Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Acetate acts as a metabolic immunomodulator by bolstering T-cell effector function and potentiating antitumor immunity in breast cancer

Nature Cancer volume 4, pages 1491–1507 (2023)Cite this article

Subjects

Abstract

Acetate metabolism is an important metabolic pathway in many cancers and is controlled by acetyl-CoA synthetase 2 (ACSS2), an enzyme that catalyzes the conversion of acetate to acetyl-CoA. While the metabolic role of ACSS2 in cancer is well described, the consequences of blocking tumor acetate metabolism on the tumor microenvironment and antitumor immunity are unknown. We demonstrate that blocking ACSS2, switches cancer cells from acetate consumers to producers of acetate thereby freeing acetate for tumor-infiltrating lymphocytes to use as a fuel source. We show that acetate supplementation metabolically bolsters T-cell effector functions and proliferation. Targeting ACSS2 with CRISPR-Cas9 guides or a small-molecule inhibitor promotes an antitumor immune response and enhances the efficacy of chemotherapy in preclinical breast cancer models. We propose a paradigm for targeting acetate metabolism in cancer in which inhibition of ACSS2 dually acts to impair tumor cell metabolism and potentiate antitumor immunity.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Inhibition of ACSS2 triggers stronger tumor growth inhibition in immune-competent mice compared to immune-deficient mice.
Fig. 2: T cells and NK cells are necessary for suppression of Acss2-KO tumor growth.
Fig. 3: ACSS2 is dispensable in T cells for antitumor immune responses.
Fig. 4: Inhibition of ACSS2 synergizes with chemotherapy in breast tumors.
Fig. 5: Single-cell RNA sequencing identifies enhanced activation of tumor-infiltrating T cells upon ACSS2 inhibition.
Fig. 6: Phenotypic marker analysis of tumor-infiltrating leukocytes and splenocytes from VY-3-135-treated tumors.
Fig. 7: CD8+ T cells and NK cells readily oxidize acetate in a time- and concentration-dependent manner.
Fig. 8: Acetate improves human CD8+ T-cell proliferation and functionality when glucose is limited.

Similar content being viewed by others

Data availability

RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE202281. Metabolomic data are available at the NIH Common Fund’s National Metabolomics Data Repository website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org where it has been assigned study ID ST002740. The data can be accessed directly via its project at https://doi.org/10.21228/M89T4G. This work is supported by NIH grant U2C-DK119886. Source data for all figures and extended data have been provided as Source Data files. Previously published human breast tissue scRNA-seq data that were re-analyzed here for expression of ACSS1 and ACSS2 mRNA are available under accession code GSE164898 (refs. 40,41). Previously published human primary breast tumor scRNA-seq data that were re-analyzed here for expression for ACSS1 mRNA are available from GSE176078 (ref. 36). Source data are provided with this paper.

References

  1. Bianchini, G., Balko, J. M., Mayer, I. A., Sanders, M. E. & Gianni, L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat. Rev. Clin. Oncol. 13, 674–690 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Anon. Atezolizumab combo approved for PD-L1-positive TNBC. Cancer Discov. 9, OF2 (2019).

  3. Emens, L. A. et al. First-line atezolizumab plus nab-paclitaxel for unresectable, locally advanced, or metastatic triple-negative breast cancer: IMpassion130 final overall survival analysis. Ann. Oncol. 32, 983–993 (2021).

    Article  CAS  PubMed  Google Scholar 

  4. Nanda, R. et al. Pembrolizumab in patients with advanced triple-negative breast cancer: phase Ib KEYNOTE-012 study. J. Clin. Oncol. 34, 2460–2467 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Adams, S. et al. Pembrolizumab monotherapy for previously treated metastatic triple-negative breast cancer: cohort A of the phase II KEYNOTE-086 study. Ann. Oncol. 30, 397–404 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Loi, S. et al. Relationship between tumor infiltrating lymphocyte (TIL) levels and response to pembrolizumab (pembro) in metastatic triple-negative breast cancer (mTNBC): results from KEYNOTE-086. Ann. Oncol. 28, v608 (2017).

    Article  Google Scholar 

  7. Winer, E. P. et al. Pembrolizumab versus investigator-choice chemotherapy for metastatic triple-negative breast cancer (KEYNOTE-119): a randomised, open-label, phase 3 trial. Lancet Oncol. 22, 499–511 (2021).

