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The lactate receptor HCAR1 drives the recruitment of immunosuppressive PMN-MDSCs in colorectal cancer

Nature Immunology (2025)Cite this article

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Abstract

Most patients with colorectal cancer do not achieve durable clinical benefits from immunotherapy, underscoring the existence of alternative immunosuppressive mechanisms. Here we found that activation of the lactate receptor HCAR1 signaling pathway induced the expression of chemokines CCL2 and CCL7 in colorectal tumor cells, leading to the recruitment of immunosuppressive CCR2+ polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) to the tumor microenvironment. Ablation of Hcar1 in mice with colorectal tumors significantly decreased the abundance of tumor-infiltrating CCR2+ PMN-MDSCs, enhanced the activation of CD8+ T cells and, consequently, reduced tumor burden. We detected immunosuppressive CCR2+ PMN-MDSCs in tumor specimens from individuals with colorectal and other cancers. The US Food and Drug Administration-approved drug reserpine suppressed lactate-mediated HCAR1 activation, impaired the recruitment of CCR2+ PMN-MDSCs, boosted CD8+ T cell-dependent antitumor immunity and sensitized immunotherapy-resistant tumors to programmed cell death protein 1 antibody therapy in mice with colorectal tumors. Altogether, we described HCAR1-driven recruitment of CCR2+ PMN-MDSCs as a mechanism of immunosuppression.

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Fig. 1: HCAR1 limits the CD8+ T cell-mediated antitumor immunity in colorectal cancer.
Fig. 2: HCAR1 promotes the recruitment of CCR2+ PMN-MDSCs by inducing expression of CCL2 and CCL7.
Fig. 3: CCR2+ PMN-MDSCs have an immunosuppressive phenotype.
Fig. 4: HCAR1 activates the 14-3-3–STAT3 pathway.
Fig. 5: Reserpine blocks the migration of CCR2+ PMN-MDSCs.
Fig. 6: Reserpine induces CD8+ T cell-dependent antitumor immunity.
Fig. 7: Reserpine improves the efficacy of PD-1 antibody therapy in preclinical models of colorectal cancer.

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Data availability

RNA-seq data of human colorectal cancer in FPKM format were obtained from TCGA database (https://portal.gdc.cancer.gov/). Spectral counts data of STAT3 and 14-3-3ζ were obtained from the Prospective Colon VU Proteome cohort (PDC study ID: PDC000109) in The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) database (https://proteomics.cancer.gov/programs/cptac). RNA-seq data of HCAR1WT and HCAR1KO MC38 cells in this study have been deposited in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE195710. scRNA-seq data of CD45+ immune cells sorted from tumor tissues of MC38-allograft-bearing mice treated with vehicle or reserpine in this study have been deposited in the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession code PRJNA1161227. Identified HCAR1-interacting proteins in the proximity labeling assay are listed in Supplementary Table 1. Source data are provided with this paper. All other data are available in the main article and Supplementary Information or from the corresponding author upon reasonable request.

References

  1. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    PubMed  Google Scholar 

  2. Williams, C. J. M. et al. Neoadjuvant immunotherapy for dMMR and pMMR colorectal cancers: therapeutic strategies and putative biomarkers of response. Nat. Rev. Clin. Oncol. 21, 839–851 (2024).

    CAS  PubMed  Google Scholar 

  3. Veglia, F., Sanseviero, E. & Gabrilovich, D. I. Myeloid-derived suppressor cells in the era of increasing myeloid cell diversity. Nat. Rev. Immunol. 21, 485–498 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Gabrilovich, D. I., Ostrand-Rosenberg, S. & Bronte, V. Coordinated regulation of myeloid cells by tumours. Nat. Rev. Immunol. 12, 253–268 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhou, J., Nefedova, Y., Lei, A. & Gabrilovich, D. Neutrophils and PMN-MDSC: their biological role and interaction with stromal cells. Semin. Immunol. 35, 19–28 (2018).

    CAS  PubMed  Google Scholar 

  6. Limagne, E. et al. Accumulation of MDSC and Th17 cells in patients with metastatic colorectal cancer predicts the efficacy of a FOLFOX-bevacizumab drug treatment regimen. Cancer Res. 76, 5241–5252 (2016).

    CAS  PubMed  Google Scholar 

  7. DePeaux, K. & Delgoffe, G. M. Metabolic barriers to cancer immunotherapy. Nat. Rev. Immunol. 21, 785–797 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Certo, M., Tsai, C.-H., Pucino, V., Ho, P.-C. & Mauro, C. Lactate modulation of immune responses in inflammatory versus tumour microenvironments. Nat. Rev. Immunol. 21, 151–161 (2020).

