Tumour lineage shapes BRCA-mediated phenotypes

An Author Correction to this article was published on 11 December 2019

This article has been updated


Mutations in BRCA1 and BRCA2 predispose individuals to certain cancers1,2,3, and disease-specific screening and preventative strategies have reduced cancer mortality in affected patients4,5. These classical tumour-suppressor genes have tumorigenic effects associated with somatic biallelic inactivation, although haploinsufficiency may also promote the formation and progression of tumours6,7. Moreover, BRCA1/2-mutant tumours are often deficient in the repair of double-stranded DNA breaks by homologous recombination8,9,10,11,12,13, and consequently exhibit increased therapeutic sensitivity to platinum-containing therapy and inhibitors of poly-(ADP-ribose)-polymerase (PARP)14,15. However, the phenotypic and therapeutic relevance of mutations in BRCA1 or BRCA2 remains poorly defined in most cancer types. Here we show that in the 2.7% and 1.8% of patients with advanced-stage cancer and germline pathogenic or somatic loss-of-function alterations in BRCA1/2, respectively, selective pressure for biallelic inactivation, zygosity-dependent phenotype penetrance, and sensitivity to PARP inhibition were observed only in tumour types associated with increased heritable cancer risk in BRCA1/2 carriers (BRCA-associated cancer types). Conversely, among patients with non-BRCA-associated cancer types, most carriers of these BRCA1/2 mutation types had evidence for tumour pathogenesis that was independent of mutant BRCA1/2. Overall, mutant BRCA is an indispensable founding event for some tumours, but in a considerable proportion of other cancers, it appears to be biologically neutral—a difference predominantly conditioned by tumour lineage—with implications for disease pathogenesis, screening, design of clinical trials and therapeutic decision-making.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The prevalence and origins of BRCA1/2 mutations.
Fig. 2: Lineage variation in selection for BRCA1/2 biallelic inactivation.
Fig. 3: BRCA phenotypes are tumour lineage and zygosity-dependent.
Fig. 4: Context-specific therapeutic sensitivity of BRCA1/2-mutant tumours.

Data availability

The whole-exome sequencing data as well as germline calls have been deposited in the NCBI dbGaP archive under accession numbers phs001783.v1.p1 and phs001858.v1.p1, respectively. All other genomic and clinical data accompanies the manuscript and are available as Extended Data.

Code availability

Source code for these analyses is available at https://github.com/taylor-lab/BRCA.

Change history

  • 11 December 2019

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Wooster, R. et al. Identification of the breast cancer susceptibility gene BRCA2. Nature 378, 789–792 (1995).

    ADS  CAS  PubMed  Google Scholar 

  2. 2.

    Miki, Y. et al. A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266, 66–71 (1994).

    ADS  CAS  PubMed  Google Scholar 

  3. 3.

    Roy, R., Chun, J. & Powell, S. N. BRCA1 and BRCA2: different roles in a common pathway of genome protection. Nat. Rev. Cancer 12, 68–78 (2011).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Kuchenbaecker, K. B. et al. Risks of breast, ovarian, and contralateral breast cancer for BRCA1 and BRCA2 mutation carriers. J. Am. Med. Assoc. 317, 2402–2416 (2017).

    CAS  Google Scholar 

  5. 5.

    Paluch-Shimon, S. et al. Prevention and screening in BRCA mutation carriers and other breast/ovarian hereditary cancer syndromes: ESMO Clinical Practice Guidelines for cancer prevention and screening. Ann. Oncol. 27 (suppl 5), v103–v110 (2016).

    CAS  PubMed  Google Scholar 

  6. 6.

    Maxwell, K. N. et al. BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers. Nat. Commun. 8, 319 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Lord, C. J. & Ashworth, A. BRCAness revisited. Nat. Rev. Cancer 16, 110–120 (2016).

    CAS  PubMed  Google Scholar 

  8. 8.

    Yu, V. P. et al. Gross chromosomal rearrangements and genetic exchange between nonhomologous chromosomes following BRCA2 inactivation. Genes Dev. 14, 1400–1406 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Moynahan, M. E., Pierce, A. J. & Jasin, M. BRCA2 is required for homology-directed repair of chromosomal breaks. Mol. Cell 7, 263–272 (2001).

    CAS  PubMed  Google Scholar 

  10. 10.

    Moynahan, M. E., Chiu, J. W., Koller, B. H. & Jasin, M. Brca1 controls homology-directed DNA repair. Mol. Cell 4, 511–518 (1999).

