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Mutations in BRCA1 and BRCA2 differentially affect the tumor microenvironment and response to checkpoint blockade immunotherapy

Nature Cancer volume 1, pages 1188–1203 (2020)Cite this article

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Abstract

Immune checkpoint blockade (ICB) has improved outcomes for patients with advanced cancer, but the determinants of response remain poorly understood. Here we report differential effects of mutations in the homologous recombination genes BRCA1 and BRCA2 on response to ICB in mouse and human tumors, and further show that truncating mutations in BRCA2 are associated with superior response compared to those in BRCA1. Mutations in BRCA1 and BRCA2 result in distinct mutational landscapes and differentially modulate the tumor-immune microenvironment, with gene expression programs related to both adaptive and innate immunity enriched in BRCA2-deficient tumors. Single-cell RNA sequencing further revealed distinct T-cell, natural killer, macrophage and dendritic cell populations enriched in BRCA2-deficient tumors. Taken together, our findings reveal the divergent effects of BRCA1 and BRCA2 deficiency on ICB outcome and have important implications for elucidating the genetic and microenvironmental determinants of response to immunotherapy.

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Fig. 1: Immunogenomic analyses of the association of homologous recombination deficiency with response to immune checkpoint inhibitors.
Fig. 2: Enhanced response to checkpoint blockade immunotherapy in syngeneic mouse models of Brca2 deficiency.
Fig. 3: Differential response of Brca1null and Brca2null tumors to treatment with immune checkpoint inhibitors and modulation of the tumor-immune microenvironment.
Fig. 4: BRCA1 and BRCA2 mutations differentially modulate the tumor-immune microenvironment and response to immune checkpoint inhibitors in the TCGA breast cancer cohort and MSK-IMPACT.
Fig. 5: Single-cell RNA-seq analysis reveals marked heterogeneity within 4T1 murine tumors and enrichment of distinct T-cell populations in Brca1null and Brca2null tumors.
Fig. 6: Single-cell RNA-seq analysis reveals enrichment of distinct myeloid populations in Brca1null and Brca2null tumors.
Fig. 7: Single-cell RNA-seq analysis reveals enrichment of distinct myeloid populations in Brca1null and Brca2null tumors treated with ICB.

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Data availability

Bulk RNA-seq from murine tumors and cell lines as well as murine single-cell RNA- seq data that support the findings of this study have been deposited in the Gene Expression Omnibus and Sequence Read Archive under accession codes GSE137818 (all murine bulk RNA-seq and ICB-untreated single-cell RNA-seq) and PRJNA632854 (WGS and ICB-treated single-cell RNA-seq). Previously published somatic genomic data that were re-analyzed here are available at https://www.cbioportal.org/study/summary?id=tmb_mskcc_2018.

Human data that were derived from TCGA Research Network are available from http://cancergenome.nih.gov/. Germline data are available via dbGAP under accession code phs000178.v11.p8. Details of which exact TCGA cases were analyzed in the present study are available in the source data for Fig. 4. Processed data based for all datasets include murine and human variant calls, DEGs for bulk and single-cell RNA-seq datasets and GSEA outputs for bulk RNA-seq analyses. A detailed description of the data available in supplementary and source data follows below.

In general, GSEA outputs for all bulk RNA-seq analyses are included in the supplementary data, with the exception of DEGs for Brca2null and Brca1null tumors in culture (Fig. 3 and Extended Data Fig. 5), which are also included in Supplementary Table 7.

Source data are included for Figs. 1–7 and Extended Data Figs. 3–8 and 10. SNVs and indels for murine WGS data are in Source Data for Fig. 2 and Extended Data Fig. 3. DEGs between murine ICB-untreated Brca2null and Brca1null tumors are in Source Data for Fig. 3 and Extended Data Fig. 5.

