Uncoupling protein 2 reprograms the tumor microenvironment to support the anti-tumor immune cycle

A Publisher Correction to this article was published on 12 March 2019

This article has been updated

Abstract

Immune checkpoint blockade therapy has shifted the paradigm for cancer treatment. However, the majority of patients lack effective responses due to insufficient T cell infiltration in tumors. Here we show that expression of mitochondrial uncoupling protein 2 (UCP2) in tumor cells determines the immunostimulatory feature of the tumor microenvironment (TME) and is positively associated with prolonged survival. UCP2 reprograms the immune state of the TME by altering its cytokine milieu in an interferon regulatory factor 5-dependent manner. Consequently, UCP2 boosts the conventional type 1 dendritic cell- and CD8+ T cell-dependent anti-tumor immune cycle and normalizes the tumor vasculature. Finally we show, using either a genetic or pharmacological approach, that induction of UCP2 sensitizes melanomas to programmed cell death protein-1 blockade treatment and elicits effective anti-tumor responses. Together, this study demonstrates that targeting the UCP2 pathway is a potent strategy for alleviating the immunosuppressive TME and overcoming the primary resistance of programmed cell death protein-1 blockade.

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Fig. 1: UCP2 expression is associated with elevated T cell infiltration and prolonged survival rates.
Fig. 2: UCP2 expression is associated with anti-tumor immune states and cDC1 and CD8+ T cell infiltration in tumors.
Fig. 3: UCP2 overexpression in melanoma cells induces anti-tumor responses and normalizes tumor vasculature.
Fig. 4: UCP2-induced anti-tumor responses are associated with the immune state and are dependent on CD103+ dendritic cells.
Fig. 5: UCP2-stimulated IRF5-CXCL10 axis supports engagement of the cDC1-CD8+ T cell anti-tumor cycle.
Fig. 6: UCP2-mediated tumor suppression is independent of the β-catenin-ATF3 pathway and PGE2 production.
Fig. 7: UCP2 induction sensitizes melanoma to PD-1 blockade therapy.

Data availability

All other relevant data are available from the corresponding author on request.

Change history

  • 12 March 2019

    In the version of this article initially published, the bars were not aligned with the data points or horizontal axis labels in Fig. 5d, and the labels along each horizontal axis of Fig. 5j–l indicating the presence (+) or absence (–) of doxycycline (Dox) were incorrectly included with the labels below that axis. Also, the right vertical bar above Fig. 7b linking ‘P = 0.0001’ to the key was incorrect; the correct comparison is αPD-1 versus Dox + αPD-1. Similarly, the right vertical bar above Fig. 7e linking ‘P = 0.0002’ to the key was incorrect; the correct comparison is αPD-1 versus Rosig + αPD-1. The errors have been corrected in the HTML and PDF versions of the article.

References

  1. 1.

    Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).

    CAS  Article  Google Scholar 

  2. 2.

    Borghaei, H. et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N. Engl. J. Med. 373, 1627–1639 (2015).

    CAS  Article  Google Scholar 

  3. 3.

    Ferris, R. L. et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 375, 1856–1867 (2016).

    Article  Google Scholar 

  4. 4.

    Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Topalian, S. L., Drake, C. G. & Pardoll, D. M. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell 27, 450–461 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Gajewski, T. F. The next hurdle in cancer immunotherapy: overcoming the non-T-cell-inflamed tumor microenvironment. Semin. Oncol. 42, 663–671 (2015).

    Article  Google Scholar 

  8. 8.

    Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    CAS  Article  Google Scholar 

  9. 9.

    Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic beta-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).

    CAS  Article  Google Scholar 

  10. 10.

    Bottcher, J. P. et al. NK cells stimulate recruitment of cdc1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037 e1014 (2018).

    CAS  Article  Google Scholar 

  11. 11.

    Spranger, S. & Gajewski, T. F. Impact of oncogenic pathways on evasion of antitumour immune responses. Nat. Rev. Cancer 18, 139–147 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Fuertes, M. B. et al. Host type I IFN signals are required for antitumor CD8+ T cell responses through CD8{alpha}+ dendritic cells. J. Exp. Med. 208, 2005–2016 (2011).