    CAS  PubMed  Google Scholar 

  11. 11.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Marquard, A. M. et al. Pan-cancer analysis of genomic scar signatures associated with homologous recombination deficiency suggests novel indications for existing cancer drugs. Biomark. Res. 3, 9 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Moore, K. et al. Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N. Engl. J. Med. 379, 2495–2505 (2018).

    CAS  PubMed  Google Scholar 

  15. 15.

    Robson, M. et al. Olaparib for metastatic breast cancer in patients with a germline BRCA mutation. N. Engl. J. Med. 377, 523–533 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Manickam, K. et al. Exome sequencing-based screening for BRCA1/2 expected pathogenic variants among adult biobank participants. JAMA Network Open 1, e182140 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Mandelker, D. et al. Mutation detection in patients with advanced cancer by universal sequencing of cancer-related genes in tumor and normal DNA vs guideline-based germline testing. J. Am. Med. Assoc. 318, 825–835 (2017).

    Google Scholar 

  18. 18.

    Cheng, D. T. et al. Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing. BMC Med. Genomics 10, 33 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. 19.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Levy-Lahad, E. & Friedman, E. Cancer risks among BRCA1 and BRCA2 mutation carriers. Br. J. Cancer 96, 11–15 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Mersch, J. et al. Cancers associated with BRCA1 and BRCA2 mutations other than breast and ovarian. Cancer 121, 269–275 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Scully, R. & Livingston, D. M. In search of the tumour-suppressor functions of BRCA1 and BRCA2. Nature 408, 429–432 (2000).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Timms, K. M. et al. Association of BRCA1/2 defects with genomic scores predictive of DNA damage repair deficiency among breast cancer subtypes. Breast Cancer Res. 16, 475 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

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

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Drilon, A. et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N. Engl. J. Med. 378, 731–739 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Coombs, C. C. et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 21, 374–382 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Karczewski, K. J. et al. Variation across 141,456 human exomes and genomes reveals the spectrum of loss-of-function intolerance across human protein-coding genes. Preprint at https://www.bioRxiv.org/content/10.1101/531210v2 (2019).

  31. 31.

    Spurdle, A. B. et al. ENIGMA—evidence-based network for the interpretation of germline mutant alleles: an international initiative to evaluate risk and clinical significance associated with sequence variation in BRCA1 and BRCA2 genes. Hum. Mutat. 33, 2–7 (2012).

    CAS  PubMed  Google Scholar 

  32. 32.

    Findlay, G. M. et al. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562, 217–222 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00011 (2017).

  34. 34.

    Niu, B. et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 30, 1015–1016 (2014).

    CAS  PubMed  Google Scholar 

  35. 35.

    Middha, S. et al. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis. Oncol. https://doi.org/10.1200/PO.17.00084 (2017).

  36. 36.

    Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    ADS  CAS  PubMed  Google Scholar 

  37. 37.

    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 (2016).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Bielski, C. M. et al. Genome doubling shapes the evolution and prognosis of advanced cancers. Nat. Genet. 50, 1189–1195 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    McGranahan, N. et al. Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Sci. Transl. Med. 7, 283ra54 (2015).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Mose, L. E., Wilkerson, M. D., Hayes, D. N., Perou, C. M. & Parker, J. S. ABRA: improved coding indel detection via assembly-based realignment. Bioinformatics 30, 2813–2815 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Lai, Z. et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 44, e108 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Chang, M. T. et al. Accelerating discovery of functional mutant alleles in cancer. Cancer Discov. 8, 174–183 (2018).

    CAS  PubMed  Google Scholar 

  45. 45.

    Chang, M. T. et al. Identifying recurrent mutations in cancer reveals widespread lineage diversity and mutational specificity. Nat. Biotechnol. 34, 155–163 (2016).

    CAS  PubMed  Google Scholar 

  46. 46.

    Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).

    CAS  PubMed  Google Scholar 

  48. 48.

    Huang, K.-L. et al. Pathogenic germline variants in 10,389 adult cancers. Cell 173, 355–370 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Knijnenburg, T. A. et al. Genomic and molecular landscape of DNA damage repair deficiency across the cancer genome atlas. Cell Rep. 23, 239–254 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank our patients and their families for participating in this study and the members of Taylor laboratory and Marie-Josée and Henry R. Kravis Center for Molecular Oncology for discussions and support. We thank the Jonathan and Mindy Gray Foundation for their support from project inception. This work was also supported by National Institutes of Health (NIH) awards P30 CA008748, U54 OD020355 (D.B.S., B.S.T.), R01 CA207244 (D.M.H., B.S.T.), P50 CA092629 (D.B.S., B.S.T.), R01 CA204749 (B.S.T.); and the American Cancer Society (RSG-15-067-01-TBG), Cycle for Survival, Sontag Foundation, Prostate Cancer Foundation, Anna Fuller Fund, and the Josie Robertson Foundation (B.S.T.).