Source data for Figs. 5 and 6 and Extended Data Fig. 8 include DEGs for ICB-untreated single-cell RNA-seq clusters. Source data for Fig. 7 and Extended Data Fig. 10 include DEGs for ICB-treated single-cell RNA-seq clusters. Source data for Extended Data Fig. 4 include DEGs between murine ICB-untreated Brca2null and WT (parental) tumors. Source data for Extended Data Fig. 6 include DEGs between murine ICB-treated Brca2null and Brca1null tumors. Source data for Fig. 7 include mutations and copy-number alterations in TCGA BRCA1/BRCA2 mutant samples. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Mouw, K. W., Goldberg, M. S., Konstantinopoulos, P. A. & D’Andrea, A. D. DNA damage and repair biomarkers of immunotherapy response. Cancer Discov. 7, 675–693 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ma, J., Setton, J., Lee, N. Y., Riaz, N. & Powell, S. N. The therapeutic significance of mutational signatures from DNA repair deficiency in cancer. Nat. Commun. 9, 3292 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Rebbeck, T. R. et al. Association of type and location of BRCA1and BRCA2 mutations with risk of breast and ovarian cancer. JAMA 313, 1347–15 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Teo, M. Y. et al. Alterations in DNA damage response and repair genes as potential marker of clinical benefit from PD-1/PD-L1 blockade in advanced urothelial cancers. J. Clin. Oncol. 36, 1685–1694 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Karzai, F. et al. Activity of durvalumab plus olaparib in metastatic castration-resistant prostate cancer in men with and without DNA damage repair mutations. J. Immunother. Cancer 6, 141 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. 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 

  14. Disis, M. L. et al. Efficacy and safety of avelumab for patients with recurrent or refractory ovarian cancer: phase 1b Results From the JAVELIN solid tumor trial. JAMA Oncol. 5, 393 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Prakash, R., Zhang, Y., Feng, W. & Jasin, M. Homologous recombination and human health: the roles of BRCA1, BRCA2, and associated proteins. Cold Spring Harb. Perspect. Biol. 7, a016600 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Powell, S. N. & Kachnic, L. A. Roles of BRCA1 and BRCA2 in homologous recombination, DNA replication fidelity and the cellular response to ionizing radiation. Oncogene 22, 5784–5791 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Koh, G., Zou, X. & Nik-Zainal, S. Mutational signatures: experimental design and analytical framework. Genome Biol. 21, 37–13 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Francis, J. C. et al. Whole-exome DNA sequence analysis of Brca2- and Trp53-deficient mouse mammary gland tumours. J. Pathol. 236, 186–200 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Chockley, P. J. & Keshamouni, V. G. Immunological consequences of epithelial-mesenchymal transition in tumor progression. J. Immunol. 197, 691–698 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Datar, I. & Schalper, K. A. Epithelial-mesenchymal transition and immune evasion during lung cancer progression: the chicken or the egg? Clin. Cancer Res. 22, 3422–3424 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lakhani, S. R. et al. Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. J. Natl Cancer Inst. 90, 1138–1145 (1998).

    Article  CAS  PubMed  Google Scholar 

  25. Lakhani, S. R. et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin. Cancer Res. 11, 5175–5180 (2005).

    Article  CAS  PubMed  Google Scholar 

  26. Nolan, E. et al. Combined immune checkpoint blockade as a therapeutic strategy for BRCA1-mutated breast cancer. Sci. Transl. Med. 9, eaal4922 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Liu, H. et al. ADORA1 inhibition promotes tumor immune evasion by regulating the ATF3-PD-L1 axis. Cancer Cell 37, 324–339.e8 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Moral, J. A. et al. ILC2s amplify PD-1 blockade by activating tissue-specific cancer immunity. Nature 579, 130–135 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Naskar, D. et al. Wnt5a-Rac1-NF-κB homeostatic circuitry sustains innate immune functions in macrophages. J. Immunol. 192, 4386–4397 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Danaher, P. et al. Pan-cancer adaptive immune resistance as defined by the tumor inflammation signature (TIS): results from the Cancer Genome Atlas (TCGA). J. Immunother. Cancer https://doi.org/10.1186/s40425-018-0367-1 (2018).

  31. Pereira, B. et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat. Commun. 7, 1–15 (2016).

    Google Scholar 

  32. Schaaf, M. B., Garg, A. D. & Agostinis, P. Defining the role of the tumor vasculature in antitumor immunity and immunotherapy. Cell Death Dis. https://doi.org/10.1038/s41419-017-0061-0 (2018).

  33. Wellenstein, M. D. & de Visser, K. E. Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity 48, 399–416 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Buckley, N. E. et al. BRCA1 regulates IFN-γ signaling through a mechanism involving the type I IFNs. Mol. Cancer Res. 5, 261–270 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Riaz, N. et al. Pan-cancer analysis of bi-allelic alterations in homologous recombination DNA repair genes. Nat. Commun. https://doi.org/10.1038/s41467-017-00921-w (2017).

