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:

B-cell-specific checkpoint molecules that regulate anti-tumour immunity

Nature volume 619, pages 348–356 (2023)Cite this article

Subjects

Abstract

The role of B cells in anti-tumour immunity is still debated and, accordingly, immunotherapies have focused on targeting T and natural killer cells to inhibit tumour growth1,2. Here, using high-throughput flow cytometry as well as bulk and single-cell RNA-sequencing and B-cell-receptor-sequencing analysis of B cells temporally during B16F10 melanoma growth, we identified a subset of B cells that expands specifically in the draining lymph node over time in tumour-bearing mice. The expanding B cell subset expresses the cell surface molecule T cell immunoglobulin and mucin domain 1 (TIM-1, encoded by Havcr1) and a unique transcriptional signature, including multiple co-inhibitory molecules such as PD-1, TIM-3, TIGIT and LAG-3. Although conditional deletion of these co-inhibitory molecules on B cells had little or no effect on tumour burden, selective deletion of Havcr1 in B cells both substantially inhibited tumour growth and enhanced effector T cell responses. Loss of TIM-1 enhanced the type 1 interferon response in B cells, which augmented B cell activation and increased antigen presentation and co-stimulation, resulting in increased expansion of tumour-specific effector T cells. Our results demonstrate that manipulation of TIM-1-expressing B cells enables engagement of the second arm of adaptive immunity to promote anti-tumour immunity and inhibit tumour growth.

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: Characterization of B cells expressing TIM-1 and several checkpoint molecules in mouse melanoma and human tumours.
Fig. 2: Screening of in vivo regulatory molecules reveals TIM-1 as a B cell immune checkpoint controlling tumour growth.
Fig. 3: Targeting of TIM-1 reduces B16F10 growth, is dependent on TIM-1 expression on B cells and augments PD-1 blockade therapy.
Fig. 4: Havcr1 deletion in B cells enhances anti-tumour T cell immunity.
Fig. 5: TIM-1 deficiency in B cells results in B cell activation, antigen presentation and co-stimulatory function.

Similar content being viewed by others

Data availability

All genomics data produced for this study have been deposited at the GEO under accession number GSE225717. All other data needed to evaluate the conclusions in this paper are available in the Article and its Supplementary Information. Source data are provided with this paper.

References

  1. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Griss, J. et al. B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma. Nat. Commun. 10, 4186 (2019).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  5. Ladanyi, A. et al. Prognostic impact of B-cell density in cutaneous melanoma. Cancer Immunol. Immunother. 60, 1729–1738 (2011).

    Article  CAS  PubMed  Google Scholar 

  6. Gupta, N. T. et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–3358 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Delaney, C. et al. Combinatorial prediction of marker panels from single-cell transcriptomic data. Mol. Syst. Biol. 15, e9005 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wolf, Y., Anderson, A. C. & Kuchroo, V. K. TIM3 comes of age as an inhibitory receptor. Nat. Rev. Immunol. 20, 173–185 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Mohib, K., Rothstein, D. M. & Ding, Q. Characterization and activity of TIM-1 and IL-10-reporter expressing regulatory B cells. Methods Mol. Biol. 2270, 179–202 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Xiao, S., Brooks, C. R., Sobel, R. A. & Kuchroo, V. K. Tim-1 is essential for induction and maintenance of IL-10 in regulatory B cells and their regulation of tissue inflammation. J. Immunol. 194, 1602–1608 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Xiao, S. et al. Defect in regulatory B-cell function and development of systemic autoimmunity in T-cell Ig mucin 1 (Tim-1) mucin domain-mutant mice. Proc. Natl Acad. Sci. USA 109, 12105–12110 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ding, Q. et al. Regulatory B cells are identified by expression of TIM-1 and can be induced through TIM-1 ligation to promote tolerance in mice. J. Clin. Invest. 121, 3645–3656 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yeung, M. Y. et al. TIM-1 signaling is required for maintenance and induction of regulatory B cells. Am. J. Transplant. 15, 942–953 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu, Y. et al. Immune phenotypic linkage between colorectal cancer and liver metastasis. Cancer Cell 40, 424–437 (2022).

    Article  PubMed  Google Scholar 

  16. 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 

  17. Bi, K. et al. Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma. Cancer Cell 39, 649–661 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Qian, J. et al. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling. Cell Res. 30, 745–762 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhang, L. et al. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181, 442–459 (2020).

    Article  CAS  PubMed  Google Scholar 

  22. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997 e924 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cerqueira, C., Manfroi, B. & Fillatreau, S. IL-10-producing regulatory B cells and plasmocytes: molecular mechanisms and disease relevance. Semin. Immunol. 44, 101323 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Floudas, A. et al. Pathogenic, glycolytic PD-1+ B cells accumulate in the hypoxic RA joint. JCI Insight https://doi.org/10.1172/jci.insight.139032 (2020).

