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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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).
References
Silva-Santos, B., Serre, K. & Norell, H. γδ T cells in cancer. Nat. Rev. Immunol. 15, 683–691 (2015).
Sebestyen, Z., Prinz, I., Déchanet-Merville, J., Silva-Santos, B. & Kuball, J. Translating gammadelta (γδ) T cells and their receptors into cancer cell therapies. Nat. Rev. Drug Discov. 19, 169–184 (2020).
Raverdeau, M., Cunningham, S. P., Harmon, C. & Lynch, L. γδ T cells in cancer: a small population of lymphocytes with big implications. Clin. Transl. Immunol. 8, e01080 (2019).
Silva-Santos, B., Mensurado, S. & Coffelt, S. B. γδ T cells: pleiotropic immune effectors with therapeutic potential in cancer. Nat. Rev. Cancer 19, 392–404 (2019).
Mensurado, S., Blanco-Domínguez, R. & Silva-Santos, B. The emerging roles of γδ T cells in cancer immunotherapy. Nat. Rev. Clin. Oncol. 20, 178–191 (2023).
Harly, C. et al. Key implication of CD277/butyrophilin-3 (BTN3A) in cellular stress sensing by a major human γδ T-cell subset. Blood 120, 2269–2279 (2012).
Rigau, M. et al. Butyrophilin 2A1 is essential for phosphoantigen reactivity by γδ T cells. Science 367, eaay5516 (2020).
Karunakaran, M. M. et al. Butyrophilin-2A1 directly binds germline-encoded regions of the Vγ9Vδ2 TCR and is essential for phosphoantigen sensing. Immunity 52, 487–498 (2020).
Groh, V., Steinle, A., Bauer, S. & Spies, T. Recognition of stress-induced MHC molecules by intestinal epithelial γδ T cells. Science 279, 1737–1740 (1998).
Strid, J., Sobolev, O., Zafirova, B., Polic, B. & Hayday, A. The intraepithelial T cell response to NKG2D-ligands links lymphoid stress surveillance to atopy. Science 334, 1293–1297 (2011).
Girardi, M. et al. Regulation of cutaneous malignancy by γδ T cells. Science 294, 605–609 (2001).
Harly, C. et al. Human γδ T cell sensing of AMPK-dependent metabolic tumor reprogramming through TCR recognition of EphA2. Sci. Immunol. 6, eaba9010 (2021).
Marlin, R. et al. Sensing of cell stress by human γδ TCR-dependent recognition of annexin A2. Proc. Natl Acad. Sci. USA 114, 3163–3168 (2017).
Chien, Y., Meyer, C. & Bonneville, M. γδ T cells: first line of defense and beyond. Annu. Rev. Immunol. 32, 121–155 (2014).
Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Payne, K. K. et al. BTN3A1 governs antitumor responses by coordinating αβ and γδ T cells. Science 369, 942–949 (2020).
Sandstrom, A. et al. The intracellular B30.2 domain of butyrophilin 3A1 binds phosphoantigens to mediate activation of human Vγ9Vδ2 T cells. Immunity 40, 490–500 (2014).
Mullen, P. J., Yu, R., Longo, J., Archer, M. C. & Penn, L. Z. The interplay between cell signalling and the mevalonate pathway in cancer. Nat. Rev. Cancer 16, 718–731 (2016).
Yu, Z. et al. Identification of a transporter complex responsible for the cytosolic entry of nitrogen-containing bisphosphonates. eLife 7, e36620 (2018).
Dang, A. T. et al. NLRC5 promotes transcription of BTN3A1-3 genes and Vγ9Vδ2 T cell-mediated killing. iScience 24, 101900 (2021).
Corvaisier, M. et al. Vγ9Vδ2 T cell response to colon carcinoma cells. J. Immunol. 175, 5481–5488 (2005).
Palakodeti, A. et al. The molecular basis for modulation of human Vγ9Vδ2 T cell responses by CD277/butyrophilin-3 (BTN3A)-specific antibodies. J. Biol. Chem. 287, 32780–32790 (2012).
Sheftel, A., Stehling, O. & Lill, R. Iron–sulfur proteins in health and disease. Trends Endocrinol. Metab. 21, 302–314 (2010).
Voss, M. et al. Shedding of glycan‐modifying enzymes by signal peptide peptidase‐like 3 (SPPL3) regulates cellular N‐glycosylation. EMBO J. 33, 2890–2905 (2014).
Park, J. H. et al. Tumor hypoxia represses γδ T cell-mediated antitumor immunity against brain tumors. Nat. Immunol. 22, 336–346 (2021).
Blevins, M. A., Huang, M. & Zhao, R. The role of CtBP1 in oncogenic processes and its potential as a therapeutic target. Mol. Cancer Ther. 16, 981–990 (2017).
Annaert, W. & Kaether, C. Bring it back, bring it back, don’t take it away from me – the sorting receptor RER1. J. Cell Sci. 133, jcs231423 (2020).
Cano, C. E. et al. BTN2A1, an immune checkpoint targeting Vγ9Vδ2 T cell cytotoxicity against malignant cells. Cell Rep. 36, 109359 (2021).
Vantourout, P. et al. Heteromeric interactions regulate butyrophilin (BTN) and BTN-like molecules governing γδ T cell biology. Proc. Natl Acad. Sci. USA 115, 1039–1044 (2018).
Quinlan, K. G. R. et al. Specific recognition of ZNF217 and other zinc finger proteins at a surface groove of C-terminal binding proteins. Mol. Cell. Biol. 26, 8159–8172 (2006).