  9. Watson, M. J. et al. Metabolic support of tumour-infiltrating regulatory T cells by lactic acid. Nature 591, 645–651 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Noe, J. T. et al. Lactate supports a metabolic-epigenetic link in macrophage polarization. Sci. Adv. 7, eabi8602 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Lundø, K., Trauelsen, M., Pedersen, S. F. & Schwartz, T. W. Why Warburg works: lactate controls immune evasion through GPR81. Cell Metab. 31, 666–668 (2020).

    PubMed  Google Scholar 

  12. Roland, C. L. et al. Cell surface lactate receptor GPR81 is crucial for cancer cell survival. Cancer Res. 74, 5301–5310 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat. Rev. Immunol. 20, 651–668 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. van der Leun, A. M., Thommen, D. S. & Schumacher, T. N. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20, 218–232 (2020).

    PubMed  PubMed Central  Google Scholar 

  15. Lucas, C. et al. Autophagy of intestinal epithelial cells inhibits colorectal carcinogenesis induced by colibactin-producing Escherichia coli in ApcMin/+ mice. Gastroenterology 158, 1373–1388 (2020).

    CAS  PubMed  Google Scholar 

  16. Neufert, C. et al. Inducible mouse models of colon cancer for the analysis of sporadic and inflammation-driven tumor progression and lymph node metastasis. Nat. Protoc. 16, 61–85 (2020).

    PubMed  Google Scholar 

  17. Mombaerts, P. et al. RAG-1-deficient mice have no mature B and T lymphocytes. Cell 68, 869–877 (1992).

    CAS  PubMed  Google Scholar 

  18. Philip, M. & Schietinger, A. CD8+ T cell differentiation and dysfunction in cancer. Nat. Rev. Immunol. 22, 209–223 (2021).

    PubMed  PubMed Central  Google Scholar 

  19. Ozga, A. J., Chow, M. T. & Luster, A. D. Chemokines and the immune response to cancer. Immunity 54, 859–874 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Chun, E. et al. CCL2 promotes colorectal carcinogenesis by enhancing polymorphonuclear myeloid-derived suppressor cell population and function. Cell Rep. 12, 244–257 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Nat. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Yang, X. et al. FAP promotes immunosuppression by cancer-associated fibroblasts in the tumor microenvironment via STAT3–CCL2 signaling. Cancer Res. 76, 4124–4135 (2016).

    CAS  PubMed  Google Scholar 

  24. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2019).

    PubMed Central  Google Scholar 

  25. Song, H., Wang, R., Wang, S. & Lin, J. A low-molecular-weight compound discovered through virtual database screening inhibits Stat3 function in breast cancer cells. Proc. Nat. Acad. Sci. USA 102, 4700–4705 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Lobingier, B. T. et al. An approach to spatiotemporally resolve protein interaction networks in living cells. Cell 169, 350–360 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Oughtred, R. et al. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30, 187–200 (2020).

    PubMed  PubMed Central  Google Scholar 

  28. Rastogi, S. & Rost, B. LocDB: experimental annotations of localization for Homo sapiens and Arabidopsis thaliana. Nucleic Acids Res. 39, D230–D234 (2011).

    CAS  PubMed  Google Scholar 

  29. Fu, H., Subramanian, R. R. & Masters, S. C. 14-3-3 proteins: structure, function, and regulation. Annu. Rev. Pharmacol. Toxicol. 40, 617–647 (2000).

    CAS  PubMed  Google Scholar 

  30. Machleidt, T. et al. NanoBRET—a novel BRET platform for the analysis of protein–protein interactions. ACS Chem. Biol. 10, 1797–1804 (2015).

    CAS  PubMed  Google Scholar 

  31. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Le Guilloux, V., Schmidtke, P. & Tuffery, P. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10, 168 (2009).

    PubMed  PubMed Central  Google Scholar 

  33. Kumar, B. A., Kumari, P., Sona, C. & Yadav, P. N. GloSensor assay for discovery of GPCR-selective ligands. Methods Cell Biol. 142, 27–50 (2017).

  34. McQueen, E. G., Doyle, A. E. & Smirk, F. H. Mechanism of hypotensive action of reserpine, an alkaloid of Rauwolfia serpentina. Nature 174, 1015 (1954).

    CAS  PubMed  Google Scholar 

  35. Andreatta, M. et al. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nat. Commun. 12, 2965 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Akkari, L. et al. Defining myeloid-derived suppressor cells. Nat. Rev. Immunol. 24, 850–857 (2024).

    CAS  PubMed  Google Scholar 

  37. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Lu, W. et al. Reprogramming immunosuppressive myeloid cells facilitates immunotherapy for colorectal cancer. EMBO Mol. Med. 13, e12798 (2020).