    Article  CAS  PubMed  Google Scholar 

  8. Dirix, L. Y. et al. Avelumab, an anti-PD-L1 antibody, in patients with locally advanced or metastatic breast cancer: a phase 1b JAVELIN Solid Tumor study. Breast Cancer Res. Treat. 167, 671–686 (2018).

    Article  CAS  PubMed  Google Scholar 

  9. Fujino, T., Kondo, J., Ishikawa, M., Morikawa, K. & Yamamoto, T. T. Acetyl-CoA synthetase 2, a mitochondrial matrix enzyme involved in the oxidation of acetate. J. Biol. Chem. 276, 11420–11426 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Yoshimura, Y., Araki, A., Maruta, H., Takahashi, Y. & Yamashita, H. Molecular cloning of rat acss3 and characterization of mammalian propionyl-CoA synthetase in the liver mitochondrial matrix. J. Biochem. 161, 279–289 (2017).

    CAS  PubMed  Google Scholar 

  11. Miller, K. D. et al. Targeting ACSS2 with a transition state mimetic inhibits triple-negative breast cancer growth. Cancer Res. 81, 1252–1264 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Luong, A., Hannah, V. C., Brown, M. S. & Goldstein, J. L. Molecular characterization of human acetyl-CoA synthetase, an enzyme regulated by sterol regulatory element-binding proteins. J. Biol. Chem. 275, 26458–26466 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Schug, Z. T., Vande Voorde, J. & Gottlieb, E. The metabolic fate of acetate in cancer. Nat. Rev. Cancer 16, 708–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bulusu, V. et al. Acetate recapturing by nuclear acetyl-CoA synthetase 2 prevents loss of histone acetylation during oxygen and serum limitation. Cell Rep. 18, 647–658 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Schug, Z. T. et al. Acetyl-CoA synthetase 2 promotes acetate utilization and maintains cancer cell growth under metabolic stress. Cancer Cell 27, 57–71 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kamphorst, J. J., Chung, M. K., Fan, J. & Rabinowitz, J. D. Quantitative analysis of acetyl-CoA production in hypoxic cancer cells reveals substantial contribution from acetate. Cancer Metab. 2, 23 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Comerford, S. A. et al. Acetate dependence of tumors. Cell 159, 1591–1602 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chen, R. et al. The acetate/ACSS2 switch regulates HIF-2 stress signaling in the tumor cell microenvironment. PLoS ONE 10, e0116515 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Chen, R., Xu, M., Nagati, J. & Garcia, J. A. Coordinate regulation of stress signaling and epigenetic events by Acss2 and HIF-2 in cancer cells. PLoS ONE 12, e0190241 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Gao, X. et al. Acetate functions as an epigenetic metabolite to promote lipid synthesis under hypoxia. Nat. Commun. 7, 11960 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Li, X. et al. Nucleus-translocated ACSS2 promotes gene transcription for lysosomal biogenesis and autophagy. Mol. Cell 66, 684–697 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, Z. et al. Acetyl-CoA synthetase 2: a critical linkage in obesity-induced tumorigenesis in Myeloma. Cell Metab. 33, 78–93 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lakhter, A. J. et al. Glucose-independent acetate metabolism promotes melanoma cell survival and tumor growth. J. Biol. Chem. 291, 21869–21879 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Oyama, N. et al. 11C-Acetate P. E. T. imaging for renal cell carcinoma. Eur. J. Nucl. Med. Mol. Imaging 36, 422–427 (2009).

    Article  PubMed  Google Scholar 

  25. Ho, C. L. et al. 11C-acetate PET/CT for metabolic characterization of multiple myeloma: a comparative study with 18F-FDG PET/CT. J. Nucl. Med. 55, 749–752 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Delbeke, D. & Pinson, C. W. 11C-acetate: a new tracer for the evaluation of hepatocellular carcinoma. J. Nucl. Med. 44, 222–223 (2003).

    PubMed  Google Scholar 

  27. Calhoun, S., Duan, L. & Maki, C. G. Acetyl-CoA synthetases ACSS1 and ACSS2 are 4-hydroxytamoxifen responsive factors that promote survival in tamoxifen treated and estrogen deprived cells. Transl. Oncol. 19, 101386 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Triplett, T. A. et al. Reversal of indoleamine 2,3-dioxygenase-mediated cancer immune suppression by systemic kynurenine depletion with a therapeutic enzyme. Nat. Biotechnol. 36, 758–764 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Leone, R. D. et al. Glutamine blockade induces divergent metabolic programs to overcome tumor immune evasion. Science 366, 1013–1021 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Scharping, N. E. et al. Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat. Immunol. 22, 205–215 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang, Y. X. et al. Nanoenabled modulation of acidic tumor microenvironment reverses anergy of infiltrating T cells and potentiates anti-PD-1 therapy. Nano Lett. 19, 2774–2783 (2019).

