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Hypoxia drives CD39-dependent suppressor function in exhausted T cells to limit antitumor immunity

Nature Immunology volume 24, pages 267–279 (2023)Cite this article

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Abstract

CD8+ T cells are critical for elimination of cancer cells. Factors within the tumor microenvironment (TME) can drive these cells to a hypofunctional state known as exhaustion. The most terminally exhausted T (tTex) cells are resistant to checkpoint blockade immunotherapy and might instead limit immunotherapeutic efficacy. Here we show that intratumoral CD8+ tTex cells possess transcriptional features of CD4+Foxp3+ regulatory T cells and are similarly capable of directly suppressing T cell proliferation ex vivo. tTex cell suppression requires CD39, which generates immunosuppressive adenosine. Restricted deletion of CD39 in endogenous CD8+ T cells resulted in slowed tumor progression, improved immunotherapy responsiveness and enhanced infiltration of transferred tumor-specific T cells. CD39 is induced on tTex cells by tumor hypoxia, thus mitigation of hypoxia limits tTex suppression. Together, these data suggest tTex cells are an important regulatory population in cancer and strategies to limit their generation, reprogram their immunosuppressive state or remove them from the TME might potentiate immunotherapy.

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Fig. 1: Terminally exhausted CD8+ T cells infiltrating tumors are functionally suppressive.
Fig. 2: Suppressive function of tTex cells affects many targets and is environment-dependent.
Fig. 3: Ex vivo tTex cell suppression is associated with tTex cell-intrinsic apoptosis.
Fig. 4: tTex cells suppress through CD39-mediated eATP depletion and adenosine production.
Fig. 5: Enforced CD39 expression in Teff cells inhibits intrinsic and neighboring T cell function.
Fig. 6: CD39 on endogenous intratumoral tTex cells suppresses newly infiltrating T cells.
Fig. 7: tTex cell-restricted CD39 deletion bolsters immunotherapeutic efficacy.
Fig. 8: Hypoxia reversibly drives CD39-dependent suppression by tTex cells.

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Data availability

Publicly available fastq files were downloaded from the Gene Expression Omnibus under accession code GSE123235. All other data are present in the article and supplementary files or are available from the corresponding author upon reasonable request. Source data are provided with this paper.

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Acknowledgements

The authors thank all members of the Delgoffe laboratory for helpful and invigorating discussions. We also thank friends and collaborators at the University of Pittsburgh, especially A. Burr and the Hand laboratory. This work was supported by a National Institutes of Health (NIH) Director’s New Innovator Award (DP2AI136598); the National Institute of Allergy and Infectious Disease (R01AI171483, R01AI166598); the Hillman Fellows for Innovative Cancer Research Program; a Stand Up to Cancer–American Association for Cancer Research Innovative Research Grant (SU2C-AACR-IRG-04-16); the Alliance for Cancer Gene Therapy; the UPMC Hillman Cancer Center Skin Cancer and Head and Neck Cancer SPOREs (P50CA121973 and P50CA097190; NIH); the Mark Foundation for Cancer Research’s Emerging Leader Award; and a Cancer Research Institute-Lloyd J. Old STAR Award; and the Sy Holzer Endowed Immunotherapy Fund (all to G.M.D.). Trainees on this manuscript were supported by grants T32CA082084 (NIH) (to P.D.A.V., M.J.W., K.D. and B.R.F.), F30CA247034 and T32GM008208 (NIH) (to P.D.A.V.), F31AI149971 (NIH) (to M.J.W.), F31CA247129 (NIH) (to K.D.). This work used the UPMC Hillman Cancer Center Flow Cytometry and Animal Facilities, supported in part by grant P30CA047904 (NIH). This work was supported by the Health Sciences Sequencing Core at UPMC Children’s Research Hospital of Pittsburgh and the University of Pittsburgh Center for Research Computing. Some images were derived from Biorender.com. We also acknowledge the authors of important papers that we could not cite due to space constraints.

