Abstract
Immune checkpoint blockade (ICB) targeting PD-1 and CTLA-4 has revolutionized cancer treatment. However, many cancers do not respond to ICB, prompting the search for additional strategies to achieve durable responses. G-protein-coupled receptors (GPCRs) are the most intensively studied drug targets but are underexplored in immuno-oncology. Here, we cross-integrated large singe-cell RNA-sequencing datasets from CD8+ T cells covering 19 distinct cancer types and identified an enrichment of Gαs-coupled GPCRs on exhausted CD8+ T cells. These include EP2, EP4, A2AR, β1AR and β2AR, all of which promote T cell dysfunction. We also developed transgenic mice expressing a chemogenetic CD8-restricted Gαs–DREADD to activate CD8-restricted Gαs signaling and show that a Gαs–PKA signaling axis promotes CD8+ T cell dysfunction and immunotherapy failure. These data indicate that Gαs–GPCRs are druggable immune checkpoints that might be targeted to enhance the response to ICB immunotherapies.
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Data availability
Gene Expression Omnibus accession numbers for the human datasets used in this study include GSE116256, GSE114727, GSE110686, GSE14018, GSE123813, GSE22898, GSE123139, GSE115978, GSE146771, GSE134520, GSE140228, GSE125449, GSE117570, GSE127465, GSE99254, GSE123813, GSE156728, GSE120575, GSE84820, GSE123235, GSE122969, GSE88987 and GSE141299. European Genome–Phenome Archive identifiers for the human datasets used in this study include EGAS00001002171, EGAS00001002486, EGAS00001002325 and EGAS00001002553. Additional information regarding these datasets can be found in Supplementary Table 1. The remaining data are available within the article and supplementary information. Source data are provided with this paper.
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Acknowledgements
This project was supported by grants from the National Cancer Institute (R01CA247551 and U54CA209891) and National Institute of Dental and Craniofacial Research (NIH/NIDCR, R01DE026870 and U01DE028227). V.H.W. was supported by an NRSA Training Award (NIH/NCI 1F31CA250488-01). R.S.-K. was supported by an NRSA Training Award (NIH/NIDCR F32DE029990-01). B.S.Y. was supported by the Pharmacological Sciences Training Program (5T32GM007752-40) and an NRSA Training Award (NIH/NIDCR1F31DE031961-01). J.P.M. was supported by grants from the National Cancer Institute (U24CA248457 and U24CA220341). A.T.W. was supported by grants from the National Cancer Institute (F31CA257344 and U54CA209891) and the National Library of Medicine (T15LM011271). R.B. was supported by grants from the National Institutes of Health NIDDK (R01-DK092590) and NIAMS (R01-AR-072368). We acknowledge A. Sharabi (University of California, San Diego) for gifting the MC38-OVA cell line, D. Vignali (University of Pittsburg) for gifting the E8iCreErt2 mouse model, R. Iglesias-Bartolome (National Cancer Institute) for gifting the Gnas-exon 1fl/fl and the Tet-GFP-PKI mouse models and R. Berdeaux (The University of Texas) for gifting the ROSA26LSLGsDREADD mice. We acknowledge A. Goldrath for expertise and guidance in the study design and direction of the project.
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Contributions
V.H.W. and B.S.Y. designed and conceived the studies, conducted most experiments described in this study and interpreted the data. F.F., Z.W. and R.S.-K. performed experiments described in the study. A.T.W., M.J.S., M.S.P., L.M.C., J.C., S.S., M.M., F.R., H.C., E.R. and J.P.M. all contributed to the bioinformatics analysis of this project. V.H.W., B.S.Y., F.F., R.S.-K., T.S.H., D.A.A.V., R.I.-B., R.B. and J.S.G. contributed to the study design and writing of the manuscript. J.S.G. provided oversight and direction of the entire project and study design, provided financial support for the study, interpreted the data and wrote the manuscript.
