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CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors

Nature Immunology volume 21, pages 298–308 (2020)Cite this article

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Abstract

Depleting regulatory T cells (Treg cells) to counteract immunosuppressive features of the tumor microenvironment (TME) is an attractive strategy for cancer treatment; however, autoimmunity due to systemic impairment of their suppressive function limits its therapeutic potential. Elucidating approaches that specifically disrupt intratumoral Treg cells is direly needed for cancer immunotherapy. We found that CD36 was selectively upregulated in intrautumoral Treg cells as a central metabolic modulator. CD36 fine-tuned mitochondrial fitness via peroxisome proliferator-activated receptor-β signaling, programming Treg cells to adapt to a lactic acid-enriched TME. Genetic ablation of Cd36 in Treg cells suppressed tumor growth accompanied by a decrease in intratumoral Treg cells and enhancement of antitumor activity in tumor-infiltrating lymphocytes without disrupting immune homeostasis. Furthermore, CD36 targeting elicited additive antitumor responses with anti-programmed cell death protein 1 therapy. Our findings uncover the unexplored metabolic adaptation that orchestrates the survival and functions of intratumoral Treg cells, and the therapeutic potential of targeting this pathway for reprogramming the TME.

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Fig. 1: Intratumoral Treg cells elevate the expression of CD36 and genes involved in lipid metabolism.
Fig. 2: Disruption of CD36 selectively impairs the accumulation and suppressive function of intratumoral Treg cells.
Fig. 3: CD36 expression selectively supports suppressive activity of intratumoral Treg cells.
Fig. 4: CD36 deficiency stimulates apoptosis in intratumoral Treg cells.
Fig. 5: PPAR-β signaling is required for metabolic adaptation in intratumoral Treg cells.
Fig. 6: CD36 targeting impairs intratumoral Treg cells and primes tumors to PD-1 blockade.

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Data availability

The RNA-Seq data for intratumoral Treg cells are available in the Gene Expression Omnibus database under accession code GSE139325. All relevant data are available from the corresponding author upon request.

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Acknowledgements

We thank L.-F. Lu and W.-L. Lo for critical reading and comments. We also thank Y. Maeda and H. Nishikawa for helpful discussion. P.-C.H. was supported in part by the SNSF (project grants 31003A_163204 and 31003A_182470), the Swiss Cancer Foundation (KFS-3949-08-2016), the Swiss Institute for Experimental Cancer Research (ISREC 26075483), a European Research Council Staring Grant (802773-MitoGuide), the Cancer Research Institute Clinic and Laboratory Integration Program award and the SITC-MRA Young Investigator Award. C.J. is supported by the SNSF (project grants PMPDP3_129022 and PZ00P3_161459). A.Z. is supported by the SNSF (project grant 320030_162575) and Cancer League Switzerland (KFS-3394-02-2014). R.S. and I.G. are supported by NIH funding (P01 HL46403, P01 HL087018 and R01 HL142152 to R.S. and HL45095 and HL073029 to I.G.). E.M. acknowledges funding from the Swiss Cancer Research Foundation (KFS-3681-08-2015-R). S.-M.F. acknowledges funding from an FWO grant and projects, as well as KU Leuven Methusalem co-funding. J.F.-G. is supported by an FWO postdoctoral fellowship. J.D.W and T.M. are supported by NIH funding (P30 CA008748 and R01 CA056821), Swim Across America, the Ludwig Institute for Cancer Research, the Parker Institute for Cancer Immunotherapy and the Breast Cancer Research Foundation. R.Z. is supported by the Parker Institute for Cancer Immunotherapy Bridge Scholar Award. We also appreciate the support provided by the Electron Microscopy Facility at the University of Lausanne and the Biomedical Sequencing Facility at the Research Center for Molecular Medicine of the Austrian Academy of Sciences.

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Authors and Affiliations

  1. Department of Oncology, University of Lausanne, Lausanne, Switzerland

    Haiping Wang, Fabien Franco, Yao-Chen Tsui, Chin-Hsien Tsai, Florence Picard, Camilla Jandus & Ping-Chih Ho

  2. Ludwig Institute of Cancer Research, University of Lausanne, Lausanne, Switzerland

    Haiping Wang, Fabien Franco, Yao-Chen Tsui, Chin-Hsien Tsai, Florence Picard, Camilla Jandus & Ping-Chih Ho