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Author notes

  1. These authors contributed equally: Kristin DePeaux, McLane J. Watson.

Authors and Affiliations

  1. Department of Immunology, University of Pittsburgh, Pittsburgh, PA, USA

    Paolo D. A. Vignali, Kristin DePeaux, McLane J. Watson, Chenxian Ye, Nicole E. Scharping, Dayana B. Rivadeneira & Greg M. Delgoffe

  2. Tumor Microenvironment Center, University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA, USA

    Paolo D. A. Vignali, Kristin DePeaux, McLane J. Watson, Chenxian Ye, Nicole K. McGaa, Nicole E. Scharping, Ashley V. Menk, Dayana B. Rivadeneira & Greg M. Delgoffe

  3. Division of Pediatric Rheumatology, Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA

    B. Rhodes Ford & Amanda C. Poholek

  4. Division of Hematology/Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Konstantinos Lontos

  5. Center for Inflammation Research, Department of Anesthesia, Critical Care & Pain Medicine and Division of Gastroenterology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Simon C. Robson

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Contributions

P.D.A.V. conceived and performed the majority of the experiments, compiled and analyzed data and wrote the manuscript. K.D. performed TIL analysis and suppression assays in the B16-ND4– and axitinib/metformin experiments and contributed to the editing of the text. M.J.W. carried out initial experiments and performed several assays characterizing CD39 overexpression in effector T cells. C.Y. performed critical experiments involving cell death in tTex cells. B.R.F. performed bioinformatic analysis of publicly available data sets comparing Foxp3+ Treg cells to CD8+ TILs and contributed to the editing of the text. K.L. performed detailed statistical analysis on many of the figures. N.K.M. helped characterize CD39 overexpression in Teff cells. N.E.S. supported generation of data characterizing tTex cells. A.V.M. supported generation of metabolic assay data in CD39 overexpression experiments. S.C.R. generously donated the Entpd1f/f mice. A.C.P. supported the bioinformatic analysis. D.B.R. performed several experiments and helped direct the research. G.M.D. conceived of the study, directed the research, obtained funding and wrote the manuscript.

Corresponding author

Correspondence to Greg M. Delgoffe.

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Competing interests

A.V.M. is currently an employee of Novasenta. G.M.D. declares competing financial interests and has submitted patents targeting exhausted T cells that are licensed or pending and is entitled to a share in net income generated from licensing of these patent rights for commercial development. G.M.D. consults for and/or is on the scientific advisory board of BlueSphere Bio, Century Therapeutics, Nanna Therapeutics, Novasenta, Pieris Pharmaceuticals and Western Oncolytics/Kalivir; has grants from bluebird bio, Novasenta, Pfizer, Pieris Pharmaceuticals, TCR2 and Western Oncolytics/Kalivir. G.M.D. owns stock in Novasenta, BlueSphere Bio and RemplirBio. The other authors declare no competing interests.

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Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 PD-1hiTim-3+ terminally exhausted CD8+ T cells are a numerically dominant in tumor and express numerous Treg cell-associated effector molecules.

(a) Gating strategy for isolating CD8+ T cells in B16-F10 tumor or tumor-draining lymph nodes. (b) Quantified distribution of inhibitory receptor expression on CD8+ TIL from Fig. 1a. (c) Total cell numbers per size-matched B16-F10 tumor or draining lymph node. (d) Quantification from Fig. 1c. Mean fluorescence intensity (MFI) of CD4+Foxp3+ Treg cell-associated genes among tumor-infiltrating T cell populations. Statistics are one-way ANOVA (C,D) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 2 Tumor infiltrating CD8+ T cells upregulate Treg cell signature upon terminal differentiation.

(a) Gene set enrichment analysis (GSEA) of tumor-infiltrating Treg cell signature on bulk SLAMF6+ progenitor and Tim-3+ terminally exhausted CD8+ T cell transcripts from Miller et al. (b, c) Heatmap displaying DESeq2 of log2 normalized transcript expression of genes from the tumor-infiltrating Treg cell signature gene set in tetramer+ (b) or bulk (c) progenitor and terminally exhausted CD8 + T cells. Values are transformed log2 (TPM) scaled to row. (d) Heatmap of log2 normalized DESeq2-defined differentially expressed genes (DEG) between progenitor and terminally exhausted CD8+ T cells.

