mycoplasma mobile, Mycoplasma magallisepticum, Mycoplasma ovipneumoniae, Mycoplasma canis, Mycoplasma synoviae, Eubacterium rectale, Streptococcus thermophilus, Eubacterium dolichum, and Lactobacillus coryniformis subsp.Torquens, Ilyobacter polytropus, Ruminococcus albus, Akkermansia muciniphila, Acidothermus cellulolyticus, Bifidobacterium longum, Bifidobacterium dentium, Corynebacterium diphtheria, Elusimicrobium minutum, Nitratifractor salsuginis, Sphaerochaeta globus, Fibrobacter succinogenes subsp. Succinogenes, Bacteroides fragilis, Capnocytophaga ochracea, Rhodopseudomonas The following bacteria are listed: *Plasmustris*, *Prevotella micans*, *Prevotella ruminicola*, *Flavobacterium columnare*, *Aminomonas paucivorans*, *Rhodospirillum rubrum*, *Candidatus Puniceispirillum marinum*, *Verminephrobactereiseniae*, *Ralstonia syzygii*, *Dinoroseobactershibae*, *Azospirillum*, *Nitrobacter hamburgensis*, *Bradyrhizobium*, *Wolinella succinogenes*, and *Campylobacter jejuni subsp.*Jejuni, Helicobacter mustelae, Bacillus cereus, Acidovorax ebreus, Clostridium perfringens, Parvibaculum lavamentivorans, Roseburia intestinalis, Neisseria meningitidis, Pasteurella multocida subsp. Multocida, Sutterella wadsworthensis, Proteobacterium, Legionella pneumophila, Parasutterella excrementihominis, Wolinella succinogenes, and Francisella novicida. .

[0056] Other RNA-mediated nucleases include Cpf1 (see, for example, Zetsche et al., Cell, Vol. 163, No. 3, pp. 759-771, October 22, 2015) and its homologs.

[0057] As used herein, the term "Cas9 ribonucleoprotein" complex and similar terms refer to complexes such as: complexes between Cas9 protein and guide RNA, complexes between Cas9 protein and crRNA, complexes between Cas9 protein and trans-activating crRNA (tracrRNA), or combinations thereof (e.g., complexes comprising Cas9 protein, tracrRNA, and crRNA guide RNA). It should be understood that in any of the embodiments described herein, the Cas9 nuclease can be replaced by another RNA-mediated nuclease, such as an alternative Cas protein or Cpf1 nuclease.

[0058] In some embodiments, the Cas protein is introduced into T cells in polypeptide form. Therefore, for example, in some embodiments, the Cas protein may be coupled or fused with cell-penetrating peptides or polypeptides well known in the art. Non-limiting examples of cell-penetrating peptides include those provided in Milletti F, “Drug Discov. Today 17:850-860, 2012,” the entire disclosure of which is incorporated herein by reference. In some cases, T cells may be engineered to produce the Cas protein.

[0059] In some implementations, Cpf1 or Cas9 nuclease and gRNA are introduced into the cell as a ribonucleoprotein (RNP) complex.

[0060] In some embodiments, the RNP complex can introduce approximately 1 × 10⁻⁶ 5 - Approximately 2 × 10 6 1 × 10 cells (e.g., 1 × 10) 5 Each cell - approximately 5 × 10 5 One cell, approximately 1 × 10 5 1 cell - approximately 1 × 10 6 1 cell, 1×10 5 1.5 × 10⁻⁶ cells 6 1 cell, 1×10 5 1 cell - approximately 2 × 10 6 One cell, approximately 1 × 10 6 Each cell s - approximately 1.5 × 10 6 One cell or approximately 1 × 10 6 1 cell - approximately 2 × 10 6 (cells). In some embodiments, cells are cultured under conditions that effectively expand the modified cell population. Cell populations are also provided herein in which at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99% or more of the genome contains the genetic modifications or heterologous polynucleotides described herein for suppressing the expression of T-cell repressive genes. In some embodiments, the population comprises cell subpopulations, each subpopulation having different genetic modifications to suppress the expression of the T-cell repressive genes described herein.

[0061] In some embodiments, the RNP complex is introduced into T cells via electroporation. Methods, compositions, and apparatus for electroporating cells to introduce the RNP complex are known in the art, see, for example, WO 2016 / 123578, WO / 2006 / 001614, and Kim, JA et al. Biosens. Bioelectron. 23, 1353–1360 (2008). Other or additional methods, compositions, and apparatus for electroporating cells to introduce the RNP complex may include those described in: U.S. Patent Application Publication Nos. 2006 / 0094095; 2005 / 0064596; or 2006 / 0087522; Li, LH et al. Cancer Res. Treat. 1,341–350 (2002); U.S. Patent Nos. 6,773,669; 7,186,559; 7,771,984; 7,991,559; 6,485,961; 7,029,916; and U.S. Patent Application Publication Nos. 2014 / 0017213; and 2012 / 0088842; Geng, T. et al. Control Release 144,91–100 (2010); and Wang, J. et al. Lab. Chip 10,2057–2061 (2010).

[0062] In some embodiments, the Cas9 protein can be in an activated endonuclease form, thus allowing double-strand breaks to be introduced into the target nucleic acid when it binds to the target nucleic acid as part of a complex containing a guide RNA or a complex containing a DNA template. In the methods provided herein, a Cas9 polypeptide or a nucleic acid encoding a Cas9 polypeptide can be introduced into T cells. Double-strand breaks can be repaired using HDR to insert the DNA template into the genome of the T cell. The methods described herein can utilize various Cas9 nucleases. For example, a Cas9 nuclease that requires an NGG prototype spacer neighbor motif (PAM) immediately adjacent to the 3' region targeted by the guide RNA can be utilized. Such a Cas9 nuclease can target the region of exon 1 of TRAC or exon 1 of TRAB, which contains an NGG sequence. As another example, Cas9 proteins with orthogonal PAM motif requirements can be used to target sequences that do not have a neighboring NGG PAM sequence. Exemplary Cas9 proteins with orthogonal PAM sequence specificity include, but are not limited to, those described in Esvelt et al. (Nature Methods 10:1116–1121 (2013)).

[0063] In some cases, Cas9 proteins are cleavage enzymes, thus introducing single-strand breaks or nicks into the target nucleic acid when they bind to it as part of a complex with the guide RNA. A pair of Cas9 cleavage enzymes, each binding to a guide RNA with a different structure, can target two proximal sites in a target genomic region and thus introduce a pair of proximal single-strand breaks into the target genomic region, such as exon 1 of the TRAC gene or exon 1 of the TRBC gene. Cleavage enzyme pairs can provide enhanced specificity because off-target effects can potentially result in a single nick, which is usually repaired without damage by base excision repair mechanisms. Illustrative Cas9 cleavage enzymes include Cas9 nucleases with D10A or H840A mutations (see, for example, Jinek et al., Science 337:816-821, 2012; Qi et al., Cell, 152(5):1173-1183, 2012; Ran et al., Cell 154:1380-1389, 2013). In one embodiment, the Cas9 polypeptide from *Streptococcus pyogenes* contains at least one mutation at positions D10, G12, G17, E762, H840, N854, N863, H982, H983, A984, D986, A987, or any combination thereof. Descriptions of such dCas9 polypeptides and their variants are provided, for example, in International Patent Publication No. WO 2013 / 176772. The Cas9 enzyme may contain mutations at D10, E762, H983, or D986, and mutations at H840 or N863. In some cases, the Cas9 enzyme may contain the D10A or D10N mutation. In other embodiments, the Cas9 enzyme may contain H840A, H840Y, or H840N. In some embodiments, the Cas9 enzyme may comprise D10A and H840A; D10A and H840Y; D10A and H840N; D10N and H840A; D10N and H840Y; or D10N and H840N substitutions. The substitutions may be conserved or non-conserved to catalytically inactivate the Cas9 peptide and enable it to bind to target DNA.

[0064] In some implementations, the Cas nuclease can be a high-fidelity or specific enhancement of the Cas9 peptide variant, with reduced off-target effects and robust on-target cleavage. Non-limiting examples of Cas9 peptide variants with improved target specificity include SpCas9(K855A), SpCas9(K810A / K1003A / R1060A) (also known as eSpCas9(1.0)), and the SpCas9(K848A / K1003A / R1060A) (also known as eSpCas9(1.1)) variant described in Slaymaker et al., Science, 351(6268):84-8(2016), and the SpCas9 variant described in Kleinstiver et al., Nature, 529(7587):490-5(2016), which contains 1, 2, 3 or 4 of the following mutations: N497A, R661A, Q695A and Q926A (e.g., SpCas9-HF1 contains all 4 mutations).

[0065] In some embodiments, the target motif can be selected to minimize off-target effects of the CRISPR / Cas system of the present invention. For example, in some embodiments, the target motif is selected to contain at least two mismatches relative to all other genomic nucleotide sequences in the cell. In some embodiments, the target motif is selected to contain at least one mismatch relative to all other genomic nucleotide sequences in the cell. Those skilled in the art will appreciate that various techniques can be used to select suitable target motifs to minimize off-target effects (e.g., bioinformatics analysis).

[0066] As used throughout, a guide RNA (gRNA) sequence is a sequence that interacts with a site-specific or targeted nuclease and specifically binds to or hybridizes with a target nucleic acid within the cellular genome, thereby co-localizing the gRNA and the targeted nuclease to the target nucleic acid in the cellular genome. Each gRNA includes a DNA targeting sequence or prototype spacer sequence of approximately 10 to 50 nucleotides in length that specifically binds to or hybridizes with a target DNA sequence in the genome. For example, the target sequence can be approximately 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 nucleotides in length. In some embodiments, the gRNA comprises a crRNA sequence and a trans-activating crRNA (tracrRNA) sequence. In some implementations, the gRNA does not contain a tracrRNA sequence.

[0067] Those skilled in the art will understand that sgRNAs can be selected based on the specific CRISPR / Cas system employed and the sequence of the target polynucleotide. As shown above, in some embodiments, one or two ribonucleic acids may also be selected to minimize hybridization with nucleic acid sequences other than the target polynucleotide sequence. In some embodiments, the one or two ribonucleic acids hybridize with a target motif that contains at least two mismatches relative to all other genomic nucleotide sequences in the cell. In some embodiments, the one or two ribonucleic acids hybridize with a target motif that contains at least one mismatch relative to all other genomic nucleotide sequences in the cell. In some embodiments, the one or two ribonucleic acids are designed to hybridize with a target motif that is directly adjacent to a deoxyribonucleic acid motif recognized by the Cas protein. In some embodiments, each of the one or two ribonucleic acids is designed to hybridize with a target motif that is directly adjacent to a deoxyribonucleic acid motif recognized by the Cas protein, the Cas protein flanking a mutant allele located between the target motifs. Alternatively, readily available software, such as the software guide RNA available at the website crispr.mit.edu, can be used. One or more sgRNAs can be transfected into T cells, where the Cas protein is presented via transfection, according to methods known in the art.

[0068] In some cases, the DNA target sequence can incorporate wobble or degenerate bases to bind multiple genetic elements. In some cases, the 19 nucleotides at the 3' or 5' end of the binding region are perfectly complementary to one or more target genetic elements. In some cases, the binding region can be modified to increase stability. For example, non-natural nucleotides can be incorporated to increase RNA resistance to degradation. In some cases, the binding region can be modified or engineered to avoid or reduce secondary structure formation within the binding region. In some cases, the binding region can be engineered to optimize GC content. In some cases, a GC content of approximately 40% to approximately 60% (e.g., 40%, 45%, 50%, 55%, 60%) is preferred.

[0069] In some embodiments, the sequence of the gRNA or a portion thereof may be designed to be complementary (e.g., perfectly complementary) or substantially complementary (e.g., 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% complementary) to a target region in a T-cell repressive gene. In some embodiments, the length of the gRNA portion complementary to and binding to the target region in the polynucleotide is or approximately 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, or 40 or more nucleotides. In some cases, the length of the gRNA portion that is complementary to and binds to the target region in the polynucleotide is between approximately 19 and approximately 21 nucleotides. In some cases, the gRNA may incorporate wobbly or degenerate bases to bind to the target region. In some cases, the gRNA may be modified to increase stability. For example, non-natural nucleotides may be incorporated to increase the RNA's resistance to degradation. In some cases, the gRNA may be modified or engineered to avoid or reduce secondary structure formation. In some cases, the gRNA may be engineered to optimize GC content. In some cases, the GC content is approximately 40%–approximately 60% (e.g., 40%, 45%, 50%, 55%, 60%). In some cases, the binding region may contain modified nucleotides, such as, but not limited to, methylated or phosphorylated nucleotides.