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J.S.G. reports consulting fees from Domain Pharmaceuticals, Pangea Therapeutics and io9 and is founder of Kadima Pharmaceuticals, unrelated to the current study. R.B. is an employee and shareholder of CellChorus, Inc. J.C. is an employee and shareholder of Pfizer, Inc. V.H.W. is an employee and shareholder of Septerna, Inc. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Patient and cancer information for integration analysis of CD8 T cells.
a, Information about patients, cancer type, and dataset used in the singe-cell RNA-seq integration. b, Visualization of 217,953 CD8 T cells after integration from 30 singe-cell RNA-seq datasets. c, Statistical comparison of calculated dysfunction score from tumor-infiltrating populations of CD8s characterized from Fig. 1b. Each dot represents one cell from groups listed in Supplementary Table 1a. Naïve: lower bound=0.147, middle bound=0.229, upper bound=0.337, 25th percentile=0.228, 75th percentile=0.231. Proliferating: lower bound=0.372, middle bound=0.564, upper bound=0.790, 25th percentile=0.556, 75th percentile=0.571. Cytotoxic: lower bound=0.184, middle bound=0.290, upper bound=0.447, 25th percentile=0.288, 75th percentile=0.292. Effector Memory: lower bound=0.209, middle bound=0.304, upper bound=0.431, 25th percentile=0.303, 75th percentile=0.305. Exhausted: lower bound=0.361, middle bound=0.555, upper bound=0.800, 25th percentile=0.551, 75th percentile=0.559.
Extended Data Fig. 2 Effect of targeting the Gαs/PKA signaling pathway in CD8 T cells.
a, Upregulation of inhibitory receptors and decrease of IFNγ and TNF in chronically versus acutely simulated CD8 T cells. The average relative expression and s.e.m. are shown (n = 6 biologically independent samples). b, Significant decrease of IFNγ or TNF with Gαs agonists in chronically stimulated CD8 T cells. The average relative expression and s.e.m. are shown (n = 6 biologically independent samples). c, Significant decrease of Ki-67 and viability with Gαs agonists in chronically stimulated CD8 T cells. The average relative expression and s.e.m. are shown (n = 6 biologically independent samples). d, Representative flow cytometry plots showing expression of Tim-3 and PD-1 in chronically stimulated CD8 T cells after treatment with 1 µM PGE2 (P), 5 µM Dobutamine (D), or 5 µM CGS-21860 (C). e, Effect of CXCL10 on PGE2-mediated decrease in IFNγ and TNFα The average frequency and s.e.m. are shown (n = 3 per group). Statistical significance was determined by two-way ANOVA. Unless indicated otherwise, statistical significance was determined by two-tailed unpaired Student’s t-test.
Extended Data Fig. 3 Development of a CD8-restricted PKI transgenic mouse model.
a, Scheme illustrating the generation of CD8-PKI mice. b, Genotyping information for CD8-PKI mice. c, Confirmation of PKI expression in CD4 or CD8 T cells isolated from splenocytes of CD8-PKI mice and littermate controls after induction by doxycycline and tamoxifen. The average relative expression and s.e.m. are shown (n = 6 mice per group). d, Quantification of IFNγ and TNF inhibition by PGE2 in chronically stimulated CD8 T cells from CD8-PKI mice. The average relative expression and s.e.m. are shown (n = 3 biologically independent samples). Statistical significance was determined by two-way ANOVA.
Extended Data Fig. 4 Development of a CD8-restricted Gαs-DREADD transgenic mouse model.
a, Genotyping confirmation for CD8-GsD mice. Primers detecting the Gαs-DREADD, ROSA26, and E8i-Cre were used to confirm recombination by the Cre-recombinase. Information about primers and genotyping is listed in Supplementary Table 4. b, Effect of DCZ on circulating CD8, CD4, NK cells, and CD11b myeloid cells in the peripheral blood of CD8-GsD mice treated with tamoxifen and 5 doses of DCZ (n = 5 mice for -DCZ; n = 6 mice for +DCZ). c, Effect of DCZ on non-tamoxifen-treated CD8-GsD mice. Quantification of IFNγ and TNF and PD-1 and Tim-3 in non-tamoxifen-treated CD8 T cells treated with or without DCZ. The average frequency and s.e.m. are shown (n = 3 biologically independent samples). Statistical significance was determined by two-tailed unpaired Student’s t-test.
