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CRISPR screens decode cancer cell pathways that trigger γδ T cell detection

Nature volume 621, pages 188–195 (2023)Cite this article

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Abstract

γδ T cells are potent anticancer effectors with the potential to target tumours broadly, independent of patient-specific neoantigens or human leukocyte antigen background1,2,3,4,5. γδ T cells can sense conserved cell stress signals prevalent in transformed cells2,3, although the mechanisms behind the targeting of stressed target cells remain poorly characterized. Vγ9Vδ2 T cells—the most abundant subset of human γδ T cells4—recognize a protein complex containing butyrophilin 2A1 (BTN2A1) and BTN3A1 (refs. 6,7,8), a widely expressed cell surface protein that is activated by phosphoantigens abundantly produced by tumour cells. Here we combined genome-wide CRISPR screens in target cancer cells to identify pathways that regulate γδ T cell killing and BTN3A cell surface expression. The screens showed previously unappreciated multilayered regulation of BTN3A abundance on the cell surface and triggering of γδ T cells through transcription, post-translational modifications and membrane trafficking. In addition, diverse genetic perturbations and inhibitors disrupting metabolic pathways in the cancer cells, particularly ATP-producing processes, were found to alter BTN3A levels. This induction of both BTN3A and BTN2A1 during metabolic crises is dependent on AMP-activated protein kinase (AMPK). Finally, small-molecule activation of AMPK in a cell line model and in patient-derived tumour organoids led to increased expression of the BTN2A1–BTN3A complex and increased Vγ9Vδ2 T cell receptor-mediated killing. This AMPK-dependent mechanism of metabolic stress-induced ligand upregulation deepens our understanding of γδ T cell stress surveillance and suggests new avenues available to enhance γδ T cell anticancer activity.

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Fig. 1: Vγ9Vδ2 T cell coculture with genome-wide KO library of Daudi cells shows killing-evasion and -enhancement KO.
Fig. 2: Systematic discovery of BTN3A surface expression regulators.
Fig. 3: Metabolic regulation of BTN3A.
Fig. 4: Regulation of BTN2A1 and Vγ9Vδ2 TCR-mediated killing of cancer cells.
Fig. 5: Schematic of regulation of the BTN2A1–BTN3A1–BTN3A2 complex.

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Data availability

Sequencing datasets for the two screens and CUT&RUN are available in the NCBI Gene Expression Omnibus repository (coculture screen, GSE192828; BTN3A screen, GSE192827; CUT&RUN, GSE226931). Publicly available paired-end IRF1 ChIP–seq fastq files were downloaded from ENCODE68,69: IRF1 K562 IP rep 1 (ENCFF031RWN, ENCFF031WIT), IRF1 K562 IP rep 2 (ENCFF602NSL, ENCFF071PWE) and IRF1 K562 input (ENCFF285JYI, ENCFF420KFM). Also, publicly available TCGA data were utilized for this study (https://www.cancer.gov/tcga). Source data are provided with this paper.

Code availability

Computer code used to analyse the TCGA dataset has been deposited at Zenodo (https://doi.org/10.5281/zenodo.8011891).

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Acknowledgements

We thank members of the Marson laboratory, I. Jain, S. Dodgson, S. Pyle, T. Tolpa and the Gladstone Flow Cytometry Core for providing valuable input and technical expertise. We also thank B. Gewurz (Harvard Medical School) for sharing Daudi-Cas9 cells. M.R.M. was a Cancer Research Institute (CRI) Irvington Fellow supported by CRI and was supported by the Human Vaccines Project Michelson Prizes for Human Immunology and Vaccine Research funded by the Michelson Medical Research Foundation. J.W.F. was funded by an NIH grant (no. R01HG008140). A.R. was supported by an NIH training grant (no. T32GM007281). M.M.A. is supported by an NSF GRFP grant (no. 2038436). M.O. was supported by Astellas Foundation for Research on Metabolic Disorder, Chugai Foundation for Innovative Drug Discovery Science and Mochida Memorial Foundation for Medicine and Pharmaceutical Research. K.A.T. was supported by the Gladstone PUMAS programme, funded by an NIH grant (no. 5R25HL121037). J.K. and Z.S. were supported by Oncode-PACT and Dutch Cancer Society grant nos. KWF 11393, 12586 and 13043. E.J.A. is funded by an NIH grant (no. R01AI155984). The Marson laboratory has received funds from the CRI Lloyd J. Old STAR grant, The Cancer League, the Innovative Genomics Institute, the Simons Foundation and the Parker Institute for Cancer Immunotherapy. We thank the Hubrecht Organoid Technology for providing patient-derived breast cancer organoids, and J. M. L. Roodhart (Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands) for providing the patient-derived colon cancer organoid. The Gladstone Flow Cytometry Core is supported by the James B. Pendleton Charitable Trust. The schematic in Fig. 3a was adapted from the BioRender ‘Electron Transport Chain’ template. Some of the results shown here are based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Author information