    Article  CAS  PubMed  Google Scholar 

  32. Zhao, F. et al. Paracrine Wnt5a-β-catenin signaling triggers a metabolic program that drives dendritic cell tolerization. Immunity 48, 147–160 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Guerra, L., Bonetti, L. & Brenner, D. Metabolic modulation of immunity: a new concept in cancer immunotherapy. Cell Rep. 32, 107848 (2020).

    Article  CAS  PubMed  Google Scholar 

  34. Tibbitt, C. A. et al. Single-cell RNA sequencing of the T helper cell response to house dust mites defines a distinct gene expression signature in airway Th2 cells. Immunity 51, 169–184 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Rogers, P. R., Song, J., Gramaglia, I., Killeen, N. & Croft, M. OX40 promotes Bcl-xL and Bcl-2 expression and is essential for long-term survival of CD4 T cells. Immunity 15, 445–455 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–352 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Cancer Genome Atlas Research, N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  Google Scholar 

  39. Bacigalupa, Z. A. et al. ACSS2 regulates HIF-2α degradation through the E3-ubiquitin ligase MUL1 in clear cell renal cell carcinoma. Preprint at bioRxiv https://doi.org/10.1101/2022.04.21.489116 (2022).

  40. Bhat-Nakshatri, P. et al. Acquisition, processing, and single-cell analysis of normal human breast tissues from a biobank. STAR Protoc. 3, 101047 (2022).

    Article  CAS  PubMed  Google Scholar 

  41. Bhat-Nakshatri, P. et al. A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells. Cell Rep. Med. 2, 100219 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wilde, L. et al. Metabolic coupling and the reverse Warburg effect in cancer: Implications for novel biomarker and anticancer agent development. Semin. Oncol. 44, 198–203 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Shin, E. & Koo, J. S. Glucose metabolism and glucose transporters in breast cancer. Front. Cell Dev. Biol. 9, 728759 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Pavlides, S. et al. The reverse Warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 8, 3984–4001 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Sousa, C. M. et al. Pancreatic stellate cells support tumour metabolism through autophagic alanine secretion. Nature 536, 479–483 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Auciello, F. R. et al. A stromal lysolipid-autotaxin signaling axis promotes pancreatic tumor progression. Cancer Discov. 9, 617–627 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mendoza, M. et al. Enzymatic transfer of acetate on histones from lysine reservoir sites to lysine activating sites. Sci. Adv. 8, eabj5688 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cai, L., Sutter, B. M., Li, B. & Tu, B. P. Acetyl-CoA induces cell growth and proliferation by promoting the acetylation of histones at growth genes. Mol. Cell 42, 426–437 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Balmer, M. L. et al. Memory CD8(+) T cells balance pro- and anti-inflammatory activity by reprogramming cellular acetate handling at sites of infection. Cell Metab. 32, 457–467 (2020).

    Article  CAS  PubMed  Google Scholar 

  50. Fachi, J. L. et al. Acetate coordinates neutrophil and ILC3 responses against C. difficile through FFAR2. J. Exp. Med. 217, e20190489 (2020).

    Article  Google Scholar 

  51. Balmer, M. L. et al. Memory CD8(+) T cells require increased concentrations of acetate induced by stress for optimal function. Immunity 44, 1312–1324 (2016).

    Article  CAS  PubMed  Google Scholar 

  52. Hanninen, A., Maksimow, M., Alam, C., Morgan, D. J. & Jalkanen, S. Ly6C supports preferential homing of central memory CD8+ T cells into lymph nodes. Eur. J. Immunol. 41, 634–644 (2011).

    Article  PubMed  Google Scholar 

  53. Araki, Y., Fann, M., Wersto, R. & Weng, N. P. Histone acetylation facilitates rapid and robust memory CD8 T cell response through differential expression of effector molecules (eomesodermin and its targets: perforin and granzyme B). J. Immunol. 180, 8102–8108 (2008).