Extended Data Fig. 3 Quantification of tumor sizes and TIL populations from preclinical models with diverse sensitivity to immunotherapy.

(a) Tumor sizes from suppression assay experiments in Figs. 1 and 2(b) (f) Average calculated percent suppression at 1:4 suppressor to responder ratio from all replicate experiments in Fig. 1 and supplementary Fig. 3r. (G) Percent of CD8+PD-1+Tim-3+ tTexh cells in various murine tumor models. Bivariant plots and histograms are representative of ≥3 experiments. Statistics are one-way ANOVA (A-C) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 4 tTex cells do not suppress via IL-10 secretion or direct cytotoxicity.

(a, b) Day 14 B16-F10 tumor sizes from Il10–/– experiments and matched CD8+ T cell infiltrate. (c) Suppression assay of B16-F10 infiltrating tTex cells co-cultured with isotype IgG or neutralizing IL-10 antibody at 2.5 or 5.0 𝜇g/mL. (d) Gating strategy for calculation of cell viability in suppression assay populations. (e) Calculated averages of ‘responder’ or ‘APC’ population cell viability per dilution series of ‘suppressor’ T cells from Fig. 1g. (f) Cell viability of responder T cell populations from Fig. 3b. (g) Suppression assay of B16-F10 infiltrating tTex cells co-cultured with CTV-labeled responding T cells with either live or Mytomycin C (MC)-killed T cell-depleted splenocytes, or anti-CD3/anti-CD28 bound microbeads. (h) Cell viability of ‘suppressor’ populations from Fig. 1g. (i) Cell viability of ‘suppressor’ populations from Fig. 3d, e. Statistics are Mann-Whitney (A,B,F,H), linear regression (C) and one-way ANOVA (G,I) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 5 tTex cells suppress through CD39-mediated extracellular ATP depletion and adenosine production.

(a, b) Human CD8+ T cell populations from five melanoma biopsies from treatment-naïve patients and corresponding CD39 expression. (c) Sorting strategy for Fig. 4b. (d) Percent CD8+ T cells expressing CD39 in various tissues in a B16-F10 tumor-bearing C57/BL6 mice. (e, f) Suppression assays of B16-F10 infiltrating CD4+ Treg cells, co-cultured (E) with activated CTV-labeled C57/BL or Nt5e– responding T cells or (F) with DMSO vehicle or A2AR/A2BR small-molecule inhibitor AB928 at 3 𝜇g/mL. Statistics are one-way ANOVA (B), and linear regression (E,F) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 6 Cd4-driven Cre recombinase expression efficiently deletes CD39 on CD8+ TIL.

(a, b) TIL analysis of CD39 and inhibitory expression in CD8+ T cells from Cd4CreEntpd1f/f mice. (c) Cell counts per milligram of tumor mass from experiments in Extended Data Fig. 6a, b. (d) Tumor areas at day 14 from experiments in Fig. 2f. (e) Suppression assay of Cd4CreEntpd1f/f Thy1.1+CD44+ OT-I Teff cells isolated from day 8 of acute VacciniaOVA infection. (f) CD39 expression on TIL tTex cells or VacciniaOVA OT-I T cells in suppression assay co-cultures. (g) Suppression assay of B16-F10-derived CD8+ tTex cells co-cultured with OT-I TCR transgenic CD8+ T cells. Culture were stimulated with either anti-CD3 antibodies as before, or OT-I specific peptide, SIIFEKL. (h–j) Flow cytometric analysis of TIL following suppression assay from Extended Data Fig. 6g. Statistics are Mann-Whitney (B–D,H-J) and linear regression (E,G) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 7 Enforced CD39 expression on CD8+ T cells limits metabolic reprogramming via disruption of TCR signaling.

