ENGINEERED T CELLS FOR EXPRESSION OF CHIMERIC ANITGEN RECEPTORS
20230364138 · 2023-11-16
Inventors
- Christine E. Brown (Duarte, CA)
- Dongrui Wang (Duarte, CA, US)
- Jeremy Rich (La Jolla, CA, US)
- QI XIE (HANGZHOU, CN)
- Briana Prager (Oakland, CA, US)
Cpc classification
C12N2310/20
CHEMISTRY; METALLURGY
C12N9/22
CHEMISTRY; METALLURGY
C07K14/705
CHEMISTRY; METALLURGY
A61K35/17
HUMAN NECESSITIES
C12N15/113
CHEMISTRY; METALLURGY
C12N15/1138
CHEMISTRY; METALLURGY
A61K48/005
HUMAN NECESSITIES
C07K14/4705
CHEMISTRY; METALLURGY
C12N15/1034
CHEMISTRY; METALLURGY
International classification
A61K35/17
HUMAN NECESSITIES
C12N15/113
CHEMISTRY; METALLURGY
C12N9/22
CHEMISTRY; METALLURGY
Abstract
Disclosed herein, inter alia, are methods of making and using engineered T cells useful for expressing a chimeric antigen receptor (CAR) targeted to a cell surface protein (e.g., a CAR targeted to IL13Rα2, which is highly expressed on glioblastoma cells).
Claims
1. A population of engineered human T cells, wherein the engineered human T cells comprise: a disrupted Transducin-Like Enhancer of Split 4 (TLE4) gene, a disrupted Transmembrane Protein 184B (MEM184B) gene, a disrupted Eukaryotic Translation Initiation Factor 5A-1 (EIF5A) gene or a disrupted Ikaros Family Zinc Finger Protein 2 (IKZF2) gene.
2. The population of engineered human T cells of claim 1, comprising a disrupted TLE4 gene.
3. The population of engineered human T cells of claim 1, comprising a disrupted MEM184B gene.
4. The population of engineered human T cells of claim 1, comprising a disrupted EIF5A gene.
5. The population of engineered human T cells of claim 1, comprising a disrupted IKZF2 gene.
6. The population of engineered human T cells of claim 2, wherein the disrupted TLE4 gene comprises an insertion of at least 10 contiguous nucleotides into SEQ ID NO: D1.
7. The population of engineered human T cells of claim 3, wherein the disrupted MEM184B gene comprises an insertion of at least 10 contiguous nucleotides into SEQ ID NO: D2.
8. The population of engineered human T cells of claim 3, wherein the disrupted EIF5A gene comprises an insertion of at least 10 contiguous nucleotides into SEQ ID NO: D3.
9. The population of engineered human T cells of claim 3, wherein the disrupted IKZF2 gene comprises a deletion of at least 10 contiguous nucleotides of SEQ ID NO: D4
10. The population of engineered human T cells of claim 2, wherein the disrupted TLE4 gene comprises a deletion of at least 10 contiguous nucleotides of SEQ ID NO: D1.
11. The population of engineered human T cells of claim 3, wherein the disrupted MEM184B gene comprises a deletion of at least 10 contiguous nucleotides of SEQ ID NO: D2.
12. The population of engineered human T cells of claim 3, wherein the disrupted EIF5A gene comprises a deletion of at least 10 contiguous nucleotides of SEQ ID NO: D3.
13. The population of engineered human T cells of claim 3, wherein the disrupted IKZF2 gene comprises a deletion of at least 10 contiguous nucleotides of SEQ ID NO: D4.
14. The population of engineered T cells of any of claims 2-5, wherein the disrupted gene is disrupted by a nucleic acid encoding a chimeric antigen receptor.
15. The population of engineered human T cells of claim 1, wherein at least 30% of the T cells comprises a nucleic acid molecule comprising a nucleotide sequence encoding a chimeric antigen receptor (CAR) wherein the chimeric antigen receptor comprises a targeting domain, a spacer, a transmembrane domain, a co-stimulatory domain, and a CD3 (signaling domain.
16. The population of engineered human T cells of claim 15, wherein the targeting domain comprises a scFv that selectively binds a tumor cell antigen.
17. The population of engineered human T cells of claim 15, wherein the targeting domain comprises a ligand for a cell surface receptor.
18. The population of engineered T cells of claim 15, wherein the nucleic acid molecule encoding the CAR is an mRNA.
19. The population of T cells of claim 1 wherein at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% of the engineered T cells do not express a detectable level of TLE4.
20. The population of T cells of claim 1 wherein at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% of the engineered T cells do not express a detectable level of MEM184B.
21. The population of T cells of claim 1 wherein at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% of the engineered T cells do not express a detectable level of EIF5A.
22. The population of T cells of claim 1 wherein at least 75%, at least 80%, at least 85%, at least 90%, or at least 95% of the engineered T cells do not express a detectable level of KZF2.
23. A method for producing an engineered T cell, the method comprising (a) delivering to a T cell: a RNA-guided nuclease, a gRNA targeting a TLE4 gene, a EMM1848 gene, or a KZF2 gene, a vector comprising a donor template that comprises a nucleic acid encoding a CAR; and (b) producing an engineered T cell suitable for allogeneic transplantation.