  3. Center for Genomics and Systems Biology, NYU Abu Dhabi, Abu Dhabi, United Arab Emirates

    Xin Xie & Syed Raza Mohmood

  4. Department of Biomedicine, Laboratory of Cancer Immunology, University Hospital and University of Basel, Basel, Switzerland

    Marcel P. Trefny & Alfred Zippelius

  5. Ludwig Collaborative and Swim Across America Laboratory, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Roberta Zappasodi, Isabell Schulze, Jedd D. Wolchok & Taha Merghoub

  6. Parker Institute for Cancer Immunotherapy, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Roberta Zappasodi, Isabell Schulze, Jedd D. Wolchok & Taha Merghoub

  7. Laboratory of Cellular Metabolism and Metabolic Regulation, VIB-KU Leuven Center for Cancer Biology, Leuven, Belgium

    Juan Fernández-García & Sarah-Maria Fendt

  8. Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute, Leuven, Belgium

    Juan Fernández-García & Sarah-Maria Fendt

  9. Swiss Institute for Experimental Cancer Research, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Etienne Meylan

  10. Department of Medicine, Medical College of Wisconsin, Wauwatosa, WI, USA

    Roy Silverstein

  11. Department of Medicine, NYU Langone Health, New York, NY, USA

    Ira Goldberg

  12. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jedd D. Wolchok & Taha Merghoub

  13. Weill Cornell Medicine, New York, NY, USA

    Jedd D. Wolchok

  14. Medical Oncology, University Hospital Basel, Basel, Switzerland

    Alfred Zippelius

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Contributions

H.W. and P.-C.H. contributed to overall project design and wrote the manuscript. H.W., F.F., Y.-C.T., C.-H.T. and F.P. performed the in vitro and in vivo animal works and data analysis. X.X. and S.R.M. performed analysis of the RNA-Seq results. H.W., M.P.T., R.Z., J.D.W., T.M., C.J., I.S. and A.Z. conducted collection and flow cytometry analysis of the human samples. J.F.-G. and S.-M.F. supported the metabolomite analysis. R.S. and I.G. provided the hybridoma clone for anti-CD36 antibody production and CD36flox mice, respectively. E.M. provided samples of NSCLC murine models.

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Correspondence to Ping-Chih Ho.

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Competing interests

H.W. and P.-C.H. are inventors on a patent application related to targeting CD36 in cancer immunotherapy. P.-C.H. is serving as a member of the scientific advisory board for Elixiron Immunotherapeutics and receiving research grants from Roche and Idorsia. J.D.W. is serving as a consultant for Adaptive Biotechnologies, Advaxis, Amgen, Apricity, Array BioPharma, Ascentage Pharma, Astellas, Bayer, BeiGene, Bristol-Myers Squibb. Celgene, Chugai, Elucida, Eli Lilly, F-Star, Genentech, Imvaq, Janssen, Kyowa Hakko Kirin, Kleo Pharmaceuticals, Linnaeus, MedImmune, Merck, Neon Therapeutics, Northern Biologics, Ono, Polaris Pharma, Polynoma, PsiOxus, PureTech, Recepta, Takara Bio, Trieza, Sellas Life Sciences, Serametrix, Surface Oncology, Syndax and Synthologic. J.D.W. received research support from Bristol-Myers Squibb, MedImmune, Merck and Genentech and has equity in Potenza Therapeutics, Tizona Pharmaceuticals, Adaptive Biotechnologies, Elucida, Imvaq, BeiGene, Trieza and Linnaeus. P.-C.H. received an honorarium from Pfizer and Chugai. J.D.W. received an honorarium from Esanex.

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Extended data

Extended Data Fig. 1 Lipid accumulation and increased CD36 expression in intratumoral Treg cells.