(a) Extracellular acidification rate (ECAR) in in-Seahorse activation of rested CD8 + T cells stability transduced with pMSCV or pMSCV-Entpd1. (b) Delta-maximal ECAR quantified by basal ECAR – maximal ECAR; minutes till maximal ECAR per sample well. (c) Oxygen consumption rate (OCR) of cells from Extended Data Fig. 7c with (d) quantified basal OCR. (e) Intracellular calcium flux in day 7 transduced T cells restimulated in a calcium-buffered solution with anti-CD3/anti-CD28-bound microbeads in the presence of calcium indicators, Fluo-4 and Fura Red. (f) Cytokine production of transduced T cells following 24-hour anti-CD3/anti-CD28-bound microbeads stimulation. (g) ELISA of supernatants from repeat experiments of Extended Data Fig. 7f. Statistics are Mann-Whitney (B,D,G) and one-way ANOVA (E,F) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 8 E8i-driven Cre recombinase expression efficiently deletes CD39 on CD8+ TIL.

(a) CD39 expression in day 14 B16-F10 tumor-infiltrating CD8+ T cells from Entpd1f/f or E8iCre-ERT2Entpd1f/f mice. (b) Cytokine production following PMA/Ionomycin re-simulation of day 14 CD8+ TIL. (c, d) Infiltration of adoptively transferred pmel-I T cells into tumor-draining lymph nodes (dLN; C) and (D) quantification of pmel-I percentages in a representative experiment. (e) Ex vivo cytokine production of pmel-I T cells following gp100 re-stimulation. (f) TIL analysis of CD39 expression in CD8+ T cells from E8iCre-ERT2Entpd1f/f mice treated for three consecutive days with tamoxifen. (g, h) Representative flow cytometry plots from Fig. 7e, f. Statistics are one-way ANOVA (B,D,E) and Mann-Whitney (F) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

Source data

Extended Data Fig. 9 Tumor hypoxia enforces CD39 expression on tTex cells.

(a) Histogram overlay displaying hypoxia exposure in CD8+ dLN and TIL from B16-F10 tumors. (b) CD39 staining in exhausted T cells from B16-F10 or MC38, and (c) Pimonidazole staining of bulk TIL. (d) Continuous Activation under Hypoxia (CS + H) assay. In brief, naïve T cells are activated for 24 hours, then split into treatment groups of removal (acute stimulation) or continued presence (continuous stimulation) of anti-CD3/anti-CD28-bound microbeads and cultured under atmospheric oxygen tensions (~20% O2) or tumor hypoxic conditions (1.5% O2) for 5 days. (e) Inhibitory receptor staining in murine day 6 CS + H cells. (f–h) Validation of humanized CS + H assay with healthy donor PBMC-derived CD8+ T cells via staining of (F) inhibitory receptors and (g) enzymes of adenosine metabolism. (h) 24-hour PMA/Ionomycin re-stimulation of human CS + H cells. Statistics are Mann-Whitney (B,C), one-way ANOVA (H) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

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Extended Data Fig. 10 Tumor hypoxia mitigation as a therapeutic target to lessen tTex cell-mediated suppression.

(a) Extracellular flux analysis showing validation of mitochondrial respiration knockdown (via OCR measurement) in B16ND4– tumor cells versus parental B16-F10. (b) PD-1 and Tim-3 staining in TIL from day 14 wild-type B16-F10 or B16ND4– tumors. (c) Schematic of treatment plan for therapeutic alleviation of tumor hypoxia. (d) Tumor sizes at treatment initiation and sacrifice in Axitinib/metformin experiments. (e) PD-1 and Tim-3 staining in TIL from Axitinib/metformin experiments in Extended Data Fig. 9d. (f) Suppression assays of CD4+Foxp3+ Treg cells from Fig. 9c. (g) Suppression assays of CD4+Foxp3+ Treg cells from Fig. 9f. Statistics are Mann-Whitney (B), one-way ANOVA (E) and linear regression (F,G) with ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001 and ∗∗∗∗p < 0.0001.

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Vignali, P.D.A., DePeaux, K., Watson, M.J. et al. Hypoxia drives CD39-dependent suppressor function in exhausted T cells to limit antitumor immunity. Nat Immunol 24, 267–279 (2023). https://doi.org/10.1038/s41590-022-01379-9

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