    PubMed  PubMed Central  Google Scholar 

  39. Song, W. et al. Synergistic and low adverse effect cancer immunotherapy by immunogenic chemotherapy and locally expressed PD-L1 trap. Nat. Commun. 9, 2237 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. Xiao, Q. et al. DKK2 imparts tumor immunity evasion through β-catenin-independent suppression of cytotoxic immune-cell activation. Nat. Med. 24, 262–270 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Brown, T. P. & Ganapathy, V. Lactate/GPR81 signaling and proton motive force in cancer: role in angiogenesis, immune escape, nutrition, and Warburg phenomenon. Pharmacol. Ther. 206, 107451 (2020).

    CAS  PubMed  Google Scholar 

  42. Bronte, V. et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7, 12150 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Zingg, D. et al. Truncated FGFR2 is a clinically actionable oncogene in multiple cancers. Nature 608, 609–617 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Varghese, F., Bukhari, A. B., Malhotra, R. & De, A. IHC Profiler: an open source plugin for the quantitative evaluation and automated scoring of immunohistochemistry images of human tissue samples. PLoS ONE 9, e96801 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. Harder, E. et al. Opls3: a force field providing broad coverage of drug-like small molecules and proteins. J. Chem. Theory Comput. 12, 281–296 (2016).

    CAS  PubMed  Google Scholar 

  46. Hezaveh, K. et al. Tryptophan-derived microbial metabolites activate the aryl hydrocarbon receptor in tumor-associated macrophages to suppress anti-tumor immunity. Immunity 55, 324–340 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The study was supported by the National Natural Science Foundation of China (grants 81972828 and 82172644 to W.Q.L. and 82230002 to M.Y.L.), National Key Scientific Infrastructure for Translational Medicine (Shanghai, grant TMSK-2021-120 to W.Q.L.), Hainan Province Clinical Medical Center and the specific research fund of the Innovation Platform for Academicians of Hainan Province (to Y.L.M.), Shanghai Key Medical Specialty Program (grant 2024ZDXK0047 to Yang Wang), Natural Science Research Funds of Minhang District, Shanghai (grant 2021MHZ071 to Yang Wang), Scientific Research Project funded by Shanghai Fifth People’s Hospital, Fudan University (grant 2020WYZT02 to Yang Wang), ECNU Multifunctional Platform for Innovation (grant 011), The Instruments Sharing Platform of School of Life Science, East China Normal University, Open Research Fund of Key Laboratory of Cancer Prevention and Intervention (Zhejiang University), Ministry of Education and Fundamental Research Funds for the Central Universities (grant 226-2024-00062). This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government. This research was supported [in part] by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Author notes

  1. These authors contributed equally: Jiacheng He, Xiaolei Chai, Qiansen Zhang, Yang Wang.

Authors and Affiliations

  1. Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China

    Jiacheng He, Xiaolei Chai, Qiansen Zhang, Yijie Wang, Xinyu Yang, Shuyin Ze, Yuanyuan Fu, Yumiao Zhao, Ying Zhang, Yao Zhang, Meizhen Liu, Xueyun Ma, Huaiyu Yang, Dali Li, Mingyao Liu & Weiqiang Lu

  2. Department of Urology and Department of Pathology, The Fifth People’s Hospital of Shanghai, Fudan University, Shanghai, China

    Yang Wang & Jingbo Wu

  3. Department of General Surgery and Department of Pathology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China

    Bo Feng, Jing Sun & Weiwei Rui

  4. Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, China

    Chuang Liu, Meifu She & Xiangfei Hu

  5. Department of Colorectal Surgery, Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China

    Dawei Li, Senlin Zhao, Guichao Li & Zhen Zhang

  6. School of Life Sciences, Fudan University, Shanghai, China

    Zhonghui Tian

  7. Hainan Provincial Key Laboratory for Human Reproductive Medicine and Genetic Research, Hainan Provincial Clinical Research Center for Thalassemia, Department of Reproductive Medicine, Key Laboratory of Reproductive Health Diseases Research and Translation (Hainan Medical University), Ministry of Education, The First Affiliated Hospital of Hainan Medical University, Haikou, China

    Yanlin Ma

  8. Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China

    Lingyan Cao

  9. Department of Gastrointestinal Surgery, The Third Xiangya Hospital, Central South University, Changsha, China

    Bo Yi

  10. Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, USA

    Ruth Nussinov

  11. Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    Ruth Nussinov

  12. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Charis Eng & Feixiong Cheng

  13. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Charis Eng & Feixiong Cheng

  14. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Charis Eng, Timothy A. Chan & Feixiong Cheng

  15. Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA

    Timothy A. Chan

  16. Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Eytan Ruppin

  17. Department of Pharmacology and Moores Cancer Center, University of California,San Diego, San Diego, CA, USA

    J. Silvio Gutkind

  18. International Center for Aging and Cancer, Hainan Medical University, Haikou, China

    Mingyao Liu

  19. University Engineering Research Center of Oncolytic & Nanosystem Development, Guangxi, China

    Weiqiang Lu

Authors
  1. Jiacheng He
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  2. Xiaolei Chai
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  38. Weiqiang Lu
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Contributions