  27. Hasan, M. M. et al. Implication of TIGIT+ human memory B cells in immune regulation. Nat. Commun. 12, 1534 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lino, A. C. et al. LAG-3 inhibitory receptor expression identifies immunosuppressive natural regulatory plasma cells. Immunity 49, 120–133 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xiao, S. et al. Checkpoint receptor TIGIT expressed on Tim-1+ B cells regulates tissue inflammation. Cell Rep. 32, 107892 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Horikawa, M., Minard-Colin, V., Matsushita, T. & Tedder, T. F. Regulatory B cell production of IL-10 inhibits lymphoma depletion during CD20 immunotherapy in mice. J. Clin. Invest. 121, 4268–4280 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Hilgenberg, E. et al. Interleukin-10-producing B cells and the regulation of immunity. Curr. Top. Microbiol. Immunol. 380, 69–92 (2014).

    CAS  PubMed  Google Scholar 

  32. Domeier, P. P. et al. B-cell-intrinsic type 1 interferon signaling is crucial for loss of tolerance and the development of autoreactive B cells. Cell Rep. 24, 406–418 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Le Bon, A. et al. Type I interferons potently enhance humoral immunity and can promote isotype switching by stimulating dendritic cells in vivo. Immunity 14, 461–470 (2001).

    Article  PubMed  Google Scholar 

  34. Hervas-Stubbs, S. et al. Direct effects of type I interferons on cells of the immune system. Clin. Cancer Res. 17, 2619–2627 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. Andreu, P. et al. FcRγ activation regulates inflammation-associated squamous carcinogenesis. Cancer Cell 17, 121–134 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lu, Y. et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell 180, 1081–1097 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Zhou, B., Lawrence, T. & Liang, Y. The role of plasmacytoid dendritic cells in cancers. Front. Immunol. 12, 749190 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ding, Q., Mohib, K., Kuchroo, V. K. & Rothstein, D. M. TIM-4 identifies IFN-γ-expressing proinflammatory B effector 1 cells that promote tumor and allograft rejection. J. Immunol. 199, 2585–2595 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Zhou, X. CD19+IL-10+ regulatory B cells affect survival of tongue squamous cell carcinoma patients and induce resting CD4+ T cells to CD4+Foxp3+ regulatory T cells. Oral Oncol. https://doi.org/10.1016/j.oraloncology.2015.11.003 (2016).

  40. de Souza, A. J., Oriss, T. B., O’Malley K, J., Ray, A. & Kane, L. P. T cell Ig and mucin 1 (TIM-1) is expressed on in vivo-activated T cells and provides a costimulatory signal for T cell activation. Proc. Natl Acad. Sci. USA 102, 17113–17118 (2005).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  41. Xiao, S. et al. Tim-1 stimulation of dendritic cells regulates the balance between effector and regulatory T cells. Eur. J. Immunol. 41, 1539–1549 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mariat, C. et al. Tim-1 signaling substitutes for conventional signal 1 and requires costimulation to induce T cell proliferation. J. Immunol. 182, 1379–1385 (2009).

    Article  CAS  PubMed  Google Scholar 

  43. Xiao, S. et al. Differential engagement of Tim-1 during activation can positively or negatively costimulate T cell expansion and effector function. J. Exp. Med. 204, 1691–1702 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Meyers, J. H. et al. TIM-4 is the ligand for TIM-1, and the TIM-1-TIM-4 interaction regulates T cell proliferation. Nat. Immunol. 6, 455–464 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Sharonov, G. V., Serebrovskaya, E. O., Yuzhakova, D. V., Britanova, O. V. & Chudakov, D. M. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat. Rev. Immunol. 20, 294–307 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Braun, D., Caramalho, I. & Demengeot, J. IFN-α/β enhances BCR-dependent B cell responses. Int. Immunol. 14, 411–419 (2002).

    Article  CAS  PubMed  Google Scholar 

  47. Bruno, T. C. et al. Antigen-presenting intratumoral B cells affect CD4+ TIL phenotypes in non-small cell lung cancer patients. Cancer Immunol. Res. 5, 898–907 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Rivera, A., Chen, C. C., Ron, N., Dougherty, J. P. & Ron, Y. Role of B cells as antigen-presenting cells in vivo revisited: antigen-specific B cells are essential for T cell expansion in lymph nodes and for systemic T cell responses to low antigen concentrations. Int. Immunol. 13, 1583–1593 (2001).