  38. Ciriello, G. et al. Comprehensive molecular portraits of invasive lobular breast cancer. Cell 163, 506–519 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Timms, K. M. et al. Association of BRCA1/2defects with genomic scores predictive of DNA damage repair deficiency among breast cancer subtypes. Breast Cancer Res. 16, 123–129 (2014).

    Article  CAS  Google Scholar 

  41. Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399–16 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Karn, T. et al. Association between genomic metrics and immune infiltration in triple-negative breast cancer. JAMA Oncol. 3, 1707–1711 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Levine, J. H. et al. Data-driven phenotypic dissection of aml reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Lange, S. M. et al. l-citrulline metabolism in mice augments CD4+ T cell proliferation and cytokine production in vitro, and accumulation in the mycobacteria-infected lung. Front. Immunol. 8, 1561 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 176, 404 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wagner, J. et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell 177, 1330–1345 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gubin, M. M. et al. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell 175, 1443 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Strickland, K. C. et al. Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget 7, 13587–13598 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Kraya, A. A. et al. Genomic Signatures predict the immunogenicity of BRCA-deficient breast cancer. Clin. Cancer Res. https://doi.org/10.1158/1078-0432.CCR-18-0468 (2019).

  50. Smid, M. et al. Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration. Nat. Commun. 7, 1–9 (2016).

    Article  CAS  Google Scholar 

  51. Ovarian Tumor Tissue Analysis (OTTA) Consortium et al. Dose–response association of CD8+ tumor-infiltrating lymphocytes and survival time in high-grade serous ovarian cancer. JAMA Oncol. 3, e173290–e173299 (2017).

    Article  Google Scholar 

  52. Andrews, H. N. et al. BRCA1 regulates the interferon-γ-mediated apoptotic response. J. Biol. Chem. 277, 26225–26232 (2002).

    Article  CAS  PubMed  Google Scholar 

  53. 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 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Emens, L. A. et al. Long-term clinical outcomes and biomarker analyses of atezolizumab therapy for patients with metastatic triple-negative breast cancer: a phase 1 study. JAMA Oncol. 5, 74–82 (2019).

    Article  PubMed  Google Scholar 

  55. Chabanon, R. M. et al. PARP inhibition enhances tumor cell-intrinsic immunity in ERCC1-deficient non-small cell lung cancer. J. Clin. Invest. 129, 1211–1228 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Pantelidou, C. et al. PARP inhibitor efficacy depends on CD8+ T-cell recruitment via intratumoral STING pathway activation in BRCA-deficient models of triple-negative breast cancer. Cancer Discov. 9, 722–737 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Reisländer, T. et al. BRCA2 abrogation triggers innate immune responses potentiated by treatment with PARP inhibitors. Nat. Commun. 10, 3143–13 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Heijink, A. M. et al. BRCA2 deficiency instigates cGAS-mediated inflammatory signaling and confers sensitivity to tumor necrosis factor-α-mediated cytotoxicity. Nat. Commun. 10, 100 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Domchek, S. M. et al. Abstract PD6-11: an open-label, multitumor, phase II basket study of olaparib and durvalumab (MEDIOLA): results in germline BRCA-mutated (gBRCAm) HER2-negative metastatic breast cancer (MBC). Cancer Res. 78, PD6-11 (2018).

  60. Matsuo, K. et al. Nivolumab use for BRCA gene mutation carriers with recurrent epithelial ovarian cancer: a case series. Gynecol. Oncol. Rep. 25, 98–101 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Cheng, D. T. et al. Memorial Sloan Kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  63. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).

    Article  CAS  Google Scholar 

  67. Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 31–21 (2014).

    Article  CAS  Google Scholar 

  70. Leek, J. T., Taub, M. A. & Rasgon, J. L. A statistical approach to selecting and confirming validation targets in -omics experiments. BMC Bioinf. 13, 150 (2012).

    Article  Google Scholar 

  71. Senbabaoglu, Y. et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. https://doi.org/10.1186/s13059-016-1092-z (2016).

  72. Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). 18, 1–15 (John Wiley & Sons, 2014).

  73. Anderson, M. J. & Santana-Garcon, J. Measures of precision for dissimilarity-based multivariate analysis of ecological communities. Ecol. Lett. 18, 66–73 (2015).

    Article  PubMed  Google Scholar 

  74. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  77. Maaten, L. & Hinton, G. Visualizing data using t-SNE. JMLR 9, 2579–2605 (2008).