a, b, Quantitative results of geometric mean (GeoMean) fluorescent intensity of Bodipy FL C12 (a) and Bodipy 493/503 (Bodipy) (b) in Treg cells from paired TILs and PBMCs of non-small cell lung cancer (NSCLC) patients (n=6 per group). c, d, Quantitative results of GeoMean fluorescent intensity of Bodipy FL C12 (c) and Bodipy 493/503 (d) in Treg cells from paired PBMC and tumor infiltrated lymph nodes (TILNs) of melanoma patients (n=19). e, f, Quantitative results of fluorescent intensity of Bodipy FL C12 in Treg cells from indicated tissues of B16 melanoma-bearing mice (dLN, n=7; Spleen, n=6; Thymus, n=7; Tumor, n=6) (e), and MC38 colon carcinoma-bearing mice (n=8) (f). g, Quantitative result of GeoMean fluorescent intensity of CD36 surface staining in Treg cells from paired TILs and PBMCs of NSCLC patients (n=6) h, i, j, k, Quantitative results of surface expression of CD36 in Treg cells of indicated tissues from B16-OVA melanoma-bearing B6 mice (n=6, one outlier was removed from dLN) (h), inducible Braf/Pten melanoma-bearing mice (n=9) (i), K-rasLSL-G12D/+/p53fl/fl mouse model of NSCLC (Blood, n=8; Tumor, n=13) (j), and MC38 colon cancer (n=8) (k). l, CD36 expression in iTreg cells cultured in different indicated conditions for 48h. (RPMI: normal cell culture RPMI 1640 medium indicated in methods; CM, cancer cell conditioned medium, n=4 per group). m, CD36 expression in iTreg cells cultured in cancer cell-conditioned medium treated with control procedure or lipid removal procedure for 48h. (n=6 per group). Data are representative result of at least two independent experiments with similar results (l, m) or cumulative results from at least two independent experiments (a, b, c, d, e, f, g, h, i, j, k). Each symbol represents one individual. Data are mean ± S.D. and were analyzed by two-tailed, unpaired Student’s t-test (e, f, h, i, j, k, l, m) or two-tailed, paired Student’s t-test (a, b) or one-tailed, paired Student’s t-test (c, d, g).

Source data

Extended Data Fig. 2 CD36 expression supports the accumulation and suppressive function of intratumoral Treg cells.

a, Body weight of WT and TregCd36-/- mice at the age of 21–23 weeks (WT male, n=4; TregCd36-/- male, n=6; WT female, n=6; TregCd36-/- female, n=5;). b, Representative plots (left) and quantitative frequency of CD44hi/CD62Llow CD4+ or CD8+ T cells (right) in aged WT and TregCd36-/- mice (n=7 per group). c, Representative images of hematoxylin and eosin (H&E) staining for indicated tissues from WT or TregCd36-/- mice at the age of 21–23 weeks. Scale bar, 200 µm. d, e, Tumor growth of B16-OVA melanoma (n=7 per group) (d) or MC38 colon carcinoma (n=6 per group) (e) from WT or TregCd36-/- mice. f, g, h, Absolute number of FoxP3+ Treg cells per gram tumor (f), percentage of CD8+ T cells out of CD3+ T cells among tumor-infiltrating T cells (g), and the ratio of CD8+ to Treg cell TIL density (h) of YUMM1.7 melanoma-bearing WT and TregCd36-/- mice (n=11 per group). i, Representative plots (left) and percentage of indicated cytokine-producing CD4+ T cells among total tumor-infiltrating CD4+ T cells from indicated mice (right) (n=5 per group). Data are representative result of at least two independent experiments with similar results (c, d, e, i) or cumulative results from at least two independent experiments (a, b, f, g, h) Each symbol represents one individual. Data are mean ± S.D. (a, b, f, g, h, i) or ± S.E.M. (d, e) and were analyzed by two-tailed, unpaired Student’s t-test.

Source data

Extended Data Fig. 3 Effects of CD36 in expression of activation markers and stability of intratumoral Treg cells.

a, Representative images of guts (a) and spleens (b) from indicated group of Rag1-/- mice. c, d, e, Expression of CD44 (n=17) (c), CD103 (n=5) (d), and KLRG1 (n=5) (e) in intratumoral Treg cells of YUMM1.7 melanoma-bearing WT and TregCd36-/- mice. f, The expression of YFP in intratumoral Treg cells of YUMM1.7 melanoma-bearing WT and TregCd36-/- mice (n=15). g, h, i, Representative plots of IFNγ and TNF production among total intratumoral Treg cells from indicated mice (g), and quantitative result of percentage of IFNγ-producing (n=19 per group) (h) and TNF-producing (n=18, one outlier was removed from TregCd36-/-) (i) Treg cells among total intratumoral Treg cells of indicated mice. j, Expression of Ki67 in intratumoral Treg cells of YUMM1.7 melanoma-bearing WT and TregCd36-/- mice (n=10 per group). k, Representative histograms (left) and quantitative analysis (right) of Annexin V staining in intratumoral Treg cells from WT and TregCd36-/- tumor-bearing mice (n=14 per group). l, Quantitative analysis of cleaved caspase-3 levels in Treg cells of indicated tissues from WT (n=13 per group except for thymus, n=9) and TregCd36-/- (n=14 per group except for thymus, n=9) tumor-bearing mice. LN: non-draining lymph node; DLN: draining lymph node. Data are representative results of two independent experiments with similar results (a, b, d, e) or cumulative results from at least three independent experiments (c, f, g, h, i, j, k, l). Each symbol represents one individual. Data are mean ± S.D. (c, d, e, f, h, i, j, k, l) and were analyzed by two-tailed, unpaired Student’s t-test.