Description
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DETAILED DESCRIPTION
[0059] GSCs represent a potentially important cellular target in GBM, as they have been linked to therapeutic resistance, invasion into normal brain, promotion of angiogenesis, and immune modulation (24,25). We hypothesized that systematic interrogation of molecular regulation of CAR T cell efficacy against GBM could be optimized by screening both CAR T cells and GBM cells, thereby informing the interplay between a cell-based therapy and its target population. Here, we developed a robust method for performing whole-genome CRISPR-knockout screens in both GBM cells and human CAR T cells. Using our well-established CAR T cell platform targeting the tumor-associated surface marker interleukin-13 receptor α2 (IL13Rα2) (7,8,26), we identified novel CAR T cell- and tumor-intrinsic targets that substantially improved CAR T cell cytotoxicity against GSCs both in vitro and in vivo. Targeted genetic modification of identified hits in CAR T cells potentiated their long-term activation, cytolytic activity, and in vivo antitumor function against GSCs, demonstrating that CRISPR screen on CAR T cells leads to the discovery of key targets for augmenting CAR T cell therapeutic potency. In parallel, knockout of identified targets in GSCs sensitized them to CAR-mediated killing both in vitro and in vivo, revealing potential avenues for combinatorial inhibitor treatment to augment CAR T cell efficacy. Our findings represent a feasible and highly effective approach to discovering key targets that mediate effective tumor eradication using CAR T cells.
Examples
[0060] The invention is further described in the following examples, which do not limit the scope of the invention described in the claims.
Materials and Methods
Lentiviral Transduction on GSCs
[0061] GSCs were acquired from patient specimens at City of Hope under protocols approved by the IRB, and maintained as tumorspheres in GSC media as previously described (4,91). GSC lines used in this study to test CAR T cell function are IDH1/2-wildtype. The sgRNA library and single-targeted sgRNA lentiviral plasmids (containing a puromycin-resistance gene) for GSC transduction were purchased from Addgene (#73179 and #52961, respectively). Lentiviral particles were generated as previously described (92). For lentiviral transduction, GSC tumorspheres were dissociated into single cells using Accutase (Innovative Cell Technologies), resuspended in GSC media and lentivirus was added at a 1:50 v/v ratio. GSCs were then washed once after 12 hours, resuspended in fresh GSC media and cultured for 3 days. To ensure that only transduced cells were expanded for further assays, GSCs were selected by puromycin (Thermo Fisher Scientific) for 7 continuous days, with a 1:10000 v/v ratio into GSC media.
Lentiviral Transduction on Primary Human T Cells
[0062] Naïve and memory T cells were isolated from healthy donors at City of Hope under protocols approved by the IRB (26,30). The constructs of IL13Ra2-targeted and HER2-targeted CARs were described in previous studies (8,26,93). Procedures of CAR-only transduction on primary human T cells were previously described (44). The sgRNA library and single-targeted sgRNA lentiviral plasmids for T cell transduction were purchased from Addgene (#73179 and #52961, respectively). All sgRNA plasmids contain a puromycin-resistance gene. Dual transduction of CAR and sgRNA were performed using modification of previously reported procedures (21). In brief, primary T cells were stimulated with Dynabeads Human T expander CD3/CD28 (Invitrogen) (T cells: beads=1:2) for 24 hours and transduced with sgRNA lentivirus (1:250 v/v ratio). Cells were washed after 6 hours and then transduced with CAR lentivirus (multiplicity of infection [MOI]=0.5). 4 days after CAR transduction, CD3/CD28 beads were removed and cells were resuspended in Lonza electroporation buffer P3 (Lonza, #V4XP-3032) (2×10.sup.8 cells/mL). Cas9 protein (MacroLab, Berkeley, 40 mM stock) was then added to the cell suspension (1:10 v/v ratio) and electroporation was performed using a 4D-Nucleofactor™ Core Unit (Lonza, #AAF-1002B). Cells were recovered in pre-warmed X-VIVO 15 media (Lonza) for 30 min before proceeding to ex vivo expansion. All T cell transduction and ex vivo expansion experiments were performed in X-VIVO 15 containing 10% FBS, 50 U/ml recombinant human IL-2 (rhIL-2), and 0.5 ng/ml rhIL-15, at 6×10.sup.5 cells/ml. To ensure that only sgRNA-transduced cells were expanded, puromycin (1:10000 v/v ratio) was added to the media 3 days after electroporation, and puromycin selection was performed for 6 continuous days before CAR T cells were used for further assays. CRISPR screening was performed on two independent donors, and other 2 donors are used to generate IL13Ra2-targeted and HER2-targeted CARs, respectively.
CRISPR Screening on GSCs
[0063] GSCs transduced with the CRISPR KO library were dissociated into single cells, and co-cultured with CAR T cells at an effector: target ratio of 1:2 in culture plates pre-coated with matrigel. After 24 hours, the media containing CAR T cells and tumor debris were removed, and same number of CAR T cells were added in fresh media. 24 hours after the second CAR T cell addition, the media were removed and remaining GSCs were washed with PBS and harvested. Genomic DNA was isolated from the remaining GSCs after co-culture with CAR T cells, as well as GSCs harvested before co-culture and GSCs after monoculture for 48 hours.