    Article  CAS  PubMed  Google Scholar 

  54. Qiu, J. et al. Acetate promotes T cell effector function during glucose restriction. Cell Rep. 27, 2063–2074 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Vodnala, S. K. et al. T cell stemness and dysfunction in tumors are triggered by a common mechanism. Science 363, eaau0135 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sugiura, A. & Rathmell, J. C. Metabolic barriers to T cell function in tumors. J. Immunol. 200, 400–407 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Arner, E. N. & Rathmell, J. C. Metabolic programming and immune suppression in the tumor microenvironment. Cancer Cell. 41, 421–433 (2023).

    Article  CAS  PubMed  Google Scholar 

  58. Chang, C. H. et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162, 1229–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhou, Y., Cheng, L., Liu, L. & Li, X. NK cells are never alone: crosstalk and communication in tumour microenvironments. Mol. Cancer 22, 34 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Huntington, N. D., Cursons, J. & Rautela, J. The cancer-natural killer cell immunity cycle. Nat. Rev. Cancer 20, 437–454 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Barry, K. C. et al. A natural killer-dendritic cell axis defines checkpoint therapy-responsive tumor microenvironments. Nat. Med. 24, 1178–1191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bottcher, J. P. et al. NK cells stimulate recruitment of cDC1 into the tumor microenvironment promoting cancer immune control. Cell 172, 1022–1037 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mocikat, R. et al. Natural killer cells activated by MHC class I(low) targets prime dendritic cells to induce protective CD8 T cell responses. Immunity 19, 561–569 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Allegrezza, M. J. et al. Trametinib drives T-cell-dependent control of KRAS-mutated tumors by inhibiting pathological myelopoiesis. Cancer Res. 76, 6253–6265 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Rutkowski, M. R. et al. Initiation of metastatic breast carcinoma by targeting of the ductal epithelium with adenovirus-cre: a novel transgenic mouse model of breast cancer. J. Vis. Exp. 26, 51171 (2014).

    Google Scholar 

  66. Rutkowski, M. R. et al. Microbially driven TLR5-dependent signaling governs distal malignant progression through tumor-promoting inflammation. Cancer Cell 27, 27–40 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Sheen, M. R. et al. Constitutively activated PI3K accelerates tumor initiation and modifies histopathology of breast cancer. Oncogenesis 5, e267 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Pfefferle, A. D. et al. Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers. Dis. Model Mech. 9, 749–757 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hollern, D. P. et al. B cells and T follicular helper cells mediate response to checkpoint inhibitors in high mutation burden mouse models of breast cancer. Cell 179, 1191–1206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Pfefferle, A. D. et al. Transcriptomic classification of genetically engineered mouse models of breast cancer identifies human subtype counterparts. Genome Biol. 14, R125 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Singh, S. et al. Chemotherapy coupled to macrophage inhibition induces T-cell and B-cell infiltration and durable regression in triple-negative breast cancer. Cancer Res. 82, 2281–2297 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Zheng, T. et al. Profiling single-guide RNA specificity reveals a mismatch sensitive core sequence. Sci Rep. 7, 40638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Shinde, R. et al. Apoptotic cell-induced AhR activity is required for immunological tolerance and suppression of systemic lupus erythematosus in mice and humans. Nat. Immunol. 19, 571–582 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Liu, Z., Gu, Y., Shin, A., Zhang, S. & Ginhoux, F. Analysis of myeloid cells in mouse tissues with flow cytometry. STAR Protoc. 1, 100029 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Casciano, J. C. et al. MYC regulates fatty acid metabolism through a multigenic program in claudin-low triple negative breast cancer. Br. J. Cancer 122, 868–884 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Melamud, E., Vastag, L. & Rabinowitz, J. D. Metabolomic analysis and visualization engine for LC-MS data. Anal. Chem. 82, 9818–9826 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinf. 12, 323 (2011).