Author information




P.J., M.F.B., D.B.S. and B.S.T. conceived the study. C.B. and P.S. generated germline variant calling and pathogenicity assessments. P.J., C.B., P.S., S.S.C., N.D.F., A.L.R., C.M.B., A.Z., M.T.A.D., N.S., M.F.B., D.B.S. and B.S.T. designed and performed data analysis. D.M., O.B., L.Z., Z.K.S., K.O. and M.E.R. aided germline variant pathogenicity assessment. N.B., S.D.S., N.D.S. and A.V. assisted with exome re-sequencing. M.L.C., E.Y.R., N.B., S.D.S., W.A., J.B., K.O., H.I.S., E.M.O., Z.K.S., M.L., M.E.R., D.M.H., M.F.B. and D.B.S. assisted with prospective genomic and clinical data collection, sample annotation, and consent infrastructure. P.J. and B.S.T. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Michael F. Berger or David B. Solit or Barry S. Taylor.

Ethics declarations

Competing interests

M.L.C. reports receiving travel/accommodation funding from Allergan, Sanofi-Aventis, and Daiichi Sankyo. W.A. reports receiving honoraria from Caret, advisory board activities for Clovis Oncology, Janssen, and MORE Health, travel/accommodation expenses from Clovis Oncology and GlaxoSmithKline, and research funding from AstraZeneca, Zenith Epigenetics, Clovis Oncology, and GlaxoSmithKline. L.Z. reports receiving honoraria from Future Technology Research LLC, Roche Diagnostics Asia Pacific, BGI, and Illumina. L.Z. has a family member with a leadership position and ownership interest in Shanghai Genome Center. J.B. is an employee of AstraZeneca, serves on the Board of Directors of Foghorn and is a past board member of Varian Medical Systems, Bristol-Myers Squibb, Grail, Aura Biosciences and Infinity Pharmaceuticals. He has performed consulting and/or advisory work for Grail, PMV Pharma, ApoGen, Juno, Lilly, Seragon, Novartis and Northern Biologics. He has stock or other ownership interests in Tango and Venthera, for which he is a co-founder. He has previously received honoraria or travel expenses from Roche, Novartis, and Lilly. E.M.O. reports receiving consulting fees from BioLineRx, Targovax, Halozyme, Celgene, Cytomx, and Bayer and research funding support from Genentech, Roche, BMS, Halozyme, Celgene, MabVax Therapeutics, and ActaBiologica. D.M.H. reports receiving research funding from AstraZeneca, Puma Biotechnology, and Loxo Oncology and personal fees from Atara Biotherapeutics, Chugai Pharma, Boehringer Ingelheim, AstraZeneca, Pfizer, Bayer, Debiophram Group, and Genetech. M.F.B. reports receiving research funding from Illumina and advisory board activities for Roche. D.B.S. reports advisory board activities for Loxo Oncology, Pfizer, Illumina, Lilly Oncology, Vivideon, and Intezyne. All stated activities were outside of the work described herein. No other disclosures were noted.

Additional information

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

Peer review information Nature thanks Clare Turnbull and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Study cohort and BRCA1/2 germline and somatic mutation distribution.

a, The number of tumour and matched normal specimens are shown by cancer type. CNS, non-glioma central nervous system tumours; CUP, cancer of unknown primary; GINET, gastrointestinal neuroendocrine tumour; GIST, gastrointestinal stromal tumour; NHL, non-Hodgkin’s lymphoma; NSCLC, non-small-cell lung cancer; SCLC, small-cell lung cancer. b, Somatic mutational burden (log2(mutations per megabase)) in tumours defined as non-hypermutated or hypermutated (via microsatellite instability, DNA polymerase epsilon mutations, or alkylating therapy-induced; see Methods). Data are shown as median and interquartile range. c, BRCA1 and BRCA2 somatic mutation rates in deciles of increasing tumour mutational burden. The highest mutational burden tumours also had the highest rate of BRCA1/2 mutations. Error bars are binomial confidence intervals. d, The percentage of tumours in each tumour type containing either somatic VUS or LoF BRCA1/2 mutations as a function of the median somatic mutational burden of that cancer type (excluding hypermutated cases). Overall, the rate of somatic LoF BRCA1/2 mutations by cancer type modestly increased with their increasing mutational burden, and this was much more pronounced for BRCA1/2 variants of uncertain significance. e, Population frequency comparisons are shown between the study cohort and gnomAD for allele frequencies (AF) of BRCA1/2 germline pathogenic and likely pathogenic (P/LP) alleles and VUS (dark and light blue, respectively). Left, all alleles; centre, only P/LP alleles; right, comparison between the study cohort and the germline results from the TCGA cohort. Ashkenzai Jewish (ASJ) founder BRCA1/2 alleles are shown. f, As in e for only the ASJ sub-populations. g, As in e and f, but for the non-ASJ white subpopulation. NFE, non-Finnish European. h, The prevalence of homozygous deletions in BRCA1 or BRCA2 in affected cancer types. Count of affected tumours in parentheses, inset is the fraction of all homozygous deletions of either gene. i, Average age of first cancer diagnosis for BRCA1/2 germline carriers compared to those patients lacking any pathogenic germline alteration (germline wild type) in BRCA-associated cancer types and all other cancer types. Error bars are 95% confidence intervals. *P < 0.01, two-sided Wilcoxon test. j, Percentage of BRCA1/2 germline carriers with several independent cancer diagnoses compared to germline wild-type patients. Error bars are 95% confidence intervals. *P = 0.02, χ2 test. k, The fraction of all germline pathogenic or somatic LoF alterations in BRCA1 versus BRCA2 (in non-hypermutated tumours).