Extended Data Fig. 5 Effect of tamoxifen and DCZ on tumor growth.
Tumor growth curve of CD8-GsD littermate control mice implanted with 4MOSC1 tumors treated with or without tamoxifen or DCZ. Mice were given 3 doses of tamoxifen, and 5 × 105 4MOSC1 cells were implanted into the tongue. Where indicated, 0.01 mg/kg DCZ was administered daily starting one day after tumor implantation.
Extended Data Fig. 6 Anti-CTLA-4 response in CD8-GsD mice bearing 4MOSC1 tumors.
Tumor growth curve (left panel) and survival plot (right panel) of CD8-GsD mice implanted with 4MOSC1 tumors treated with anti-CTLA-4 with or without DCZ (n = 7 mice per group). Mice were given three doses of tamoxifen before orthotopic tumor implantation and treated with checkpoint inhibitors and DCZ as previously described. Statistical significance was determined by two-way ANOVA. Statistical significance of survival data was calculated by the log-rank test.
Extended Data Fig. 7 Effect of Gnas deletion CD8 T cells in mice bearing 4MOSC1 tumors.
a, Tumor growth curve (left panel) and quantification of endpoint tumor volume (right panel) of CD8-Gnas+/+ (n = 14 mice) and CD8-Gnas−/− mice ( = 12 mice) implanted with 4MOSC1 tumors. Mice were given 3 doses of tamoxifen prior to orthotopic tumor implantation. The average tumor volume and s.e.m. are shown. Statistical significance was determined by two-way ANOVA. b, Quantification of PD-1+TIGIT+ CD8 T cells in 4MOSC1 tumors and draining lymph nodes at endpoint. The average frequency and s.e.m. are shown (n = 5 mice per group). Statistical significance was determined by two-tailed unpaired Student’s t-test. c, Frequency of CD8+ T cells in 4MOSC1 tumors in CD8-Gnas KO mice versus littermate controls. The average frequency and s.e.m. are shown (n = 5 mice per group). Statistical significance was determined by two-tailed unpaired Student’s t-test.
Extended Data Fig. 8 Gating strategy for chronic stimulation and in vivo experiments.
a, For chronic stimulation experiments, lymphocytes were gated from forward scatter area (FSC-A) and side scatter area (SSC-A). Single cells were distinguished from doublet cells in forward scatter height (FSC-H) and forward scatter width (FSC-W), and then side scatter height (SSC-H) and side scatter width (SSC-W). Live CD8 cells were then gated. b, For in vivo experiments, lymphocytes were gated from forward scatter area (FSC-A) and side scatter area (SSC-A). Single cells were distinguished from doublet cells in forward scatter height (FSC-H) and forward scatter width (FSC-W), and then side scatter height (SSC-H) and side scatter width (SSC-W). Live CD45 cells were then gated. T cells were distinguished by NK1.1 negative, CD19 negative, and CD3 positive. CD8 T cells were then gated as CD4 negative.
Supplementary information
Supplementary Table
Supplementary Tables 1–6.
Source data
Source Data Figs. 2–4 and Extended Data Figs. 1–7
Statistical source data.
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Wu, V.H., Yung, B.S., Faraji, F. et al. The GPCR–Gαs–PKA signaling axis promotes T cell dysfunction and cancer immunotherapy failure. Nat Immunol 24, 1318–1330 (2023). https://doi.org/10.1038/s41590-023-01529-7
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DOI: https://doi.org/10.1038/s41590-023-01529-7
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