Author notes

  1. These authors contributed equally: Shane Vedova, Jacob W. Freimer

Authors and Affiliations

  1. Gladstone-UCSF Institute of Genomic Immunology, San Francisco, CA, USA

    Murad R. Mamedov, Shane Vedova, Jacob W. Freimer, Maya M. Arce, Mineto Ota, Peixin Amy Chen, Vinh Q. Nguyen, Kirsten A. Takeshima & Alexander Marson

  2. Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    Murad R. Mamedov, Shane Vedova, Jacob W. Freimer, Maya M. Arce, Mineto Ota, Peixin Amy Chen & Alexander Marson

  3. Department of Genetics, Stanford University, Stanford, CA, USA

    Jacob W. Freimer, Mineto Ota & Jonathan K. Pritchard

  4. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Avinash Das Sahu

  5. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Avinash Das Sahu

  6. UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, USA

    Avinash Das Sahu

  7. Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA

    Amrita Ramesh & Erin J. Adams

  8. Center for Translational Immunology, University Medical Center Utrecht, Utrecht, the Netherlands

    Angelo D. Meringa, Jürgen Kuball & Zsolt Sebestyen

  9. Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA

    Kristina Hanspers

  10. Department of Surgery, University of California, San Francisco, San Francisco, CA, USA

    Vinh Q. Nguyen

  11. Diabetes Center, University of California, San Francisco, San Francisco, CA, USA

    Vinh Q. Nguyen & Alexander Marson

  12. UCSF CoLabs, University of California, San Francisco, San Francisco, CA, USA

    Vinh Q. Nguyen

  13. Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands

    Anne C. Rios

  14. Oncode Institute, Utrecht, the Netherlands

    Anne C. Rios

  15. Department of Biology, Stanford University, Stanford, CA, USA

    Jonathan K. Pritchard

  16. Department of Hematology, University Medical Center Utrecht, Utrecht, the Netherlands

    Jürgen Kuball

  17. Committee on Immunology, University of Chicago, Chicago, IL, USA

    Erin J. Adams

  18. Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA

    Alexander Marson

  19. Innovative Genomics Institute, University of California-Berkeley, Berkeley, CA, USA

    Alexander Marson

  20. UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Alexander Marson

  21. Parker Institute for Cancer Immunotherapy, University of California, San Francisco, San Francisco, CA, USA

    Alexander Marson

  22. Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA

    Alexander Marson

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  1. Murad R. Mamedov
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Contributions

M.R.M. and A.M. designed the study. M.R.M., S.V., A.R., M.M.A., A.D.M., P.A.C., V.Q.N. and K.A.T. performed experiments and generated essential reagents. J.W.F. performed computational analysis and data visualization. A.D.S. and M.O. performed TCGA data analysis and visualization. K.H. generated pathway enrichment visualization. A.C.R. helped in obtaining patient-derived tumour organoids. E.J.A., J.K., Z.S. and J.K.P. helped design assays and interpret results. M.R.M. and A.M. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Murad R. Mamedov or Alexander Marson.