Extended Data Fig. 2 BRCA1/2 zygosity.

a, Diagrammatic representation of the integration of allele-specific copy number with purity and mutant allele frequencies to determine the zygosity of the germline pathogenic allele in the corresponding tumour (and the mechanism of its selection). CN-LOH, copy-neutral LOH. b, In only a subset of cases of low tumour cell content (<30%) does the LOH inference become increasingly analytically challenging (increasing rate of indeterminant calls). c, The percentage of cases with LOH affecting the germline pathogenic or somatic LoF BRCA1/2 mutations (as labelled) as a function of tumour purity (see Methods). Although somatic mutant allele frequencies are affected by tumour purity, this does not affect the sensitivity for LOH detection for germline variants and only affects sensitivity for LOH of somatic mutations in tumours of less than 30% purity. Error bars are 95% confidence intervals in all panels. d, In tumours with benign germline variants in BRCA1 and BRCA2, the ratio of zygosity changes affecting the wild-type or mutant BRCA1/2 allele is approximately 0.5, indicating neutral selection. By contrast, the rate of zygosity changes leading to loss of the wild-type allele in patients with germline pathogenic BRCA1 or BRCA2 mutations (>80%) is consistent with selective pressure for biallelic inactivation. e, Integrating all measurable sources of biallelic inactivation (inset, somatic sequence variants as the source of second hits to wild-type BRCA1/2), the percentage of tumours by cancer type containing a biallelic BRCA1 or BRCA2 loss. f, The rate of biallelic inactivation of BRCA1 versus BRCA2 in patients with germline pathogenic or somatic LoF mutations (in hypermutated and non-hypermutated tumours). P values determined by two-sided Fisher’s exact test. g, The rate of loss of wild-type BRCA1 or BRCA2 (LOH) in patients with germline deleterious BRCA1 or BRCA2 mutations compared with rare benign variants in either gene in BRCA-associated cancer types and in those not conventionally associated with BRCA germline carriers. P values determined by Fisher’s exact test. h, The rate of biallelic inactivation of BRCA1/2 in patients with germline pathogenic or somatic LoF mutations pan-cancer as a function of primary or metastatic specimen type. Right, the four BRCA-associated cancer types are shown individually. P values determined by two-sided Fisher’s exact test. ns, not significant. i, The rate of LOH spanning germline or somatic mutant BRCA1 and BRCA2 in breast cancers (coloured as in Fig. 2b, c) as well as other somatically mutated tumour-suppressor genes.

Extended Data Fig. 3 Somatic loss of the pathogenic germline BRCA1 or BRCA2 allele.

a, Schematic representation of the different allelic configurations that would lead to the retention or loss of a germline allele in the presence of a somatically mutated tumour-suppressor gene (TSG) responsible for driving biallelic inactivation. b, Among tumours with loss of the pathogenic germline allele (in either BRCA1 or BRCA2, as indicated), the pattern of somatic mutations in known TSGs on their respective chromosomes (TP53 and NF1 are encoded on chromosome 17 on which BRCA1 also appears, whereas RB1 is encoded on chromosome 13 on which BRCA2 also appears) arising in the same tumours and in trans with, and presumed to drive the loss of, the germline allele. c, In a representative EML4-ALK-positive lung adenocarcinoma diagnosed in a BRCA1 E23Vfs*17 carrier, LOH preceding whole-genome doubling spanned chromosome 17 encoding TP53 R248Q arising in trans with the mutant BRCA1 allele. Dark and light blue represent the major and minor copy number at the indicated loci. d, Somatic mutant allele fractions (for case in c) are consistent with deletion of the allele containing the BRCA1 founder mutation as compared to the observed and expected values for clonal heterozygous somatic mutations (RAD50) or biallelic inactivation of mutant TP53 (tumour purity is Φ). The selective pressure for biallelic TP53 inactivation driven by the initial R248Q mutation probably precipitated the subsequent heterozygous loss of the wild-type TP53 allele, leading to deletion of the BRCA1 pathogenic mutation and retention of the wild-type BRCA1 allele, indicating that mutant BRCA1 was dispensable for its pathogenesis. Error bars are binomial confidence intervals.