    Google Scholar 

  78. Shen, R. & Seshan, V. E. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res. 44, e131–e131 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Shukla, S. A. et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes. Nat. Biotechnol. 33, 1152–1158 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Hoof, I. et al. NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61, 1–13 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

We thank the members of the Chan laboratory, the Immunogenomics and Precision Oncology Platform, the Marie-Josée and Henry R. Kravis Center for Molecular Oncology, the Medical Genetics Service at MSKCC and the Department of Pathology for helpful discussions. We also thank MSKCC core laboratories including the Anti-Tumor Assessment, Molecular Cytology, Cytogenetics and Flow Cytometry cores. J.S.R.-F. and S.N.P. are funded in part by the Breast Cancer Research Foundation. This study was funded in part through the National Institutes of Health (NIH)/National Cancer Institute Cancer Center Support Grant P30 CA008748. We acknowledge funding sources, including Pershing Square Sohn Cancer Research grant (T.A.C.), the PaineWebber Chair (T.A.C.), Stand Up 2 Cancer (T.A.C.), NIH R01 CA205426, NIH R35 CA232097 (T.A.C.), the STARR Cancer Consortium (T.A.C.), AACR Translational Immunology (R.M.S.), NIH Director’s Early Independence Award, DP5-OD028171 (R.M.S.), Burroughs Wellcome Fund Career Award for Medical Scientists (R.M.S.), Precision Immunotherapy Kidney Cancer Fund (T.A.C., R.J.M.), Frederick Adler Fund (L.G.T.M.) and NIH U54 OD020355 (E.D.S.).

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Author notes

  1. These authors contributed equally: Robert M. Samstein, Chirag Krishna, Xiaoxiao Ma.

Authors and Affiliations

  1. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Robert M. Samstein, Xin Pei, Jeremy Setton, Rachna Shah, Lior Braunstein, Pedro Blecua Carrillo Albornoz, Simon N. Powell, Timothy A. Chan & Nadeem Riaz

  2. Department of Radiation Oncology, Mount Sinai Hospital, New York, NY, USA

    Robert M. Samstein

  3. Precision Immunology Institute at Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Robert M. Samstein & Jonathan Chung

  4. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Chirag Krishna

  5. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Xiaoxiao Ma, Ken-Wing Lee, Rajarsi Mandal, Wei Wu, Luc G. T. Morris, Timothy A. Chan & Nadeem Riaz

  6. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Vladimir Makarov, Fengshen Kuo, Raghvendra M. Srivastava, Tanaya A. Purohit, Douglas R. Hoen, Sviatoslav Kendall, Luc G. T. Morris, Timothy A. Chan & Nadeem Riaz

  7. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Besnik Qeriqi, Qing Chang & Elisa de Stanchina

  8. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Britta Weigelt, Diana L. Mandelker & Jorge S. Reis-Filho

  9. Cancer and Leukemia Epigenetics and Biology Program, Josep Carreras Leukaemia Research Institute, Barcelona, Spain

    Pedro Blecua Carrillo Albornoz

  10. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Luc G. T. Morris

  11. Center for Immunotherapy and Precision Immuno-Oncology, Department of Radiation Oncology, Cleveland Clinic, Cleveland, OH, USA

    Timothy A. Chan

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R.M.S., C.K., X.M., T.A.C. and N.R. wrote the manuscript with constructive feedback from all the authors. R.M.S., C.K., V.M., F.K., S.K., X.P., P.B.C.A. and D.R.H. performed computational analyses. R.M.S., X.M., K.W.L., R.M., J.S., B.W., E.dS., S.N.P., T.A.C. and N.R. were involved in experimental design. R.M.S., K.W.L., X.M., J.C., W.W., R.M.S., T.A.P., R.S., B.Q. and Q.C. performed experiments. R.M.S., C.K., T.A.C., N.R., S.N.P., J.S.R.-F., L.B., L.G.T.M. and D.L.M. were involved in data interpretation and manuscript preparation.

Corresponding authors

Correspondence to Timothy A. Chan or Nadeem Riaz.