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Competing interests

A.M. is a cofounder of Arsenal Biosciences, Spotlight Therapeutics and Survey Genomics; serves on the boards of directors at Spotlight Therapeutics and Survey Genomics; is a board observer (and former member of the board of directors) at Arsenal Biosciences; is a member of the scientific advisory boards of Arsenal Biosciences, Spotlight Therapeutics, Survey Genomics, NewLimit, Amgen, Lightcast and Tenaya; owns stock in Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, PACT Pharma, Lightcast, and Tenaya; and has received fees from Arsenal Biosciences, Spotlight Therapeutics, NewLimit, Survey Genomics, Tenaya, Lightcast, 23andMe, PACT Pharma, Juno Therapeutics, Trizell, Vertex, Merck, Amgen, Genentech, AlphaSights, Rupert Case Management, Bernstein and ALDA. A.M. is an investor in, and informal advisor to, Offline Ventures and a client of EPIQ. J.W.F. was a consultant for NewLimit, is an employee of Genentech and has equity in Roche. The Marson laboratory has received research support from Juno Therapeutics, Epinomics, Sanofi, GlaxoSmithKline, Gilead and Anthem. J.K. is a shareholder of Gadeta B.V. J.K. and Z.S. are inventors on patents with γδTCR-related topics. A.M. and M.R.M. are inventors on patent applications that have been filed based on the findings described here.

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Nature thanks Dmitry Gabrilovich, Thomas Herrmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Mevalonate pathway effects, co-culture screen consistency, and gene set enrichment analysis (GSEA).

(a) Schematic of the mevalonate pathway, adapted from WikiPathways. Phosphoantigens highlighted in blue. (b) Survival of eGFP+ Daudi cells co-cultured with primary Vγ9Vδ2 T cells at different effector-to-target (E:T) ratios with or without zoledronate (ZOL). Cells were quantified using real-time quantitative live-cell imaging (Incucyte). Survival was normalized to Daudi cells cultured without T cells. Mean ± SD. n = 3 per condition. (c–e) Pairwise comparisons of log2(fold change [FC]) of screen results among the three healthy human PBMC donors. (f) Number of genes contained within each negatively enriched KEGG gene set after filtering out genes that were not in the screen dataset, FDR q-values, and (g) number of genes found in two KEGG gene sets.

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Extended Data Fig. 2 Gene, gene set, and signature TCGA analysis.

(a) Heatmap of the hazard ratios (natural log-transformed) associated with the co-culture screen gene signature in TCGA patients for 33 cancer types (a positive log-ratio indicates a worse prognosis and a negative one indicates a protective effect of the gene signature). The co-culture screen gene signature was scaled to mean = 0, SD = 1. Values shown only for cancer types with significant survival and signature association in patient tumours, as determined by a Wald test with Benjamini-Hochberg multiple comparisons correction (two-sided padj < 0.05). (b, d) Correlation of tumour gene expression and survival in the low-grade glioma (LGG) patient cohort, (b) with the entire cohort and (d) with the cohort split according to TRGV9/TRDV2 tumour transcript abundance. Patients with high and low expression of every given gene were compared across the 1040 genes in the co-culture screen signature. Positive Wald test Z score indicates a positive correlation with survival, and negative Z score indicates a negative correlation with survival. (c, e) Correlation of KEGG pathway-derived and type I interferon response pathway-derived signature scores and survival in the low-grade glioma (LGG) patient cohort, (c) with the entire cohort and (e) with the cohort split according to TRGV9/TRDV2 tumour transcript abundance. Patients with high and low pathway signature scores were compared. (f, g) Survival of (f) all LGG patients and (g) TRGV9/TRDV2-high or TRGV9/TRDV2-low LGG patients split by high and low expression of the TCA cycle pathway signature. (h, i) Survival of TRAC/TRBC-high/low (h) LGG and (i) BLCA patients split by high and low expression of the co-culture screen gene signature. (b-e) Significance was determined by a Wald test (Cox regression) with Bonferroni multiple comparisons correction (two-sided padj < 0.05). (f-i) Log-rank test (Kaplan-Meier survival analysis), adjusted (padj) with Benjamini-Hochberg multiple comparisons correction. (c, e-g) Pathway signature levels were estimated by limiting the comparison to genes that overlapped between the co-culture screen hits signature and the pathway.

Extended Data Fig. 3 Correlation of expression between BTN3A1 and screen hits across thousands of samples.

(a) BTN3A1 expression correlation with gene ontology (GO) pathways across thousands of healthy samples (all tissues combined) collated by Correlation AnalyzeR. To determine pathways that correlate with BTN3A1, genome-wide Pearson correlations for BTN3A1 are used as a ranking metric in the GSEA algorithm, which determines the padj-value. (b) Pairwise correlations between expression of BTN3A1 and shown genes across thousands of healthy samples (all tissues combined) (Correlation AnalyzeR). (c) Each vertical line indicates the correlation between expression of BTN3A1 and one of the genes in the KEGG oxidative phosphorylation (OXPHOS) gene set, overlaid over a density plot of the BTN3A1 pairwise correlations with genes across the entire human genome. Data from healthy immune tissues (Correlation AnalyzeR).

Extended Data Fig. 4 BTN3A expression screen consistency, cross-screen correlation, FDPS validation, and GSEA.

(a–c) Pairwise comparisons of significant (FDR < 0.01) and not significant results among the three replicates (Rep) of Daudi-Cas9 cell populations used for the BTN3A expression screen. (d) Correlation of screen effect sizes (LFC) among concordant hits separated into positive and negative regulators of BTN3A surface expression. Linear regression line with a 95% confidence interval is shown. (e) Surface BTN3A staining on live Daudi-Cas9 cells treated for 72 hours with zoledronate (ZOL), an inhibitor of FDPS. n = 3 per ZOL dose, representative data from one of three independent experiments. One-way ANOVA comparison to no treatment with Dunnett’s multiple comparisons test. (f) Surface BTN3A staining on live Daudi-Cas9 FDPS KO or control AAVS1 KO cells at indicated days after lentiviral sgRNA transduction. n = 4 for each KO. One-way ANOVA comparison to Daudi-Cas9 AAVS1 (#5) KO cells with Dunnett’s multiple comparisons test. Data from one experiment. (e, f) Mean ± SD. p < 0.0001 (****), p < 0.001 (***), p < 0.01 (**). (g) GSEA of KEGG gene sets that positively or negatively regulate surface BTN3A expression. Number of genes contained within each KEGG gene set after filtering out genes that were not in the screen dataset and FDR q-values are shown.

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Extended Data Fig. 5 Pathway Enrichment Visualization.

(a–d) Schematics of the depletion and enrichment of KOs within the (a) oxidative phosphorylation, (b) iron-sulphur (Fe-S) cluster biogenesis, (c) N-glycan biosynthesis, (d) and sialylation pathways across both screens. Shading indicating log2FC shown only for significant hits (FDR < 0.05). All pathways were adapted from WikiPathways. (a) OXPHOS subunits are shown with abridged names without accompanying prefixes (C I: NDUF; C II: SDH; C III: UQCR, C IV: COX; C V: ATP5) (e.g., B4 in CI is NDUFB4). Subunits encoded by mitochondrial genes are not included in the visualization.

Extended Data Fig. 6 BTN3A expression screen gene signature.

(a) Heatmap of the hazard ratios (natural log-transformed) associated with the BTN3A screen gene signature in TCGA patients for 33 cancer types (a positive log-ratio indicates a worse prognosis and a negative one indicates a protective effect of the gene signature). The BTN3A screen gene signature was scaled to mean = 0, SD = 1. Values shown only for cancer types with significant survival and signature association in patient tumours, as determined by a Wald test with Benjamini-Hochberg multiple comparisons correction (two-sided padj < 0.05). (b-d) Survival of (b) total, (c) TRGV9/TRDV2-high/low, or (d) TRAC/TRBC-high/low LGG patients split by high and low expression of the BTN3A expression screen gene signature. Log-rank test (Kaplan-Meier survival analysis) and Wald test (Cox regression), adjusted (padj) with Benjamini-Hochberg multiple comparisons correction.

Extended Data Fig. 7 Effects of screen hit KOs on Vγ9Vδ2 TCR tetramer staining and BTN3A2 and BTN2A1 expression.

(a) Representative histograms of surface BTN3A fluorescence for a subset of single gene Daudi-Cas9 KOs and the AAVS1 control. (b) G115 clone Vγ9Vδ2 TCR tetramer staining fluorescence (MFI) at 14 days after lentiviral sgRNA transduction. Data from one experiment. AAVS1 KO n = 12, BTN3A1 KO n = 3, all other deletions n = 6. (c, d) qPCR data for (c) BTN3A2 and (d) BTN2A1 transcripts normalized to ACTB transcripts. n = 5-6 (except RER (#1) KO n = 4 for BTN2A1), AAVS1 KO n = 12, data combined from two independent experiments. (b-d) One-way ANOVA with Dunnett’s multiple comparisons test. Mean ± SD. p < 0.0001 (****), p < 0.001 (***), p < 0.01 (**), p < 0.05 (*).

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Extended Data Fig. 8 IRF1 and ZNF217 CUT&RUN, ChIP-Seq, and Vγ9Vδ2 T cell killing.

(a) Publicly available IRF1 ChIP-Seq for the butyrophilin locus in K562 cells stably expressing C-terminal eGFP-tagged IRF1 (ENCODE). (b–d) CUT&RUN data for IRF1 and ZNF217 binding at promoters in (b) BTN3A1, (c) BTN3A2, and (d) BTN3A3 loci in WT Daudi-Cas9 cells. n = 3 per condition. The algorithm SEACR calls peaks and verifies them above a stringent background signal threshold. (e) Daudi-Cas9 KO survival after 24-hour co-culture with expanded Vγ9Vδ2 T cells in the presence of ZOL at an E:T ratio of 2:1. For each γδ T cell donor, Daudi survival is calculated relative to Daudi cells cultured without T cells and normalized to the Daudi-Cas9 AAVS1 KO control cell survival. Combined data from three donors and two independent experiments. AAVS1 KO n = 6, IRF1 KO n = 3. One-way ANOVA comparison to AAVS1 KO cells with Dunnett’s multiple comparisons test. Mean ± SD. p < 0.01 (**).

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Extended Data Fig. 9 Metabolic effects on surface BTN3A expression.

(a) Surface BTN3A MFI in Daudi-Cas9 KOs cultured in different pyruvate concentrations for 3 days in RPMI (no glucose, no pyruvate). Normalized to cells grown without pyruvate (0 mM). (b-d, f) Surface BTN3A MFI in Daudi-Cas9 cells treated for 72 h with (b) an mTOR inhibitor (rapamycin), an ISR inhibitor (ISRIB), ISR agonists (guanabenz, Sal003, salubrinal, raphin1, sephin1), and DMSO (vehicle) (KO cells); (c) metformin (WT cells); (d) A-769662 compared to equivalent amounts of DMSO (vehicle) (WT cells); or (f) the shown compounds (KO cells). (e) Surface BTN3A MFI in WT Daudi-Cas9 cells co-treated with AICAR and increasing amounts of Compound C (AMPK inhibitor) or DMSO (vehicle). (a) n = 4 per condition (n = 3, TIMMDC1 (#2) at 0 mM), data combined from two independent experiments, each individually normalized. (b) n = 6 per condition (except n = 5 for AAVS1 (#5) with guanabenz and for PPAT (#1) with salubrinal), data combined from two independent experiments, each individually normalized to DMSO (vehicle)-treated cells. (c) n = 8 per condition, data combined from two independent experiments. One-way ANOVA comparison to cells that received no treatment with Dunnett’s multiple comparisons test. (d) n = 3 per condition, representative data from one of two independent experiments. (e) n = 3 per conditions, representative data from one of two independent experiments. Two-tailed unpaired Student’s t test with Bonferroni correction. (f) n = 3 per condition (n = 2 for AMPKα1 (#1) treated with DMSO), representative data from one of two independent experiments. One-way ANOVA comparison to AAVS1 (#5) KO cells with Dunnett’s multiple comparisons test. (a-f) Mean ± SD. p < 0.0001 (****), p < 0.001 (***), p < 0.01 (**), p < 0.05 (*), p > 0.05 (N.S.).

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Extended Data Fig. 10 Isotype Control Staining, qPCR, and BTN3A blocking.

(a) G115 clone Vγ9Vδ2 TCR tetramer staining MFI of WT Daudi-Cas9 cells treated with 80 µM C991 (DMSO), DMSO (vehicle), 0.5 mM AICAR (aqueous), or without treatment for 72 h. Two-tailed unpaired Student’s t test. (b) Vγ4Vδ1 TCR (clone DP10.7) tetramer staining fluorescence (MFI) of Daudi-Cas9 KO cells treated with 80 µM C991 (DMSO), DMSO (vehicle), 0.5 mM AICAR (aqueous), or water for 72 hours. This staining with a tetramer of an irrelevant γδTCR clone defines the background for Vγ9Vδ2 TCR tetramer staining in Fig. 4a. (c) qPCR data for BTN2A1, BTN3A1, and BTN3A2 transcripts in Daudi-Cas9 cells treated with C991, internally normalized to ACTB transcripts and normalized to DMSO (vehicle)-treated cells. Two-tailed unpaired Student’s t test. (d) IgG1κ isotype control staining in Daudi-Cas9 KO cells treated with 80 µM Compound 991 (DMSO), DMSO (vehicle), 0.5 mM AICAR (aqueous), or water (vehicle) treatment for 72 h. (e) Survival of eGFP+ Daudi cells treated for 3 days with AICAR or water prior to co-culture (E:T 2:1) with primary Vγ9Vδ2 T cells in the presence of an anti-BTN3A antibody (clone 103.2). Cells were quantified using real-time quantitative live-cell imaging (Incucyte). Survival was normalized to Daudi cells cultured without T cells. (a) n = 4 per condition, representative data from one of two independent experiments. (b) n = 3 per condition, representative data from one of two independent experiments. (c) n = 4 per condition, representative data from one of three independent experiments. (d) n = 3, representative data from one of two independent experiments. (e) n = 4 per condition. (a-e) Mean ± SD. p < 0.0001 (****).

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Supplementary information

Supplementary Information

Table of contents and Supplementary Fig. 1.

Reporting Summary

Supplementary Table 1

Coculture screen results by individual genes. Statistical significance was assessed by the MAGeCK RRA test followed by adjustment for multiple comparisons. FDR values were used to determine significance.

Supplementary Table 2

Coculture screen results by individual sgRNAs. Statistical significance was assessed by the MAGeCK RRA test followed by adjustment for multiple comparisons. FDR values were used to determine significance.

Supplementary Table 3

Negatively enriched coculture screen KEGG pathway gene sets.

Supplementary Table 4

LGG TCGA analysis with individual genes from the coculture screen signature. Wald test z-scores and unadjusted two-sided P values.

Supplementary Table 5

LGG TCGA analysis with pathways from the coculture screen signature. Wald test z-scores and unadjusted two-sided P values.

Supplementary Table 6

BTN3A expression screen results by individual genes. Statistical significance was assessed by the MAGeCK RRA test followed by adjustment for multiple comparisons. FDR values were used to determine significance.

Supplementary Table 7

BTN3A expression screen results by individual sgRNAs. Statistical significance was assessed by the MAGeCK RRA test followed by adjustment for multiple comparisons. FDR values were used to determine significance.

Supplementary Table 8

Primers relevant to the methods.

Supplementary Table 9

Validation sgRNAs.

Supplementary Table 10

qPCR probe assay details.

Supplementary Table 11

DP10.7 gdTCR tetramer sequences.

Supplementary Table 12

Additional gene signatures.

Source data

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Mamedov, M.R., Vedova, S., Freimer, J.W. et al. CRISPR screens decode cancer cell pathways that trigger γδ T cell detection. Nature 621, 188–195 (2023). https://doi.org/10.1038/s41586-023-06482-x

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