    CAS  Article  Google Scholar 

  13. 13.

    Woo, S. R. et al. STING-dependent cytosolic DNA sensing mediates innate immune recognition of immunogenic tumors. Immunity 41, 830–842 (2014).

    CAS  Article  Google Scholar 

  14. 14.

    Horimoto, M. et al. Expression of uncoupling protein-2 in human colon cancer. Clin. Cancer Res. 10, 6203–6207 (2004).

    CAS  Article  Google Scholar 

  15. 15.

    Pons, D. G. et al. UCP2 inhibition sensitizes breast cancer cells to therapeutic agents by increasing oxidative stress. Free Radic. Biol. Med. 86, 67–77 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Derdak, Z. et al. The mitochondrial uncoupling protein-2 promotes chemoresistance in cancer cells. Cancer Res. 68, 2813–2819 (2008).

    CAS  Article  Google Scholar 

  17. 17.

    Esteves, P. et al. Mitochondrial retrograde signaling mediated by UCP2 inhibits cancer cell proliferation and tumorigenesis. Cancer Res. 74, 3971–3982 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Imai, K. et al. UCP2 expression may represent a predictive marker of neoadjuvant chemotherapy effectiveness for locally advanced uterine cervical cancer. Oncol. Lett. 14, 951–957 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Pecqueur, C. et al. Uncoupling protein-2 controls proliferation by promoting fatty acid oxidation and limiting glycolysis-derived pyruvate utilization. FASEB J. 22, 9–18 (2008).

    CAS  Article  Google Scholar 

  20. 20.

    Bouillaud, F. UCP2, not a physiologically relevant uncoupler but a glucose sparing switch impacting ROS production and glucose sensing. Biochim. Biophys. Acta 1787, 377–383 (2009).

    CAS  Article  Google Scholar 

  21. 21.

    Gatza, M. L., Silva, G. O., Parker, J. S., Fan, C. & Perou, C. M. An integrated genomics approach identifies drivers of proliferation in luminal-subtype human breast cancer. Nat. Genet. 46, 1051–1059 (2014).

    CAS  Article  Google Scholar 

  22. 22.

    Harlin, H. et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 69, 3077–3085 (2009).

    CAS  Article  Google Scholar 

  23. 23.

    Parikh, J. R., Klinger, B., Xia, Y., Marto, J. A. & Bluthgen, N. Discovering causal signaling pathways through gene-expression patterns. Nucleic Acids Res. 38, W109–W117 (2010).

    CAS  Article  Google Scholar 

  24. 24.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 17, 1206 (2016).

    CAS  Article  Google Scholar 

  27. 27.

    Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  Article  Google Scholar 

  28. 28.

    Hildner, K. et al. Batf3 deficiency reveals a critical role for CD8alpha+ dendritic cells in cytotoxic T cell immunity. Science 322, 1097–1100 (2008).

    CAS  Article  Google Scholar 

  29. 29.

    Spranger, S., Dai, D., Horton, B. & Gajewski, T. F. Tumor-residing batf3 dendritic cells are required for effector T cell trafficking and adoptive T cell therapy. Cancer Cell 1, 711–723.e4 (2017).

    Article  Google Scholar 

  30. 30.

    Roberts, E. W. et al. Critical role for cd103(+)/cd141(+) dendritic cells bearing ccr7 for tumor antigen trafficking and priming of T cell immunity in melanoma. Cancer Cell 30, 324–336 (2016).

    CAS  Article  Google Scholar 

  31. 31.

    Ho, P. C. et al. Immune-based antitumor effects of BRAF inhibitors rely on signaling by CD40L and IFNgamma. Cancer Res. 74, 3205–3217 (2014).

    CAS  Article  Google Scholar 

  32. 32.

    Dalla Pozza, E. et al. Role of mitochondrial uncoupling protein 2 in cancer cell resistance to gemcitabine. Biochim. Biophys. Acta 1823, 1856–1863 (2012).