    Article  CAS  Google Scholar 

  80. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  82. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H.-Y. Tang, N. Gorman, A. Goldman and T. Beer of the Wistar Institute Proteomic and Metabolomic core. We also acknowledge the staff of the Wistar Institute Genomics Core, Wistar Flow Cytometry Core and Wistar Animal Facility. The authors thank E. Cento, Z. Chen, M.A. Eldabbas and E. Maddox of the Human Immunology Core (HIC) and the Division of Transfusion Medicine and Therapeutic Pathology at the Perelman School of Medicine at the University of Pennsylvania for providing de-identified, T cells, B cells, monocytes and NK cells that were purified from healthy donor apheresis using StemCell RosetteSep kits. HIC RRID, SCR_022380. This work was supported by grants from the National Institutes of Health (NIH) National Cancer Institute (NCI) DP2 CA249950-01 (Z.T.S.), NIH NCI P01 CA114046 (Z.T.S.), NIH R21 CA259240-01 (R.S.S.), the W.W. Smith Charitable Trust (Z.T.S.), Susan G. Komen CCR19608782 (Z.T.S.) and the V Foundation for Cancer Research (Z.T.S.). This research and project is funded, in part, by a contract with the Pennsylvania Breast Cancer Coalition (Z.T.S.). The PBCC takes no part in and is in no way responsible for any analyses, interpretations or conclusions contained herein. We acknowledge funding from the NIH NCI T32 CA009171 (K.D.M. and S.H.) and the American Cancer Society Rena and Victor Damone Postdoctoral Fellowship PF-20-1225-01-CCG (K.D.M.). The Wistar Molecular Screening Facility and Genomics Facility are supported by NIH grant P30 CA010815. The Wistar Proteomic and Metabolomic Facility is supported, in part, by NIH grants R50 CA221838 and S10 OD023586. The HIC is supported, in part, by NIH P30 AI045008 and P30 CA016520.

Author information

Authors and Affiliations

  1. The Wistar Institute of Anatomy and Biology, Molecular and Cellular Oncogenesis Program, Philadelphia, PA, USA

    Katelyn D. Miller, Seamus O’Connor, Katherine A. Pniewski, Toshitha Kannan, Sara Papp, Michael Hulse, Dzmitry Mukha, Sabina I. Hlavaty, Kelsey N. Salcido, Fabrizio Bertolazzi, Yellamelli V. V. Srikanth, Andrew Kossenkov, Joseph M. Salvino & Zachary T. Schug

  2. The Wistar Institute of Anatomy and Biology, Vaccine and Immunotherapy Center, Philadelphia, PA, USA

    Reyes Acosta & Daniel T. Claiborne

  3. The Wistar Institute of Anatomy and Biology, Immunology, Microenvironment & Metastasis Program, Philadelphia, PA, USA

    Gauri Mirji & Rahul S. Shinde

  4. Cellular and Molecular Biology Program, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy

    Fabrizio Bertolazzi

  5. Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA

    Steven Zhao & Kathryn E. Wellen

  6. Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA, USA

    Kathryn E. Wellen

Authors
  1. Katelyn D. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seamus O’Connor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katherine A. Pniewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Toshitha Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Reyes Acosta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gauri Mirji
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sara Papp
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael Hulse
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dzmitry Mukha
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sabina I. Hlavaty
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kelsey N. Salcido
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Fabrizio Bertolazzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yellamelli V. V. Srikanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Steven Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Kathryn E. Wellen
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Rahul S. Shinde
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Daniel T. Claiborne
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Andrew Kossenkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Joseph M. Salvino
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Zachary T. Schug
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.D.M. and Z.T.S. conceived and planned the experiments. K.D.M. and Z.T.S. interpreted and prepared the data presented in this manuscript. K.D.M., S.O.C., K.A.P., S.P., S.H., K.N.S., F.B., S.Z., K.E.W. and S.H. assisted in the execution and interpretation of experiments. M.H. generated revertant cell lines. Y.V.V.S. and J.M.S. generated and validated VY-3-135. T.K. and A.K. assisted in the analysis, interpretation and generation of figures related to transcriptomics. D.M. optimized the metabolomics method and analyzed LC–MS data. K.D.M. and Z.T.S. prepared the figures and initial drafts of this manuscript. R.A., F.B. and D.T.C. performed proliferation and polyfunctional assays. G.M. and R.S.S. performed phenotypic marker flow cytometry profiling. All authors revised and approved the final version of this manuscript.

Corresponding author

Correspondence to Zachary T. Schug.