CRISPR Screening on CAR T Cells
[0064] T cells transduced with CAR and the CRISPR KO library were co-cultured with GSC at an effector: target ratio of 1:4 in culture plates pre-coated with matrigel. After 48 hours, CAR T cells were re-challenged by GSCs doubling the number of the initial co-culture. 24 hours after the rechallenge, the co-culture was harvested and stained with fluorescence-conjugated antibodies against human CD45 (BD Biosciences Cat #340665, RRID:AB_400075), PD1 (BioLegend Cat #329922, RRID:AB_10933429) and IL13 (BioLegend Cat #501914, RRID:AB_2616746). Different subsets were sorted using an Aria SORP (BD Biosciences): total CAR T cells (CD45+, IL13+), PD1.sup.+ CART cells (CD45+, IL13+, PD1.sup.+) and PD1− CART cells (CD45+, IL13+, PD1−). Genomic DNA was isolated from the sorted subsets of cells, as well as CAR T cells harvested before co-culture and CAR T cells after monoculture for 72 hours.
CRISPR-Cas9 Screen Analysis
[0065] FASTQ files were trimmed to 20 bp CRISPR guide sequences using BBDuk from the BBMap (https://jgi.doe.gov/data-and-tools/bbtools) (RRID:SCR_016965) toolkit and quality control as performed using FastQC (RRID:SCR_014583, https://www.bioinformatics.babraha-m.ac.uk/projects/fastqc/). FASTQs were aligned to the library and processed into counts using the MAGECK-VISPR ‘count’ function (https://bitbucket.org/liulab/mageck-vispr/src/master/). β-values were calculated using an MLE model generated independently for each comparison. Non-targeting sgRNAs were used to derive a null distribution to determine p-values.
In Vitro Cytotoxicity and Flow Cytometry Assays
[0066] For in vitro cytotoxicity test, CAR T cells were co-cultured with GSCs at an effector: target ratio of 1:40. After 48 hours of co-culture, the numbers of CAR T cells and GSCs were evaluated by flow cytometry. Flow cytometry assays were performed on GSCs, CAR T cells from monoculture or co-culture with procedures described previously (30). For co-culture, anti-CD45 (BD Biosciences Cat #340665, RRID:AB_400075) staining was used to distinguish GSCs with T cells, and CAR T cells were identified by anti-IL13 (BioLegend Cat #501914, RRID:AB_2616746) staining. Other antibodies used for flow cytometry target: PD-L1 (Thermo Fisher Scientific Cat #17-5983-42, RRID:AB_10597586), TIM3 (Thermo Fisher Scientific Cat #17-3109-42, RRID:AB_1963622), LAG3 (Thermo Fisher Scientific Cat #12-2239-41, RRID:AB_2572596), PD1 (BioLegend Cat #329922, RRID:AB_10933429), CD69 (BD Biosciences Cat #340560, RRID:AB_400523), CD137 (BD Biosciences Cat #555956, RRID:AB_396252) and IL13Ra2 (BioLegend Cat #354404, RRID:AB_11218789). All samples were analyzed via a Macsquant Analyzer (Miltenyi Biotec) and processed via FlowJo v10 (RRID:SCR_008520).
RNA-Sequencinq Analysis
[0067] Total mRNA from GSCs or CAR T cells was isolated and purified by RNeasy Mini Kit (Qiagen Inc.) and sequenced with Illumina protocols on a HiSeq 2500 to generate 50-bp reads. Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) (RRID:SCR_011847) was used to trim adaptors and remove low quality reads. Reads were quantified against Gencode v29 using Salmon (RRID:SCR_017036, https://combine-lab.github.io/salmon/) with correction for fragment-level GC bias, positional bias and sequence-specific bias. Transcripts were summarized to gene level and processed to transcripts per million (TPM) using the R/Bioconductor (https://www.bioconductor.org/) package DESeq2 (RRID:SCR_000154, https://bioconductor.org/packages/release/bio-c/html/DESeq2.html). Comparisons were performed using contrasts in DESeq2 followed by Benjamini-Hochberg adjustment to correct for false discovery rate.
Gene Set Enrichment Analysis
[0068] ClueGO gene set enrichment plots were generated using the ClueGO plugin (http://apps.cytoscape.org/apps/cluego, RRID:SCR_005748) for GO BP, KEGG or Reactome gene sets and visualized in Cytoscape v3.7.2 (https://cytoscape.org/).
[0069] GSEA (RRID:SCR_003199) plots were generated from preranked lists using the mean β value as the ranking metric. Reactome networks were created using the Reactome FI plugin (https://reactome.org/tools/reactome-fiviz) with network version 2018 and visualized in Cytoscape. Networks were clustered using built-in network clustering algorithm, which utilizes spectral partition-based network clustering, and node layout and color were determined by module assignment. GSEA plots from RNA-sequencing data were generated from preranked lists. Weighting metrics for preranked lists were generated using the DESeq2 results from the gene knockdown vs. non-targeting control and applying the formula: −log 10(FDR)*log 2(fold change). ssGSEA scores for specific immune or functional pathways were generated using the ssGSEA function from the R/Bioconductor package GSVA (https://bioconductor.org/packages/release/bioc/html/GSVA.html) (94) (93) (93) and plotted using pheatmap (https://cran.r-project.org/web/packages/pheatmap/). ChEA enrichments were performed using Enrichr (https://amp.pharm.mssm.edu/Enrichr/). Barplots for positive or negative gene set enrichments were performed using Metascape (https://metascape.org/gp/index.html) for significantly up- or down-regulated genes (FDR<0.05 and log 2 fold change>1 or <−1).
