Immunogenetic metabolomics revealed key enzymes that modulate CAR-T metabolism and function

Abstract

Immune evasion is a critical step of cancer progression that remains a major obstacle for current T cell-based immunotherapies. Hence, we seek to genetically reprogram T cells to exploit a common tumor-intrinsic evasion mechanism, whereby cancer cells suppress T cell function by generating a metabolically unfavorable tumor microenvironment (TME). Specifically, we use an in silico screen to identify ADA and PDK1 as metabolic regulators, in which gene overexpression (OE) enhances the cytolysis of CD19-specific CD8 CAR-T cells against cognate leukemia cells, and conversely, ADA or PDK1 deficiency dampens such effect. ADA -OE in CAR-T cells improves cancer cytolysis under high concentrations of adenosine, the ADA substrate and an immunosuppressive metabolite in the TME. High-throughput transcriptomics and metabolomics in these CAR-Ts reveal alterations of global gene expression and metabolic signatures in both ADA- and PDK1- engineered CAR-T cells. Functional and immunological analyses demonstrate that ADA -OE increases proliferation and decreases exhaustion in α-CD19 and α-HER2 CAR-T cells. ADA-OE improves tumor infiltration and clearance by α-HER2 CAR-T cells in an in vivo colorectal cancer model. Collectively, these data unveil systematic knowledge of metabolic reprogramming directly in CAR-T cells, and reveal potential targets for improving CAR-T based cell therapy.

Synopsis: The authors identify the adenosine deaminase gene (ADA) as a regulatory gene that reprograms T cell metabolism. ADA-overexpression (OE) in α-CD19 and α-HER2 CAR-T cells increases proliferation, cytotoxicity, memory, and decreases exhaustion, and ADA-OE α-HER2 CAR-T cells have enhanced clearance of HT29 human colorectal cancer tumors in vivo .

Fig. 1:. In silico screen and mRNA-seq revealed ADA and PDK1 as metabolic reprogramming genes that induce immune response-related transcriptomic changes in CD8 T cells.

A, Flowchart of the multi-tier selection for identification of candidate genes that modulate T cell metabolism. B, Schematic of mutant T cell and CAR T cell generation, multi-omics profiling and functional analyses. C-D, Quantification of CRISPR-mediated indel formation to ADA and PDK1 genes. Sample sequences and indel statistics are displayed in C and D, respectively. E, Heatmap of significant differentially expressed (DE) transcripts in ADA-gRNA11 or PDK1-gRNA13 T cells. F-G, Volcano plots of differential expression (DE) in (F) ADA-gRNA11 or (G) PDK1-gRNA13 T cells. Expression fold-changes and significance are represented by the beta-value and q value, respectively. H-I, Waterfall plots of significantly enriched Biological Process ontologies in (H) ADA-gRNA11 or (I) PDK1-gRNA13 T cells. Results are shown for pathway analyses of DE genes, and significance is provided as the Benjamini-adjusted p value (adj. p). Enrichment was assessed separately for up- and down-regulated genes, but results are shown together. DE genes had a beta-change > 1 and q < 0.05. Indel quantification and DE were analyzed with 3 biological replicates for each condition.

Fig. 2:. Metabolic reprogramming in ADA and PDK1 mutant human primary T cells.

A-B, Heatmaps of differential metabolite levels in (A) ADA and (B) PDK1 T cell mutants. Significant metabolite changes had an adjusted-p < 0.10 and log2 fold-changes > 0.50. Heatmap values depict the z-score of log2 fold-changes. C-D, Waterfall plots of metabolic pathway analyses (MetPA) in (C) ADA and (D) PDK1 T cell mutants. Bar color, size and opacity represent pathway significance and impact scores, which summarizing metabolite fold-changes of each pathway. MetPA results are given for metabolites-only in the overexpression mutants and for integrated metabolite-gene expression in the knockout mutants. E-F, Flow cytometric analyses of functional markers in ADA and PDK1 T cell mutants. Histograms and are provided for (E) cytotoxicity and (F) exhaustion markers. Metabolomics and flow cytometry data were analyzed with n >= 3 for each condition and control.

Fig. 3:. Overexpression of ADA or PDK1 in α-CD19 CAR-T cells enhanced cytotoxicity.

A, Schematic maps of the α-CD19 CAR, CAR-ADA-OE, and CAR-PDK1-OE lentiviral vectors. B, Flow cytometry dot plots of α-CD19 CAR protein expression in CAR-T cell populations from a representative sample. C-E, CRISPR-mediated perturbations to ADA and PDK1 gene loci in CAR-T cell populations. Indel formation and protein expression were assessed by (C-D) Nextera DNA sequencing and (E) Immunoblot, respectively. F-G, Bar plots and line plots of co-culture cytotoxicity assay results for different CAR-T cell populations. Cytotoxicity was quantified as NALM6-GL cancer cell survival in co-culture assays with CAR-T cells at different effector-target ratio concentrations (E:T). NALM6-GL levels were quantified by luciferase bioluminescence, relative to no treatment. Results are shown for (F) ADA/PDK1 vs control CAR-T cells via Dunnett’s two-way ANOVA test, and the (G) ADA vs control CAR-T cells in 30 nM and 120 nM adenosine. All analyses were performed with n = 3 biological replicates for each condition.

Fig. 4:. Multi-omics profiling and phenotypic characterization of ADA mutant CAR-T cells.