Ethics declarations

Competing interests

J.M.S. and Z.T.S. are scientific co-founders and consultants for Syndeavor Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Mauricio J. Reginato and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Knockout of Acss2 in mouse breast cancer cell lines often leads to tumor clearance.

a, LC-MS analysis of 13C2-acetate labeling of metabolites and fatty acids in T12 tumor tissue from vehicle versus VY-3-135 treated BALB/c mice. Lines represent the mean ± standard deviation (SD). P values were generated using two-sided t-test. n = 5 tumours per group. AU = arbitrary units b, Allograft tumour growth of Cas9 (WT) or sgAcss2 (Acss2-KO) A7C11 cells in C57Bl/6 mice. Adjusted p values were generated using two-tailed multiple unpaired Welch t-test. Values represent the mean tumour volume ± SEM. n = 5 mice per group. c, Western blots for ACSS2 expression in T11 and 4T1 sgAcss2 cell lines. d, Allograft tumour growth of Cas9 (WT) or sgAcss2 (Acss2-KO) T11 cells in BALB/c mice. n = 8 mice per group. e, Kaplan-Meier survival plot of C57Bl/6 tumour-bearing mice injected with either wild type (Cas9) or Acss2-KO (sgAcss2) T11 cells. P values were generated using a log-rank (Mantel-Cox) test. n = 16 mice per group. f, Allograft tumour growth of Cas9 (WT) or sgAcss2 (Acss2-KO) 4T1 cells in BALB/c mice. Adjusted p values were generated using multiple two-sided Welch t-test. Values represent the mean tumour volume ± SEM. n = 8 mice per group. g, Syngeneic allograft tumour growth of 4T1 sgAcss2 revertant (sgAcss2 rev) tumours in BALB/c mice. n = 7 to 8 mice per group as indicated on plot. Take rates indicate the number of mice with tumours in each group at the end of the study. h, i, Kaplan-Meier survival plot of C57Bl/6 tumour-bearing mice injected with either wild type (Cas9) or Acss2-KO (sgAcss2) Brpkp110 cells or with wild type (Cas9) Brpkp110 cells that were treated daily from day 4 to day 31 (marked red triangles) with VY-3-135. P values were generated using a log-rank (Mantel-Cox) test. n = 8 mice per group. j, Western blot for ACSS2 expression in Brpkp110 wild type (Cas9 #1), sgAcss2 and sgAcss2 rev. k, Syngeneic allograft tumour growth of Brpkp110 cells from panel j in C57Bl/6 mice. n = 5 mice per group. Western blotting results were independently validated twice.

Source data

Extended Data Fig. 2 Clearance of Acss2-KO tumours depends on the presence of functional T cells.

a, Representative flow plots of peripheral blood mononuclear cells collected from mice depleted of CD4+, CD8+, or CD4+ and CD8+ T cells. b, Final tumour weights of WT or Acss2-KO Brpkp110 tumours grown in C57Bl/6, C57Bl/6 Rag2−/−, or NSG mice. Values represent the mean tumour volume ± SD. P values generated using two-tailed Mann–Whitney U test. c, d, Representative flow plots of peripheral blood mononuclear cells collected from mice treated with IgG control (c) or an NK-cell depleting antibody against NK1.1 (d).

Source data

Extended Data Fig. 3 ACSS2 inhibitor-treated tumours have gene signatures associated with increased immune surveillance and activation.

a, Volcano plots showing differentially expressed genes (DEGs) in tumours from NSG or C57Bl/6 mice treated with VY-3-135. NSG tumour-bearing mice were treated for 15 days. C57Bl/6 mice were treated for 5 days. b, Venn diagram illustrating the overlap of genes differentially regulated by VY-3-135 treatment in tumours grown in NSG versus C57Bl/6 mice. c, Heat map of 64 genes in common from the data in panel a. d, e, GSEA of the top 20 most significantly activated functions in tumours after VY-3-135 treatment in NSG and C57Bl/6 mice. Heat-mapping represents -log10 FDR values. The number of genes within that function that were altered is displayed. The x-axis shows the predicted Z score based on the gene expression differences. f, g, IPA analysis of the top 20 significant regulators enriched by VY-3-135 treatment in NSG or C57Bl/6 mice. Heat-mapping on bar plots represents -log10 FDR values. The number next to the bar displays the number (N) of genes within that regulator or function gene set that were altered. The x-axis is the predicted Z score based on the gene expression differences.