Reactome Networks and KEGG Pathways
[0070] Reactome networks were derived from RNA-seq data using the Cytoscape Reactome FI plugin (RRID:SCR_003032). A gene list of upregulated (FDR<0.05 and log 2 fold change>1) or downregulated (FDR<0.05 and log 2 fold change<−1) genes plus the target gene (as knockout by CRISPR-Cas9 would not be detected by RNA-seq) was input into Reactome FI and all genes with at least one edge were included in the network plot. Node color (light to dark) and size (small to large) are proportional to node degree. Pathway enrichment was performed on this network of genes using the Reactome FI enrichment option. Boxplots for genes from selected pathways were generated using RNA-seq TPM data. KEGG pathway visualizations were generated using the R/Bioconductor package pathview (https://www.bioconductor.org/packages/release/bioc/html/pathview.html) from for selected pathways and genes were colored based upon the log 2 fold change knockout vs. control.
Single Cell RNA-Sequencinq Analysis
[0071] Single cell RNA-sequencing files were processed using the Cell Ranger workflow (https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome). FASTQ files were generated using the Cell Ranger ‘mkfastq’ command with default parameters. FASTQs were aligned to the hg19 genome build using the ‘count’ function and aggregated using the default Cell Ranger ‘aggr’ parameters with normalization performed by subsampling wells to equalize read depth across cells. Downstream analyses were performed using the R/Bioconductor package Seurat (https://satijalab.org/seurat/) (95)(94)(95). Specifically, datasets of stimulated and unstimulated cells in knockout or control populations were merged using the “FindintegrationAnchors” Seurat function. Clustering was performed using UMAP using PCA for dimensional reduction and a resolution of 0.6 from 1 to 20 dimensions. Dead cell clusters were determined by high expression of mitochondrial genes and removed. Samples were then reclustered. Clusters with similar CD4 or CD8, Ki67 and marker expression, determined using the “FindAllMarkers” function that were proximal on the UMAP projection were merged. All plots for gene expression were generated using normalized data from the default parameters of the “NormalizeData” function. Gene expression was visualized on the UMAP projection using the “FeaturePlot” function with a maximum cutoff or gene expression determined on a gene-by-gene basis.
Functional Analysis of CAR T Cells in Orthotopic GBM Models
[0072] All mouse experiments were performed using protocols approved by the City of Hope IACUC. Orthotopic GBM models were generated using 6- to 8 week-old NOD/SCID/IL2R.sup.−/− (NSG) mice (IMSR Cat #JAX:005557, RRID:IMSR_JAX:005557), as previously described (96). Briefly, ffLuc-transduced GSCs (1×10.sup.5/mouse) were stereotactically implanted (intracranially) into the right forebrain of NSG mice. Randomization was performed after 8 days of tumor injection based on bioluminescent signal, and mice were then treated intracranially with CAR T cells (2×10.sup.4 or 5×10.sup.4/mouse as indicated for each experiment). To ensure statistical power, all treatment groups include ≥6 animals. Mice were monitored by the Department of Comparative Medicine at City of Hope for survival and any symptoms related to tumor progression, with euthanasia applied according to the American Veterinary Medical Association Guidelines. Studies were done in both male and female animals. Investigators were not blinded for randomization and treatment.
TCGA Data Analysis
[0073] Analysis of genes in the TCGA dataset was performed using RNA-sequencing TCGA GBM data. Immune infiltration signatures were previously reported (97). GSEA plots for each gene in the context of TCGA GBM data were generated by using the normalized gene expression as a continuous phenotype.
CAR T Cell Responder Analysis
[0074] Gene sets derived from TLE4 or IKZF2 knockout were analyzed in the context of CAR T cell non-responder vs. responders from a previous report on patients with CLL (27). Genes upregulated in bulk RNA-seq of CAR T cells following knockout of TLE4 or IKZF2 (FDR<0.05 and log 2 fold change>1) were plotted by their fold change expression in stimulated vs. unstimulated CAR T cells for responders or non-responders. Fold change was calculated using DESeq2 for stimulated vs. unstimulated cells independently for each group (non-responder or complete responder). Cluster 10-enriched genes in the TLE4 knockout and control sc-seq data, identified by the “FindAllMarkers” function in Seurat subsetted for overexpressed genes, were plotted similarly. Genes upregulated (>0.4 log 2 fold change of normalized counts) in sc-seq for IKZF2 knockout vs. control in stimulated CAR T cells were plotted similarly.
Statistical Analysis
[0075] CAR T cell functional data (tumor killing, expansion, survival of tumor-bearing mice) were analyzed via GraphPad Prism. Group means±SEM were plotted. Methods of p-value calculations are indicated in figure legends.
Example 1:Genome-Wide CRISPR Screening of CAR T Cells Identifies Essential Regulators of Effector Activity
[0076] The fitness of CAR T cell products correlates with clinical responses (27,28), indicating that key regulators of CAR T cell function can be targeted to potentiate therapeutic efficacy. T cell exhaustion resulting from chronic tumor exposure limits CAR T cell antitumor responses (29). To identify the essential regulators of T cell functional activity in an unbiased manner, we performed genome-wide CRISPR screen adapting our previously developed in vitro tumor rechallenge assay, which differentiates CAR T cell potency in the setting of high tumor burden and reflects in vivo antitumor activity (30,31). IL13Ra2-targeted CAR T cells from two human healthy donors were lentivirally transduced to express the Brunello short-guide RNA (sgRNA) library (32) and the CAR construct, then electroporated with Cas9 protein.
[0077] CAR T cells harboring CRISPR-mediated knockouts were recursively exposed to an excess amount of PBT030-2 GSCs (
Example 2: CRISPR Screening Empowers Discovery of Targets that Enhance CAR T Cell Cytotoxic Potency
[0078] We interrogated the 220 targets enriched in PD1-negative cells common between two T cell donors, focusing on four representative genes identified in the top third of hits, which have not been previously explored for their role in enhancing CAR T cell function. These included the high-ranking hits: Eukaryotic Translation Initiation Factor 5A-1 (EIF5A; Gene ID 1984), transcription factor Transducin Like Enhancer of Split 4 (TLE4; Gene ID 7091), Ikaros Family Zinc Finger Protein 2 (IKZF2; Gene ID 22807), and Transmembrane Protein 184B (TMEM184B; Gene ID 25829) (
[0079] TLE4 is a transcriptional co-repressor of multiple genes encoding inflammatory cytokines (45) and IKZF2 is upregulated in exhausted T cells (37,46,47), supporting potential roles in inhibiting CAR T cell function. To elucidate molecular mechanisms underlying the regulation of CAR T cell activity, we compared the transcriptomes of CAR T cells with individual knockouts against cells transduced with non-targeted sgRNA (sgCONT). TLE4 KO in CAR T cells upregulated critical regulators of T cell activation, including the transcription factor EGR1, which promotes Th1 cell differentiation (48), and the metabolic regulator BCAT, which mediates metabolic fitness in activated T cells (49) (
[0080] Whole-transcriptome analyses following TMEM184B or EIF5A KO revealed convergence of altered pathways, similar to those induced by TLE4 or IKZF2 KO, including the upregulation of BCAT1, EGR1, and IL17RB (
Example 3: Targeting TLE4 and IKZF2 Modify CAR T Subsets Associated with Effector Potency
[0081] To determine the impact of TLE4 or IKZF2 KO on specific subpopulations of CAR T cells, we performed comparative single-cell RNA-sequencing (scRNAseq) on KO and control CAR T cells with or without stimulation by tumor cells. Comparing TLE4-KO cells with control CAR T cells by unbiased clustering of pooled data identified 10 different clusters, the distribution of which was greatly influenced by stimulation (
[0082] Comparison between IKZF2-KO cells and control CAR T cells identified 10 clusters using unbiased clustering of pooled data (
Example 4: Genome-Wide Screening of GSCs Identified Genes Mediating Resistance to CAR T Cells
[0083] Augmenting efficacy of CAR T cells against GBM can be approached by studying T cells themselves, as above, which may inform targeted KOs in addition to CAR engineering for enhancing CAR activity. Reciprocal screening of GBM cells, especially GSCs, potentially informs interactions with CAR T cells to predict clinical responsiveness to CAR T cell therapy. To identify potential genes in GSCs that promote resistance to CAR-mediated cytotoxicity, we performed genome-wide CRISPR screens on two independent patient-derived GSC lines (PBT030-2 and PBT036), both derived from primary GBM tumors with high expression of IL13Ra2 (33). To identify tumor cell targets that rendered GBM cells more susceptible to T cell immunotherapy, we subjected GSCs to two rounds of co-culture with IL13Ra2-targeted CAR T cells (
Example 5: Knockout of RELA or NPLOC4 Sensitizes GSCs to CAR-Mediated Antitumor Activity
[0084] Next, we sought to confirm and further characterize the function of common top hits whose deletion promoted CAR killing (
[0085] RELA (also known as p65) is an NF-κB subunit that regulates critical downstream effectors of immunosuppressive pathways in tumors (60,61). NPLOC4 mediates nuclear pore transport of proteins, but its role in cancer or immune modulation remains unclear. To elucidate the mechanism by which these genes mediate GSC sensitivity to CAR T cell killing, we performed in-depth characterization of GSCs harboring knockout of each gene. The increased sensitivity was not a result of alterations in target antigen expression on GSCs (
Example 6: CRISPR Screening Identified Targets with Functional and Clinical Relevance in GSCs
[0086] Next, we used an orthotopic intracranial patient-derived xenograft model to evaluate whether modulating the identified targets on GSCs enhanced the antitumor function of CAR T cells in a preclinical setting. Established GBM PDXs were treated with CAR T cells delivered intracranially into the tumors, mimicking our clinical trial design of CAR T cell administration to patients with GBMs (7,66). First, we used CAR T cells without CRISPR knockout to treat control, RELA-KO, or NPLOC4-KO tumors. A limited number of CAR T cells (50,000/mouse) completely eradicated xenografts derived from RELA-KO or NPLOC4-KO GSCs, whereas the same CAR T cells were only partially effective against tumors established with sgCONT-GSCs (
[0087] To further dissect the roles of RELA and NPLOC4 in immune modulation in GBM, we analyzed 41 GSC samples, and found that high RELA- or NPLOC4-expressing GSCs showed enrichment in immune-suppression signatures (
Example 7: CRISPR Screening Identified Targets with Functional and Clinical Relevance in CAR T Cells
[0088] We next evaluated the molecular targets identified in our CAR T cell screen in vivo, with the goal of establishing clinically translatable strategies to improve CAR T cell function. The antitumor function of different CAR T cells were tested against tumors without CRISPR knockouts, with a further limited CAR T cell dose (20,000/mouse) showing enhanced survival benefit as compared to the control CAR T cells failed to achieve long-term tumor eradication (
[0089] We then investigated whether the CAR T cell targets indicate the potency of clinical therapeutic products. We then mapped upregulated genes in IKZF2-KO CAR T cells compared to control CAR T cells after tumor stimulation, with the transcriptomes of CAR T cell products from patients with chronic lymphocytic leukemia (CLL) achieving complete responses (CR) or no responses (NR) (27). Supporting our results, these genes were induced to a greater degree after CAR stimulation in the products from patients achieving CR (
[0090] To further understand how TLE4 and IKZF2 contribute to the function of clinical CAR T cell products, we analyzed scRNAseq from 24 patient-derived CD19-CAR T cell products (68). An unbiased clustering of the scRNAseq data revealed that IKZF2 expression was highly enriched in cluster 7 (
Example 8: Advantages and Additional Targets
[0091] T cell-based therapies may offer several advantages in GBM therapy. T cell-based therapies, especially when delivered into the cerebrospinal fluid (CSF), traffic to multifocal tumor populations within the central nervous system (CNS) (8,70-72), thus overcoming challenges associated with the blood-brain barrier that limits the CNS penetration of most pharmacologic agents. T cell therapies compensate for cellular plasticity within brain tumors more effectively than traditional pharmacologic agents. GBMs display striking intratumoral heterogeneity, and tumor cells readily compensate for targeted agents against specific molecular targets. With T cell therapy targeting different antigens, personalized treatments based on the antigen expression profile of individual tumors may be designed. T cell-based therapies induce secondary responses that augment endogenous anti-tumor responses. Adoptive cell transfer, especially CAR T therapies, have been investigated in clinical trials for GBM patients, but efficacy has been restricted to limited cases (11). Our focus on CAR T cells was prompted not only by the potential value for clinical translation, but also as our findings inform a broader understanding of T cell function in brain tumor biology.
[0092] Previous genetic screens used to identify interactions between immune cells and tumor cells have largely focused on the tumor cells (18,19,29), as these cells are easier to manipulate genetically. Screens on tumor-reactive mouse T cells have also been reported (20,73,74) given the establishment of Cas9-knockin mouse strain (75), as well as the convenience to acquire large numbers of these cells. Here, we interrogated both the human CAR T cell and tumor cell compartments. The screening strategy on CAR T cells was greatly facilitated by the development of the non-viral Cas9 expression system in primary human T cells (21). Here, the screening on tumor cells was performed on two independent GSCs, displaying a relatively narrow range of shared molecular targets involved in mediating responses to CAR T cells in our studies, which might be a consequence of subtype difference between these GSC lines (33). The screening identified both rational targets (RELA/p65) and novel targets (NPLOC4) in immune regulation, which were not restricted to a specific GBM molecular subclass. NPLOC4 displayed unexpected associations with GBM-targeting immune cell activity, as NPLOC4-KO in GSCs led to enhanced potency of CAR T cells and increased cytokine production in GSCs, although the detailed mechanism awaits further investigation. In the analyses of GSC models and TCGA database, high RELA and NPLOC4 expression was associated with immunosuppressive signatures. More specifically, higher expression of RELA and NPLOC4 in GBMs correlated with low infiltration of both CD4+ and CD8+ T cells, indicating that targeting these genes may confer immune modulatory effect and enhance antitumor T cell responses in GBMs.
[0093] The assay used for CRISPR screening in T cells is crucial for reliable readouts and is required for its sensitivity to differentiate effective versus non-effective therapies. Although the in vivo antitumor efficacy in mouse models has been the standard to evaluate the functional quality of T cells in adoptive transfer, the utilization of this system in screening has been controversial. Tumor-infiltrating T cells harvested after the injection of therapeutic cells display signatures of tumor reactivity (73) or, conversely, T cell exhaustion (40). The differential results appear model dependent, leading to mixed interpretation of the results. The co-culture assays that we used in this study identified key regulators by creating challenging screening environments. For the screening on GSCs, two rounds of short-term (24 h) killing with relatively large number of T cells (total E:T=1:1) was performed and GSCs were harvested immediately after the second round of killing, minimizing the effect of knocking out genes essential for the GSC growth. For the screening on CAR T cells, a repetitive challenge assay was used with excessive number of GSCs (total E:T=1:12), which we have shown to induce CAR T cell exhaustion (30). The screen was performed by comparing a less exhausted (PD1-negative) with a more exhausted (PD1-positive) subset, informing prioritization for maintenance of recursive killing function, while reducing the noise from tumor cell or T cell growth. The screening was performed with two independent CAR T cell donors, and the relatively small proportion of overlapping hits between the two donors was expected and consistent with previous studies (21,76), due to the variation in T cell populations between individuals. The target validation was done with different T cell donors and CAR platforms; therefore, the discovered immunotherapy targets may be generalizable to multiple CAR designs. While we validated 4 representative genes, the screening on CAR T cells resulted in over 200 potential targets involved in critical pathways of T cell biology and activation, offering additional targets for future investigation of CAR refinement. One limitation of our approach, however, is the exclusion of apoptosis pathways in tumor cells due to its critical role in tumor cell growth, which have been demonstrated as important regulators of CAR T cell-mediated tumor killing as well as tumor-induced CAR T cell exhaustion (29).