    Article  CAS  PubMed  Google Scholar 

  49. Rossetti, R. A. M. et al. B lymphocytes can be activated to act as antigen presenting cells to promote anti-tumor responses. PLoS ONE 13, e0199034 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Germain, C., Gnjatic, S. & Dieu-Nosjean, M. C. Tertiary lymphoid structure-associated B cells are key players in anti-tumor immunity. Front. Immunol. 6, 67 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  52. Helmink, B. A. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Khalil, A. M., Cambier, J. C. & Shlomchik, M. J. B cell receptor signal transduction in the GC is short-circuited by high phosphatase activity. Science 336, 1178–1181 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Engblom, C. et al. Osteoblasts remotely supply lung tumors with cancer-promoting SiglecFhigh neutrophils. Science https://doi.org/10.1126/science.aal5081 (2017).

  55. Chihara, N. et al. Induction and transcriptional regulation of the co-inhibitory gene module in T cells. Nature 558, 454–459 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  56. Li, B. et al. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nat. Methods 17, 793–798 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Fleming, S. J. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Preprint at bioRxiv https://doi.org/10.1101/791699 (2022).

  58. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jerby-Arnon, L. & Ruppin, E. Moving ahead on harnessing synthetic lethality to fight cancer. Mol. Cell. Oncol. 2, e977150 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Jerby-Arnon, L. et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158, 1199–1209 (2014).

    Article  CAS  PubMed  Google Scholar 

  62. Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lee, J. S. et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat. Commun. 9, 2546 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  64. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  65. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 46, W537–W544 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mahoney, K. M. et al. A secreted PD-L1 splice variant that covalently dimerizes and mediates immunosuppression. Cancer Immunol. Immunother. 68, 421–432 (2019).

    Article  CAS  PubMed  Google Scholar 

  69. Ye, C. J. et al. Genetic analysis of isoform usage in the human anti-viral response reveals influenza-specific regulation of ERAP2 transcripts under balancing selection. Genome Res. 28, 1812–1825 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).

    Article  CAS  PubMed  Google Scholar 

  71. Tang, Z., Kang, B., Li, C., Chen, T. & Zhang, Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 47, W556–W560 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank all of the members of the Kuchroo laboratory; A. C. Anderson, S. H. Krovi, A. Kohl and M. Collins for discussions; J. Xia, H. Stroh, D. Kozoriz and R. Kumar for laboratory support; and C. Lambden for computational advice. The work was supported by the grants Melanoma Research Alliance (MRA; 926682); P01AI129880, P01AI039671, P01AI073748, P01AI056299 and R01AI144166 from the National Institutes of Health (to V.K.K.); by the Klarman Cell Observatory and HHMI (to A.R.). Y.-C.K. was supported by the NMSS FG-2007-36929; L.B. by the Philippe Foundation; and L.A. by the LabEx MAbImprove (ANR-10-LABX-53-01).

Author information

Author notes

  1. Lloyd Bod

    Present address: Massachusetts General Hospital Cancer Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

  2. Jingwen Shi

    Present address: BeiGene, Beijing, China

  3. Johannes Fessler

    Present address: Division of Immunology and Pathophysiology, Medical University of Graz, Graz, Austria

  4. Aviv Regev

    Present address: Genentech, San Francisco, CA, USA

Authors and Affiliations

  1. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, MA, USA

    Lloyd Bod, Yoon-Chul Kye, Jingwen Shi, Alexandra Schnell, Johannes Fessler, Juhi R. Kuchroo, Rocky M. Barilla, Sarah Zaghouani, Sheng Xiao, Nadine Slingerland, Arlene H. Sharpe, Lionel Apetoh & Vijay K. Kuchroo

  2. Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Lloyd Bod, Yoon-Chul Kye, Jingwen Shi, Elena Torlai Triglia, Alexandra Schnell, Elena Christian, Toni Marie Delorey, Nadine Slingerland, Christopher J. Giuliano, Orr Ashenberg, Orit Rozenblatt-Rosen, Francisco J. Quintana, Aviv Regev & Vijay K. Kuchroo

  3. Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Lloyd Bod, Yoon-Chul Kye, Alexandra Schnell, Rocky M. Barilla & Vijay K. Kuchroo

  4. Department of Dermatology, Massachusetts General Hospital, Boston, MA, USA

    Stephen M. Ostrowski, Max Y. Von-Franque & David E. Fisher

  5. Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA

    Juhi R. Kuchroo & Arlene H. Sharpe

  6. Thomas E. Starzl Transplantation Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Kanishka Mohib & David M. Rothstein

  7. Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Zhaorong Li

  8. Howard Hughes Medical Institute, Department of Biology and Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Orit Rozenblatt-Rosen, Francisco J. Quintana & Aviv Regev