    CAS  Article  Google Scholar 

  33. 33.

    Kageyama, Y. et al. Leu-574 of human hif-1alpha is a molecular determinant of prolyl hydroxylation. FASEB J. 18, 1028–1030 (2004).

    CAS  Article  Google Scholar 

  34. 34.

    Tian, L. et al. Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming. Nature 544, 250–254 (2017).

    CAS  Article  Google Scholar 

  35. 35.

    Huang, Y. et al. Improving immune-vascular crosstalk for cancer immunotherapy. Nat. Rev. Immunol. 18, 195–203 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Algood, H. M. & Flynn, J. L. CCR5-deficient mice control Mycobacterium tuberculosis infection despite increased pulmonary lymphocytic infiltration. J. Immunol. 173, 3287–3296 (2004).

    Article  Google Scholar 

  37. 37.

    Ren, J., Chen, X. & Chen, Z. J. IKKbeta is an IRF5 kinase that instigates inflammation. Proc. Natl Acad. Sci. USA 111, 17438–17443 (2014).

    CAS  Article  Google Scholar 

  38. 38.

    Andrilenas, K. K. et al. DNA-binding landscape of IRF3, IRF5 and IRF7 dimers: implications for dimer-specific gene regulation. Nucleic Acids Res. 46, 2509–2520 (2018).

    CAS  Article  Google Scholar 

  39. 39.

    Uccellini, L. et al. IRF5 gene polymorphisms in melanoma. J. Transl. Med. 10, 170 (2012).

    CAS  Article  Google Scholar 

  40. 40.

    Zelenay, S. et al. Cyclooxygenase-dependent tumor growth through evasion of immunity. Cell 162, 1257–1270 (2015).

    CAS  Article  Google Scholar 

  41. 41.

    Curran, M. A., Montalvo, W., Yagita, H. & Allison, J. P. PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors. Proc. Natl Acad. Sci. USA 107, 4275–4280 (2010).

    CAS  Article  Google Scholar 

  42. 42.

    Bugge, A. et al. A novel intronic peroxisome proliferator-activated receptor gamma enhancer in the uncoupling protein (UCP) 3 gene as a regulator of both UCP2 and -3 expression in adipocytes. J. Biol. Chem. 285, 17310–17317 (2010).

    CAS  Article  Google Scholar 

  43. 43.

    Villarroya, F., Iglesias, R. & Giralt, M. PPARs in the control of uncoupling proteins gene expression. PPAR Res. 2007, 74364 (2007).

    Article  Google Scholar 

  44. 44.

    Bechmann, I. et al. Brain mitochondrial uncoupling protein 2 (UCP2): a protective stress signal in neuronal injury. Biochem. Pharmacol. 64, 363–367 (2002).

    CAS  Article  Google Scholar 

  45. 45.

    Hass, D. T. & Barnstable, C. J. Uncoupling protein 2 in the glial response to stress: implications for neuroprotection. Neural Regen. Res. 11, 1197–1200 (2016).

    Article  Google Scholar 

  46. 46.

    Sun, S. & Zhou, J. Molecular mechanisms underlying stress response and adaptation. Thorac. Cancer 9, 218–227 (2018).

    Article  Google Scholar 

  47. 47.

    Schmittnaegel, M. et al. Dual angiopoietin-2 and VEGFA inhibition elicits antitumor immunity that is enhanced by PD-1 checkpoint blockade. Sci. Transl. Med. 9, eaak9670 (2017).

  48. 48.

    Tsapogas, P. et al. In vivo evidence for an instructive role of fms-like tyrosine kinase-3 (FLT3) ligand in hematopoietic development. Haematologica 99, 638–646 (2014).

    CAS  Article  Google Scholar 

  49. 49.

    Dankort, D. et al. Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat. Genet. 41, 544–552 (2009).

    CAS  Article  Google Scholar 

  50. 50.

    Meeth, K., Wang, J. X., Micevic, G., Damsky, W. & Bosenberg, M. W. The YUMM lines: a series of congenic mouse melanoma cell lines with defined genetic alterations. Pigment Cell Melanoma Res. 29, 590–597 (2016).