Mick, E. et al. Distinct mitochondrial defects trigger the integrated stress response depending on the metabolic state of the cell. eLife 9, e49178 (2020).
Herzig, S. & Shaw, R. J. AMPK: guardian of metabolism and mitochondrial homeostasis. Nat. Rev. Mol. Cell Biol. 19, 121–135 (2018).
Marcu-Malina, V. et al. Redirecting αβ T cells against cancer cells by transfer of a broadly tumor-reactive γδT-cell receptor. Blood 118, 50–59 (2011).
Gründer, C. et al. γ9 and δ2CDR3 domains regulate functional avidity of T cells harboring γ9δ2TCRs. Blood 120, 5153–5162 (2012).
Straetemans, T. et al. GMP-grade manufacturing of T cells engineered to express a defined γδTCR. Front. Immunol. 9, 1062 (2018).
Dekkers, J. F. et al. Uncovering the mode of action of engineered T cells in patient cancer organoids. Nat. Biotechnol. 41, 60–69 (2023).
Gassart, A. D. et al. Development of ICT01, a first-in-class, anti-BTN3A antibody for activating Vγ9Vδ2 T cell-mediated antitumor immune response. Sci. Transl. Med. 13, eabj0835 (2021).
Mu, X. et al. Glucose metabolism controls human γδ T-cell-mediated tumor immunosurveillance in diabetes. Cell. Mol. Immunol. 19, 944–956 (2022).
Meizlish, M. L., Franklin, R. A., Zhou, X. & Medzhitov, R. Tissue homeostasis and inflammation. Annu. Rev. Immunol. 39, 557–581 (2021).
Warburg, O., Posener, K. & Negelein, E. Über den Stoffwechsel der Tumoren. Biochem. Z. 152, 319–344 (1924).
Heiden, M. G. V., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).
Tzelepis, K. et al. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep. 17, 1193–1205 (2016).
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971 (2018).
Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).
Ma, Y. et al. CRISPR/Cas9 screens reveal Epstein-Barr virus-transformed B cell host dependency factors. Cell Host Microbe 21, 580–591 (2017).
Jiang, S. et al. CRISPR/Cas9‐mediated genome editing in Epstein‐Barr virus‐transformed lymphoblastoid B‐cell lines. Curr. Protoc. Mol. Biol. 121, 31.12.1–31.12.23 (2018).
Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Miller, H. E. & Bishop, A. J. R. Correlation AnalyzeR: functional predictions from gene co-expression correlations. BMC Bioinformatics 22, 206 (2021).
Lachmann, A. et al. Massive mining of publicly available RNA-seq data from human and mouse. Nat. Commun. 9, 1366 (2018).
Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224–226 (2019).
Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).
Davodeau, F. et al. Close correlation between Daudi and mycobacterial antigen recognition by human gamma delta T cells and expression of V9JPC1γ/V2DJCδ-encoded T cell receptors. J. Immunol. 151, 1214–1223 (1993).
Bai, L. et al. The majority of CD1d‐sulfatide‐specific T cells in human blood use a semiinvariant Vδ1 TCR. Eur. J. Immunol. 42, 2505–2510 (2012).
Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 (2018).
Pleguezuelos‐Manzano, C. et al. Establishment and culture of human intestinal organoids derived from adult stem cells. Curr. Protoc. Immunol. 130, e106 (2020).
Zheng, Y., Ahmad, K. & Henikoff, S. CUT&Tag data processing and analysis tutorial. Protocol.io https://doi.org/10.17504/protocols.io.bjk2kkye (2020).
Meers, M. P., Tenenbaum, D. & Henikoff, S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics Chromatin 12, 42 (2019).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Kutmon, M., Lotia, S., Evelo, C. T. & Pico, A. R. WikiPathways app for Cytoscape: making biological pathways amenable to network analysis and visualization. F1000Res. 3, 152 (2014).
Tsuchiya, S. et al. Implementation of GlycanBuilder to draw a wide variety of ambiguous glycans. Carbohydr. Res. 445, 104–116 (2017).
Varki, A. et al. Symbol nomenclature for graphical representations of glycans. Glycobiology 25, 1323–1324 (2015).
Gustavsen, J. A., Pai, S., Isserlin, R., Demchak, B. & Pico, A. R. RCy3: network biology using Cytoscape from within R. F1000Res. 8, 1774 (2019).
Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).
Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).
Dunham, I. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).
Luo, Y. et al. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 48, D882–D889 (2019).
Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38, 276–278 (2020).
Lopez-Delisle, L. et al. pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37, 422–423 (2020).
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
Authors and Affiliations
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
Ethics declarations
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.
Peer review
Peer review information
Nature thanks Dmitry Gabrilovich, Thomas Herrmann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
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 (*).
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 (**).
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.).
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 (****).
Supplementary information
Supplementary Information
Table of contents and Supplementary Fig. 1.
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.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-023-06482-x
This article is cited by
-
CRISPR–Cas9 applications in T cells and adoptive T cell therapies
Cellular & Molecular Biology Letters (2024)
-
Unlocking γδ T cell power: pathways that boost cancer defense
Molecular Biomedicine (2024)
-
Novel insights into regulation of butyrophilin molecules: critical components of cancer immunosurveillance by γδ T cells
Cellular & Molecular Immunology (2024)
-
Unsynchronized butyrophilin molecules dictate cancer cell evasion of Vγ9Vδ2 T-cell killing
Cellular & Molecular Immunology (2024)
-
Dual targeting of cancer metabolome and stress antigens affects transcriptomic heterogeneity and efficacy of engineered T cells
Nature Immunology (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.