Conceptualization: W.Q.L. Methodology: J.H., X.C., S. Ze, Y.F., Yao Zhang, Y. Zhao, Yijie Wang, X.Y., M.S., X.H., Z.T., C.L., W.R., Miezhen Liu, J.W., X.M., C.Z. and B.Y. Investigation: J.H. and W.L. Visualization: J.H., X.C. and Q.Z. Funding acquisition: Yang Wang, Mingyao Liu and W.L. Project administration: Mingyao Liu and W.L. Supervision: Mingyao Liu and W.L. Writing—original draft: J.H. and W.L. Writing—review and editing: R.N., C.E., F.C., T.A.C., E.R., J.S.G., J.H. and W.L. Resources: Yang Wang, B.F., J.S., S. Zhao, Z.Z., Ying Zhang, H.Y., G.L., Dawei Li, Y.M., L.C., Dali Li, F.C., Mingyao Liu and W.L.

Corresponding authors

Correspondence to Mingyao Liu or Weiqiang Lu.

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Competing interests

The authors declare no competing interests.

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Nature Immunology thanks Ivan Ballesteros, María Casanova-Acebes and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ioana Staicu, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 HCAR1 expression is aberrantly high in colorectal cancer and is associated with decreased CD8+ T cell infiltration, related to Fig. 1.

a, Volcano plot showing the fold changes in HCAR1 mRNA expression and corresponding P-values for tumor tissues versus adjacent normal tissues across 23 cancer types. Data were obtained from TCGA database. b, Violin plot showing HCAR1 mRNA expression in tumor tissues and adjacent normal mucosa tissues of patients with colorectal cancer (n = 46). Data were obtained from TCGA-COAD and TCGA-READ database. c, Violin plot showing HCAR1 mRNA expression in tumor tissues of colorectal cancer patients with separated TNM stages. Data were obtained from TCGA-COAD and TCGA-READ database. d, Percentages of low (HCAR1lo, n = 42) or high (HCAR1hi, n = 45) HCAR1 staining of tumor sections from distinct TNM stages of colorectal cancer in CRC-87 cohort. e, Kaplan-Meier overall survival curves of colorectal cancer patients stratified by HCAR1lo and HCAR1hi (n = 87) in CRC-87 cohort. f, Representative immunoblotting analysis and corresponding quantification of HCAR1 expression in EpCAM+ and EpCAM− cells sorted from colorectal tumors of patients with colorectal cancer (n = 3 biological replicates). g, Representative DNA sequencing data of Hcar1 gene in Hcar1−/− mice. h, Representative immunoblotting analysis of HCAR1 expression in colorectal tumors from ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15. i, Representative gross view of colorectal tumors in ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15. j, Representative hematoxylin and eosin staining of colorectal tumors from ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15 (n = 6 biological replicates). Scale bar, 50 μm. k, Representative flow cytometric dot plots of the percentages of CD8+ T cells (among CD45+ cells) and GzmB+ cells (gated on CD45+CD8+ T cells) in colorectal tumors from ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15. l-m, Representative flow cytometric dot plots and corresponding percentages of IFNγ+ cells (l) and TNF+ (m) cells gated on CD45+CD8+ T cells in colorectal tumors of ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15 (n = 6 biological replicates). n, Quantification of Hcar1 gene expression in EpCAM+ and EpCAM− cells sorted from colorectal tumors of ApcMin/+ mice (n = 6 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (b) two-tailed paired student’s t test; (a, f, l, m, n) two-tailed unpaired student’s t test; (c) one-way ANOVA with Tukey’s multiple comparison test; (e) log-rank test.

Source data

Extended Data Fig. 2 HCAR1 limits the CD8+ T cell mediated anti-tumor immunity in colorectal cancer, related to Fig. 1.

a, Representative DNA sequencing data of Hcar1 gene in intestinal epithelial cells, heart and liver tissues of Vil1-cre Hcar1fl/fl mice at week 8. b, Representative immunoblotting analysis of HCAR1 expression in intestinal epithelial cells of Vil1-cre Hcar1fl/fl and Hcar1fl/fl littermates at week 8. c, Representative gross view of colorectums and quantifications of colorectal tumor numbers in AOM/DSS-treated Vil1-cre Hcar1fl/fl and Hcar1fl/fl littermates at week 8 following the third DSS cycle (n = 6 mice per group). d, Flow cytometry analysis of frequencies (among CD45+ cells) and absolute number (per mg tumor) of CD8+ T cells in colorectal tumors from AOM/DSS-treated Vil1-cre Hcar1fl/fl and Hcar1fl/fl littermates at week 8 following the third DSS cycle (n = 6 biological replicates). e, Flow cytometry analysis of frequencies of GzmB+ cells (n = 6 biological replicates), IFNγ+ cells (n = 5 biological replicates), TNF+ cells (n = 5 biological replicates), and CD44+CD62L− cells (n = 5 biological replicates) gated on CD45+CD8+ T cells in colorectal tumors as in (d). f, Flow cytometry analysis of frequencies PD-1+ cells, LAG-3+ cells, TIM-3+ cells, and CD44−CD62L+ cells gated on CD45+CD8+ T cells in colorectal tumors as in (d) (n = 5 biological replicates). g, Representative immunoblotting analysis of HCAR1 expression in HCAR1WT, HCAR1KO and HCAR1KO (subline 2) MC38 cells. h-i, Cell proliferation, cell apoptosis and cell cycle of HCAR1WT, HCAR1KO (h) and HCAR1KO (subline 2) (i). MC38 cells were treated with vehicle or 25 mM lactate for 24 and 48 hours (n = 3 biological replicates). j, Growth curves of HCAR1WT, HCAR1KO and HCAR1KO (subline 2) MC38 cells at day 1-day 13 post-inoculation in C57BL/6 mice (n = 6 mice per group). k, Growth curves of HCAR1WT or HCAR1KO (subline 2) MC38 cells at day 1-day 14 post-inoculation in Rag1−/− mice (n = 6 mice per group). l, Representative flow cytometry dot plots and corresponding frequencies (among CD45+ cells) of CD8+ T cells in peripheral blood from C57BL/6 mice at day 13 post-inoculation with MC38 cells, treated with 10 mg/kg IgG isotype or 10 mg/kg CD8α antibody at day 5, 8, and 12 post-inoculation (n = 5 biological replicates). m, Growth curves of HCAR1WT or HCAR1KO (subline 2) MC38 cells at day 1-day 13 post-inoculation in C57BL/6 mice, followed by intraperitoneal injection with IgG isotype or CD8α antibodyas in (l) (n = 5 mice per group). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (c, d, e, f, j, k, l) two-tailed unpaired student’s t test; (h, i, m) one-way ANOVA with Tukey’s multiple comparison. Ab, antibody.

Source data

Extended Data Fig. 3 HCAR1 promotes the recruitment of CCR2+ PMN-MDSCs by inducing expression of CCL2 and CCL7, related to Fig. 2.

a, Flow cytometry analysis of the frequencies and/or absolute number (per mg tumor) of CD45+CD11b+Ly-6G+ cells (PMN-MDSCs, among CD45+ cells), CD45+CD11b+Ly-6ChiLy-6G− cells (M-MDSCs, among CD45+ cells), CD45+CD11b+F4/80+MHC-II+ cells (M1-like Macrophages, M1-like Mac, among CD45+ cells), CD45+CD11b+F4/80+CD206+ cells (M2-like Macrophages, M2-like Mac, among CD45+ cells), CD45+CD11c+MHC-II+ cells (DCs, among CD45+ cells), CD45+CD4+ cells (among CD45+ cells), CD25+Foxp3+ cells gated on CD4+ cells (Treg), IFNγ+CD4+ cells gated on CD4+ cells (Th1), IL-4+CD4+ cells gated on CD4+ cells (Th2) and CD45+NK1.1+ (NK, among CD45+ cells) from colorectal tumors in AOM/DSS-treated Vil1-cre Hcar1fl/fl and Hcar1fl/fl littermates at week 8 following the third DSS cycle (n = 5 biological replicates). b, ELISA of CCL2 and CCL7 expression in the culture supernatants from HCAR1WT or HCAR1KO (subline 2) MC38 cells treated with vehicle or 25 mM lactate for 24 hours (n = 5 biological replicates). c, ELISA of CCL2 and CCL7 expression in EpCAM+ and EpCAM− cells sorted from colorectal tumors of ApcMin/+Hcar1−/− and ApcMin/+ littermates at week 15 (n = 5 biological replicate). d, Flow cytometry analysis of the frequency of CCR2+CD11b+CD33+CD15+HLA-DR- cells (CCR2+ hPMN-MDSCs) among hPMN-MDSCs in human colorectal cancer specimens (n = 5 biological replicates). e, Representative hematoxylin staining of CCR2+ hPMN-MDSCs and CCR2+ PMN-MDSCs sorted from peripheral blood of colorectal cancer patients and C57BL/6 mice at day 21 post-inoculation with MC38 cells (n = 3 biological replicates). f, Flow cytometry analysis of the CCR2+ hPMN-MDSC absolute number (per mg tissue) in tumor tissues (n = 12 biological replicates) and adjacent normal mucosa tissues (n = 5 biological replicates) from patients with colorectal cancer. g, Relative numbers of CCR2+ PMN-MDSCs and CCR2− PMN-MDSCs isolated from the MC38 tumors of C57BL/6 mice at day 21 post-inoculation that migrated towards supernatant of HCAR1WT or HCAR1KO (subline 2) MC38 cells, relative to the vehicle-treated group (n = 3 biological replicates). h, Flow cytometry analysis of the frequency (among CD45+ cells) and absolute counts (per mg tumor) of CCR2+ PMN-MDSCs form colorectal tumors in AOM/DSS-treated Vil1-cre Hcar1fl/fl and Hcar1fl/fl littermates at week 8 following the third DSS cycle (n = 6 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (a, f, h) two-tailed unpaired student’s t test; (b, c, g) one-way ANOVA with Tukey’s multiple comparison.

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Extended Data Fig. 4 CCR2+ PMN-MDSCs have an immunosuppressive phenotype, related to Fig. 3.

a, Flow cytometry analysis of the frequencies (among CD45+ cells) of PMN-MDSCs and M-MDSCs from the MC38 tumors of C57BL/6 mice at day 7 and 14 post-inoculation, colorectal tumors of ApcMin/+ mice at week 15, and colorectal tumors of AOM/DSS-treated mice at week 8 following the third DSS cycle (n = 5 biological replicates). b, Quantification of CD8+ T cell proliferation by co-culturing activated T cells with CCR2+ M-MDSC or CCR2+ PMN-MDSCs from the tumors of C57BL/6 mice at day 7 and 14 post-inoculation with MC38 cells, colorectal tumors of ApcMin/+ mice at week 15, and colorectal tumors of AOM/DSS-treated mice at week 8 following the third DSS cycle at a ratio of 2:1 (n = 3 biological replicates). c, Representative histogram plot of Fig. 3e. d, Flow cytometry analysis of ARG1 expression, NO levels, and ROS levels of CCR2+ and CCR2− hPMN-MDSCs from peripheral blood of patients with colorectal cancer (n = 6 biological replicates). e, Representative histogram plot of Fig. 3f. f, Flow cytometry analysis of ARG1 expression, NO and ROS levels of CCR2+ and CCR2− hPMN-MDSCs from peripheral blood of patients with renal cell cancer (RCC, n = 4 biological replicates), bladder cancer (BCa, n = 4 biological replicates) and prostate cancer (PCa, n = 4 biological replicates). g, Quantification of CCR2+ hPMN-MDSC abundance (number per mg tissue) in tumor tissues from patients with different TNM stages of colorectal cancer (n = 12 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (d, f) two-tailed paired student’s t test; (middle and right panels of a, g) two-tailed unpaired student’s t test; (left panel of a, b) one way ANOVA with Tukey’s multiple comparison. TN, naïve T cells; TAct, active T cells.

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Extended Data Fig. 5 HCAR1 activates the 14-3-3/STAT3 pathway, related to Fig. 4.

a-b, Fast gene set enrichment analysis (FGSEA) showing enrichment of IL6_JAK_STAT3 signaling (hallmark gene sets) in RNA-seq from HCAR1WT MC38 cells treated with 25 mM lactate versus vehicle (a) and HCAR1KO MC38 cells versus HCAR1WT MC38 cells (both treated with 25 mM lactate) (b). The ranked hallmark pathways using hallmark gene sets related to cell signaling were determined by normalized enrichment scores (NES). c, Representative immunofluorescence staining with a specific phosphorylated STAT3 (p-STAT3) antibody in HCAR1WT and HCAR1KO (subline 2) MC38 cells treated with vehicle or 25 mM lactate for 24 hours. Scale bar, 25 μm. d, Representative immunoblotting analysis of phosphorylated and total STAT3 in HCAR1WT and HCAR1KO (subline 2) MC38 cells treated with vehicle or 25 mM lactate for 24 hours. e, Quantification of the luciferase activities of MC38 cells co-transfected with a Ccl2 promoter reporter or a Ccl7 promoter reporter, and a STAT3-overexpressing plasmid or a vehicle plasmid for 12 hours (n = 5 biological replicates). f, Diagram of predicted STAT3 binding region on Ccl2 and Ccl7 promoter loci via JASPAR database. g, Quantification of the luciferase activities of MC38 cells co-transfected with a wildtype Ccl7 promoter reporter or a mutant Ccl7 promoter reporter, and a STAT3-overexpressing plasmid or a vehicle plasmid for 12 hours (n = 6 biological replicates). h, Quantification of the luciferase activities of MC38 cells transfected with a Ccl2 promoter reporter or a Ccl7 promoter reporter, and treated with vehicle, 25 mM lactate and/or 10 μM of the STAT3 inhibitor STA-21 (STAT3i) for 12 hours (n = 5 biological replicates). i, Pearson correlation analysis of STAT3 and 14-3-3ζ protein expressions in human colorectal cancer tissues. Data were obtained from VU cohort (PDC000109) in CPTAC database (n = 100). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (e) two-tailed unpaired student’s t test; (g-h) one-way ANOVA with Tukey’s multiple comparison; (i) two-tailed Pearson correlation test.

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Extended Data Fig. 6 Reserpine blocks the migration of CCR2+ PMN-MDSCs, related to Fig. 5.

a, Chemical structure of reserpine. b, Inhibitory effects of reserpine on other ten metabolite-sensing membrane receptors in GlosensorTM cAMP assay (n = 3 biological replicates). c, Docked pose of reserpine into the model of the human HCAR1. The receptor is displayed in cartoon representation, the binding-related residues (bule) and reserpine (cyan) are shown as stick models. Schematic 2D representation of the binding pocket. Lipophilic amino acids are colored in grey, hydrophilic ones in yellow, aromatic ones in orange, and amino acid residues with mixed properties in green. d, Cell proliferation analysis of MC38, CT26, HT29, HCT116, LS174T and HCT8 cells treated with vehicle or indicated concentrations of reserpine for 48 hours (n = 3 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m.

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Extended Data Fig. 7 Reserpine induces CD8+ T cell-dependent anti-tumor immunity, related to Fig.6.

a, Growth curves and Kaplan-Meier survival curves in CT26 tumor-bearing BALB/cmice that were intraperitoneally injected with vehicle, 0.5 or 1 mg/kg reserpine every three days starting at day 7 post-inoculation with CT26 cells (n = 6 mice per group). b, Body weight curves of MC38 tumor-bearing C57BL/6 mice and CT26 tumor-bearing BALB/c mice treated with vehicle, 0.5 mg/kg, or 1 mg/kg reserpine every two or three days (n = 6 mice per group). c, Quantification of serum liver function markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB) and gamma-glutamyl transferase (GGT) as well as kidney function markers including blood urea nitrogen (BUN) and creatinine (CREA) of 8-week-old C57BL/6 mice treated with vehicle or 1 mg/kg reserpine (every three days) for 14 days by using an automatic biochemical analyzer (n = 3 biological replicates). d, Representative hematoxylin and eosin staining of heart, liver, spleen, lung and kidney tissues from 8-week-old C57BL/6 mice treated as in (c) (n = 3 biological replicates). Scale bar, 100 μm. e, Flow cytometry analysis of the frequencies of PD-1+ cells, LAG-3+ cells, TIM-3+ cells, and CD44−CD62L+ cells gated on CD45+CD8+ T cells from the MC38 tumors of C57BL/6 mice at day 21 post-inoculation and treated with vehicle or 1 mg/kg reserpine (every three days), starting at day 7 post-inoculation (n = 5 biological replicates). f, Flow cytometry analysis of the frequencies of CD45+CD11b+F4/80+MHC-II+ cells (M1-like Macrophages, M1-like Mac), CD45+CD11b+F4/80+CD206+ cells (M2-like Macrophages, M2-like Mac), CD45+CD11c+MHC-II+ cells (DCs), CD45+CD4+ cells, CD25+Foxp3+ cells gated on CD4+ cells (Treg), IFNγ+CD4+ cells gated on CD4+ cells (Th1), IL-4+CD4+ cells gated on CD4+ cells (Th2) and CD45+NK1.1+ (NK) from the MC38 tumors of C57BL/6 mice as in (e) (n = 5 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (c, e, f) two-tailed unpaired student’s t test; (left panel of a, b) one-way ANOVA with Tukey’s multiple comparison test; (right panel of a) Log-rank test.

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Extended Data Fig. 8 Reserpine enhances CD8+ T cell-dependent anti-tumor immune response, related to Fig.6.