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Competing interests

T.A.C. is a co-founder of Gritstone Oncology and holds equity, holds equity in An2H and acknowledges grant funding from Bristol-Myers Squibb, AstraZeneca, Illumina, Pfizer, An2H and Eisai. L.G.T.M. receives research funding from AstraZeneca. T.A.C. has served as an advisor for Bristol-Myers Squibb, Illumina, Eisai and An2H. MSK has licensed the use of TMB for the identification of patients who benefit from immune checkpoint therapy to PGDx. MSK, R.M.S., L.G.T.M. and T.A.C. receive royalties as part of this licensing agreement. J.S.R.-F. reports personal/ consultancy fees from Goldman Sachs and membership of the advisory boards of VolitionRx, Page.AI, Invicro, Roche, Genentech and Ventana, outside the submitted work. N.R. has received research funding from BMS, Pfizer and REPARE Therapeutics and has served as an ad hoc consultant to Illumina, REPARE Therapeutics and Mirati Therapeutics.

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Extended data

Extended Data Fig. 1 Sequence traces of Brca2null cell lines demonstrating frameshift mutations in all alleles.

PCR amplification and TOPO-TA cloning of individual allele copies was performed surrounding the CRISPR target guide sites to confirm that all alleles contain a frameshift or truncating mutation in Exon 3 of 4T1 Brca2null a, and CT26 Brca2null b, cell lines. Arrows indicate primer or probe locations. c-d, qRT-PCR amplication of Brca2 WT-specific allele in cDNA generated from RNA isolated from cell lines using SYBR green based assay (c) and TaqMan based assay (d), n=3 technical replicates for c and d. Data are presented as mean values +/- SD. Data represent results from a single experiment and were repeated twice with similar results.

Extended Data Fig. 2 Characterization of the Brca2null cell line.

a-b Representative images of parental (a) and Brca2null (b) CT26 cell karyotype analysis demonstrating increased double strand breaks. c, Quantification of karyotype analysis shown in a representing n=100 metaphases analyzed demonstrating the percent of metaphase nuclei containing at least 1 of indicated abnormality. Data represent results from a single experiment and were repeated twice with similar results. d, Quantification of karyotype analysis in 4T1 cells shown in Fig. 2d representing n=100 metaphases analyzed demonstrating the percent of metaphase nuclei containing at least 1 of indicated abnormality. Data represent results from a single experiment and were repeated twice with similar results. e, Quantification of immunofluorescent images of parental and Brca2null CT26 murine colorectal adenocarcinoma cells 4 hours after 10 Gy irradiation stained with DAPI and antibodies to Rad51 and gamma-H2ax. f, In vitro relative viable cell count of parental and Brca2null CT26 cell lines in the presence of PARP inhibitor olaparib at indicated concentrations after 96 hours in n = 3 independent assays in technical triplicate. Data are presented as mean values +/- SEM. Data represent results from a single experiment and were repeated three times with similar results. g-h, Tumor growth curve of parental and additional Brca2null clones with ICB treatment in both the 4T1(g) and CT26 models (h). (* p<0.05, ** p<0.005,*** p<0.0005, **** p<0.00005). All p values represent two-sided unpaired t test at respective timepoint. Data are presented as mean values +/- SEM, n = 15 mice per group.

Extended Data Fig. 3 Characterization of the genomic alternation in 4T1 Brca2null clones.

a, The 96 base substitution pattern for Brca2null and Brca1null single-cell clones after subtraction of background mutagenesis pattern derived from parental clones. Signature 3, previously associated with HRD (that is Signature 3), is provided for reference. b, Cosine similarity of BRCA2 null experimentally derived mutational signature to previously described mutational signature related to COSMIC Signature 3. c, Proportion of deletions with microhomology in 4T1 Brca2null single-cell clones compared to parental single clones (two-sided Fisher’s exact test P<0.0001, for all mutant single cell clones compared to parental). d, Cosine similarity of Brca1null experimentally derived mutational signature to previously described mutational signature related to COSMIC Signature 3. e, Proportion of deletions with microhomology Brca1null single-cell clones compared to parental single clones using a two-sided Fisher’s exact test P<0.0001, for all mutant single cell clones compared to parental).