  9. INSERM, Tours, France

    Lionel Apetoh

  10. Faculté de Médecine, Université de Tours, Tours, France

    Lionel Apetoh

Authors
  1. Lloyd Bod
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yoon-Chul Kye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingwen Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elena Torlai Triglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexandra Schnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Johannes Fessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stephen M. Ostrowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Max Y. Von-Franque
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Juhi R. Kuchroo
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Rocky M. Barilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sarah Zaghouani
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Elena Christian
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Toni Marie Delorey
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Kanishka Mohib
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Sheng Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Nadine Slingerland
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Christopher J. Giuliano
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Orr Ashenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Zhaorong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. David M. Rothstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. David E. Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Orit Rozenblatt-Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Arlene H. Sharpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Francisco J. Quintana
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Lionel Apetoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Vijay K. Kuchroo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.B. and V.K.K. conceived the study. L.B., with assistance from Y.-C.K., J.S., A.S., S.M.O., M.Y.V.-F., D.E.F., J.F., R.M.B., S.Z. and S.X., designed, performed and analysed the biological experiments. L.B., with assistance from Y.-C.K., J.S., A.S., E.C. and T.M.D., performed the sequencing experiments with guidance from A.R. and O.R.-R. L.B., N.S., C.J.G., Z.L., F.J.Q., O.A. and E.T.T. designed and performed the computational analysis with guidance from A.R. L.B., J.S., E.T.T., L.A., V.K.K. and A.R. interpreted the results. J.R.K. and A.H.S. generated and provided the Pdcd1fl/fl mice. K.M. and D.M.R. generated and performed the experiments using the Cd19cre/+ × Il10fl/fl mice. The manuscript was written by L.B. with assistance from E.T.T. and was edited by L.A., A.R. and V.K.K. with input from all of the authors.

Corresponding authors

Correspondence to Aviv Regev or Vijay K. Kuchroo.

Ethics declarations

Competing interests

V.K.K. has an ownership interest in and is a member of the scientific advisory board for Tizona Therapeutics, Bicara Therapeutics, Compass Therapeutics, Larkspur Biosciences and Trishula Therapeutics. L.B., S.X. and V.K.K. are named as inventors on a provisional patent that has been filed including work from this study. L.A. performed consultancy work for Roche, Merck, Bristol-Myers Squibb and Orega Biotech, and was a recipient of a research grant from Sanofi. A.R. and V.K.K. are co-founders of and have an ownership interest in Celsius Therapeutics. A.R. is also a co-founder and equity holder in Immunitas Therapeutics and was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov and Neogene Therapeutics until 31 July 2020. A.R. and O.R.-R. are listed as co-inventors on patent applications filed by the Broad Institute to inventions relating to single-cell genomics. The interests of V.K.K. were reviewed and managed by the Brigham and Women’s Hospital and Partners Healthcare in accordance with their conflict-of-interest policies. The interests of A.R. were reviewed and managed by the Broad Institute and HHMI in accordance with their conflict-of-interest policies. Since 1 August 2020, A.R. has been an employee of Genentech, a member of the Roche group. O.R.-R. is currently an employee of Genentech. The other authors declare no competing interests.

Peer review

Peer review information

Nature thanks Menna Clatworthy 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 figures and tables

Extended Data Fig. 1 Total B cells but not plasma cells limit tumour growth and B16F10-infiltrating B cells have a distinct phenotype.

a, Frequencies of B cells among CD45+ cells derived from tumour, dLN, ndLN from C57Bl6/J mice 16 days post tumour implantation. b,c, B16F10 tumour growth in C57Bl/6J treated with anti-CD20 (48h prior to tumour injections) or isotype control antibodies (n = 5 mice per group) (b) or CD19Cre/+ and CD19Cre/+xPrdm1fl/fl (n = 5 mice per group). d–g, Bulk RNAseq analysis of B cells derived from tumour, dLN, ndLN and spleen of B16F10-bearing wild-type mice (n = 3). Experimental design and PCA plot (d), Heatmap of global gene expression (e), Pathway enrichment analysis of genes up-regulated in tumour-derived B cells (f) and heatmap of a selected set of genes (g). h, Flow cytometry analysis of B cells derived from tumour, dLN, ndLN and spleen of C57Bl6/J mice implanted with B16F10 s.c. Representative FACS plot and percentage of B cell subsets. Heatmap depicting the MFI of various B cell markers in B cells derived from tumours or dLN from C57Bl6/J mice (n = 5) (h). Data are mean ± s.e.m and pooled or representative of at least two to three independent experiments. * p < 0.05, ** p < 0.01, *** p < 0.0001. Repeated measures two-way ANOVA test in b and c. two-tailed Student’s t-test in a. two-way ANOVA with Tukey’s multiple comparisons test in h.