[0094] T cell exhaustion has been considered as one of the major hurdles for reducing CAR T cell potency (77-79). Blocking/knockout of inhibitory receptors is being rigorously investigated to augment CAR activity or other tumor targeting T cells (29,80,81). T cell exhaustion is a feedback mechanism after activation, occurring upon recursive exposure to antigens in the contexts of chronic infection or the tumor microenvironment (78,82) compromising their antitumor potency (79). Here, we observed that TLE4 or IKZF2 KO resulted in unstimulated CAR T cells to express transcriptional profiles of activation, while prohibiting exhaustion. AP-1 family transcription factors FOS and JUN, which were induced after both TLE4- and IKZF2-KO, provide a possible mechanism by which CAR T cell fitness was protected. The protein c-Jun forms homodimers or c-Fos/c-Jun heterodimers to initiate transcription of proinflammatory cytokines, and heterodimers with other co-factors (including BATF, IRF4, JUNB, and JUND) induce inhibitory receptors or suppress transcriptional activity of c-Jun (83-86). FOS was more upregulated than suppressive co-factors after TLE4-KO; therefore, driving T cell activation together with a protection from exhaustion, which was reminiscent of the effect after expressing c-Jun in CAR T cells with tonic signaling (55). In IKZF2-KO cells, however, the uncoupling of activation from exhaustion signatures was likely influenced by the upregulation of cytokines CCL3 and CCL4, which inversely correlated with PD-1 expression during T cell exhaustion (87). Both TLE4 or IKZF2 KO in CAR T cells upregulated essential regulators for Th1 cell differentiation (BCAT and EGR1, respectively), consistent with a previously identified role of this T cells population in mediating antitumor immunity (88,89). Consequently, targeted KOs in CAR T cells enhanced not only killing, but also expansion potential, which is correlated with clinical responses (90). Although it remains unresolved if these KOs potentiate CAR activity in immune-competent settings, our results have revealed the feasibility that CAR T cells can be modified for their activation/exhaustion signals to achieve functional improvement in clinically-relevant models. Consistent with these findings, we explored public databases of scRNAseq on patient-derived CAR T cell products and discovered that high IKZF2 expression and TLE4 activity were associated with other suppressive/exhaustion signatures of CAR T cells as well as poor clinical responses.
[0095] Single cell analyses reveal subset composition within a mixed cell sample, such as CAR T cells, in which minority populations serve critical roles. scRNAseq revealed that CAR activation, rather than genetic modification of CAR T cells (TLE4 or IKZF2 KO), resulted in a major cluster switch, which is consistent with the observation that TLE or IKZF2 KO in monoculture CAR T cells did not dramatically alter transcriptional profiles, as suggested by bulk RNA-seq. Following tumor challenge, knockout of targeted genes upregulated T cell activation markers and proinflammatory cytokines across different clusters, especially IFNG and CCL3, which showed similar induction by both TLE-KO and IKZF2-KO. Further, after CAR activation, TLE4 KO maintained a specific cluster, which existed pre-activation, and IKZF2 KO led to the emergence of a new cluster. The transcriptional signature of these clusters (expression of several costimulation molecules and cytokines) indicated their critical role in mediating effector function of CAR T cells. Therefore, the superior functions of TLE4-KO or IKZF2-KO CAR T cells were likely the result of a generally elevated activation state, as well as the stimulatory effect from critical subsets. Our scRNAseq results also suggested the existence of Treg-like populations, the expansion of which was seen after CAR activation and can be reduced by IKZF2-KO. The suppressive function of these cells still requires further investigation, but these results indicate the potential of enhancing CAR function through inhibiting differentiation towards Treg-like cells. Both TLE4-KO and IKZF2-KG CAR T cells appear to modify specific CD4+ T cell subsets, which supports our previous observation that CD4+ CAR T cells play a critical role in mediating potent effector function (30).
Additional T Cell Gene Targets
[0096] Additional genes that can be knocked out in T cells harboring a CAR to improve CAR T cell function can include.