Source data

Extended Data Fig. 4 Bulk RNA-seq analysis between untreated Brca2null and WT tumors yields gene expression programs related to adaptive and innate immune activation enriched in Brca2null tumors.

a, Heatmap displaying all 4,637 genes significantly differentially expressed (FDR < 0.05) between untreated Brca2null and Brca2-WT murine tumors from the 4T1 model. Data show drastic gene expression changes upon Brca2 inactivation in-vivo. P-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and corrected for multiple testing using the Benjamini-Hochberg method b, Pathway analysis on genes from a using GO pathways. Top enriched pathways in either Brca2null or Brca2WT tumors are shown. Color gradient of red to purple indicates decreasing adjusted p-value; circle size indicates number of genes in pathway. GeneRatio on x-axis indicates number of significantly differentially expressed genes from RNA-seq overlapping with genes in each pathway. P-values for overlap between input genes and pathway genesets calculated via two-sided hypergeometric test and corrected for multiple comparisons using the Benjamini Hochberg method. c, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05, two-sided Wald test from DESeq2) between Brca2null and WT tumors from the Immune Response pathway. d, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05, two-sided Wald test from DESeq2) between Brca2null and WT tumors from the T cell activation pathway. e, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05, two-sided Wald test from DESeq2) between Brca2null and WT tumors from the Biocarta cytokines pathway. f, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05, two-sided Wald test from DESeq2) between Brca2null and WT tumors from the Hallmark IFNA Signaling pathway. g, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05, two-sided Wald test from DESeq2) between Brca2null or Brca2WT tumors from the NK-mediated cytotoxicity pathway. For c-g, P-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and correct for multiple testing using the Benjamini-Hochberg method.

Source data

Extended Data Fig. 5 Bulk RNA-seq analysis between untreated Brca2null and Brca1null tumors yields gene expression programs related to adaptive and innate immunity enriched in Brca2null tumors.

a, Heatmap displaying all 6,881 genes significantly differentially expressed (FDR < 0.05) between untreated Brca2null and Brca1null murine tumors from the 4T1 model. Data show drastic gene expression changes upon Brca2 inactivation. P-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and corrected for multiple testing using the Benjamini-Hochberg method. b, Pathway analysis on genes from a using GO pathways. Top enriched pathways in either Brca2null or Brca1null tumors are shown. Color gradient of red to purple indicates decreasing adjusted p-value; circle size indicates number of genes in pathway. GeneRatio on x-axis indicates number of significantly differentially expressed genes from RNA-seq overlapping with genes in each pathway. P-values for overlap between input genes and pathway genesets calculated via two-sided hypergeometric test and corrected for multiple comparisons using the Benjamini Hochberg method. c, Heatmap for genes significantly differentially expressed in a associated with interferon gamma signaling between Brca2null and Brca1null murine tumors from the 4T1 model. d, Heatmap displaying for genes significantly differentially expressed in a associated with T cell activation between Brca2null and Brca1null murine tumors from the 4T1 model. e, Heatmap for genes significantly differentially expressed in a associated with antigen presentation between Brca2null and Brca1null murine tumors from the 4T1 model. f, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the Kurozumi Response to Cytolytic Virus pathway, evaluated using GSEA (FDR P <= 0.05). g, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the Reactome chemokines pathway, evaluated using GSEA (FDR P <= 0.05).h, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the NK-mediated cytotoxicity pathway, evaluated using GSEA (FDR P <= 0.05). For c-h, P-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and correct for multiple testing using the Benjamini-Hochberg method. i, Heatmap of mean log-normalized expression of differentially expressed genes for genes promoting immune activity/evasion in between Brca1null and Brca2null deficient cell lines in vitro. For all GSEA analyses, p-values calculated using two-sided pre-ranked analysis using log fold changes as input.

Source data

Extended Data Fig. 6 Bulk RNA-seq analysis between Brca2null and Brca1null tumors after treatment with anti-PD1 yields gene expression programs related to adaptive and innate immune activation enriched in treated Brca2 null tumors.

a, Heatmap displaying all genes significantly differentially expressed (FDR < 0.05) between Brca2null and Brca1null murine tumors from the 4T1 model after treatment with anti-PD1. Data show drastic gene expression changes upon Brca2 inactivation with treatment. P-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and corrected for multiple testing using the Benjamini-Hochberg method. b, Pathway analysis on genes from a using GO pathways. Top enriched pathways in either treated Brca2null or treated Brca1null tumors are shown. Color gradient of red to purple indicates decreasing adjusted p-value; circle size indicates number of genes in pathway. GeneRatio on x-axis indicates number of significantly differentially expressed genes from RNA-seq overlapping with genes in each pathway. P-values for overlap between input genes and pathway genesets calculated via hypergeometric test and corrected for multiple comparisons using the Benjamini Hochberg method. c, Heatmap displaying genes significantly differentially expressed in a associated with innate immunity between treated Brca2null and Brca1null murine tumors from the 4T1 model. d, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the Bosco interferon antiviral module pathway, evaluated using GSEA (FDR P <= 0.05). e, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the Brown myeloid cell development up pathway, evaluated using GSEA (FDR P <= 0.05). f, Heatmap showing significantly differentially expressed genes (FDR P <= 0.05) between Brca2null and Brca1null tumors from the Hecker IFNB1 targets pathway, evaluated using GSEA (FDR P <= 0.05). For c-f, p-values calculated from two-sided differential expression analysis using DESeq2 (Wald test) and correct for multiple testing using the Benjamini-Hochberg method. For all GSEA analyses, p-values calculated using two-sided pre-ranked analysis using log fold changes as input.