[0070] In some implementations, gRNA expression can be optimized by substituting, deleting, or adding one or more nucleotides. In some cases, nucleotide sequences that provide inefficient transcription of the coding template nucleic acid can be deleted or substituted. For example, in some cases, the gRNA is transcribed from a nucleic acid operatively linked to the RNA polymerase III promoter. In such cases, gRNA sequences that cause inefficient transcription by RNA polymerase III can be deleted or substituted, such as those described in Nielsen et al., Science. 2013 Jun 28; 340(6140):1577-80. For example, one or more consecutive uracils in the gRNA sequence can be deleted or removed. In some cases, if uracil is hydrogen-bonded to the corresponding adenine, the gRNA sequence can be altered to exchange adenine and uracil. This “AU flip” can preserve the overall structure and function of the gRNA molecule while improving expression by reducing the number of consecutive uracil nucleotides.

[0071] In some implementations, gRNA can be optimized for stability. Stability can be enhanced by optimizing the stability of gRNA:nuclease interactions, optimizing the assembly of the gRNA:nuclease complex, removing or altering unstable RNA sequence elements, or adding stable RNA sequence elements. In some implementations, the gRNA includes a 5' stem-loop structure located proximal to or near the region of gRNA-mediated nuclease interactions. Optimizing the 5' stem-loop structure can enhance the stability or assembly of the gRNA:nuclease complex. In some cases, the 5' stem-loop structure is optimized by increasing the length of the stem portion of the stem-loop structure.

[0072] gRNA can be modified by methods known in the art. In some cases, modification may include, but is not limited to, the addition of one or more of the following sequence elements: a 5' cap (e.g., a 7-methylguanylic acid cap); a 3' polyadenylated tail; a riboswitch sequence; a stability control sequence; a hairpin; a subcellular localization sequence; a detection sequence or marker; or a binding site for one or more proteins. Modification may include the introduction of one or more non-natural nucleotides, including but not limited to, fluorescent nucleotides and methylated nucleotides.

[0073] This document also provides expression cassettes and vectors for generating gRNA in host cells. The expression cassette may contain a promoter (e.g., a heterologous promoter) that is operatively linked to a multinucleotide encoding the gRNA. The promoter may be inducible or constitutive. The promoter may be tissue-specific. In some cases, the promoter is a U6, H1, or spleen lesion-forming virus (SFFV) long terminal repeat promoter. In some cases, the promoter is a weak mammalian promoter relative to the human elongation factor 1 promoter (EF1A). In some cases, the weak mammalian promoter is a ubiquitin C promoter or a glycerol phosphokinase 1 promoter (PKG). In some cases, in the absence of an inducer, the weak mammalian promoter is the TetOn promoter. In some cases, when the TetOn promoter is used, the host cell is also contacted with a tetracycline transactivator. In some embodiments, the strength of the selected gRNA promoter is chosen to express an amount of gRNA proportional to the amount of Cas9 or dCas9. The expression cassette may be in a vector (such as a plasmid), a viral vector, or a lentiviral vector. In some cases, the expression cassette is located within the host cell. gRNA expression cassettes can be free or integrated into the host cell.

[0074] Modification using other targeted nuclease systems

[0075] In some implementations, targeted nucleases are applied to modify T cells to suppress the expression of T cell repressive genes, such as transcription activator-like effector nucleases (TALENs), zinc finger nucleases (ZFNs), and megaTALs (see, for example, Merkert and Martin, “Site-Specific Genome Engineering in Human Pluripotent Stem Cells”, Int. J. Mol. Sci. 18(7):1000 (2016)).

[0076] Zinc finger nucleases that inhibit the expression of repressive genes in T cells

[0077] In some implementations, modified T cells containing targeted alterations to T cell repressive genes are generated by using ZFNs to suppress expression. Methods of using ZFNs to reduce gene expression are described, for example, in U.S. Patent No. 9,045,763, and in Durai et al., Nucleic Acid Research 33:5978-5990, 2005; Carroll et al., Genetics Society of America 188:773-782, 2011; and Kim et al., Proc. Natl. Acad. Sci. USA 93:1156-1160.

[0078] ZFNs contain a FokI nuclease domain (or a derivative thereof) fused to a DNA-binding domain. In the case of ZFNs, the DNA-binding domain contains one or more zinc fingers. Zinc fingers are small protein structural motifs stabilized by one or more zinc ions. Zinc fingers may contain, for example, Cys2His2 and can recognize sequences of approximately 3 bp. Various zinc fingers with known specificity can be combined to generate multi-finger polypeptides that recognize sequences of approximately 6, 9, 12, 15, or 18 bp. A variety of selective and modular assembly techniques exist to generate zinc fingers (and combinations thereof) that recognize specific sequences, including phage display, yeast one-hybrid systems, bacterial one-hybrid and two-hybrid systems, and mammalian cells.

[0079] ZFN dimerize to cleave DNA. Therefore, a pair of ZFNs can be used to target non-palindromic DNA sites. Two separate ZFNs bind to the opposite strand of DNA with the nuclease at the correct interval (see, for example, Bitinaite et al., Proc. Natl. Acad. Sci. USA 95:10570-5, 1998). ZFNs can induce double-strand breaks in DNA, which, if improperly repaired, can lead to frameshift mutations, resulting in decreased expression and expression levels of target genes in the cell.

[0080] TALEN suppresses T-cell repressor genes

[0081] In some implementations, T cells containing targeted alterations are generated by inhibiting desired T cell repressive genes using transcription activator-like effector nucleases (TALENs). TALENs are similar to ZFNs in that they bind in pairs around genomic sites and guide nonspecific nucleases (e.g., FoKI) to cleave the genome at specific sites, but instead of recognizing DNA triplets, each domain recognizes a single nucleotide. Methods for using TALENs to reduce gene expression are disclosed, for example, in U.S. Patent No. 9,005,973; Christian et al., "Genetics 186(2):757-761, 2010"; Zhang et al., 2011 Nature Biotech. 29:149-53, 2011; Geibler et al., 2011 PLoS ONE 6:e19509, 2011; Boch et al., 2009 Science 326:1509-12; Moscou et al., 2009 Science 326:3501.

[0082] To generate TALENs, the TALE protein is typically fused with a FokI endonuclease, which can be wild-type or mutant. Various mutations have been performed on FokI to suit its use in TALENs; for example, they improve cleavage specificity or activity. Cermak et al., Nucl. Acids Res. 39:e82, 2011; Miller et al., Nature Biotech. 29:143-8, 2011; Hockemeyer et al., Nature Biotech. 29:731-734, 2011; Wood et al., Science 333:307, 2011; Doyon et al., Nature Methods 8:74-79, 2010; Szczepek et al., Nature Biotech. 25:786-793, 2007; and Guo et al., J. Mol. Biol. 200:96, 2010.

[0083] The FokI domain functions as a dimer and typically utilizes two constructs, each with a unique DNA-binding domain targeting a site in the target genome, and with appropriate orientation and spacing. The number of amino acid residues between the TALE DNA-binding domain and the FokI cleavage domain, as well as the number of bases between the two separate TALEN binding sites, appear to be important parameters for achieving high levels of activity (e.g., Miller et al., 2011, cited above).

[0084] Meganucleases

[0085] "Large-scale nucleases" are highly specific sparse-cut endonucleases or homing nucleases that recognize DNA target sites of at least 12 base pairs in length, such as 12-40 or 12-60 base pairs. Large-scale nucleases can be modular DNA-binding nucleases, such as any fusion protein containing at least one endonuclease catalytic domain and at least one DNA-binding domain or protein specifying a nucleic acid target sequence. The DNA-binding domain may contain at least one motif recognizing single-stranded or double-stranded DNA. Large-scale nucleases can be monomers or dimers.

[0086] In some embodiments of the methods described herein, a wide range of nucleases can be used to inhibit the expression of T-cell repressive genes or to inhibit the expression of genes described herein that suppress immune function. In some cases, the wide range of nucleases are naturally occurring (present in nature) or wild-type, and in others, the wide range of nucleases are non-natural, artificial, engineered, synthetic, or rationally designed. In some embodiments, the wide range of nucleases that can be used in the methods described herein include, but are not limited to: I-CreI wide range of nucleases, I-CeuI wide range of nucleases, I-MsoI wide range of nucleases, I-SceI wide range of nucleases, their variants, their mutants, and their derivatives.

[0087] A wide range of useful nucleases and their applications in gene editing are described in, for example, Silva et al., Curr Gene Ther, 2011, 11(1):11-27; Zaslavoskiy et al., BMC Bioinformatics, 2014, 15:191; Takeuchi et al., Proc Natl Acad Sci USA, 2014, 111(11):4061-4066, and U.S. Patent Nos. 7,842,489; 7,897,372; 8,021,867; 8,163,514; 8,133,697; 8,021,867; 8,119,361; 8,119,381; 8,124,36; and 8,129,134.

[0088] The efficiency of inhibiting the expression of any T-cell regulatory gene using the methods described herein can be assessed by measuring the amount of mRNA or protein using methods well-known in the art, such as quantitative PCR, Western blotting, flow cytometry, etc. In some embodiments, protein levels are assessed to evaluate the efficiency of inhibition. In some embodiments, the efficiency of reducing target gene expression relative to corresponding cells without targeted modification is at least 5%, at least 10%, at least 20%, at least 30%, at least 50%, at least 60%, or at least 80%, or at least 90% or higher. In some embodiments, the reduction efficiency is from about 10% to about 90%. In some embodiments, the reduction efficiency is from about 30% to about 80%. In some embodiments, the reduction efficiency is from about 50% to about 80%. In some embodiments, the reduction efficiency is greater than or equal to about 80%.

[0089] Treatment methods and compositions

[0090] Any of the methods described herein can be used to modify T cells obtained from human subjects, such as CD8+ T cells. T cells modified according to the present invention can be used to treat any number of diseases or conditions, including cancer, autoimmune diseases, infectious diseases, or diseases or conditions related to graft rejection.

[0091] Methods of treating cancer

[0092] In some embodiments, T cells are modified to reduce the expression of T cell repressive genes, as described herein. In some embodiments, the modified T cell repressive gene is CBLB, CD5, SOCS1, TMEM222, TNFAIP3, DGKZ, RASA2, TCEB2, UBASH3A, or ARID1A. In some embodiments, the T cell repressive gene is CD5, SOCS1, TMEM222, TNFAIP3, RASA2, or TCEB2. In some embodiments, the T cell repressive gene is SOCS1, TCEB2, RASA2, or CBLB. In some embodiments, the T cell repressive gene is SOCS1, TCEB2, or RASA2. In some embodiments, the modified T cell repressive gene is RASA2, TCEB2, SOCS1, CBLB, FAM105A, ARID1A, or TMEM222. In some embodiments, the modified T-cell repressive gene is AGO1, ARIH2, CD8A, CDKN1B, DGKA, FIBP, GNA13, MEF2D, or SMARCB1. Therefore, in some embodiments, this document provides a method for treating cancer in a human subject, the method comprising: a) obtaining T cells, such as CD8+ T cells, from the subject; b) modifying the T cells using any of the methods provided herein to reduce the expression of a T-cell repressive gene (e.g., a gene disclosed in this paragraph); and c) administering the modified T cells to the subject.

[0093] In some implementations, T cells derived from a subject with cancer, such as CD8+ T cells, can be expanded in vitro. The characteristics of the subject's cancer can identify a set of customized cellular modifications (e.g., selection of one or more repressive gene targets), and these modifications can be applied to T cells using any of the methods described herein. The modified T cells can then be reintroduced into the subject. This strategy leverages and enhances the function of the subject's natural reservoir of cancer-specific T cells, providing a diverse therapeutic library for the rapid elimination of mutagenic cancer cells.

[0094] The genetically modified T cells described herein can be used to treat any cancer. In some embodiments, the cancer is carcinoma or sarcoma. In some embodiments, the cancer is a blood cancer. In some embodiments, the cancer is breast cancer, prostate cancer, testicular cancer, renal cell carcinoma, bladder cancer, liver cancer, ovarian cancer, cervical cancer, endometrial cancer, lung cancer, colorectal cancer, anal cancer, pancreatic cancer, stomach cancer, esophageal cancer, hepatocellular carcinoma, kidney cancer, head and neck cancer, glioblastoma, mesothelioma, melanoma, chondrosarcoma, or bone or soft tissue sarcoma. In some embodiments, the cancer is adrenocortical carcinoma, anal cancer, appendiceal cancer, astrocytoma, basal cell carcinoma, bile duct cancer, bone tumor, brainstem glioma, brain cancer, cerebellar astrocytoma, cerebral astrocytoma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumor, visual pathway and hypothalamic glioma, or bronchial adenoma. In some implementations, the cancer is acute lymphoblastic leukemia, acute myeloid leukemia, Burkitt's lymphoma, central nervous system lymphoma, chronic lymphocytic leukemia, chronic myeloid leukemia, hairy cell leukemia, chronic myelodysplastic disease, myelodysplastic syndrome, adult acute myelodysplastic disease, multiple myeloma, cutaneous T-cell lymphoma, Hodgkin's lymphoma, or non-Hodgkin's lymphoma.In some implementations, the cancer is a proliferative small round cell tumor, ependymoma, epithelial hemangioendothelioma (EHE), Ewing's sarcoma, extracranial germ cell tumor, gonadal germ cell tumor, extrahepatic bile duct carcinoma, intraocular melanoma, retinoblastoma, gallbladder carcinoma, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor (GIST), germ cell tumor, gestational trophoblastic tumor, gastric carcinoid, heart disease, hypopharyngeal cancer, hypothalamic and optic pathway glioma, childhood cancer, intraocular melanoma, islet cell carcinoma, Kaposi's sarcoma, laryngeal cancer, lip and oral cancer, liposarcoma, non-small cell lung cancer, small cell lung cancer, macroglobulinemia, male breast cancer, malignant fibrous histiocytoma of bone, medulloblastoma, melanoma, Merkel cell carcinoma, mesothelioma, metastatic squamous neck cancer, oral cancer, multiple endocrine tumors, mycosis fungoides. Fungoides, chronic, myxoma, nasal cavity and paranasal sinus carcinoma, nasopharyngeal carcinoma, neuroblastoma, oligodendroglioma, oral cancer, oropharyngeal cancer, osteosarcoma, ovarian epithelial carcinoma, ovarian germ cell tumor, low-potency ovarian tumor, sinus and nasal cavity carcinoma, parathyroid carcinoma, penile cancer, pharyngeal carcinoma, pheochromocytoma, pineal astrocytoma, pineal germ cell, osteoblastoma, supratentorial primitive neuroectodermal tumor, pituitary adenoma, plasmacytoma, pleural pulmonary blastoma Primary central nervous system lymphoma, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, uterine sarcoma, Sézary syndrome, non-melanoma skin cancer, melanoma Merkel cell skin cancer, small intestine cancer, squamous cell carcinoma, squamous neck cancer, laryngeal cancer, thymoma, thyroid cancer, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, pregnancy, urethral cancer, uterine cancer, vaginal cancer, vulvar cancer, Waldenström macroglobulinemia, and Wilms tumor.

[0095] In some embodiments, genetically modified T cells, or populations of individuals of genetically modified T cell subtypes, are provided to the subject in the following ranges: approximately 1 million to approximately 100 billion cells, for example, 1 million to approximately 50 billion cells (e.g., approximately 5 million cells, approximately 25 million cells, approximately 50 billion cells, approximately 1 billion cells, approximately 5 billion cells, approximately 20 billion cells, approximately 30 billion cells, approximately 40 billion cells, or a range defined between any two of the foregoing values), for example, approximately 10 million to approximately 100 billion cells (e.g., approximately 20 million cells, approximately 30 million cells, approximately 40 million cells, approximately...). 60 million cells, approximately 70 million cells, approximately 80 million cells, approximately 90 million cells, approximately 10 billion cells, approximately 25 billion cells, approximately 50 billion cells, approximately 75 billion cells, approximately 90 billion cells, or a range defined between any two of the foregoing values), and in some cases, approximately 100 million cells to approximately 50 billion cells (e.g., approximately 120 million cells, approximately 250 million cells, approximately 350 million cells, approximately 450 million cells, approximately 650 million cells, approximately 800 million cells, approximately 900 million cells, approximately 3 billion cells, approximately 30 billion cells, approximately 45 billion cells) or any value between these ranges.

[0096] In some implementations, the total cell dose and / or the dose of individual cell subpopulations are in the range of: between or about 10 4 The sum is or approximately 10 9 Cells / kg body weight, such as between 10 5 and 10 6 Between cells / kg body weight, for example, at least approximately 1 x 10-1 5 Cells / kg, 1.5 x 10 5 cells / kg, 2x10 5 Cells / kg, 5 x 10 5 Cells / kg or 1x 10 6 Cells / kg body weight.

[0097] The appropriate dosage can be determined based on the type of cancer to be treated, the severity and course of the disease, previous treatments, the subject's clinical history and response to the cells, and the discretion of the attending physician. In some embodiments, the composition and cells are suitable for administration to the subject at one point in time or as a series of treatments.

[0098] The cells can be administered by any suitable method, such as by bolus infusion, by injection, for example, intravenous or subcutaneous injection, intraocular injection, fundus injection, subretinal injection, intravitreal injection, anti-septal injection, subscleral injection, intrachoroidal injection, anterior chamber injection, subconjunctival injection, subconjuntival injection, suprascleral injection, retrobulbar injection, periocular injection, or peribulbar delivery. In some embodiments, they are administered parenterally, intrapulmonaryly, and intranasally, and, if local treatment is desired, intralesionally. Extraperitoneal infusion includes intramuscular, intravenous, intraarterial, intraperitoneal, or subcutaneous administration. In some embodiments, a given dose is administered by a single bolus injection of cells. In some embodiments, cells are administered by multiple bolus injections, for example, over a period not exceeding 3 days, or by continuous infusion of cells.

[0099] In some embodiments, cells are administered as part of a combination therapy, such as simultaneously with or sequentially in any order with another therapeutic intervention (e.g., an antibody or engineered cell or receptor, or a drug (e.g., a cytotoxic or therapeutic agent)). In some embodiments, cells are administered co-administered with one or more other therapeutic agents or in combination with another therapeutic intervention, or simultaneously or sequentially in any order. In some cases, cells are administered co-administered with other treatments, close enough in time that the cell population enhances the effect of one or more other therapeutic agents, or vice versa. In some embodiments, cells are administered before one or more other therapeutic agents. In some embodiments, cells are administered after one or more other therapeutic agents.

[0100] Methods for treating autoimmune diseases or graft rejection

[0101] This article also provides methods for treating autoimmune diseases or other conditions where it is desirable to suppress the immune system (e.g., graft rejection), said methods by administering T cells (e.g., CD8+ T cells) modified to suppress the expression of the following genes: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP21, EXOSC6, RPN1, VARS, CD3D, GRAP2, TRMT112, ALG8, VAV1, EXOSC6, SH2D1A, HSPA8, ZAP70, DDX54, CD247, ALDOA, ZNF131, WDR36, AK2, LCP2, CD247, VHL, EIF2B2, PRELID 1, GRPEL1, NAA10, ALDOA, ALG2, MARS, C4orf45, RAC2, LCK, SUPT4H1, SLC25A3, LUC7L3, C3orf17, RPP21, HARS, ZNRD1, CCNH, MYC, CCDC25, EEF1G, CCND2, GCLC, TAF2, EIF6, SEC63, EXOSC6, RPS19BP1, SEC61B, VHL, DAD1, BEND6, FBL, VARS, EIF2B4, RAC2, PAGR1, MYC, CD3E, LCP2, MYC, ENOSF1, POLR3H, NOP14, C LNS1A, POLR2L, ZPR1, CARD11, SLC35B1, TRMT112, FARSA, PRELID1, LARS, NOP16, POLR2L, HSPA8, CD247, GEMIN8, TTC27, PMPCA, PWP2, TAF1C, DDOST, ZNF 654, FAU, EIF2B3, YARS, DDX20, DDX56, DDX49, UTP20, EPRS, RSL1D1, ATP1B3, EXOSC4, ARMC7, EIF2B4, AUP1, VAV1, PAK1IP1, EIF6, FAM157A, HSPE1-MOB4, LAT, DCAF13, PPP1R10, EXOSC2, SRP9, POLR3K, TAF6, EIF3H, ABCF1, FLJ44635, PTP4A2, EIF3CL, ABCB7, GTF2H4, MARS, TAF4, RPL5, FTSJ3, CD28, ALG13, C ARD11, EIF4G1, UTP3, GARS, CACNB4, HSPA8, POP7, ERCC3, GDPD2, SUPT5H, POLR3D, RPP30, C12orf45, DPH3, EIF3B, LACTBL1, THAP11, IMP4, EXOSC7, NOB1,EIF4E, PLCG1, HUWE1, RBM19, GATA3, CCND2, TTI2, THG1L, TAF1C, URI1, TRMT112, EIF3H, CCND2, GCLM, RBSN, QARS, POP7, TAF4, HUWE1, CARS, PTP4A2, PES1, ZNF785, WDR26, PRR20D, STK11, PIK3CD, YARS, STRAP, WDR77, NANS, TARS, HSPA8, TMEM127, FAM35A, ZBTB8OS, BPT F, INO80D, NOP14, KARS, SH2D1A, RHOH, DIMT1, CMPK1, TAF6, QTRT1, LCK, NOL10, MYBBP1A, NHP2, DDX54, LAT, TAF2, MBTPS1, GNL3, DEF6, BCL10, NFKBIA, PHB, CD3G, CD3D, QARS, EIF3C, GRPEL1, MBTPS2, ORAOV1, SLC4A2, GATA3, ODF3, SLC7A6OS, ORAOV1, ALG13, or TAF1B gene. In some implementations,The gene is any one of the following genes: HSPA8, RPP21, EXOSC6, LCP2, MYC, CD247, NOP14, VAV1, RHOH, TAF1C, TRMT112, CCND2, SH2D1A, MARS, CD3D, NELFCD LCK, LUC7L3, EIF2B4, ORAOV1.VARS, NOL10, ZBTB8OS, SLC35B1, NAA10, EIF2B3, DHX37, LAT, EMG1, ALDOA, GRPEL1, ARMC7, POLR2L, NOP56, PS ENEN, RELA, SUPT4H1, VHL, GFER, BPTF, RAC2, TSR2, TAF6, PMPCA, EIF6, STT3B, POP7, GMPPB, TP53RK, CCNH, TEX10, DHX33, QARS, EID2, IRF4, T AF2, IARS, GTF3A, NOP2, IMP3, RPL28, UTP3.EIF4G1, GPN1, UTP6, DAD1, ALG2, CDK6, MED19, RASGRP1, PHB2, NFS1, POLR2E, CDIPT, POLR3H, HA RS, SEH1L, EIF2B5, TTC27, RRP12, JUNB, HSPE1, GMPS, EIF2S3, SRP14, FAM96B, RPL8, RRP36, MED11, ISG20L2, ROMO1, ATP6V1B2, RPN2, or WASH1. In one embodiment, the method includes a) obtaining T cells from a subject exhibiting the condition (e.g., autoimmune disease, or graft); b) modifying the T cells using any of the methods provided herein to suppress the expression of the following genes: CYP2R1, LCP2, RPP21, VAV1, EIF2B3, RPP21, EXOSC6, RPN1, VARS, CD3D, GRAP2, TRMT112, ALG8, VAV1, EXOSC6, SH2D1A, HSPA8, ZAP70, DDX54, CD247, ALDOA, ZNF131, WDR36, A K2, LCP2, CD247, VHL, EIF2B2, PRELID1, GRPEL1, NAA10, ALDOA, ALG2, MARS, C4orf45, RAC2, LCK, SUPT4H1, SLC25A3, LUC7L3, C3orf17 ,RPP21,HARS,ZNRD1,CCNH,MYC,CCDC25,EEF1G,CCND2,GCLC,TAF2,EIF6,SEC63,EXOSC6,RPS19BP1,SEC61B,VHL,DAD1,BEND6,FBL,VARS,EIF2B4,RAC2,PAGR1,MYC,CD3E,LCP2,MYC,ENOSF1,POLR3H,NOP14, CLNS1A,POLR2L,ZPR1,CARD11,SLC35B1,TRMT112,FARSA,PRELID1,LARS,N OP16,POLR2L,HSPA8,CD247,GEMIN8,TTC27,PMPCA,PWP2,TAF1C,DDOST,Z NF654,FAU,EIF2B3,YARS,DDX20,DDX56,DDX49,UTP20,EPRS,RSL1D1,ATP1 B3,EXOSC4,ARMC7,EIF2B4,AUP1,VAV1,PAK1IP1,EIF6,FAM157A,HSPE1-M OB4,LAT,DCAF13,PPP1R10,EXOSC2,SRP9,POLR3K,TAF6,EIF3H,ABCF1,FLJ 44635,PTP4A2,EIF3CL,ABCB7,GTF2H4,MARS,TAF4,RPL5,FTSJ3,CD28,ALG 13,CARD11,EIF4G1,UTP3,GARS,CACNB4,HSPA8,POP7,ERCC3,GDPD2,SUPT5 H,POLR3D,RPP30,C12orf45,DPH3,EIF3B,LACTBL1,THAP11,IMP4,EXOSC7 ,NOB1,EIF4E,PLCG1,HUWE1,RBM19,GATA3,CCND2,TTI2,THG1L,TAF1C,URI 1,TRMT112,EIF3H,CCND2,GCLM,RBSN,QARS,POP7,TAF4,HUWE1,CARS,PTP4 A2,PES1,ZNF785,WDR26,PRR20D,STK11,PIK3CD,YARS,STRAP,WDR77,NANS ,TARS,HSPA8,TMEM127,FAM35A,ZBTB8OS,BPTF,INO80D,NOP14,KARS,SH2 D1A,RHOH,DIMT1,CMPK1,TAF6,QTRT1,LCK,NOL10,MYBBP1A,NHP2,DDX54,L AT,TAF2,MBTPS1,GNL3,DEF6,BCL10,NFKBIA,PHB,CD3G,CD3D,QARS,EIF3C ,GRPEL1,MBTPS2,ORAOV1,SLC4A2,GATA3,ODF3,SLC7A6OS,ORAOV1,ALG13,Or the TAF1B gene; and c) administering modified T cells to the subject. In some embodiments, the gene is any one of the following genes: HSPA8, RPP21, EXOSC6, LCP2, MYC, CD247, NOP14, VAV1, RHOH, TAF1C, TRMT112, CCND2, SH2D1A, MARS, CD3D, NELFCD LCK, LUC7L3, EIF2B4, ORAOV1.VARS, NOL10, ZBTB8OS, SLC35B1, NAA10, EIF2B3, DHX37, LAT, EMG1, ALDOA, GRPEL1, ARMC7, POLR2L, NOP56, PS ENEN, RELA, SUPT4H1, VHL, GFER, BPTF, RAC2, TSR2, TAF6, PMPCA, EIF6, STT3B, POP7, GMPPB, TP53RK, CCNH, TEX10, DHX33, QARS, EID2, IRF4, T AF2, IARS, GTF3A, NOP2, IMP3, RPL28, UTP3.EIF4G1, GPN1, UTP6, DAD1, ALG2, CDK6, MED19, RASGRP1, PHB2, NFS1, POLR2E, CDIPT, POLR3H, HA RS, SEH1L, EIF2B5, TTC27, RRP12, JUNB, HSPE1, GMPS, EIF2S3, SRP14, FAM96B, RPL8, RRP36, MED11, ISG20L2, ROMO1, ATP6V1B2, RPN2, or WASH1. ,