TABLE-US-00005 Gene Enrichment Gene Enrichment Gene Enrichment Gene Enrichment SEL1L3 3.15565 TIMM22 1.8449 DNAH11 1.55245 AADAT 1.27385 RXRG 2.77905 PIGR 1.83365 CARD16 1.54205 DNAH5 1.2674 EIF5A 2.773 ATP6V1E2 1.8199 EGFL7 1.53965 TSSK3 1.26545 C14orf166 2.75935 GNRHR 1.81665 ST7L 1.5348 SMR3B 1.25785 MLC1 2.71775 GPR83 1.8162 MLLT4 1.52635 C12orf42 1.257 PSORS1C2 2.65995 MMACHC 1.8149 TMEM95 1.52295 MCUR1 1.25645 COG7 2.6155 JADE2 1.81365 CCL8 1.49605 RGPD3 1.246 ZBTB10 2.467 GPR20 1.8119 HMMR 1.4945 GPR39 1.2433 ERCC6L 2.46515 MAGEB1 1.80755 GMEB2 1.4789 SGCD 1.2405 SYK 2.46395 TCEANC2 1.8067 ABCA2 1.472 ZNF592 1.2371 YPEL3 2.45765 PAQR9 1.8047 C4B 1.47 DHRS9 1.23215 HTR4 2.42875 ADPRH 1.7967 ST3GAL4 1.46945 HUNK 1.23115 MME 2.41685 RNF222 1.79155 LRFN2 1.4687 RALBP1 1.22875 CDNF 2.36145 PHF6 1.7733 ZNF354C 1.4542 ZBTB48 1.2277 PLXNA4 2.34925 NCDN 1.77005 PCDH11X 1.4502 ZNF70 1.22385 CHML 2.2995 RNF138 1.76685 GLIPR1L2 1.44445 CXCR3 1.22145 TAC4 2.29825 NDEL1 1.7655 CEP120 1.43855 FAM57A 1.2199 TMEM39B 2.28175 CFHR5 1.76405 CCDC77 1.43785 FARP1 1.2197 TBX10 2.27345 ATP2A1 1.7624 ACSL1 1.4311 CLSTN3 1.2123 WARS 2.2684 DNASE1L1 1.7612 ANTXR1 1.4308 DDB2 1.21165 APOL4 2.23055 C14orf132 1.75275 ICOS 1.42615 IL1RN 1.21105 DHX16 2.2013 SLC43A3 1.74675 CTNNAL1 1.4236 LRRC49 1.21055 CADM3 2.1881 FBXO27 1.7368 ERLIN2 1.42305 FZD9 1.2082 TLE4 2.18255 RAB23 1.71885 ARSJ 1.4221 CSH2 1.208 SLC18B1 2.1752 XIRP2 1.7147 SNRPD3 1.42095 OR13F1 1.2039 RNASEL 2.16995 NLRP1 1.70695 NMNAT1 1.41815 DPYSL3 1.18675 TMEM80 2.10835 POLR3G 1.6797 SPATA4 1.4178 C20orf141 1.18245 SV2B 2.1055 FAM219A 1.67465 C7orf60 1.4033 ARPC3 1.1788 KLHL33 2.09825 SNX29 1.6654 ELK3 1.40115 PTPRG 1.17805 TMEM184B 2.09715 NTPCR 1.65555 CYB5A 1.3928 ZNF99 1.1734 DOT1L 2.05795 RBBP8NL 1.6539 BIRC6 1.38515 TGFBR3L 1.1726 SLC35D2 2.04645 CAMTA1 1.6529 PRKAA1 1.38025 RANBP6 1.1702 EID3 2.0363 NR0B1 1.65125 ZNF235 1.37775 OXGR1 1.1598 SPG7 2.0277 ASAP3 1.64915 FAM25C 1.3498 PRORY 1.15255 CBWD2 2.0193 TT12 1.64665 MRPL11 1.3403 BPI 1.147 ANKUB1 2.01795 ZBTB41 1.6302 ZSWIM8 1.339 C4orf48 1.14665 SLC35D3 2.01715 LRP1 1.61915 SLC44A5 1.33545 DSPP 1.14605 CENPBD1 1.9954 PYY 1.6188 LOC730183 1.32535 TBPL2 1.14425 RAB3A 1.9727 ANKRD28 1.616 EIF2S2 1.3214 PADI6 1.1437 EAF2 1.97225 ADAMTSL1 1.59735 ERCC4 1.3193 AKAP1 1.13805 IKZF2 1.9635 ADAL 1.5968 NR2E1 1.31535 DEFB126 1.13135 LSM5 1.9485 C9orf172 1.59035 PSMA2 1.31475 ZNF141 1.1284 SORBS2 1.9438 ZSCAN1 1.5901 CAMTA2 1.3113 NUBP2 1.12765 SMAD2 1.93745 MORN3 1.5892 GTF2H3 1.30825 AGAP3 1.1209 VWA9 1.93245 PCDHB2 1.58755 RPH3A 1.30715 DOPEY2 1.11875 ZRANB2 1.93115 LTB 1.58465 ZNF766 1.2979 OR10V1 1.11265 MRPL42 1.93085 TSPAN5 1.5797 INTS12 1.29365 RPL19 1.10805 EPPK1 1.92875 FAM199X 1.5794 KPNA2 1.2872 MYO1F 1.10185 SMG1 1.91575 NT5C1A 1.5745 AMY2A 1.28325 MYH14 1.0942 CAPRIN1 1.91475 DNAJC16 1.57085 CPNE5 1.28315 SPHKAP 1.08525 KRBA2 1.89985 TCP11L1 1.5663 MAFG 1.2828 TBCE 1.0817 TNFAIP8L1 1.88815 CLEC6A 1.56605 FILIP1 1.27905 OR10T2 1.0479 S100A5 1.8843 KIAA2026 1.5625 ZNF878 1.27765 REP15 1.04175 OR14C36 1.86325 GCG 1.5595 EPGN 1.27595 TEC 1.02885 TBC1D22B 1.8585 C14orf93 1.55485 FGF13 1.27465 ZIC2 1.0158
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Other Embodiments
[0194] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.