Source data

Extended Data Fig. 7 Whole exome sequencing analysis of BRCA2-mutant and BRCA1-mutant tumors from the TCGA breast cancer cohort.

a, Principal component analysis (PCA) of ssGSEA scores for T cell populations for patients with either BRCA1 or BRCA2 germline or somatic biallelic mutations. P value computed on separation between BRCA1 mutant and BRCA2 mutant samples using a two-sided PERMANOVA test. b, Principal component analysis (PCA) of ssGSEA scores for T cell populations for patients with either BRCA1 or BRCA2 germline or somatic biallelic mutations. P-value computed on separation between BRCA1 mutant and BRCA2 mutant samples using a two-sided PERMANOVA test. c. Oncoprint for 35 breast cancers with biallelic BRCA1 or BRCA2 mutations. Mutations in the top 20 most frequently mutated genes in breast cancer are illustrated, along with the mutational profile of each individual tumor. d, Comparison of indel, SNV, and neopeptide counts between triple negative BRCA1-mutant tumors (BRCA1 TN n=15, individual patients) vs all triple negative (All TN, n=153, individual patients) tumors. Data show no difference in mutation counts between BRCA1-mutant tumors and histology-matched control tumors. P-values calculated using two-sided Wilcoxon test, which is also used in e-i and l. For all the boxplot in this figure pannels d-h and l, the minima, maxima are plotted as the whiskers, 1st and 3rd quartiles are plotted as the bounds of the boxes, and medians are plotted as the center. e, Comparison of indel, SNV, and neopeptide counts between non-triple negative BRCA2-mutant tumors (BRCA2 NTN, n=17, individual patients) vs all non-triple negative (All NTN, n=700, individual patients) tumors. The same groups of patients, All TN (n=153), BRCA1 TN (n=15), ALL NTN (n=700), BRCA2 NTN (n=17) are used in the figure panel d-h and l. Data show increased levels of all alterations in BRCA2-mutant tumors compared to histology-matched control tumors. P-values calculated using two-sided Wilcoxon test. f, Comparison of fraction contribution of the six SNV substitutions between triple negative BRCA1-mutant tumors vs all triple negative tumors. P-values calculated using two-sided Wilcoxon test g, Comparison of fraction contribution of the six SNV substitutions between non-triple negative BRCA2-mutant tumors vs all non-triple negative tumors. The labels for p values are as the following: ns: p > 0.05, *: p <= 0.05, **: p <= 0.01, ***: p <= 0.001, ****: p <= 0.0001. h-i, Comparison of fraction contribution of COSMIC signature 3 (h) and distribution of microhomology mediated deletion (i) between BRCA1-mutated triple negative tumors or non-triple BRCA1-mutated negative tumors with their histology control. P-values calculated using two-sided Wilcoxon test j-k, Copy number plots for biallelic mutated BRCA1(j) and BRCA2 (k) tumors from TCGA breast cancer cohort. (L). Comparison of fraction copy number altered genome (FCNA) between BRCA1 and BRCA2-mutant tumors and histology-matched control tumors. Data show significantly higher FCNA in BRCA1-mutant tumors relative to histology-matched controls, while no such effect was observed for BRCA2-mutant tumors. P-values calculated using two-sided Wilcoxon test.