Source data

Extended Data Fig. 2 scRNAseq and BCRseq of TILs, dLN and ndLN derived from B16F10 melanoma bearing mice.

a, Gating strategy for the sorting of singlet viable cells prior to scRNAseq. b, Flow cytometry analysis depicting proportions of cell types infiltrating tumours across time. Data are mean ± s.e.m from two experiments. n = 3 mice per group. c, UMAP of expression of different lineage marker transcripts. d,e, UMAPs and quantification of immunoglobulin class-switch (d) and clonal expansion (e) in B cells. f and g, UMAPs of B cells coloured by time points or relative expression of the indicated genes (f) and g, Panels I-VIII, cells are coloured by Cd19 expression (I) or by their signature score that reflects the relative average expression of the genes overlapping with the signatures for several indicated B cell subsets (II-VIII). Follicular (FO_B), Marginal zone (MZ_B), Immature (Imm_B), Antibody secreting cells (ASC), germinal centre (GC) B cells derived from the dark zone (DZ_B) or light zone (LZ_B). h, Heatmap depicting the log fold change of the top 100 genes uniquely up-regulated in each Leiden cluster (t-test; fold change >2). Selected genes are shown.

Source data

Extended Data Fig. 3 TIM-1 expressing B cells characterization.

a, Proportions of TIM-1+ cells among CD19+ cells derived from tumour, dLN, ndLN, and spleen from B16F10 bearing C57Bl6/J mice 16 days post tumour injection together with inguinal LN (iLN) and spleen from tumour-free WT mice (n = 5 for pLN, n = 9 for spleens, n = 16 for Tumour, dLN and ndLN). b, TIM-1+ B cells derived from dLN and ndLN were sorted and analysed by bulk RNAseq (n = 3). Experimental design, PCA plot and heatmap of selected genes are shown. c, Flow cytometry analysis of subsets and marker expression of TIM-1+ B cells derived from dLN vs ndLN from B16F10-bearing WT mice (n = 6). d) FACS-sorted TIM-1− and TIM-1+ B cells were stained with CTV and stimulated in vitro with anti-IgM, anti-CD40 or LPS for 72h. Cell proliferation and plasma cell differentiation was analysed by flow cytometry. Representative FACS plot (left) and quantification (right) are shown (n = 7 for medium, for stimulation n = 11 for TIM-1- and n = 9 for TIM-1+). e, f, scRNAseq analysis depicting the experimental design, UMAPs coloured by tissue of origin (I), TIM-1 sorting (II), expression of havcr1 (III) and gene signature score of cell cycle S-phase (IV), germinal centre cells (V) and antibody secreting cells (VI). Dotplot of Havcr1 expression (III. right). f, UMAP coloured by B cell clusters annotated according to TIM-1 expression. Pie chart depicting the frequency of the two main TIM-1-expressing subsets and foldchange of cell numbers between dLN and ndLN for each subset (n = 7). g, Top 5 differentially expressed genes (FDR < 0.05 and LFC > 1) (x axis) by cluster (y axis). Dot size represents the fraction of cells in the cluster that express the gene; colour indicates the mean expression (logTP10K (see Methods)) in all cells, relative to other clusters. h, FACS-sorted TIM-1- B cells were stained with CTV and stimulated in vitro with LPS, anti-IgM, anti-CD40 (n = 3) or both anti-IgM+anti-CD40 (n = 4) for 72 h. TIM-1 surface expression across cell divisions was analysed by flow cytometry. Representative FACS plot (left) and TIM-1 MFI quantification (right). Flow cytometry data are mean ± s.e.m and pooled or representative of at least two to three independent experiments. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, two-way ANOVA test in h. two-tailed Student’s t-test in a,c,d and f.

Source data

Extended Data Fig. 4 TIM-1 expressing B cells express higher levels of checkpoint molecules and IL-10.