Source data

Extended Data Fig. 4 Single cell RNA sequencing identifies enhanced activation of tumour-infiltrating myeloid cells after ACSS2 inhibition.

a, Seurat plot of cell populations following subclustering of myeloid cells from clusters 3 and 4 from Fig. 5a. b, Heat map illustrating gene expression of the top five most highly expressed gene markers within each subcluster. c, Heat-mapping of gene marker expression onto the myeloid cell subclusters. d, Volcano plot illustrating DEGs in VY-3-135 treated tumours of subcluster 0. P values were generated with a nonparametric Wilcoxon rank-sum test. e, GSEA of the top 20 functions and regulators in subcluster 0 that were significantly activated in VY-3-135 treated tumours. f, Volcano plot illustrating DEGs in subcluster 1 in VY-3-135 treated tumours. P values were generated with a nonparametric Wilcoxon rank-sum test. g, GSEA of the top 20 functions and regulators in subcluster 1 that were significantly activated in VY-3-135 treated tumours. Heat-mapping on bar plots represents -log10 FDR values. The number next to the bar displays the number (N) of genes within that regulator or function gene set that were altered. The x-axis is the predicted Z score based on the gene expression differences.

Source data

Extended Data Fig. 5 Gene markers used to identify the cell types within the subclusters of T cells and NK cells.

a, b, Heat-mapping of the relative expression of canonical genes used to distinguish T cells and NK cells within all five lymphocyte subclusters from Fig. 5d. c, Predicted distribution of NK, NKT, and Treg cells within the subclusters.

Source data

Extended Data Fig. 6 Lymphocytes express relatively high amounts of ACSS1 and oxidize acetate better than macrophages.

a, Representative dot plots displaying enrichment for CD8+ and CD4+ T cells from total splenocytes collected from mice. b, mRNA expression for macrophage markers of polarization. Data are presented as relative expression normalized to BMDM. n = 1 quantitation of mRNA expression. c, ACSS1 mRNA expression in T cells and macrophages. Data are presented as a mean ± SD. P values generated using two-way ANOVA Dunnett’s multiple comparisons test against CD8+ T cells. n.s. = not significant. n = 3 independent samples for T cells and n = 6 independent samples for macrophages. d, Stable isotope tracing of 0.1 versus 0.5 mM 13C2-acetate into T cells and macrophages for 1 h and LC-MS based analysis of citrate labeling. M + 2, M + 3, and M + 4 plots show the percent labeling of citrate by 13C2-acetate for each isotopologue. n = 2 independent cultures of CD8+ T cells and macrophages.

Source data

Extended Data Fig. 7 Acetate increases expression of cytokines and activation markers in T cells cultured in low glucose.

a, ACSS1, ACSS2 and ACSS3 expression in lymphocytes and monocytes from one male and one female healthy human donor. b, ACSS1 mRNA expression represented as normalized transcripts per kilobase million (nTPM) from a scRNA-seq analysis of normal human breast tissue. Where n ≥ 3 data are presented as a mean ± SEM. c, ACSS1 mRNA expression from a scRNA-seq analysis of human breast tumors. Violin plots with hashed lines representing median and dotted lines mark upper and lower quartiles. P values generated using log-rank test. d, Expression of degranulation and activation markers in human CD8+ T cells after stimulation in the presence of increasing concentrations of acetate and two different concentrations of glucose. Dotted lines represent expression levels in standard culture medium. n = 2 independent CD8+ T-cell cultures. Data are presented as a percent of total CD8+ T cells. e, Glucose and acetate consumption by human CD8+ T cells during polyfunctional assay. Data are expressed as change in concentration during overnight stimulation. n.d. = not detected. n = 2 independent CD8+ T-cell cultures. Western blotting results were independently validated at least twice.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Spearman correlation of ACSS1 and ACSS2 expression with immune infiltrate gene signatures.

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Fig. 7

Statistical Source Data.

Source Data Fig. 8

Statistical Source Data.

Source Data Extended Data Fig. 1

Statistical Source Data.

Source Data Extended Data Fig. 2

Statistical Source Data.

Source Data Extended Data Fig. 3

Statistical Source Data.

Source Data Extended Data Fig. 4

Statistical Source Data.

Source Data Extended Data Fig. 5

Statistical Source Data.

Source Data Extended Data Fig. 6

Statistical Source Data.

Source Data Extended Data Fig. 7

Statistical Source Data.

Source Data Gels

Unprocessed western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miller, K.D., O’Connor, S., Pniewski, K.A. et al. Acetate acts as a metabolic immunomodulator by bolstering T-cell effector function and potentiating antitumor immunity in breast cancer. Nat Cancer 4, 1491–1507 (2023). https://doi.org/10.1038/s43018-023-00636-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00636-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer