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:

Translational Therapeutics

B cell-related gene signature and cancer immunotherapy response

British Journal of Cancer volume 126, pages 899–906 (2022)Cite this article

Subjects

Abstract

Background

B lymphocytes have multifaceted functions in the tumour microenvironment, and their prognostic role in human cancers is controversial. Here we aimed to identify tumour microenvironmental factors that influence the prognostic effects of B cells.

Methods

We conducted a gene expression analysis of 3585 patients for whom the clinical outcome information was available. We further investigated the clinical relevance for predicting immunotherapy response.

Results

We identified a novel B cell-related gene (BCR) signature consisting of nine cytokine signalling genes whose high expression could diminish the beneficial impact of B cells on patient prognosis. In triple-negative breast cancer, higher B cell abundance was associated with favourable survival only when the BCR signature was low (HR = 0.68, p = 0.0046). By contrast, B cell abundance had no impact on prognosis when the BCR signature was high (HR = 0.93, p = 0.80). This pattern was consistently observed across multiple cancer types including lung, colorectal, and melanoma. Further, the BCR signature predicted response to immune checkpoint blockade in metastatic melanoma and compared favourably with the established markers.

Conclusions

The prognostic impact of tumour-infiltrating B cells depends on the status of cytokine signalling genes, which together could predict response to cancer immunotherapy.

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: Relation between B cell lineage abundance score (B.cell) and T lymphocytes module (T.cell).
Fig. 2: Cytokine signalling genes modulating B lymphocytes prognosis.
Fig. 3: Survival analysis of B cell-related gene (BCR) signature in the validation cohorts.
Fig. 4: B cell-related gene (BCR) signature association with anti-PD1 and anti-CTLA4 immune checkpoint blockade response.

Similar content being viewed by others

Data availability

All original data sets used in this article are publicly available with accession codes provided in Supplemental Table 5.

References

  1. Tsou P, Katayama H, Ostrin EJ, Hanash SM. The emerging role of B cells in tumor immunity. Cancer Res. 2016;76:5597–601.

    Article  CAS  Google Scholar 

  2. Sharonov GV, Serebrovskaya EO, Yuzhakova DV, Britanova OV, Chudakov DM. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat Rev Immunol. 2020;27:1–14.

  3. Wouters MCA, Nelson BH. Prognostic significance of tumor-infiltrating B cells and plasma cells in human cancer. Clin Cancer Res. 2018;24:6125–35.

    Article  CAS  Google Scholar 

  4. Cillo AR, Kürten CHL, Tabib T, Qi Z, Onkar S, Wang T, et al. Immune landscape of viral- and carcinogen-driven head and neck cancer. Immunity. 2020;52:183.e9–99.e9.

    Article  Google Scholar 

  5. Shalapour S, Font-Burgada J, Di Caro G, Zhong Z, Sanchez-Lopez E, Dhar D, et al. Immunosuppressive plasma cells impede T-cell-dependent immunogenic chemotherapy. Nature. 2015;521:94–8.

    Article  CAS  Google Scholar 

  6. Lu Y, Zhao Q, Liao J-Y, Song E, Xia Q, Pan J, et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell. 2020;180:1081.e24–97.e24.

    Article  Google Scholar 

  7. Shen M, Sun Q, Wang J, Pan W, Ren X. Positive and negative functions of B lymphocytes in tumors. Oncotarget. 2016;7:55828–39.

    Article  Google Scholar 

  8. Sautès-Fridman C, Petitprez F, Calderaro J, Fridman WH. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer. 2019;19:307–25.

    Article  Google Scholar 

  9. Helmink BA, Reddy SM, Gao J, Zhang S, Basar R, Thakur R, et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature. 2020;577:549–55.

    Article  CAS  Google Scholar 

  10. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015;21:938–45.

    Article  CAS  Google Scholar 

  11. Milne K, Köbel M, Kalloger SE, Barnes RO, Gao D, Gilks CB, et al. Systematic analysis of immune infiltrates in high-grade serous ovarian cancer reveals CD20, FoxP3 and TIA-1 as positive prognostic factors. PLoS ONE. 2009;4:e6412.

    Article  Google Scholar 

  12. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KRM, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.

    Article  Google Scholar 

  13. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10.

    Article  CAS  Google Scholar 

  14. Curtis C, Shah SP, Chin S-F, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346–52.

    Article  CAS  Google Scholar 

  15. Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17:218.

    Article  Google Scholar 

  16. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7.

    Article  CAS  Google Scholar 

  17. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008.

  18. Altman DG. Practical statistics for medical research. Boca Raton, FL: CRC Press; 1990. 624 p.

  19. Stouffer SA, Suchman EA, Devinney LC, Star SA, Williams Jr. RM. The American soldier: adjustment during army life (studies in social psychology in World War II). Vol. 1. Oxford: Princeton Univ. Press; 1949. xii, 599 p.

  20. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  21. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36:1–48.

    Article  Google Scholar 

  22. Liu D, Schilling B, Liu D, Sucker A, Livingstone E, Jerby-Amon L, et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat Med. 2019;25:1916–27.

    Article  CAS  Google Scholar 

  23. Allen EMV, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015;350:207–11.

    Article  Google Scholar 

  24. Schwartz LH, Litière S, de Vries E, Ford R, Gwyther S, Mandrekar S, et al. RECIST 1.1 – update and clarification: from the RECIST Committee. Eur J Cancer. 2016;62:132–7.

    Article  Google Scholar 

  25. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018;24:1550–8.

    Article  CAS  Google Scholar 

  26. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, et al. IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017;127:2930–40.

  27. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18:248–62.

    Article  CAS  Google Scholar 

  28. Nishino M, Ramaiya NH, Hatabu H, Hodi FS. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat Rev Clin Oncol. 2017;14:655–68.

    Article  CAS  Google Scholar 

  29. Shukla SA, Bachireddy P, Schilling B, Galonska C, Zhan Q, Bango C, et al. Cancer-germline antigen expression discriminates clinical outcome to CTLA-4 blockade. Cell. 2018;173:624.e8–33.e8.

    Article  Google Scholar 

  30. Maaten LJP, van der, Hinton GE. Visualizing high-dimensional data using t-SNE. J Mach Learn Res. 2008;9:2579–605.

    Google Scholar 

  31. Ise W, Kohyama M, Schraml BU, Zhang T, Schwer B, Basu U, et al. Batf controls the global regulators of class switch recombination in both B and T cells. Nat Immunol. 2011;12:536–43.

    Article  CAS  Google Scholar 

  32. Xu Z, Pone EJ, Al-Qahtani A, Park S-R, Zan H, Casali P. Regulation of aicda expression and AID activity: relevance to somatic hypermutation and class switch DNA recombination. Crit Rev Immunol. 2007;27:367–97.

    Article  CAS  Google Scholar 

  33. Mintz MA, Felce JH, Chou MY, Mayya V, Xu Y, Shui J-W, et al. The HVEM-BTLA axis restrains T cell help to germinal center B cells and functions as a cell-extrinsic suppressor in lymphomagenesis. Immunity. 2019;51:310.e7–23.e7.

    Article  Google Scholar 

  34. Derré L, Rivals J-P, Jandus C, Pastor S, Rimoldi D, Romero P, et al. BTLA mediates inhibition of human tumor-specific CD8+ T cells that can be partially reversed by vaccination. J Clin Invest. 2010;120:157–67.

    Article  Google Scholar 

  35. Harrop JA, McDonnell PC, Brigham-Burke M, Lyn SD, Minton J, Tan KB, et al. Herpesvirus entry mediator ligand (HVEM-L), a novel ligand for HVEM/TR2, stimulates proliferation of T cells and inhibits HT29 cell growth. J Biol Chem. 1998;273:27548–56.

    Article  CAS  Google Scholar 

  36. Pasero C, Barbarat B, Just-Landi S, Bernard A, Aurran-Schleinitz T, Rey J, et al. A role for HVEM, but not lymphotoxin-beta receptor, in LIGHT-induced tumor cell death and chemokine production. Eur J Immunol. 2009;39:2502–14.

    Article  CAS  Google Scholar 

  37. Li Q, Lao X, Pan Q, Ning N, Yet J, Xu Y, et al. Adoptive transfer of tumor reactive B cells confers host T-cell immunity and tumor regression. Clin Cancer Res. 2011;17:4987–95.

    Article  CAS  Google Scholar 

  38. Al-Shibli KI, Donnem T, Al-Saad S, Persson M, Bremnes RM, Busund L-T. Prognostic effect of epithelial and stromal lymphocyte infiltration in non–small cell lung cancer. Clin Cancer Res. 2008;14:5220–7.

    Article  CAS  Google Scholar 

  39. Goswami S, Chen Y, Anandhan S, Szabo PM, Basu S, Blando JM, et al. ARID1A mutation plus CXCL13 expression act as combinatorial biomarkers to predict responses to immune checkpoint therapy in mUCC. Sci Transl Med. 2020. https://doi.org/10.1126/scitranslmed.abc4220.

  40. Torres A, Casanova JF, Nistal M, Regadera J. Quantification of immunocompetent cells in testicular germ cell tumours. Histopathology. 1997;30:23–30.

    Article  CAS  Google Scholar 

  41. Neesse A, Michl P, Frese KK, Feig C, Cook N, Jacobetz MA, et al. Stromal biology and therapy in pancreatic cancer. Gut. 2011;60:861–8.

    Article  Google Scholar 

Download references

Funding

This research was partially supported by the National Institutes of Health grant R01 CA222512.

Author information

Authors and Affiliations

  1. Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA

    Arian Lundberg, Bailiang Li & Ruijiang Li

  2. Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA

    Arian Lundberg

Authors
  1. Arian Lundberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bailiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruijiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AL and RL contributed to the study concept and design. AL contributed to the acquisition and analyses of data. All authors interpreted the data and did the manuscript drafting and critical revision. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ruijiang Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Publicly available data—not applicable.

Consent for publication

Publicly available data—not applicable.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lundberg, A., Li, B. & Li, R. B cell-related gene signature and cancer immunotherapy response. Br J Cancer 126, 899–906 (2022). https://doi.org/10.1038/s41416-021-01674-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41416-021-01674-6

This article is cited by

Search

Advanced search

Quick links