a–b, TIM-1+ and TIM-1− B cells derived from dLN and ndLN from B16F10 bearing C57Bl6/J mice were analysed ex vivo. MFI of various checkpoint molecules (n = 4 mice per group) (a), IL-10 secretion 24 h post anti-IgM stimulation as determined by LegendPlex (n = 5 mice per group) (b). c, FACS-sorted TIM-1− and TIM-1+ B cells were stimulated in vitro with anti-IgM, anti-CD40 or LPS for 72 h. MFI of checkpoint molecules was analysed by flow cytometry. d–f, UMAP plot of published scRNAseq data depicting 2615 B cells(dots) isolated from human tumours, coloured by their signature score that reflects the relative average expression of the genes overlapping with the signature of human melanoma exhausted T cells from Tirosh et al. 2016 (d), known B-cell subsets4 (e) or Leiden clusters (f). g, Beeswarm plots of the distribution of log fold change across Pre and Post ICB treatment from the Merge SS2 datasets using miloR70. h–j) UMAPs depicting each single cell dots coloured by Leiden clusters (h), Immune checkpoint signature score (i) or density plot for treatment-naive samples (j). Flow cytometry data are mean ± s.e.m and pooled or representative of at least two to three independent experiments. k, Survival map depicting the association of HAVCR1 high expression and clinical outcome in 32 cancer types. High log10 Hasard ratio (HR) (Reds) indicates a negative correlation with survival which would be outlined if p ≤ 0.05. l and m, Kaplan Meier disease free (top row) or Overall (bottom row) survival curves for TIM-1 expression (l) or IC B cells signature (m) in Lung (LUAD), pancreatic (PAAD), stomach (STAD) and colon (COAD) adenocarcinomas. For each signature gene set, the cohorts were divided into high and low expression groups by median value (50% cutoff).Analyses were performed with log-rank Mantel-Cox test using web server GEPIA271, based on TCGA and GTEx databases. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, paired two-tailed t-test in a,b, and c.

Source data

Extended Data Fig. 5 TIM-1 loss in B cells but not T cells limits tumor growth and anti-TIM-1 treatment requires MHC II expression on B cells.

a–e, Tumour growth in CD19Cre/+ and TIM-1BKO mice implanted with B16-OVA (n = 5 control vs 5 TIM-1BKO) (b), intravenously (n = 5 control vs 5 TIM-1BKO) (c), intradermally (n = 4 control vs 5 TIM-1BKO) (d) or subcutaneous MC38 colon adenocarcinoma (n = 6 control vs 6 TIM-1BKO) (e). f, Tumour growth curve of B16F10 implanted into TIM-1fl/fl and CD4Cre/+xTIM-1fl/fl mice (n = 4). g, Subcutaneous B16F10 melanoma were subcutaneously implanted into CD19Cre/+, TIM-1BKO, TIM-1fl/fl and CD4Cre/WTxTIM-1fl/fl mice. On day 16 dLN were harvested followed by flow cytometric analysis of TIM-1 expression of CD19+ or CD3e+ cells. n = 4 mice per group. h, B16F10 melanoma growth in TIM-1iBKO and hCD20ert2Cre mice treated with tamoxifen on days indicated prior to tumour inoculation (n = 6 mice per group). i–k, B16F10 tumour growth with anti-isotype control or anti-TIM-1 treatment in C57Bl/6J (n = 7 treated with isotype control vs n = 9 treated with anti-TIM-1), µMT (n = 5 per group) (j) or μMT mice were reconstituted with WT or MHCII KO B cells and treated with anti-TIM-1 antibody (n = 5 mice per group) (k). Experimental design (k, left), tumour growth curves (k, right). l–n, Survival curves (l) and flow cytometry immunophenotyping of TILs depicting frequencies of B cells, CD4+ and CD8+ TILs among living CD45+ cells (m, left), FOXP3+ cells among CD4+ TILs (m, right) and granzyme B+ cells among CD8+ TILs (n) of C57Bl/6J implanted with B16F10 melanoma and treated with either anti-TIM-1, anti-PD-1, anti-TIM-1 + anti-PD-1 (combo), or isotype controls (n = 8 mice per group for tumour growth analysis and 5 mice per group for flow cytometry analysis). Data are mean ± s.e.m and pooled from two to three independent experiments. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Repeated measures two-way ANOVA test in b,d,e,f,h,I,j and k. unpaired two-tailed t-test in c and g. Differences between survival curves were analysed by log-rank (Mantel–Cox) test (l). One or two-way ANOVA with Tukey’s multiple comparisons test in m and n.

Source data

Extended Data Fig. 6 Immunophenotyping of tumour-bearing CD19Cre/+ and Havcr1/TIM-1BKO mice.

a–k, Flow cytometry analysis of TILs, dLN and ndLN derived from CD19Cre/+ and TIM-1BKO mice implanted with B16F10 s.c. Absolute number of live CD45+ cells per gram of tumour (n = 12 controls and n = 11 TIM-1BKO mice) (b), Macs, DCs (n = 12 controls and n = 6 TIM-1BKO mice), mono, PMN (n = 4 controls and n = 4 TIM-1BKO mice), B cells (n = 12 controls and n = 15 TIM-1BKO mice), CD4+ and CD8+ T cells frequencies among CD45+ cells (n = 16 controls and n = 15 TIM-1BKO mice) (c), Frequency of Tregs among CD4+ T cells (n = 16 mice per group) (d), CD8+ T cells vs Tregs ratio (e). CD107a-expressing CD4+(n = 6 controls and n = 12 TIM-1BKO mice) and CD8+ T cells (n = 7 controls and n = 9 TIM-1BKO mice) (f), Eomes and/or Tbet fraction (n = 5 mice per group) (g), MFI of TCF1 (n = 4 controls and n = 3 TIM-1BKO mice) (h) and Frequency PD-1+ TIM-3+ among CD8+ T cells (d). j, pie charts depicting the proportions of various immune cell populations with dLN and ndLN. k, frequencies of FOXP3+ cells among CD4+ T cells (n = 8 mice per group). l, Flow cytometry analysis of TILs from derived from CD19Cre/+ and TIM-1BKO mice implanted with MC38 colon adenocarcinoma s.c. Experimental design, pie chart of immune population and frequencies of FOXP3+ CD4+ T cells and of IFNγ or TNFα expressing CD8+ and CD4+ T cells (n = 4 mice per group). Data are mean ± s.e.m and pooled from two to three independent experiments. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, two-tailed Student’s t-test in b,c,d,e,f,g,h,i,k and l.