Source data

Extended Data Fig. 4 CD36-deficiency results in a metabolic shift and elevated apoptosis in Treg cells.

a, Indicated iTreg cells cultured in cancer cell-conditioned medium for 48 hrs (n=3 per group). Oxygen consumption rate (OCR) of indicated iTreg cells was measured and then followed by treatment with oligomycin, FCCP, and antimycin A plus Rotenone (n≥4 per group). b, Indicated iTreg cells cultured in cancer cell-conditioned medium for 48 hrs (n≥4 per group) and then media were refreshed with Seahorse Flux assay media without glucose. Basel extracellular acidification rate (ECAR) of indicated iTreg cells was measured and then followed by treatment with glucose, oligomycin, FCCP and 2-DG (n=4 per group). c, Quantitative result of glycolysis and glycolytic capacity based on the measurement of b. d, The viability of either WT or TregCd36-/- iTreg cells cultured under indicated conditions for 72 hrs (n=6 per group). Data are representative results of three independent experiments with similar results (a, b, c, d). Data are mean ± S.D. and were analyzed by two-tailed, unpaired Student’s t-test (c, d).

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Extended Data Fig. 5 Intratumoral Treg cells require PPAR-β, not PPAR-γ, signaling for metabolic adaptation.

a, Enrichment plots of signals controlling mitochondrial matrix (left) and mitochondrial envelope in intratumoral Treg (n=4) compared to PBMC Treg cells (n=6), identified by GSEA computational method. ES: enrichment score; NES: normalized enrichment score; FDR: false discovery rate; NOM p-val: Nominal p value. b, c, d, Percentage of FoxP3+ Treg cells among CD4+ tumor-infiltrating T lymphocytes (n=5) (b), tumor growth (n=5) (c) and tumor weight (n=7) (d) from tumor-bearing WT and TregPPARγ-/- mice. e, Percentage of CD8+ T cells among tumor-infiltrating T cells from tumor-bearing WT and TregPPARβ-/- mice (n=10). f, Quantitative result of CD36+ intratumoral Treg cells from YUMM1.7 melanoma-bearing WT and TregPPARβ-/- mice (WT, n=14; TregPPARβ-/-, n=11). g, NAD/NADH ratio of indicated iTreg cells cultured in cancer cell-conditioned medium with DMSO or PPAR-β agonist for 48h (DMSO, n=8; PPAR-β agonist, n=10). Data are representative results of at least two independent experiments with similar results (b, c, d) or cumulative results from at least two independent experiments (e, f, g). Each symbol represents one individual. Data are mean ± S.D. (b, d, e, f, g) or ± S.E.M. (c) and were analyzed by two-tailed, unpaired Student’s t-test.

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Extended Data Fig. 6 CD36-targeting unleashes host antitumor immunity.

a, b, c, d, Absolute number of FoxP3+ Treg cells per gram tumor (n=10 per group) (a), percentage of CD8+ T cells among tumor-infiltrating T cells (n=10 per group) (b) and representative plots and percentage of indicated cytokine-producing CD8+ T cells among total tumor-infiltrating CD8+ T cells (c) and CD4+ T cells among total tumor-infiltrating CD4+ T cells (d) from YUMM1.7 melanoma-bearing mice treated with indicated treatments (n=10 per group). e, f, Tumor growth (e) and survival curves (f) of YUMM1.7 melanoma-bearing B6 mice treated with indicated treatments (Ctrl, n = 10; α-PD1, n = 10; α-CD36, n = 11; α-CD36 + αPD-1, n = 11). Arrows indicate the date of treatment. Dotted lines indicate the tumor volume of 800 mm3. Data are cumulative results from at least two independent experiments. Each symbol represents one individual. Data are mean ± S.D. and were analyzed by two-tailed, unpaired Student’s t-test (a, b, c, d). Difference between survival curves was analyzed by Log-rank (Mantel-Cox) test (f).

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Wang, H., Franco, F., Tsui, YC. et al. CD36-mediated metabolic adaptation supports regulatory T cell survival and function in tumors. Nat Immunol 21, 298–308 (2020). https://doi.org/10.1038/s41590-019-0589-5

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