Source data

Extended Data Fig. 8 Single cell library sizes, replicates, and proportions of clusters across ICB-untreated Brca2null and Brca1null tumors and parental tumors.

a, Library size distribution for ICB-untreated single cells b, Library size distribution for ICB-treated single cells. c, Number of cells for each replicate used in scRNA-seq analyses. d, Proportion of all cells attributed to each T cell cluster from Fig. 5 (ICB-untreated mice) from Brca2null, Brca1null, and parental tumors. e, Proportion of all myeloid cells attributed to each myeloid cell cluster from Fig. 5 (ICB-untreated mice) from Brca2null, Brca1null, and parental tumors. Stars represent FDR p-value < 0.05 from two-sided Fisher’s exact test comparing cluster proportions in Brca2null and Brca1null mice. Cluster proportions derived from ICB-untreated mice: n = 3 biologically independent samples for Brca2 null and Parental mice, and n = 2 biologically independent samples for Brca1 null mice.

Source data

Extended Data Fig. 9 Experimental validation of sc-RNA-seq and bulk RNA-seq results on untreated 4T1 Brca1null and Brca2null tumors.

a. Flow cytometry validation of several key immune cell populations including the Gmzb+ activated Cd8+ and Cd4+ T cells, CD206+ suppressive TAM, pDC and Cd103+ conventional DC, n=5 independent biological replicates. Data are presented as mean values +/- SEM. Data represent results from a single experiment and were repeated twice with similar results. b. QRT-PCR validation of the most upregulated inflammation related genes in Brca1null vs Brca2null tumors, n=5 independent biological replicates. Data are presented as mean values +/- SEM. Data represent results from a single experiment and were repeated twice with similar results.

Extended Data Fig. 10 Single cell RNA-seq analysis reveals marked heterogeneity within 4T1 murine tumors and enrichment of distinct T cell populations in post anti-PD1 antibody therapy Brca1null and Brca2null tumors.

a, t-SNE plot after dimensionality reduction and Phenograph clustering yields 31 distinct clusters afrom post-treated Brca1null and Brca2null tumors as well as parental tumor. b, t-SNE plots from a overlaid with log-normalized expression of select markers spanning T cell and NK cell clusters. c, t-SNE plots from a overlaid with log-normalized expression of select markers spanning myeloid cell clusters. d, Proportion of all cells attributed to each T cell cluster from a (ICB-untreated mice) from Brca1null, Brca2null and parental tumors, for clusters with more than 100 cells. e, Proportion of all cells attributed to each T cell cluster from a (ICB-untreated mice) from Brca1null, Brca2null, and parental tumors, for clusters with more than 100 cells. Stars represent FDR P < 0.05 from two-sided Fisher’s exact test comparing cluster proportions in Brca2null mice and Brca1null mice. Cluster proportions derived from ICB-untreated mice: n = 2 biologically independent samples for Brca1 null, Brca2 null and Parental mice.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–19

Source data

Source Data Fig. 1

Statistical source data: pathway analysis.

Source Data Fig. 2

Statistical source data: SNVs and indels.

Source Data Fig. 2

Flow cytometry gating strategy.

Source Data Fig. 3

Statistical source data: DEGs between murine ICB-untreated Brca2 null and Brca1 null tumors.

Source Data Fig. 3

Unprocessed western blots.

Source Data Fig. 4

All TCGA breast cancer samples analyzed in the paper.

Source Data Fig. 5

Statistical source data: DEGs for ICB-untreated T-cell/NK clusters.

Source Data Fig. 6

Statistical source data: DEGs for ICB-untreated myeloid clusters.

Source Data Fig. 7

Statistical source data: DEGs for ICB-treated clusters.

Source Data Extended Data Fig. 3

Statistical source data: SNVs and indels.

Source Data Extended Data Fig. 4

Statistical source data: DEGs between murine ICB-untreated Brca2 null and WT (parental) tumors.

Source Data Extended Data Fig. 5

Statistical source data: DEGs between murine ICB-untreated Brca2 null and Brca1 null tumors.

Source Data Extended Data Fig. 6

Statistical source data: DEGs between murine ICB-treated Brca2 null and Brca1 null tumors.

Source Data Extended Data Fig. 7

Statistical source data: mutations and copy-number alterations in TCGA BRCA1/2 mutant samples.

Source Data Extended Data Fig. 8

Statistical source data: DEGs for ICB-untreated clusters.

Source Data Extended Data Fig. 10

Statistical source data: DEGs for ICB-treated clusters.

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Samstein, R.M., Krishna, C., Ma, X. et al. Mutations in BRCA1 and BRCA2 differentially affect the tumor microenvironment and response to checkpoint blockade immunotherapy. Nat Cancer 1, 1188–1203 (2020). https://doi.org/10.1038/s43018-020-00139-8

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