Source data

Extended Data Fig. 7 scRNAseq of TILs, dLN and ndLN derived from B16F10 bearing CD19Cre/+ and Havcr1/TIM-1BKO mice.

a,b, scRNA/TCR-seq of TILs, dLN and ndLN from CD19Cre/+ and TIM-1BKO mice bearing B16F10 melanoma. UMAPs coloured by genotype (a, top), biological replicates (a, bottom) or the relative expression of the indicated genes (b). c, UMAPs of T cells coloured by tissue, T cell types, Mki67 relative expression or clonal expansion as indicated. d, Gene expression for functional marker genes in T cells. For each gene (columns) in each group (rows), the proportion of cells in the group expressing the gene (dot size) and the relative mean expression of expressing cells (colour) is plotted.

Extended Data Fig. 8 Analysis of the humoral immunity and B-cell subsets in B16F10 bearing CD19Cre/+ and Havcr1/TIM-1BKO mice.

a, Frequencies of B cells among CD45+ TILs derived from CD19Cre/+ and TIM-1BKO mice implanted with B16F10 s.c. b,c, Representative FACS plot (b) and percentage (c) of plasma cells (B220low CD138high) or plasmablasts (B220+ CD138high) or TFh cells (d) from CD19Cre/+ and TIM-1BKO mice implanted with B16F10 s.c. e–h, serum immunoglobulins or CICs from naive (n = 5 CD19Cre/+ and n = 3 TIM-1BKO) or B16F10-bearing CD19Cre/+ and TIM-1BKO mice (n = 9 per group) and measured by LegendPlex (e–g) or ELISA (h). i, Flow-cytometric analysis of the presence of antitumor antibodies in the sera of CD19Cre/+ and TIM-1BKO mice implanted with B16F10 s.c. Representative histograms (light grey, staining with the secondary antibody alone; blue, CD19Cre/+ mice serum (n = 4); red, TIM-1BKO mice serum (n = 8)), and MFI ratios were calculated by dividing the MFI obtained with a given serum by the MFI obtained with the secondary antibody. j, Quantification of Immunoglobulin class-switch (left) and BCR clonality (right) in CD19Cre/+ and TIM-1BKO B cells. k–m, Flow cytometry analysis of B cell subsets in Tumour, dLN, ndLN and spleen from isotype vs anti-TIM-1 (3B3) treatment mice or in control vs TIM-1BKO mice (n = 5 mice per group). k, Gating strategy used. l and m, Bar plots depicting the frequencies of major B-cell subsets (l) or subsets within B2 cells (n = 5 mice per group) (m). Data are mean ± s.e.m and pooled from two to three independent experiments. Two-tailed Student’s t-test in a,c,d,h and i. two-way ANOVA with Tukey’s multiple comparisons test in e,f,g.

Source data

Extended Data Fig. 9 Havcr1/TIM-1BKO B cells exhibit enhanced antigen presentation and co-stimulation capacity.