a, Growth curves of MC38 tumors at day 7-day 16 post-inoculation in Rag1−/− mice that were injected intraperitoneally with vehicle or 1 mg/kg reserpine (every three days), starting at day 6 post-inoculation (n = 6 mice per group). b, Representative flow cytometry dot plots and corresponding frequencies (among CD45+ cells) of PMN-MDSCs in peripheral blood from C57BL/6 mice at day 16 post-inoculation with MC38 cells, treated with 10 mg/kg IgG isotype or 10 mg/kg Ly-6G antibody at day 5, 8, 12 and 15 post-inoculation (n = 3 biological replicates). c, Quantification of tumor burden in the proximal, middle and distal small intestine and colorectum in ApcMin/+ mice at week 15, injected intraperitoneally with vehicle or 1 mg/kg reserpine, starting at week 8 (n = 6 mice per group). d, Representative hematoxylin and eosin staining in colorectal tumors from ApcMin/+ mice as in (c). Scale bar, 50 μm. e, Kaplan-Meier survival curves of ApcMin/+ mice injected intraperitoneally with vehicle or 1 mg/kg reserpine (every three days), starting at 8 weeks of age (n = 6 mice per group). f, Flow cytometry analysis of frequencies (among CD45+ cells) of CCR2+ PMN-MDSCs in colorectal tumors from ApcMin/+ mice as in (c) (n = 6 biological replicates). g, Flow cytometry analysis of frequencies of CD8+ T cells among CD45+ cells, and GzmB+ cells gated on CD45+CD8+ T cells in colorectal tumors from ApcMin/+ mice as in (c) (n = 6 biological replicates). h, Representative immunofluorescence staining for CD8α and GzmB in colorectal tumors from ApcMin/+ mice as in (c). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (a-c, f-g) two-tailed unpaired student’s t test; (e) Log rank test. Ab, antibody.

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Extended Data Fig. 9 Reserpine improves the efficacy of PD-1 Ab therapy in preclinical models of colorectal cancer, related to Fig. 7.

a, Representative gross view of colorectums of the ApcMin/+mice at week 15, injected intraperitoneally with vehicle, 1 mg/kg reserpine (every three days), 2.5 mg/kg PD-1 antibody (twice weekly), or their combination (Com), starting at week 8 (n = 5 mice per group). b, Representative hematoxylin and eosin staining for colorectal tumors from ApcMin/+ mice treated as in (a). Scale bar, 50 μm. c, Body weight curves of ApcMin/+ mice treated as in (a) (n = 6 mice per group). d, Flow cytometry analysis of PD-1 expression on CD8+ T cells in colorectal tumors from ApcMin/+ mice treated as in (a) (n = 5 biological replicates). e, Quantification of immunofluorescence staining for CD8α and GzmB expression in colorectal tumors from ApcMin/+ mice as in Fig. 7k (n = 5 biological replicates). f, Representative immunohistochemistry staining for phosphorylated STAT3 in colorectal tumors of ApcMin/+ mice treated as in (a). g, ELISA of CCL2 and CCL7 expression in colorectal tumors of ApcMin/+ mice treated as in (a) (n = 5 biological replicates). h, Representative gross view of colorectums and quantifications of colorectum tumor burden of the AOM/DSS-treated C57BL/6 mice injected intraperitoneally with vehicle, 1 mg/kg reserpine (every three days), 2.5 mg/kg PD-1 antibody (twice weekly), or Com at week 8 following the third DSS cycle (n = 5 mice per group). i, Flow cytometry analysis of the frequencies (among CD45+ cells) of CCR2+ PMN-MDSCs and CD8+ T cells in colorectal tumors as in (h) (n = 5 biological replicates). Results are representative of two or three independent experiments. All bars in graph represent mean ± s.e.m. (c-e, g-i) one-way ANOVA with Tukey’s multiple comparison. Ab, antibody.

Extended Data Fig. 10 Gating strategies.

a, Gating strategies for mouse lymphocytes, including CD8+ T cells, NK1.1+ NK cells and CD4+ T cells gated on CD45+ cells, GzmB+ cells, IFNγ+ cells, TNF+ cells, CD44+CD62− cells, CD44−CD62+ cells, PD-1+ cells, LAG-3+ cells and TIM-3+ cells gated on CD45+CD8+ cells, IFNγ+ cells (Th1), IL-4+ cells (Th2), CD25+Foxp3+ cells (Treg) gated on CD45+CD4+ cells. b, Gating strategies for mouse myeloid cells, including CD11b+Ly-6G+ PMN-MDSCs, CD11b+Ly-6ChiLy-6G− M-MDSCs, CD11c+MHC-II+ dendritic cells, gated on CD45+ cells, MHC-II+ M1-like macrophage (M1-like Mac) and CD206+ M2-like macrophage (M2-like Mac) gated on CD45+CD11b+F4/80+ macrophages, and CCR2+ cells gated on CD11b+Ly-6G+ PMN-MDSCs or CD11b+Ly-6ChiLy-6G− M-MDSCs. c, Gating strategies for human CCR2+/CCR2− hPMN-MDSCs (CD11b+HLA-DR−CD33+CD15+).

Supplementary information

Supplementary Information

Supplementary Tables 2–5.

Reporting Summary

Supplementary Table 1

Identified HCAR1-interacting proteins in proximity labeling assay, related to the Methods.

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He, J., Chai, X., Zhang, Q. et al. The lactate receptor HCAR1 drives the recruitment of immunosuppressive PMN-MDSCs in colorectal cancer. Nat Immunol (2025). https://doi.org/10.1038/s41590-024-02068-5

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