[0102] In some implementations, the modified T cells described in the preceding paragraphs are given to the patient to treat or prevent graft rejection.

[0103] In some embodiments, the T cells modified herein to suppress immune function are given to patients suffering from autoimmune diseases or inflammatory conditions. In some embodiments, autoimmune or inflammatory diseases are osteoarthritis, rheumatoid arthritis, juvenile rheumatoid arthritis or idiopathic arthritis, multiple sclerosis, psoriasis, psoriatic arthritis, Crohn's disease, inflammatory bowel disease, ulcerative colitis, celiac disease, lupus, Graves' disease, Hashimoto's thyroiditis, Addison's disease, myasthenia gravis, Sjögren's syndrome, type I diabetes, vasculitis, and ankylosing spondylitis.

[0104] In some embodiments, genetically modified T cells, or populations of individuals of genetically modified T cell subtypes, are provided to the subject in the following ranges: approximately 1 million to approximately 100 billion cells, for example, 1 million to approximately 50 billion cells (e.g., approximately 5 million cells, approximately 25 million cells, approximately 50 billion cells, approximately 1 billion cells, approximately 5 billion cells, approximately 20 billion cells, approximately 30 billion cells, approximately 40 billion cells, or a range defined between any two of the foregoing values), for example, approximately 10 million to approximately 100 billion cells (e.g., approximately 20 million cells, approximately 30 million cells, approximately 40 million cells, approximately...). 60 million cells, approximately 70 million cells, approximately 80 million cells, approximately 90 million cells, approximately 10 billion cells, approximately 25 billion cells, approximately 50 billion cells, approximately 75 billion cells, approximately 90 billion cells, or a range defined between any two of the foregoing values), and in some cases, approximately 100 million cells to approximately 50 billion cells (e.g., approximately 120 million cells, approximately 250 million cells, approximately 350 million cells, approximately 450 million cells, approximately 650 million cells, approximately 800 million cells, approximately 900 million cells, approximately 3 billion cells, approximately 30 billion cells, approximately 45 billion cells) or any value between these ranges.

[0105] In some implementations, the total cell dose and / or the dose of individual cell subpopulations are in the range of: between or about 10 4 The sum is or approximately 10 9 Cells / kg body weight, such as between 10 5 and 10 6 Between cells / kg body weight, for example, at least approximately 1 x 10-1 5 Cells / kg, 1.5 x 10 5 cells / kg, 2x10 5 Cells / kg, 5 x 10 5 Cells / kg or 1x 10 6 Cells / kg body weight.

[0106] The appropriate dosage can be determined based on the type of cancer to be treated, the severity and course of the disease, previous treatments, the subject's clinical history and response to the cells, and the discretion of the attending physician. In some embodiments, the composition and cells are suitable for administration to the subject at one point in time or as a series of treatments.

[0107] The cells can be administered by any suitable method, such as by bolus infusion, by injection, for example, intravenous or subcutaneous injection, intraocular injection, fundus injection, subretinal injection, intravitreal injection, anti-septal injection, subscleral injection, intrachoroidal injection, anterior chamber injection, subconjunctival injection, subconjuntival injection, suprascleral injection, retrobulbar injection, periocular injection, or peribulbar delivery. In some embodiments, they are administered parenterally, intrapulmonaryly, and intranasally, and, if local treatment is desired, intralesionally. Extraperitoneal infusion includes intramuscular, intravenous, intraarterial, intraperitoneal, or subcutaneous administration. In some embodiments, a given dose is administered by a single bolus injection of cells. In some embodiments, cells are administered by multiple bolus injections, for example, over a period not exceeding 3 days, or by continuous infusion of cells.

[0108] In some embodiments, cells are administered as part of a combination therapy, such as simultaneously with or sequentially in any order with another therapeutic intervention (e.g., an antibody or other immunosuppressive agent). In some embodiments, cells are administered co-administered with one or more other therapeutic agents or in combination with another therapeutic intervention, or simultaneously or sequentially in any order. In some cases, cells are administered co-administered with other treatments, close enough in time that the cell population enhances the effect of one or more other therapeutic agents, or vice versa. In some embodiments, cells are administered prior to one or more other therapeutic agents. In some embodiments, cells are administered after one or more other therapeutic agents.

[0109] All publications cited in this article and the relevant materials cited therein are specifically included in this article through citation.

[0110] Example

[0111] The following examples are provided to illustrate, rather than limit, the claimed invention.

[0112] Example 1. A hybrid approach for introducing traceable genetic perturbations into primary human T cells.

[0113] We have set out to establish a high-throughput CRISPR screening platform that works directly in ex vivo artificial blood cells. Current pooled CRISPR screening methods rely on establishing cell lines with a stably integrated Cas9 expression cassette. Our attempts to stably express *Streptococcus pyogenes* Cas9 in primary T cells via lentivirus resulted in extremely low transduction efficiency. This inefficiency hinders large-scale pooled screening of primary cells that are not immortalized and can only be expanded in culture for a limited time. We previously demonstrated efficient gene editing of primary human T cells by electroporating Cas9 protein pre-loaded with small guide RNA (sgRNA) in vitro (Patel et al., 2017). We envision a hybrid system that introduces a trackable sgRNA cassette via lentivirus followed by electroporation with Cas9 protein. Figure 1A To test this strategy, we targeted genes encoding candidate cell surface proteins (i.e., the α chain of the CD8 receptor (CD8A)) because they are involved in human CD8 receptor cytokines. + Highly and uniformly expressed in T cells. We optimized multiple steps in cell stimulation, lentiviral transduction, and Cas9 electroporation to efficiently deliver various components while maintaining cell viability. Figure 7A -D). In short, CD8 + T cells were isolated from peripheral blood of healthy donors, stimulated, and then transduced with a lentivirus encoding an sgRNA cassette and mCherry fluorescent protein reporter gene. After transduction, T cells were transfected with recombinant Cas9 protein via electroporation. On day 4 post-electroporation, the majority (>80%) of the transduced cells (mCherry+) were CD8 negative. Figure 1B and Figure 7E This indicates that the Cas9-sgRNA combination was successfully targeted. The loss of CD8 protein was specifically programmed through the targeting of the sgRNA, as cells transduced with the non-targeting control sgRNA retained high levels of CD8 expression. By targeting PTPRC (CD45) using the same delivery strategy, we confirmed the successful knockout at the second target and demonstrated the presence of CD8. + and CD4 + The efficacy of the T cell system ( Figure 7F Consistent with the observed loss of target protein expression, efficient gene editing was confirmed by sequencing of the genomic target sites. Figure 7G We conclude that... C as9 protein electricity Perforation s gRNA slow Virus feel The conclusion is that SLICE staining leads to effective and specific destruction of target genes.

[0114] We next tested whether SLICE could be expanded to allow large-scale loss-of-function screening in primary cells using a lentiviral-encoded sgRNA library. We performed screening to identify gene targets that regulate T cell proliferation in response to T cell receptor (TCR) stimulation. For preliminary studies, we generated a custom sgRNA plasmid library that targets all annotated cell surface proteins and multiple standard members of the TCR signaling pathway (approximately 5000 guides, targeting a total of 1211 genes, plus 48 non-targeted guides). CD8 isolates were obtained from two healthy human donors. + T cells were transduced with a lentivirus encoding the sgRNA library, electroporated with Cas9, and then cultured (experimental procedure). On day 10 after electroporation, cells were labeled with CFSE to track cell division, followed by TCR stimulation. Four days after stimulation, CFSE levels indicated that the cells had undergone multiple divisions. Cells were sorted into two populations by FACS: (1) non-proliferating cells (high CFSE) and (2) highly proliferating cells (low CFSE). Figure 1A , Figure 7G (and experimental procedures). We quantified the sgRNA abundance of each population by deep sequencing of the amplified sgRNA cassettes. Consistent with the good sgRNA coverage in the experimental procedures, we were able to detect infected CD8 cells. + All library guides in T cells showed relatively uniform distribution of sgRNA abundance across individual donors and throughout the biological replicates. Figure 7H To identify sgRNAs regulating T cell proliferation, we calculated abundance-based ranking differences between highly dividing and non-dividing cells. Highly enriched sgRNAs in both dividing and non-dividing cells pointed to key biological pathways. We found that, as expected, sgRNAs targeting essential components of TCR signaling, such as CD3D and LCP2, inhibited cell proliferation (de Saint Basile et al., 2004; Shen et al., 2009). We also found that targeting CD5 or CBLB can enhance human T cell proliferation, and their role in negatively regulating T cell stimulation responses has been reported (Azzam et al., 2001; Naramura et al., 2002; Voisinne et al., 2016). The ranking differences of sgRNAs targeting these genes were in the top 1% in both biological replication pathways. Figure 1C Furthermore, the consistent action of multiple sgRNAs targeting these genes increases our confidence that the phenotype is not due to off-target effects. Figure 7I Importantly, this method provides significantly stronger sgRNA sequence enrichment than simple growth-based screening using CFSE to sort dividing and non-dividing primary cells along the same experimental timeline. Figure 7JCell proliferation-based screening has been largely successful using immortalized cell lines that can be cultured for extended periods, but this has not been applied to screening in primary human T cells (Shalem et al., 2014; Wang et al., 2014). In summary, these data suggest that SLICE pooled CRISPR screening can be used to identify both positive and negative regulators of primary human T cell proliferation.

[0115] Genome-wide pooled CRISPR screening reveals regulators of TCR response

[0116] To fully utilize this platform, we expanded targeted lead screening to a genome-wide (GW) scale (Doench et al., 2016), transducing a library of 77,441 sgRNAs (19,114 genes) into T cells from two healthy donors. After confirming successful transduction of these primary human T cells (Fig. 8, AB), the cells were restimulated and then sorted by FACS into non-proliferating and highly proliferating populations based on CFSE levels. Figure 8C And experimental procedures). MAGeCK software (Li et al., 2014) was used to systematically identify genes that were positively or negatively selected in a proliferating T cell population. Positive and negative regulators ranked first from the initial screening, as well as many other hits, were confirmed in two biological replicates of GW screening. Figure 2A (B). To train the top-ranked candidate list, we performed independent secondary screening using cells from two other blood donors. The results showed a good correlation between the primary and secondary screenings. Figure 2C Furthermore, a comprehensive analysis of two independent screenings of a total of four blood donors improved the ability to identify targets, particularly for negative regulators of T cell proliferation. Figure 8D To confirm that hits are indeed dependent on TCR stimulation, we performed GW screening with increased TCR stimulation levels. While similar gene targets acted as both positive and negative regulators throughout the conditions, the intensity of their effects was weakened at higher levels of TCR stimulation, suggesting that stronger TCR stimulation can mask the effects of these gene agitation. Figure 2D and Figure 8E The observed dose-response confirmed that most of the selected candidates depended on TCR stimulation and contributed to regulating the resulting proliferative response. In summary, these screenings identified dozens of genetic perturbations that positively and negatively regulate T cell proliferation.

[0117] Genes identified in the integrated screening analysis were enriched using annotation pathways associated with TCR stimulation. Gene set enrichment analysis (GSEA) revealed overexpression of gene targets that deplete self-proliferating cells in the TCR signaling pathway (FDR < 0.01). Figure 2E and Figure 8FWe also found significant enrichment of highly targeted genes in dividing cells from publicly available shRNA screening designed to discover gene targets (FDR < 0.01) that promote T cell proliferation in in vivo tumor tissues (Zhou et al., 2014). This is surprising because studies were conducted in different organisms using different gene perturbation platforms, yet our screening showed significant enrichment of high-ranking positive hits based on a hit list found in in vivo animal models. These global analyses confirm that our functional screening can identify key gene targets, now achievable directly in primary human cells across the entire genome.

[0118] In this GW screening, the target encoding a key protein complex crucial for TCR signaling was consumed by self-proliferating cells. Figure 2F For example, gene targets that impair TCR-dependent proliferation include the δ and ζ chains of the TCR complex itself (negative rank 18 and 6, respectively), and LCK (negative rank 20), which directly phosphorylates and activates the TCR ITAM and the central signaling mediator ZAP70 (Dave et al., 1998; Tsuchihashi et al., 2000; Wang et al., 2010). LCK and ZAP70 translocate to the immune synapse via RhoH (negative rank 2) (Chae et al., 2010). The ZAP70 target LCP2 (negative rank 4) is an adaptor protein required for TCR-induced activation and mediates the integration of TCR and CD28 co-stimulatory signals by activating VAV1 (negative rank 8), which is required for TCR-induced calcium flux and signal transduction (Dennehy et al., 2007; Raab et al., 1997; Tybulewicz, 2005). LAT (ranked 38th in the negative category) is another ZAP70 target that recruits several key adaptor proteins after phosphorylation for signal transduction downstream of TCR binding (Bartelt and Houtman).

[0119] Genes that negatively regulate T cell proliferation have therapeutic potential to enhance T cell function. While functions have been assigned to some negative regulators, many are poorly annotated within the standard TCR signaling pathway. Diacylglycerol (DAG) kinases, DGKA (ranked 17) and DGKZ (ranked 1) (negative regulators of DAG-mediated signaling), were found to inhibit human T cell proliferation upon stimulation (Arranz-Nicolás et al., 2018; Chen et al., 2016; Gharbi et al., 2011). The E3 ubiquitin ligase CBLB (ranked 4) and its interacting partner CD5 (ranked 12) work together to inhibit TCR activation through ubiquitination, which leads to TCR degradation (Voisinne et al., 2016). TCEB2 (ranked 5th) in combination with RNF7 (ranked 34th), CUL5 (ranked 162nd), and SOCS1 (ranked 3rd) is a key inhibitor of JAK / STAT signaling in activated T cells (Kamura et al., 1998; Liau et al., 2018). UBASH3A (ranked 10th), TNFAIP3, and its chaperone TNIP1 (ranked 13th and 24th, respectively) inhibit TCR-induced NF-κB signaling, which is crucial for CD8+ signaling. + Key survival and growth signals for T cells (Düwel et al., 2009; Ge et al., 2017). In addition to these key complexes, genes encoding other less characteristic cell surface receptors (e.g., TMEM222, GNA13), cytosol signaling components (e.g., RASA2, FIBP), and nuclear factors (e.g., CDKN1B, ARIH2, ZFP36L1) have been found to inhibit proliferation. Figure 2F This reveals a promising set of candidate targets to enhance the effects of T cell stimulation.

[0120] Array delivery of Cas9 RNPs reveals hit-altered stimulus responses

[0121] We then confirmed the biological role of high-resolution genes in promoting T cell activation and proliferation by array electroporation of single Cas9 ribonucleoproteins (RNPs) (Hultquist et al., 2016; Schumann et al., 2015). Our validation focused primarily on a group of top-ranked negative proliferation regulators due to their therapeutic potential to enhance T cell function when targeted. We further examined the extent to which positive hits affected T cell proliferation following TCR stimulation. Briefly, CD8+ T cells from four human blood donors were stimulated, electroporated with RNPs, rested for 10 days, labeled with CFSE, and restimulated ( Figure 3A(and experimental procedures). High-throughput flow cytometry, based on CFSE dilution, determined the proliferative response in edited and control cells. This validated the ability of many tested gene targets to increase T cell proliferation upon stimulation, consistent with their robust role in pooled screening. Figure 9A For example, compared to controls, CBLB and CD5 knockout cells showed a significant increase in cell division number after stimulation, which persisted in guide RNA and blood donors. Figure 3B To systematically quantify cell proliferation, we fitted the CFSE distribution of our samples using a mathematical model (Roederer, 2011). Figure 9B This analysis showed that, compared to controls, multiple negative regulators of perturbed T cell stimulation increased the proliferation index score (7 of the 10 gene perturbed negative regulators are shown here). Compared to untargeted control cells, UBASH3A, CBLB, CD5, and RASA2 knockout T cells all showed a more than 2-fold increase in proliferation index. Figure 3C Notably, targeting these genes did not increase the proliferation of unstimulated cells, suggesting that they are not general regulators of proliferation, but rather appear to regulate proliferation induced by TCR signaling. Conversely, the guides targeting gene targets consumed in proliferating cells during pooled screening showed a reduced proliferation index compared to the untargeted control. Therefore, we demonstrate, using an orthogonal gene targeting system, that most of the top-ranked genes identified by our screening robustly regulate stimulation-dependent proliferation in human CD8 T cells.

[0122] We then examined whether these hits modulated the typical response to TCR stimulation in addition to cell proliferation. The phenotype of cells edited in array format could be assessed using multiple biomarkers at different time points. We analyzed two distinct cell surface biomarkers, CD69 and CD154, for early CD8+ T cell activation (López-Cabrera et al., 1993; Shipkova and Wieland, 2012). Cells were assessed on day 10 after electroporation, 6 hours after restimulation. We found that, compared to untargeted control cells, T cells engineered to lack negative regulators of proliferation, such as SOCS1, CBLB, and CD5, also showed increased surface expression levels of CD69 and CD154. Figure 3D and Figure 8C -D). Conversely, the positive regulator LCP2, which targets TCR signaling, reduces the expression of CD69 and CD154 in stimulated cells. In summary, compared to the non-targeted control guide, positive hits resulted in a higher percentage of cells expressing these activation markers under various conditions, consistent with the two sgRNAs for each gene, for all four donors ( Figure 3ETherefore, array editing and phenotypic analysis characterized the effects of genetic perturbation and revealed targets that promote stimulus-dependent proliferation and activation programs.

[0123] SLICE paired with single-cell RNA-Seq is used for molecular phenotypic analysis of modified primary human cells.

[0124] We then went a step further, characterizing the stimulus-dependent transcriptional program by excising alterations in key target genes in human T cells. Recently, combining pooled CRISPR screening with single-cell RNA-seq has enabled high-level analysis of transcriptional changes induced by genetic perturbations in immortalized cell lines (Adamson et al., 2016; Datlinger et al., 2017; Dixit et al., 2016) or cells from transgenic mice (Jaitin et al., 2016). Here, we combined SLICE with droplet-based single-cell transcriptome readings for high-dimensional phenotypic analysis of pooled perturbations in primary human T cells. We chose the CROP-Seq platform because it provides barcode-free pooled CRISPR screening and single-cell RNA-Seq using the readily available 10X Genomics platform (Datlinger et al., 2017). For a total of 48 sgRNAs, we generated a custom library targeting top-ranked hits from our GW screening (2 sgRNAs per gene), known checkpoint genes (PDCD1, TNFRSF9, C10orf54, HAVCR2, LAG3, BTLA), and 8 non-targeted controls. Human T cells from two donors were transduced using this library, encoded using Cas9 protein electroporation, and enriched with puromycin selection (experimental procedure). Figure 10A Single-cell transcriptomic analysis was performed on cells with or without restimulation to characterize changes in cellular state and stimulus response caused by various genetic modifications.

[0125] First, we analyzed the transcriptional state of over 15,000 resting and stimulated single cells, in which we were able to identify sgRNA barcodes. A large-scale gene expression profile revealed that stimulated cells upregulated multiple cell cycle genes, indicating a response to TCR stimulation. Figure 10B We then used the Uniform Manifold Approximation and Projection (UMAP) (McInnes and Healy, 2018) to visualize the distribution of these single-cell transcriptomes at a reduced scale. Figure 4AWhile unstimulated T cells exhibit donor-dependent basal gene expression patterns, stimulated cells from two donors tend to share transcriptional signatures and cluster together. For example, stimulated cells typically induce expression of cell cycle genes (e.g., MKI67) and cell-soluble granzymes (e.g., GZMB). Figure 4B Conversely, unstimulated cells highly expressed markers of resting state, such as IL7R and CCR7. Therefore, TCR stimulation has a powerful role in inducing activated cell states across biological repeats, although more cells in donor 1 appeared to be strongly stimulated than in donor 2. To systematically estimate cell states, we clustered them by gene expression based on their shared nearest neighbors (...). Figure 4C Stimulated cells were enriched in clusters 9-12, characterized by preferential expression of mitotic cell cycle and T cell activation programs (…). Figure 10C This analysis of single-cell transcriptomes revealed the cellular state characteristics of human T cells before and after restimulation.

[0126] We next evaluated the effects of CRISPR-mediated genetic perturbation on cell state. We validated the efficient editing of most gene targets by reducing the expression of sgRNA target transcripts compared to cells with non-targeted control sgRNA. Figure 10D We tested whether gene perturbation led to changes in the genetic program. Cells with the non-targeted control sgRNA were relatively evenly distributed among clusters. In contrast, cells with CBLB and CD5 sgRNA were enriched in clusters associated with proliferation and activation, while cells with LCP2 sgRNA were mostly found in clusters characterized by a resting profile. Figure 4D Then, based on their transcriptional profiles, we quantified which sgRNA targets propelled cells toward different cellular state clusters. Figure 4E Several negative regulators identified in the GW screening, such as CD5, RASA2, SOCS1, and CBLB, promoted the cluster 10-12 program. Markers of the activation states (IL2RA, TNFRSF18 / GITR), cell cycle genes (MKI67, UBE2S, CENPF, and TOP2A), and effector molecules (GZMB, XCL1) induced by perturbations of these negative regulators were also identified. Figure 4F and Figure 10E Conversely, sgRNAs targeting CD3D or LCP2 inhibited the cluster 10 activation program and promoted the cluster 1-2 program. SLICE paired with single-cell RNA-Seq revealed how target gene manipulation shapes stimulus-dependent cellular states.

[0127] Targeting different negative regulators of proliferation leads to different transcriptional outcomes. CBLB knockout tends to induce cellular state characteristics similar to those targeting known checkpoint genes BTLA or LAG3, as evidenced by similarities in cluster representations. Figure 10F Different shared activation programs were observed when targeting CD5, TCEB2, RASA2, or CDKN1B. The integration of CRISPR screening and single-cell RNA-Seq in SLICE pools provides a powerful method for discovering and characterizing key genetic pathways in human primary cells. These data also suggest that targeting negative regulators of proliferation can induce specific stimulus-dependent effector gene programs that can enhance T cell potency.

[0128] Screening for engineered human T cells to promote tumor killing in vitro

[0129] Engineering cells to enhance proliferative responses and promote enhanced effector gene programming in response to TCR stimulation holds promise for cancer immunotherapy. We tested the effects of target gene knockout in an antigen-specific in vitro tumor-killing system. Figure 5A Specifically, we used the A375 melanoma cell line expressing RFP (which expresses the tumor antigen NY-ESO) as target cells (Robbins et al., 2008). Antigen-specific T cells were generated by transducing NY-ESO1-responsive α95:LY TCRs (Wargo et al., 2009). Figure 11A These transduced T cells were able to induce caspase-mediated cell death in target A375 cells, manifested by increased caspase levels and decreased RFP-labeled A375 nuclear levels over time. Figure 11B NY-ESO TCR+ T cells were generated from four donors using lentiviral transduction and then edited with RNPs in an array of 24 guides targeting 11 genes, including a non-targeted control (method). Antigen-specific T cells with or without gene deletions were then co-cultured with A375 cells, and cytotoxicity was assessed by quantifying RFP-labeled A375 cells using real-time time-lapse microscopy over four days.

[0130] We compared the tumor-killing kinetics between gene-edited and control NY-ESO TCR+ T cells. NY-ESO-specific T cells began to aggregate around RFP+ tumor cells at 12 hours, and certain sgRNA targets showed higher tumor clearance efficiency at 36 hours compared to the non-targeted control. Figure 5BAs expected, LCP2 knockout (identified as essential for a strong TCR-stimulated response in our screening) severely disrupted T cell killing of A375 cells. In contrast, CRISPR ablation with the negative modulators SOCS1, TCEB2, RASA2, and CBLB significantly increased tumor cell clearance compared to control T cells electroporated with non-targeted guide RNA. Figure 5C Compared to the non-targeted control condition, in our trial, targeting loss of these four genes led to improved tumor clearance kinetics. Figure 5D and Figure 11C Among them, CBLB has been best studied as an intracellular immune checkpoint that can be targeted in T cells to improve tumor control in mouse models (Peer et al., 2017). SOCS1, a negative regulator of JAK / STAT signaling in T cells, has shown enhanced T cell clearance compared to CBLB (Liau et al., 2018). Ablation of TCEB2 (a binding partner of SOCS1) has also provided an advantage in tumor clearance for T cells, suggesting that the SOCS1 / TCEB2 complex inhibits T cell responses and is a potential target for immunotherapy (Ilangumaran et al., 2017; Kamizono et al., 2001; Liau et al., 2018). RASA2 (a GTPase activator that stimulates the GTPase activity of wild-type RAS (Maertens and Cichowski, 2014)) has not been well studied in primary T cells and the immune system, but our findings suggest that it may be a regulator of TCR signaling and anti-tumor immunity. Surprisingly, cells with TCEB2, SOCS1, CBLB, and RASA2 gene ablation showed more intense activation of key genes, including granzyme B (GZMB) and interleukin-2 receptor α (IL2RA), than control cells. Figure 4F In summary, several gene targets identified in the genome-wide screening targeting the proliferative response to stimuli also enhanced in vitro tumor-killing activity.

[0131] SLICE screening for resistance to immunosuppressive adenosine signaling

[0132] Effective adoptive cell therapy for solid organ tumors will require cells that respond robustly to tumor antigens even in immunosuppressive tumor microenvironments. Through genome editing, T cells can become resistant to specific immunosuppressive signals (cues), which will be crucial for identifying relevant T cell modification pathways. We believe our SLICE screening platform can also be used to identify gene deletions that allow T cells to escape various forms of suppression. We focused on adenosine, a key immunosuppressive factor in the tumor microenvironment (Allard et al., 2017). We performed a four-day genome-wide proliferation screening by stimulating T cells with a 20 μM inhibitor of an adenosine receptor 2 (A2A) agonist (CGS-21680) relative to the load control. We searched for sgRNAs enriched in the proliferating cell population (low CFSE) compared to the load under A2A treatment conditions. Figure 12B ).

[0133] Although many gene modifications promote TCR proliferative responses to stimulation in the presence or absence of adenosine receptor agonists, we identified several sgRNAs that were enriched in dividing cells only in the presence of CGS-21680. Figure 6A These gene targets appear to play a selective role in adenosine receptor-mediated T-cell suppression. Importantly, ADORA2A (encoding the receptor specifically targeted by CGS-21680) showed a significant difference in ranking between the two treatment conditions (ranked 19th in CGS-21680 versus 7399th in the load control), suggesting that its knockout provides a specific escape from CGS-21680. Figure 6A and Figure 12C Conversely, ADORA2B did not show any proliferative advantage when exposed to this selective A2A agonist, CGS-21680. Figure 6A These findings encourage us to investigate other gene targets with similar patterns of selective resistance to ADORA2A in CGS-21680. Several guanine nucleotide-binding proteins with potential roles in adenosine-responsive signaling showed high positive rankings in the adenosine agonist GW screening, including GCGR (rank 35 vs. 1149), GNG3 (rank 199 vs. 12976), and GNAS (rank 836 vs. 2803). Surprisingly, we found that several guides targeting the previously uncharacterized gene FAM105A (rank 15 in CGS-21680, compared to rank 13390 in the load control) were specifically enriched to almost the same extent as ADORA2A. Figure 6BAlthough little is known about the function of FAM105A, GWAS in allergic diseases suggest the existence of credible missense risk variants of this gene (Ferreira et al., 2017). The neighboring paralogous gene Otulin (FAM105B) encodes a deubiquitinase that plays an important role in immune regulation (Damgaard et al., 2016). Figure 12D The screening results indicate that FAM105A plays a crucial role in mediating adenosine immunosuppressive signaling in T cells.

[0134] To validate our findings, we used an arrayed RNP platform to edit ADORA2A and FAM105A in two donors with CFSE proliferation reads. We found that, as predicted by our screening, targeting each of these genes with two different sgRNAs resulted in resistance to CGS-21680 inhibition. Figure 6C Importantly, these edits did not lead to increased T cell proliferation in the absence of TCR stimulation, indicating that they selectively overcame CGS-21680 inhibition under TCR stimulation. Finally, we demonstrated in in vitro cancer cell killing assays that T cells targeting ADORA2A and FAM105A were resistant to CGS-21680 inhibition. Figure 12E Therefore, we identified extracellular and intracellular targets that can be modified to produce T cells resistant to adenosine suppression. In summary, these findings demonstrate that SLICE can identify known and novel components of pathways required for primary cell responses to specific extracellular signals. This illustrates the potential of using this platform to discover gene targets that can enhance specific T cell function. SLICE in vivo pooling screening to reveal regulators of T cell tumor invasion.

[0135] Primary human CD8+ T cells were isolated from two donors and stimulated with anti-CD3 / CD28 beads. Cells were then transduced with concentrated frozen lentivirus encoding NY-ESO1-reactive α95:LY TCR and lentivirus containing the previously described CROPseq plasmid library (experimental procedure). Forty-eight hours post-transduction, cells were electroporated as previously described (experimental procedure). Forty-eight hours post-electroporation, cells were exposed to 2.5 μg / mL puromycin to select cells transduced with the CROPseq library (20 target genes (2 guides per gene) and 8 non-target control guides, for a total of 48 guides). Cells were then expanded in Xvivo medium at 50 U / mL IL-2 at a concentration of 1E6 cells / mL for a total of 14 days post-transduction. Seven days after initial T cell isolation, tumors were inoculated into 8-12 week old NOD / SCID / IL-2Rγ-empty (NSG) male mice (Jackson Laboratory) by subcutaneous injection of 1 million A375 human melanoma cells into one side of the abdomen. Seven days post-tumor inoculation, T cells transduced from the aforementioned NY-ESO-specific CROPseq library (now day 14) were resuspended at 1 million cells in 100 μl of serum-free RPMI and injected retro-orbitally into two mice from each donor. Seven days post-T cell transfer, tumors and spleens were collected from each of four mice, and T cells were isolated using FACS. Genomic DNA was isolated from these cells and amplified and barcoded by PCR as previously described (experimental procedure). Samples were then sequenced on a HiSeq 4000 (Illumina), and guide frequencies between tumors and spleens from each mouse and donor were compared. In this preliminary study using a limited number of mice, we found that the ARID1A-targeting guide was significantly enriched in tumors compared to the spleens of both donors. We further found that the CD5-targeting guide also showed a trend toward enrichment in tumors relative to the spleen. Having demonstrated the technical feasibility of this experiment, we expanded the library size and plan to perform in vivo pooling screening in 5 mice from each of the 2 donors.

[0136] Table 1-4 lists the gene targets identified by our SLICE screening platform.

[0137] discuss

[0138] SLICE provides a novel platform for genome-wide CRISPR loss-of-function screening in primary human T cells, a cell type that has revolutionized cancer immunotherapy. SLICE screening can be performed routinely at scale in primary cells from multiple human donors, ensuring biologically reproducible discoveries. Here, we selected genetic perturbations that enhance stimulation-responsive T cell proliferation. Proliferation is a broad phenotype regulated by complex genetics. Array editing using Cas9 RNPs enabled us to further characterize the effects of individual perturbations using multiplex proteomics measured by flow cytometry. Finally, combining SLICE with single-cell transcriptomics allows for a more comprehensive assessment of the functional consequences of perturbation hits from genome-wide screening. Integrating these CRISPR-based functional genetic studies rapidly identified genes in human T cells that can be targeted to enhance stimulation-dependent proliferation, activation responses, effector programs, and in vitro cancer cell killing.

[0139] The potential for screening human primary T cell loss-of-function was incidentally demonstrated in patients receiving CAR T-cell therapy for chronic lymphocytic leukemia (CLL) (Fraietta et al., 2018). Lentiviral proviruses that integrate non-targeted into the encoding of the CAR construct can disrupt endogenous genes. Lentiviral integration disrupting TET2 in patient T cells exhibited preferred and near-clonal large-scale expansion at peak response and may contribute to complete remission in the patient. This patient happened to have a pre-existing isotype mutation in the second allele of TET2. SLICE now offers the opportunity to more systematically search for genetic perturbations that enhance cellular expansion and effector function in adoptive T-cell therapy.

[0140] We found that ablation of at least four targets (SOCS1, TCEB2, RASA2, and CBLB) in human T cells enhances proliferation and anti-cancer function. Among these, CBLB, already studied in mouse models, serves as an intracellular checkpoint that can be targeted to enhance CD8 T cell control of tumors. Our data suggest that RASA2 and members of the SOCS1 / TCEB2 complex may also be potent targets for regulation in adoptive T-cell cancer immunotherapy. These studies demonstrate the potential of SLICE as a tool in primary human CD8 T cells to rapidly identify and validate relevant candidate targets for developing novel immunotherapies. Looking ahead, SLICE pooling screening could be applied to select perturbations that confer more complex phenotypes on human T cells, including in vivo functions relevant to T-cell therapy.

[0141] The SLICE pooling screening method is highly flexible because it can be applied to a variety of genetic programs that regulate the biology of primary T cells. Primary T cells can be screened using various extracellular selective pressures and / or FACS-based phenotypic selection. We focused on CD8+ T cells, but showed that SLICE can also be used for CD4+ T cells and can be extended to many other primary cell types. We demonstrated that inhibitory pressures can be added to the screening, in our case an adenosine agonist, to identify resistance-conferring gene perturbations. Future screenings could be designed to overcome other key inhibitory forces in the tumor microenvironment, such as repressive cytokines, metabolites, nutrient depletion, or repressive cell types, including regulatory T cells or myeloid-derived repressive cells. In conclusion, we have developed a novel pooling CRISPR screening technology with the potential for virtually limitless exploration of the unknown biology of human primary cells.

[0142] Experimental scheme: The methods used in the examples

[0143] Isolation and culture of human CD8 T cells

[0144] Primary human T cells for all experiments were derived from one of two sources: (1) remnants of leukoreduction chambers following Trima Apheresis (Pacific Blood Center), or (2) fresh whole blood samples collected according to protocols approved by the UCSF Human Research Committee (CHR #13-11950). Peripheral blood mononuclear cells (PBMCs) were isolated from the samples using SepMate tubes (STEMCELL, catalog #85460) via Lymphoprep centrifugation (STEMCELL, catalog #07861). CD8 T cells were isolated from the PBMCs using the EasySep Human CD8+ T Cell Isolation Kit (STEMCELL, catalog #17953) via magnetic negative selection and used directly. When using frozen cells (IncuCyte assay), previously isolated PBMCs frozen in Bambanker cryopreservation medium (Bulldog Bio, catalog BB01) were thawed, CD8 T cells were isolated using the previously described EasySep isolation kit, and the cells were left to stand in unstimulated medium for one day before stimulation. The cells were then cultured in X-Vivo medium (Lonza, catalog 04-418Q), which consists of X-Vivo15 medium containing 5% fetal bovine serum, 50 mM 2-mercaptoethanol, and 10 mM N-acetyl L-cysteine. After isolation, cells were stimulated at a concentration of 1e6 cells / ml using either plate-bound 10 μg / mL anti-human CD3 (catalog number 40-0038, clone UCHT1) and 5 μg / mL CD28 (clone CD28.2) (Tonbo, catalog number 40-0289) or ImmunoCult human CD3 / CD28 / CD2 T cell activator (Stem Cell Corporation, catalog number 10970), along with 50 U / mL IL-2. Plate-bound stimulation was used for the first T cell stimulation, while ImmunoCult was used for the second. Immunocult was used at 1 / 16, 1 / 8, and 1 / 2 of the manufacturer's recommended dose, i.e., 25 μL / mL of cells.

[0145] Lentiviral generation

[0146] Sixteen hours prior to transfection, HEK 293T cells were seeded at 18 million cells per cell in 15 cm poly-L-lysine-coated culture dishes and cultured in DMEM + 5% FBS + 1% pen / strep medium. Cells were transfected using the Lipofectamine 3000 transfection reagent according to the manufacturer's protocol (catalog number L3000001) with the sgRNA transfer plasmid and the second-generation lentiviral packaging plasmids pMD2.G (Addgene, catalog number 12259) and psPAX2 (Addgene, catalog number 12260). The following day, the culture medium was changed by adding 500x viral accelerator according to the manufacturer's protocol (Alstem, catalog number VB100). Forty-eight hours post-transfection, the viral supernatant was collected and centrifuged at 300g for 10 minutes to remove cell debris. To concentrate the lentiviral particles, Alstem precipitation solution (Alstem, catalog number VC100) was added, mixed, and frozen at 4°C for 4 hours. The virus was then concentrated by centrifugation at 1500g for 30 minutes at 4°C. Finally, the lentivirus pellet was resuspended 100 times its original volume in cold PBS and stored at -80°C for later use.

[0147] Lentiviral transduction and Cas9 electroporation

[0148] 24 hours post-stimulation, lentivirus was added directly to cultured T cells at a ratio of 1:300 v / v and gently mixed by tilting. After 24 hours, cells were collected, pelleted, and resuspended at 20e6 cells / 100 μl in Lonzalone electroporation buffer P3 (Lonzalone, catalog V4XP-3032). Cas9 protein (MacroLab, Berkeley, 40 μM stock solution) was then added to the cell suspension at a ratio of 1:10 v / v. Electroporation was performed using pulse code EH115 (Lonzalone, catalog VVPA-1002) at 20e6 cells per cuvette. The total number of cells used for electroporation was scaled up as needed. Immediately after electroporation, 1 mL of preheated culture medium was added to each cuvette, and the cuvettes were incubated at 37°C for 20 minutes. Cells were then transferred to culture vessels in X-Vivo medium containing 50 U / ml IL-2 at a density of 1e6 cells / ml in appropriate tissue culture containers. Cells were expanded every two days by adding fresh medium containing 50 U / ml IL-2, and the cell density was maintained at 1e6 cells / ml.

[0149] CFSE staining

[0150] Collect cultured cells, rotate, wash with PBS, and then resuspend in PBS at 1 million to 10 million cells / ml. Prepare CFSE (Biolegend, catalog number 423801) according to the manufacturer's protocol to prepare a 5 mM stock solution in DMSO. Dilute this stock solution 1:1000 in PBS to obtain a 5 μM working solution, and then add it to the cell suspension at a 1:1 v / v ratio. After mixing, incubate the cells in the dark at room temperature for 5 minutes. Then quench the stain with at least 5 times the volume of chromosome-rich medium (e.g., 2 ml + 10 ml) and incubate in the dark at room temperature for 1 minute. Then centrifuge and resuspend the cells in medium, and then restimulate.

[0151] Screening Pipeline

[0152] PBMCs from multiple healthy human donors were isolated from TRIMA remnants (see Methods). TRIMA remnants were purchased from the Blood Centers of the Pacific. Following CD8 T cell isolation as described above (Day 0), cells were incubated overnight in X-Vivo medium and then stimulated on Day 1 with plate-bound anti-human CD3 / CD28 and IL-2 (50 U / mL). 24 hours post-stimulation (Day 2), cells were transduced with concentrated lentivirus encoding a pooled sgRNA library (see Methods). 48 hours post-transduction (Day 3), cells were electroporated with CAS9 protein (Methods). Cells were then cultured and expanded in medium containing 50 U / mL IL-2, maintaining a target density of 1e6 cells / ml. On Day 14, cells were stained with CFSE (see Methods), separated into relevant selection fractions, and then restimulated with immunoculturation (Methods). Four days later, cells were sorted by FACS based on CFSE levels. Specifically, we defined non-proliferating cells as those with the highest CFSE peak and highly proliferating cells as those with the third highest CFSE peak or lower. Genomic DNA was isolated from the sorted cell pellet and then prepared for next-generation sequencing. Barcoded amplified PCR products were sequenced on a HiSeq4000. Data were analyzed pipelined using MAGeCK software.

[0153] Preparation and electroporation of arrayed Cas9 ribonucleotide protein (RNP)

[0154] Lyophilized crRNA and tracrRNA (Dharmacon) were resuspended at a stock solution concentration of 160 μM in 10 mM Tris-HCl (pH 7.4) with 150 mM KCl and stored at -80°C until use. To prepare Cas9-RNPs, crRNA and tracrRNA were thawed, mixed at a 1:1 v / v ratio, and incubated at 37°C for 30 min to form complex gRNA. Cas9 protein (stock solution 40 μM) was added at a 1:1 v / v ratio and incubated at 37°C for 15 min. The assembled RNPs were dispensed at 3 μL / well into 96W V-type plates. Cells were centrifuged, resuspended at 1e6 cells / 20 μL in Lonzalkon P3 buffer, and added to V-type plates containing RNPs. The cell and RNP mixture was transferred to 96-well electroporation cuvettes (Lonza, catalog number VVPA-1002) for nuclear transfection using pulse code EH115. Immediately after electroporation, 80 μL of preheated medium was added to each well, and the cells were incubated at 37°C for 20 min. The cells were then transferred to culture containers containing 50 U / ml IL-2 at a concentration of 1e6 cells / ml in appropriate tissue culture containers.

[0155] Arrayed CFSE staining

[0156] To perform CFSE staining on arrayed cells edited with RNP, cells were collected from multiple replicate plates and merged into 96-well deep-well plates. Cells were centrifuged in the deep-well plates, and after decanting the culture medium, the cells were resuspended in 1 mL of PBS per well using a manual multichannel pipette. CFSE was prepared according to the manufacturer's protocol to prepare a 5 μM working solution in PBS. Then, 1 mL of 5 μM CFSE was added to each well of cells at a 1:1 v / v ratio using a multichannel pipette. After mixing, the cells were incubated in the dark at room temperature for 5 minutes. The staining was then quenched with 2 mL of X-Vivo medium using a multichannel pipette and incubated in the dark at room temperature for 1 minute. The cells were then centrifuged in the deep-well plates, the CFSE was decanted, and the cells were resuspended in X-Vivo medium for restimulation.

[0157] Construction of sgRNA libraries

[0158] For cloning the cell surface sub-library, we followed a custom sgRNA library cloning protocol as described by Joung et al. (Joung et al., 2016). We utilized a humanized pgRNA backbone (Adgen Biotech, plasmid #44248). To optimize this plasmid for library cloning, we first replaced the sgRNA with a 1.9 kb filler sequence derived from the lentiGuide-Puro plasmid (Adgen Biotech, plasmid #52963). This filler sequence was digested with BfuAI restriction enzymes, and the backbone was purified by gel electrophoresis. We selected a cell surface library containing 1211 gene targets (4 guides per gene) and a total of 5000 guides, including non-target controls. The guides were derived from the Brunello sgRNA library, and the pooled oligonucleotide library was purchased from Twist Bioscience. The oligonucleotides were amplified by PCR and cloned into the humanized pgRNA backbone using Gibson assembly as described by Joung et al. (Joung et al., 2016). For whole-genome screening, the Brunello plasmid library (AiGene, catalog number 73178) in the lentiGuide-Puro backbone was purchased from AiGene. The Endura ElectroCompetent cell expansion library was used according to the manufacturer's protocol (Endura, catalog number 60242-1).

[0159] Preparation of gDNA for next-generation sequencing

[0160] After cell sorting and collection, genomic DNA was isolated from the cell pellet using a genomic DNA isolation kit (Machery-Nagel, catalog number 740954.20). sgRNA amplification and barcoding of the cell surface sub-library was performed as described by Gilbert et al. (Gilbert et al., 2014). For whole-genome screening, after gDNA isolation, sgRNA was amplified and barcoded using a two-step PCR protocol. Each sample was first divided into multiple 100 μL reactions, each containing 4 μg of gDNA. Each reaction consisted of: 50 μL of NEBNext 2x high-fidelity PCR master mix (NEB, catalog number M0541L), 4 μg of gDNA, 2.5 μL each of 10 μM read1-stagger-u6 and tracer-read2 primers, and water added to a total volume of 100 μL. The PCR cycling conditions were as follows: 3 minutes at 98°C, followed by 20 cycles of 98°C for 10 seconds, 62°C for 10 seconds, and 72°C for 25 seconds; a final extension at 72°C for 2 minutes. After PCR, all reactions from each sample were combined and purified using Agencourt AMPure XPSPRI beads (Beckman Coulter, catalog number A63880) according to the manufacturer's specifications. Then, 5 μL of each purified PCR product was extracted for a second PCR reaction for indexing. Each reaction consisted of: 5 μL of PCR product, 25 μL of NEBNext 2x master mix (NEB, catalog number M0541L), 1.25 μL of 10 μM p5-i5-read1 and read2-i7-p7 indexing primers, and water added to a total volume of 50 μL for each reaction. The PCR cycling conditions for indexing PCR were as follows: 3 minutes at 98°C, followed by 10 cycles of 98°C for 10 seconds, 62°C for 10 seconds, and 72°C for 25 seconds; a final extension at 72°C for 2 minutes. After PCR, the samples were purified using SPRI, quantified using the Qubit ssDNA High Sensitivity Assay Kit (Thermo Fisher Scientific, catalog number Q32854), and analyzed on a 2100 Bioanalyzer. The samples were then sequenced using a HiSeq 4000 (Yimingda).

[0161] A375 and T cell co-culture assay

[0162] A375 melanoma cells were transduced with a lentivirus to establish an RFP-nuclear tag (IncuCyte, catalog 4478) for optimal imaging on the IncuCyte cell imaging system. One day after stimulation, CD8 T cells from healthy donors were transduced with a virus containing the NY-ESO1-responsive α95:LYTCR construct. Five days after transduction, cells were FACS-sorted using HLA-A2+-restricted NY-ESO-1 peptide (SLLMWITQC) dextran-PE (Immundex, catalog WB2696) targeting a pure cell population expressing the construct. Cells were then expanded for 14 days in X-Vivo medium containing 50 U / ml IL-2 following initial stimulation. The day before co-culture, A375 cells were seeded at 5,000 cells / well in 100 μL of complete RPMI medium on 96W plates. The complete RPMI medium consisted of: RPMI (Gibco, catalog number 11875093), 10% fetal bovine serum, 1% L-glutamine, 1% NEAA, 1% HEPES, 1% pen / strep, 50 mM 2-mercaptoethanol, and 10 mM N-acetyl-L-cysteine. The following day, 14-day-old NY-ESO-1 specific T cells were added to the top of each well containing 5,000 A375 cells to indicate T cell to cancer cell ratios of 1:2, 1:4, and 1:8. The T cells were then added to 50 μL of complete RPMI containing 150 U / ml IL-2 and 6 g / dL glucose. The plates were then imaged using the IncuCyte live-cell imaging system, where the number of A375 RFP-positive nuclei was counted over time. To initially optimize the system, A375 cells were seeded at 24,000 cells / well, supplemented with T cells from two donors transduced with NY-ESO-specific TCRs at the following T cell to tumor cell ratios (8:1, 4:1, 2:1, 1:1). IncuCyte caspase-3 / 7 red cell apoptosis reagent (IncuCyte, catalog number 4704) was added to each well according to the manufacturer's instructions, and imaging was performed every 4 hours on the IncuCyte live-cell imaging system. In parallel, A375 cells with RFP nuclear tags were seeded at 4,000 cells / well, supplemented with the same T cells from the two donors transduced with NY-ESO-specific TCRs at the same ratio as in the caspase experiment described above, and they were imaged in parallel.

[0163] CROPseq library generation

[0164] The backbone plasmid used to clone the CROPseq library was purchased from AdGene (AdGene, plasmid #86708). This library contained 20 gene targets (two guides for each gene, selected from the Brunello library (Doench et al., 2016)) and 8 non-target control guides, for a total of 48 guides. The oligonucleotides for these library guides were purchased from Integrated DNA Technologies (IDT) and cloned into the CROPseq-Guide-Puro plasmid backbone using the method described by Datlinger et al., enabling amplification-free cloning of the pooled gRNA library into the CROPseq-Guide-Puro plasmid. As previously described, lentiviruses were generated from this pooled plasmid library and used to transduce CD8 T cells from two healthy donors. Forty-eight hours after transduction, cells were treated with 1 μM puromycin for three days, followed by viable cell sorting using a Ghost Dye 710 (Tonbo Biosciences, catalog number 13-0871). Cells were then collected, counted, and loaded into a 10X Chromium single-cell sequencing system with v2 chemistry.

[0165] CROPseq wizard reamplification

[0166] For guide re-amplification, a two-step PCR protocol was used to amplify and barcode the samples. First, each sample was divided into eight PCR reactions, each containing 0.1 ng of cDNA template. Each 25 μL reaction consisted of: 1.25 μL P5 forward primer, 1.25 μL Nextera Read 2 reverse primer, initiating the U6 promoter to enrich the guide, 12.5 μL NEBNext UltraII Q5 master mix (NEB, catalog number M0544L), 0.1 ng template, and water to a final volume of 25 μL. The PCR cycling conditions were: 98 °C for 3 minutes, followed by 10 cycles of 98 °C for 10 seconds, 62 °C for 10 seconds, and 72 °C for 25 seconds; a final extension at 72 °C for 2 minutes. After PCR, all reactions from each sample were combined and purified using Agencourt AMPure XP SPR beads according to the manufacturer's instructions. Then, 1 μL of each purified PCR product was extracted for a second PCR for indexing. Each reaction consisted of: 1 μL PCR product, 12.5 μL NEBNext Ultra II Q5 master mix (NEB, catalog number M0544L), 1.25 μL P5 forward primer, 1.25 μL Yimingda i7 primer, and water added to a final volume of 25 μL. The PCR cycling conditions were: 98°C for 3 minutes, followed by 10 cycles of 98°C for 10 seconds, 62°C for 10 seconds, and 72°C for 25 seconds, with a final extension at 72°C for 2 minutes. After PCR, all reactions were purified and quantified using the Qubit dsDNA High Sensitivity Assay Kit (Thermo Fisher Scientific, catalog number Q32854) using SPRI, and the samples were run on gels to confirm size. The samples were then sequenced using MiniSeq (Yimingda).

[0167] Array-validated flow cytometry

[0168] All array-based validation studies were performed on 96-well round-bottom plates and read out on an Attune NxT flow cytometer equipped with a 96-well plate reader. For RNP-based proliferation validation assays targeting top-ranked targets from whole-genome screening, cells were CFSE stained in 96-well format prior to restimulation, as described above. To assess the levels of activation markers in array-edited RNP cells, the following antibodies were used: CD69 (Byratsuin, catalog number 310904), CD154 (Byratsuin, catalog number 310806), PD-1 (Byratsuin, catalog number 329908), TIM-3 (Byratsuin, catalog number 345005), LAG-3 (Byratsuin, catalog number 369308), and CD8a (Byratsuin, catalog number 01038).

[0169] Quantitative and statistical analysis

[0170] A compilation of CRISPR screening analyses

[0171] To identify negative and positive hits in our screening, we used MAGeCK software to quantify and test guide enrichment (Li et al., 2014). First, guide abundance was determined using MAGeCK's "Count" module on the raw fastq files. For whole-genome Brunello libraries, the 5' trim length was set to eliminate interleaving offsets introduced by library preparation using the parameter "-trim-5 23,24,25,26,28,29,30". For targeted libraries, a constant 5' trim was automatically detected by MAGeCK. Guides with absolute counts below 50 were removed in over 80% of the samples. To test robust guide and gene-level enrichment, MAGeCK's "Test" module was used with default parameters. This step included median ratio normalization to account for the depth of read variation. We used a non-targeted control guide to estimate the size factor for normalization and a mean-variance model for null distribution to detect significant guide enrichment. All donor replicates in each screening were grouped for analysis of biological noise. MAGeCK generates guide-level enrichment scores in each direction (i.e., positive and negative) and then uses them for alpha-robust rank aggregation to obtain gene-level scores. The p-value for each gene is determined by a permutation test, guide assignment is randomized, and adjusted for false discovery rate using the Benjamini-Hochberg method. The Log2 fold change (LFC) for each gene is calculated and defined throughout the text as the median LFC of all guides for each gene target. Where indicated, the LFC is standardized to have a mean of 0 and a standard deviation of 1 to obtain the LFC Z-score.

[0172] Enrichment analysis of the selected gene sets

[0173] To find enriched annotations in the filter clicks, we used Gene Set Enrichment Analysis, implemented in the fgsea R package. The input for enrichment consisted of the LFC values ​​of all genes tested in the filter. We used the KEGG pathway dataset as the reference gene annotation database, including only gene sets with more than 15 members and less than 500 members. Figure 2EThe external gene set for in vivo immunotherapy shown in the figure used 43 genes identified by Zhou et al., which had 3 or more shRNA guides with more than 4-fold enrichment. Standardized enrichment scores and p-values ​​were determined by permutation tests with 10,000 iterations, with the same size randomized gene set adjusted by the FDR method.

[0174] Fitting the CFSE distribution for arrayed validation screening

[0175] We used the FlowFit R package to extract quantitative parameters from the CFSE profiles of all samples. Because CFSE staining of the array was performed on individual populations of edited cells, the signal peaks of the parental populations may have slight shifts between wells. Therefore, for each well, the stimulated wells were compared with the same unstimulated wells, expecting a single peak at the end of the experiment. The FlowFit package implemented the Levenberg-Marquadt algorithm to estimate the size and position of the parental population peaks. We then used fitting parameters from the unstimulated wells to fit the CFSE profiles of the corresponding stimulated cells. These CFSE profiles were modeled as Gaussian distributions with log2 distance peaks due to cell division and CFSE dilution. The fitted model was visually inspected, and the fitting parameters were adjusted to minimize the deviation from the original CFSE signal. The fitted model was used to calculate the proliferation index, defined as the total number of cells at the end of the experiment divided by the calculated initial number of parental cells. This parameter is robust to changes in the initial CFSE staining intensity. SLICE paired with single-cell RNA-Seq was analyzed.

[0176] Preprocessing of Emindata sequencing results from the 10X Genomics V2 library was performed using CellRanger version 2.1 software. This pipeline generated sparse numerical matrices for each sample and gene-level counts of unique molecular markers (UMIs) identified for all single cells that passed the default quality control metrics. These gene expression matrices were processed using the Seurat R package, as described elsewhere (https: / / satijalab.org / seurat / pbmc3k_tutorial.html). Only cells with more than 500 identified genes were used for downstream analysis. Using Seurat, the counts were log-normalized, regressing out the total UMI counts for each cell and the percentage of mitochondrial genes detected in each cell, and scaled to obtain z-scores at the gene level. Principal component analysis (PCA) was then performed using the 1,000 most variable genes in the cells. Figure 4A As shown, the first 30 PCA components were used to construct a unified manifold approximation and projection (UMAP) to display single cells in a two-dimensional image. Figure 4B The gene expression of the single cells shown is calculated as log10(UMI count + 1) and scaled. Figure 4C Clustering in the dataset is performed using the Louvain algorithm on a shared nearest neighbor graph, implemented via the FindClusters command in the Seurat R package. For Figure 4B The synthesis of a large number of differentially expressed genes was performed. For all cells in each sample with non-targeted control guides, the UMI counts of each gene were summed, and the differentially expressed genes were identified using the DESeq2 R software package.

[0177] To associate the guide with the identified cell barcodes, we processed fastq files from 10X libraries and from reamplified PCR. The read2 files were matched against the guide library using matchPattern, implemented in the R ShortRead package. The pattern used was a U6 promoter sequence preceding the guide sequence, appended to a 20bp library guide sequence (e.g., TGGAAAGGACGAAACACCGNNNNNNNNNNNNNNNNNN, where N represents the guide sequence), allowing a total of four mismatches. Paired Read1 reads with matching guide reads were used to determine cell barcodes and UMI assignments. We filtered out reads appearing less than twice and cells assigned to more than one guide. The chi-square test was used to identify overexpression in guide-guided cells with the same gene target within a cell state-driven cluster. The normalized residuals of the chi-square test were scaled and used to generate... Figure 4E and 10F .

[0178] Data and software availability

[0179] The raw sequencing files for all screenings can be obtained at PENDING. Raw files for single-cell RNA-Seq experiments are saved to GEO PENDING. All code for data analysis and graph generation can be provided upon request.

[0180] The introduction and examples include references cited by author and year.

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[0240] It should be understood that the embodiments and implementations described herein are for illustrative purposes only, and those skilled in the art will recognize that various modifications or changes can be made accordingly, and these are included within the scope of the spirit and benefits of this application and the appended claims. All publications, patents, and patent applications cited herein are incorporated herein by reference to their cited content.

[0241] Table 1: Positive hits of GW screening

[0242]

[0243] Table 2: Hit Rates from In Vitro and In Vitro Analyses

[0244] In vitro tumor killing

[0245]

[0246] Tumor infiltration in the body

[0247] Gene sgRNA sequence

[0248] ARID1A CAGCAGAACTCTCACGACCA

[0249] SOCS1 CGGCGTGCGAACGGAATGTG

[0250] Table 3: Adenosine resistance

[0251]

[0252] Table 4: Negative Hit Rates – GW Screening

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260]

Claims

1. A genetically modified primary hematopoietic cell, wherein the primary hematopoietic cell is a T cell, comprising a genetic modification of a gene that inhibits the expression or activity of a polypeptide product encoded by the RASA2 gene, wherein the expression or activity of the polypeptide product is inhibited by at least 60% compared to control wild-type hematopoietic cells, and the inhibition of expression exhibits increased cell proliferation, wherein the expression or activity of the polypeptide product encoded by the RASA2 gene is inhibited using a clustered regular interspaced short palindromic repeat (CRISPR) system, a transcription activator-like effector nuclease (TALEN) system, a zinc finger protease system, or a large-scale nuclease system.

2. The genetically modified primary hematopoietic cells as described in claim 1, wherein, The genetic modification of the gene inactivates the RASA2 gene.

3. The genetically modified primary hematopoietic cells as described in claim 1, wherein, The T cells are CD8+ T cells or CD4+ T cells.

4. The genetically modified primary hematopoietic cells of claim 1, wherein the expression of inhibition increases T-cell receptor signaling-induced proliferation.

5. The genetically modified primary hematopoietic cells of claim 1, wherein the cells are obtained from a person suffering from cancer.

6. The genetically modified primary hematopoietic cells as described in claim 5, wherein the cancer is melanoma.

7. The genetically modified primary hematopoietic cells according to any one of claims 1-6, wherein, The expression or activity of the polypeptide product encoded by the RASA2 gene was inhibited using the clustered regular interspaced short palindromic repeat (CRISPR) system.

8. The genetically modified primary hematopoietic cells according to any one of claims 1-6, wherein, The expression or activity of the polypeptide product encoded by the RASA2 gene is inhibited using a transcription activator-like effector nuclease (TALEN) system, a zinc finger protease system, or a broad range of nuclease systems.

9. A cell population comprising genetically modified primary hematopoietic cells according to any one of claims 1-6.

10. The use of a population of genetically modified primary hematopoietic cells for the preparation of a medicament for treating melanoma, wherein the genetically modified primary hematopoietic cells are T cells containing a genetic modification that inhibits the expression or activity of a polypeptide product encoded by the RASA2 gene, wherein the expression or activity of the polypeptide product is inhibited by at least 60% compared to control wild-type hematopoietic cells, and the inhibition of expression exhibits increased cell proliferation, wherein the expression or activity of the polypeptide product encoded by the RASA2 gene is inhibited using a clustered regular interspaced short palindromic repeat (CRISPR) system, a transcription activator-like effector nuclease (TALEN) system, a zinc finger protease system, or a large-scale nuclease system.

11. The use as described in claim 10, wherein, The genetic modification of the gene inactivates the RASA2 gene.

12. The use as described in claim 10 or 11, wherein, The T cells are CD8+ or CD4+ T cells.

13. The use as described in claim 10 or 11, wherein, The expression of the polypeptide product encoded by the RASA2 gene in the genetically modified primary hematopoietic cells was inhibited using a CRISPR system, TALEN system, zinc finger nuclease system, or a large-scale nuclease system.

14. The use as described in claim 10 or 11, wherein the expression of the polypeptide product encoded by the RASA2 gene is suppressed using CRISPR cells.

15. The use as described in claim 10 or 11, wherein the T cells are obtained from the object.

16. A method for generating genetically modified primary hematopoietic cells as described in claim 1, the method comprising: Inhibiting the expression or activity of the RASA2 gene in one or more cells from a hematopoietic cell population obtained from a patient, wherein the hematopoietic cells are T cells, and wherein the expression or activity of the polypeptide product encoded by the RASA2 gene is inhibited using a clustered regular spaced short palindromic repeat (CRISPR) system, a transcription activator-like effector nuclease (TALEN) system, a zinc finger protease system, or a large-scale nuclease system; and The hematopoietic cell population was expanded in vitro.

17. The method of claim 16, wherein, The expression or activity of the polypeptide product encoded by the RASA2 gene was inhibited using the clustered regular interspaced short palindromic repeat (CRISPR) system.

18. The method of claim 16 or 17, further comprising selecting hematopoietic cells with suppressed RASA2 gene expression prior to expanding the hematopoietic cell population in vitro.

Images