a and b, Violin plots displaying the distribution of the type I interferon response signature score (a) or the antigen processing and presentation of peptide antigen (APC) signature score (b) between TIM-1BKO and CD19Cre/+ B cells derived from ndLN, dLN and TILs. c and d, MFI and histograms of MHC I and II as well as co-stimulation molecules ex vivo (n = 8 mice per group) (c) or in vitro co-cultured with OT II CD4+ T cells (n = 3) (d). d and e, OVA323–339 peptide-pulsed TIM-1BKO and CD19Cre/+ B cells were co-cultured with CTV-labelled OVA-restricted CD4+ T cells for 4 days with or without anti-MHC II antibody. T cell proliferation was determined by dilution of CTV. Quantitative analysis of proliferation indices is shown (e). f, B16F10 melanoma growth in CD19Cre/+ and TIM-1BKO mice treated with anti-MHC II or isotype control antibodies (n = 5 mice per group). g, Naive CD45.1+ OVA-restricted CD4+ T cells were transferred i.v. 1 day prior to B16-OVA melanoma cells s.c implantation into CD45.2+ CD19Cre/+ and TIM-1BKO mice (n = 5 mice per group). Tumour-infiltrating OT II cells were examined for expression of KI67 as proportions of expressing cells or MFI of FOXP3, CD25 and Helios (n = 3 CD19Cre/+ and n = 4 TIM-1BKO mice). Schematic of the experimental and quantitative results are depicted. h, TIM-1BKO and CD19Cre/+ B cells cultured with anti-IgM/anti-CD40 for 72 h in the absence (medium) or with 20 ng/ml of IFN-β. Representative histograms (left) and quantitative analysis of the MFI of TIM-1, CD86 and MHC II (n = 4 mice per group). i and j, Flow cytometry analysis of TILs of indicated mice implanted with B16F10 melanoma and treated with isotype control (n = 3 mice per group) or neutralizing anti-IFNAR-1 antibody (n = 4 mice per group). Frequencies of CD8+ T cells (i, left), FOXP3+ and IFNγ+ cells among CD4+ T cells (i, middle and right), B cells (j, left) and MFI of MHC I, MHC II and CD86 among B cells (j, right) are depicted. k–n, Analysis of published scRNAseq20 data depicting 1462 B cells (dots) isolated from human melanoma tumours, projected onto UMAPs coloured by treatment group (top left), density of cells associated with responder, non-responder lesions (top middle and right) or signature scores of tumour-derived TIM-1BKO B cells, GO type I interferon response and GO antigen processing and presentation gene signatures as detailed in Methods. l, UMAP coloured by Leiden cell clusters (resolution 1). m, stacked bar graph displaying the frequencies of B cells derived from Responder and Non-responder samples among each Leiden cluster and n, violin plots displaying the signature scores of the indicated signatures across clusters. Data are mean ± s.e.m and pooled from two to three independent experiments. * p < 0.05, ** p < 0.01, *** p < 0.001, ****p < 0.0001. Kruskal-Wallis test in a and b. Two-tailed Student’s t-test in c,d,e,g,h,i and j. Repeated measures two-way ANOVA test in f.

Source data

Extended Data Fig. 10 Source of Interferons in B16F10 tumours and impact on TIM-1-mediated anti-tumour immunity.

a) GSEA analysis for the “Response to type II IFN pathway” of tumour-infiltrating TIM-1BKO and CD19Cre/+ B cells. b and c) Murine (b) or human (c) B cells were stimulated with IgM/CD40 for 3 and 7 days respectively in the presence or not of IFNβ, IFNγ or IFNλ (n = 3). TIM-1 expression (MFI) was analysed by flow cytometry. d) Tumour growth in indicated mice implanted with B16F10 melanoma and treated with isotype control or neutralizing anti-IFNGR-1 antibody (n = 5 mice per group). e and i) B16F10 tumour and dLN supernatants derived from CD19Cre/+ and TIM-1BKO mice were collected, and levels of IFNβ were determined by ELISA (n = 5 CD19Cre/+ vs n = 6 TIM-1BKO mice in e). f) Matrixplot depicting IFNb1 mRNA expression profile across immune populations in B16F10 tumours by scRNAseq. g) Tumour growth in indicated mice implanted with B16F10 melanoma and treated with isotype control or depleting anti-PDCA1 antibody (two i.p injections 48 and 24 h prior to tumour injection, n = 7 mice per group). h) Flow cytometry analysis of pDC (MHCII+ CD11c+B220+PDCA1+) frequencies in B16F10 CD19Cre/+ and TIM-1BKO tumours (n = 5 istoype treated and n = 3 anti-PDCA1 treated mice). Data are mean ± s.e.m and pooled from two to three independent experiments. Two-tailed Student’s t-test in b,c,e,h and i. * p < 0.05, ** p < 0.01, *** p < 0.001, ****p < 0.0001, repeated measures two-way ANOVA test in d and g.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

B cell lineage gene signatures used in Fig. 1.

Supplementary Table 2

Ranked genes in B cell clusters from the time-course dataset.

Supplementary Table 3

Differentially expressed genes in dLN TIM-1+ B cells from bulk mRNA-seq.

Supplementary Table 4

Human datasets used and clinical characteristics.

Supplementary Table 5

Ranked genes from Havcr1BKO compared with control B cells derived from tumour, dLN and ndLN.

Source data

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

Bod, L., Kye, YC., Shi, J. et al. B-cell-specific checkpoint molecules that regulate anti-tumour immunity. Nature 619, 348–356 (2023). https://doi.org/10.1038/s41586-023-06231-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06231-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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