A, Heatmaps of significant differentially expressed (DE) transcripts in ADA-gRNA11 and ADA-OE CAR-T cell mutants. B, D, Volcano plots of DE analyses in (B) ADA-gRNA11 and (D) ADA-OE CAR-T cells. Expression fold-changes and significance are represented by the beta-value and q value, respectively. C, E, Waterfall plots of significantly enriched Biological Process ontologies in (C) ADA-gRNA11 and (E) ADA-OE CAR-T cells. Results are shown for DAVID analyses of DE genes, and significance is provided as Benjamini-adjusted p values (adj. p). Enrichment was assessed separately for up- and downregulated genes, but results are shown together. F, Heatmap of Significant differential metabolite (DM) levels in ADA-OE CAR-T cell mutants. G, Volcano plot of differential metabolite levels in ADA-OE CAR-T cell mutants. H, Waterfall plot of integrated transcriptomic-metabolomic MetPA in ADA-OE CAR-T cells. Bar color, size and opacity represent pathway significance and impact scores, which summarizing metabolite fold-changes of each pathway. I, Partial pathway map of extracellular adenosine metabolism in ADA CAR-T cell mutants. Log2-fold-changes of normalized metabolites and enzyme genes are depicted by ovals and rectangles, respectively (heatmap color-scale). Multi-gene enzyme levels are presented as log2-fold-changes of the sum of normalized gene counts. Metabolomics and transcriptomics data were analyzed with n = 5 and n = 3 biological replicates, respectively, for each condition. DE genes had a beta-change > 1 and q < 0.05, while DM had adjusted-p < 0.10 and a log2 fold-change > 0.50. Volcano plot labels are given to genes/metabolites with the highest significance and fold-changes, for which significant increases and decreases are shown in red and blue, respectively.

Fig. 5:. Multi-omics profiling and phenotypic characterization of PDK1 mutant CAR-T cells.

A, Heatmaps of significant differentially expressed (DE) transcripts in PDK1-gRNA13 and PDK1-OE CAR-T cell mutants. B, Volcano plot of DE analyses in PDK1-OE CAR-T cells. Expression fold-changes and significance are represented by the beta-value and q value, respectively. C, Waterfall plots of significantly enriched Biological Process ontologies in PDK1-OE CAR-T cells. Results are shown for DAVID analyses of DE genes, and significance is provided as the Benjamini-adjusted p value (adj. p). Enrichment was assessed separately for up- and downregulated genes, but results are shown together. D, Heatmaps of differential metabolite (DM) levels in PDK1-gRNA13 and PDK1-OE CAR-T cells. E, Volcano plot of DM levels in PDK1-gRNA13 and PDK1-OE CAR-T cells. F, Waterfall plots of integrated transcriptomic-metabolomic MetPA in PDK1-gRNA13 and PDK1-OE CAR-T cells. Bar color, size and opacity represent pathway significance and impact scores, which summarizing metabolite fold-changes of each pathway. G, Partial pathway maps of the TCA cycle in PDK1-gRNA13 and PDK1-OE CAR-T cell mutants. Log2 fold-changes of metabolite and gene levels are depicted by ovals and rectangles, respectively (heatmap color-scale). Multi-gene enzyme levels were calculated as the log2-fold-changes of the sum of normalized gene counts. Metabolomics and transcriptomics data were analyzed with n = 5 and n = 3 samples, respectively, for each condition and control. DE genes had a beta-change > 1 and q < 0.05, while DM had adjusted-p < 0.10 and a log2 fold-change > 0.50. Volcano plot labels are given to genes/metabolites with the highest significance and fold-changes, for which significant increases and decreases are shown in red and blue, respectively.

Fig. 6:. Phenotypic profiling of ADA-OE CAR-T cells.

A-B, Flow cytometric analyses for effector molecule production and NALM6-GL cytolysis in ADA-OE vs control CAR-T cells at 0, 2, and 4 days post-stimulation in a co-culture assay. Cancer lysis was measured as GFP+ cell numbers or percent of live lymphocytes. C, Proliferation assay of stimulated CAR-T cells, measured as the dissipation of Cell Trace Violet dye across cell divisions (modeled by FlowJo software). Proliferation is compared at 0 vs any divisions and across each number of divisions. D-F, Flow cytometry analyses of markers for memory, exhaustion, and downstream targets of A2AR-PKA signaling. G, Schematic of A2AR-PKA signaling pathway. Experiments in (A-F) were performed with >= 4 biological replicates in >= 2 independent experiments. When indicated, CAR-T cells were stimulated with NALM6-GL at a 1:2 E:T ratio.

Fig. 7:. ADA-OE enhances α-HER2 CAR-T cell function in an in vivo colorectal cancer model.

A, Schematic maps of the ADA-OE and control α-HER2 CAR lentiviral vector constructs. B, Tumor growth curves of ADA-OE and control α-HER2 CAR-T treatments in an in vivo HT29-GL tumor model. The left panel is shown with summary curves for each treatment, while the right panels show spider plot curves of the individual growth curves, separately graphed by treatment group. C, Flow cytometry analyses of the percentages of tumor infiltrating ADA-OE vs control α-HER2 CAR-T cells. Tumor infiltration percentage is relative to live, single cells. D-E, Flow cytometry analyses of ADA-OE vs control CAR-T (D) proliferation (KI-67) and (E) memory phenotypes in CD4 or CD8 CAR-T cells. Tumor growth and flow cytometry experiments included >= 4 mice per treatment with 2 independent experiments. Metabolomics data were analyzed by unpaired t test; indel and flow cytometry data were analyzed with Welch’s unpaired t tests; and tumor growth experiments were analyzed by 2-way ANOVA tests with Sidak’s multiple-comparison test. Flow cytometry plots shown with representative example data for each treatment. All analyses are presented with two-tailed results; error bars represent mean +/− SD; adjusted p values are FDR-corrected, unless stated otherwise. **** p < 1e-4, *** p < 1e-3, ** p < 0.01, * p < 0.05.