    CAS  Article  Google Scholar 

  51. 51.

    Barbie, D. A. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009).

    CAS  Article  Google Scholar 

  52. 52.

    Schelker, M. et al. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).

    Article  Google Scholar 

  53. 53.

    Ho, P. C. et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell responses. Cell 162, 1217–1228 (2015).

    CAS  Article  Google Scholar 

  54. 54.

    Gerard, A. et al. Secondary T cell-T cell synaptic interactions drive the differentiation of protective CD8+ T cells. Nat. Immunol. 14, 356–363 (2013).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This study was supported in part by the Swiss Institute for Experimental Cancer Research (no. ISREC 26075483), the Swiss Cancer Foundation (no. KFS-3949-08-2016), a SNSF project grant (no. 31003A_163204), a Clinic and Laboratory Integration Program award from the Cancer Research Institute, a Harry J. Lloyd Charitable Trust Career Development Grant, a Roch-pRED grant and a SITC-MRA Young Investigator Award to P.-C.H. A.Z. and P.R. are supported by SNSF project grants (no. 320030_162575 to A.Z., nos. CRSII3_160708 and 31003A_156469 to P.R.). T.P. is supported by the MEDIC Foundation and Swiss Cancer League (no. KLS 3406-02-2016) and G.C. is supported by the Giorgi-Cavaglieri Foundation. We also thank Camilla Jandus for providing human melanoma cell lines.

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W.-C.C. and P.-C.H. contributed to overall project design and wrote the manuscript. W.-C.C., Y.-C.T., S.R. and F.F. performed in vitro and in vivo animal experiments and data analysis. V.H.K., H.L., A.Z. and K.M. conducted the collection and immunohistochemical staining of human melanoma samples. V.H.K. and K.M. examined pathological sections. M.M. and G.C. performed computational analyses of TCGA datasets and single-cell RNA-seq. B.T., D.S. and P.R. provided essential materials and data analysis. S.R. and T.V.P. conducted and analyzed tumor blood vessel morphology and T cell infiltration.

Corresponding author

Correspondence to Ping-Chih Ho.

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Competing interests

W.-C.C., Y.-C.T., G.C. and P.-C.H. are inventors of patent application related to targeting of UCP2 in cancer immunotherapy. P.-C.H. received research grants from Roche and Idorsia. P.-C.H. also serves as a scientific advisory member for Elixiron Immunotherapeutics.

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Integrated supplementary information

Supplementary Figure 1 Functional enrichment analysis in patients characterized by computational separation of T cell anti-tumor immunity.

a, Gene ontology term enrichment analysis for top 20 biological process controlled by differentially expressed genes between of patients with either high or low combined score for T cell anti-tumor immunity. Top panel: top 20 up-regulated biological processes in patients with high T cell anti-immunity. Bottom panel: top-20 up-regulated biological processes genes in patients with low T cells anti-tumor immunity. b, Pearson correlation plots of expression of other members of UCP family, including UCP1, UCP3, UCP4 or UCP5, with transcripts of CD8A, IFNG, GZMB, TNF, LCK, and SYK. Data were analyzed form 472 melanoma patients in TCGA database (biologically independent melanoma tumor samples), and analyzed by Pearson correlation coefficient.

Supplementary Figure 2 Single-cell RNA-seq analysis and UCP2 signature identification.

Single-cell RNA sequencing analysis from Tirosh et al. dataset27. a, The violin plots of the complete distribution of UCP2 expression by cell type. Each dot represents a single cell. Minimum values: all group (0). Middle value: B cells (5.6588); CAF (0); Endothelial cell (0); Macrophage (5.4573); NK cells (5.75925), T cells (5.12575); Tumor (0), Unclear (3.7592). Maximum values: B cells (9.0913); CAF (4.5699); Endothelial cell (5.1351); Macrophage (8.8324); NK cells (8.2642), T cells (9.5713); Tumor (7.671), Unclear (9.5402). b, Gene association plot base on UCP2 expression. The association was determined by splitting the TCGA melanoma samples in low, mid, and high UCP2 expressing samples, and then computing a right-sided Wilcoxon Rank-sum test for each gene from the panel of genes specifically expressed in malignant cells according in the Tirosh et al. dataset27. The horizontal line corresponds to a P value of 0.05. The 49 genes with P value < 0.05 were selected as UCP2 signature. c, Heatmap of the average expression of the 49 genes in the UCP2 signature across the different cell populations. Data were analyzed form 472 melanoma patients in TCGA cohort and 19 melanoma patients form single cell RNA-seq dataset (biologically independent melanoma tumor samples).

Supplementary Figure 3 Immunohistochemical staining analysis of UCP2 expression with anti-tumor score in tumor sections from melanoma patients.

a, b, Quantitative results of IHC staining against UCP2, CD8A and PD-L1 in 66 melanoma patients. PD-L1 expression percentage in melanoma cells (a) and the combined scores of CD8A intensity and PD-L1 expression percentage and intensity (b) in sections with escalating UCP2 expression in melanoma cells. Each symbol represents an individual patient. (UCP2 expression level: 0, n = 33; 1, n = 25; 2, n = 8). c, Representative histology slides form 66 melanoma patients showing staining of haematocylin and eosin (H&E), UCP2 (stained in red), Melanine A (Melan A) (stained in red) and CD8 (stained in brown), and PD-L1 in patients with different UCP2 expression levels. Data are mean±s.e.m.; unpaired, two-tailed Student’s t-test.

Supplementary Figure 4 Characterization of immune infiltrates in B16-OVA melanomas upon UCP2 induction.

a, b, Percentage (a) and absolute number of NK cells per gram of tumor (b) from indicated tumor-bearing mice as illustrated in Figs. 3a. (a, 3F, n = 10; 3F+Dox, n = 13; 3F-UCP2, n = 11; 3F-UCP2+Dox, n = 9. b, 3F, n = 10; 3F+Dox, n = 13; 3F-UCP2, n = 10; 3F-UCP2+Dox, n = 9). c, d, Percentage (c) and absolute number of CD4+ T cells per gram of tumor (d) from indicated tumor-bearing mice. (c, 3F, n = 11); 3F+Dox, n = 13; 3F-UCP2, n = 11; 3F-UCP2+Dox, n = 9. d, 3F, n = 9; 3F+Dox, n = 12; 3F-UCP2, n = 10; 3F-UCP2+Dox, n = 9). e, f, Percentage of regulatory T cells (Tregs) (e) and B cells (f) among CD45+ cells in melanomas from indicated tumor-bearing mice. (e, 3F, n = 11; 3F+Dox, n = 13; 3F-UCP2, n = 11; 3F-UCP2+Dox, n = 9. f, 3F, n = 8; 3F+Dox, n = 9; 3F-UCP2, n = 7; 3F-UCP2+Dox, n = 5). g, h, Percentage of IFN-γ-producing (g) and TNF producing (h) CD8+ T cells among total tumor-infiltrating CD8+ T cells from indicated mice. (g, 3F, n = 15; 3F+Dox, n = 11; 3F-UCP2, n = 17; 3F-UCP2+Dox, n = 17. h, 3F, n = 8; 3F+Dox, n = 8; 3F-UCP2, n = 8; 3F-UCP2+Dox, n = 7). i, Quantitative result of MHC-I expression in indicated B16-OVA cell lines treated with or without Dox. (n = 9 in each group). j, Quantitative result of OT-I T cells interacting with indicated B16-OVA cell lines treated with control vehicle or Dox. (n = 6 in each group). k, Quantitative result (left panel) and representative FACS plot (right panel) of PD-L1 expression in B16-OVA 3F-UCP2 cell lines treated with control vehicle or Dox. (n = 9 in each group). l, Survival percentage of indicated B16 melanoma cells in the in vitro effector:target cell titration assay. (n = 9 in each group). All data are mean±s.e.m. Data are cumulative results from at least three independent experiments. n.s., not significant; unpaired, two-tailed Student’s t-test.

Supplementary Figure 5 Sustaining mTOR-HIF1 activity fails to prevent UCP2-induced anti- tumor responses.

a, b, Total reactive oxygen species, (a) and mitochondrial reactive oxygen species (b) in 3F and 3F-UCP2 B16-OVA melanoma cell lines treated with control vehicle or doxycycline were measured by CM-H2DCFDA and MitoSox Red (MitoSox), respectively. (n = 9 in each group). c, Immunoblot analysis of 3F-UCP2 B16-OVA cell and dual inducible B16-OVA cell, which simultaneously overexpress flag-tagged UCP2 and stabilized myc-tagged HIF-1α, treated with or without Dox. See also Supplementary Fig. 7a. d, e, Tumor growth (d) and tumor weight (e) of dual inducible B16-OVA melanoma from mice treated with or without Dox. (Ctrl, N = 6; Dox, n = 10). Data are mean±s.e.m. and analyzed by unpaired, two-tailed Student’s t-test. Data are cumulative results from at least three independent experiments (a-b, d-e), or representative images of three independent experiments (c). Each symbol represents one individual mouse.

Supplementary Figure 6 UCP2 induction enhances CD8+ T cell infiltration and normalizes tumor vessels.

a, Relative number of CD8a+ cells in tumor cores and margins of 3F and 3F-UCP2 B16-OVA melanomas from control or doxycycline treated co-engrafted mice. (ANOVA p values: Core, p<0.0001; Margins, p = 0.0202). b, Normalized individual vessel surface area in tumor cores and margins of indicated melanomas. (ANOVA p value: Core, p = 0.0028). c, relative mural SMA+ cell coverage of CD31+ tumor endothelial cells in the indicated melanomas. (ANOVA p value = 0.0001). Data are mean±s.e.m. and analyzed by one-way ANOVA with Tukey's Multiple Comparison test (a, b, c). d, Characterization of the tumor vasculature in the indicated tumors. Proportion of single VCAM-1+ or mural cell-covered (SMA+), double positive (SMA+VCAM-1+) and immature (SMAnegVCAM-1neg) vessels were quantified. Endothelial cells were identified by staining for pan-endothelial marker CD31, Data are mean±s.e.m. (a, 3F, n = 5; 3F = Dox, n = 6; 3F-UCP2, n = 6; 3F-UCP2+Dox, n = 7. b-d, 3F, n = 5; 3F = Dox, n = 6; 3F-UCP2, n = 6; 3F-UCP2+Dox, n = 6). e, Representative images of immunofluorescent staining for indicated proteins and DNA of YUMM1.7-OVA melanomas from wild type, Rag1-KO or CD8+ T cell-depleted mice. Scale bar, 50 µm. f-h, Quantification of the data shown in e. Relative number of tumor infiltrating CD8a+ cells (f); relative single tumor vessel area (g); and relative mural SMA+ cell coverage of VE-cadherin+ blood vessels (h). Data are mean±s.d. and analyzed by two-way ANOVA with Tukey's Multiple Comparison test (f, g, h). *p ≤0.05; **p ≤0.01; ***p ≤0.001; ****p ≤0.0001. Sample size: e-h, each group (n = 5). Data are cumulative results from two independent experiments (a-d, f-g) and representative images of two independent experiments (e). Each symbol represents one individual mouse.

Supplementary Figure 7 All raw and unprocessed immunoblots.

a, Immunoblots related to Supplementary Fig. 5c. b, Immunoblots related to Fig. 7c. c, Immunoblots related to Fig. 7g

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Supplementary Text and Figures

Supplementary Figures 1–7, Supplementary Tables 2 and 3

Reporting Summary

Supplementary Table 1

T cell infiltration gene sets

Supplementary Table 4

Differentially expressed genes between UCP2hi and UCP2lo patients

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Cheng, WC., Tsui, YC., Ragusa, S. et al. Uncoupling protein 2 reprograms the tumor microenvironment to support the anti-tumor immune cycle. Nat Immunol 20, 206–217 (2019). https://doi.org/10.1038/s41590-018-0290-0

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