Extended Data Fig. 4 HRD phenotype in BRCA1/2-mutant cancers characterized by whole-exome sequencing.

a, Total number of prospectively sequenced cases by cancer type for which exome re-sequencing was obtained. PNS, peripheral nervous system; other abbreviations are as in Extended Data Fig. 1. b, The distribution of cancer types among BRCA1/2-mutant (germline or somatic) cases with exome re-sequencing data. c, The proportion of BRCA1/2-mutant cases with exome re-sequencing data by germline or somatic mutational origin. d, e, The somatic single-nucleotide mutational signature 3 of HRD (d), and the DNA copy number-based large-scale transitions metric of HRD as inferred from exome sequencing data (e) are shown as a function affected cancer types (left) and BRCA1/2 mutational origin and zygosity (right) as in Fig. 3. *P < 0.01, **P < 1 × 10−10, ***P < 1 × 10−20, two-sided Student’s t-test. Circles and horizontal lines denote median and lower and upper quartiles, respectively. The individual metrics are highly correlated with the composite HRD score (rho = 0.89, P = 1 × 10−270; see Methods) and consequently the qualitative results based on lineage, mutational origin and zygosity are similar. f, The rate of BRCA1 promoter methylation in ovarian, breast and other cancer types (no evidence of BRCA2 silencing via promoter methylation was apparent). Inset, BRCA1 germline mutations and promoter methylation leading to BRCA1 silencing are mutually exclusive in affected cancers, indicating that heterozygous BRCA1-mutant tumours typically do not acquire biallelic inactivation via epigenetic silencing of the remaining allele. Epigenetic silencing is therefore unlikely to fully explain the modest HRD phenotype in heterozygous mutant tumours (Fig. 3b). Both germline and somatic mutational data and DNA methylation data was acquired from The Cancer Genome Atlas (see Methods). g, The composite measure of HRD in homologous-recombination-wild-type tumours (light grey) and in tumours with either germline or somatic BRCA1 or BRCA2 mutations (dark grey) grouped by BRCA-associated cancer types (dark red; breast, ovary, pancreas and prostate) versus other cancer types (red), and tumours with somatic hypermutation (light red). P values determined by two-sided Student’s t-test. h, As in g and Fig. 3c, grouped by a combination of lineage, origin and zygosity.

Extended Data Fig. 5 Intra-individual BRCA phenotypic divergence.

Exome sequencing of two cancer diagnoses in a founder BRCA2 S1982Rfs*22 germline carrier revealed two independent and clonally unrelated cancers—one a HRD serous ovarian cancer (left) with loss of wild-type BRCA2; the other a co-incident cholangiocarcinoma with intact wild-type BRCA2 (right). The latter had a different pattern of somatic abnormality and lacked any evidence of HRD (top) despite the shared germline pathogenic BRCA2 allele.

Extended Data Fig. 6 Tumour mutational burden by BRCA1/2 genotype.

The somatic mutational burden of tumours as a function of cancer type, BRCA1/2 mutational origin, and somatic BRCA1/2 zygosity. P values determined by two-sided Student’s t-test. Boxes and error bars denote median and 95% confidence intervals, respectively.

Extended Data Fig. 7 Mutations in BRCA1 or BRCA2 are attributable to other mutational signatures.

The somatic mutations in each of the evaluable cancers in Fig. 3d in which a BRCA1 or BRCA2 somatic heterozygous mutation arose in a motif consistent with an alternative non-HRD mutational signature. The mutation (trinucleotide context, base change, and protein annotation) is indicated in each case as is its cancer type.

Extended Data Fig. 8 PARP inhibitor therapy by BRCA1/2 mutational origin and zygosity.

A single PARP inhibitor outcome analysis of all four BRCA genotypes as independent classes (BRCA1/2 mutational origin and zygosity) with BRCA-associated cancer types (as in Fig. 4). All four classes of BRCA-mutant patients (as indicated) achieve significantly greater clinical benefit to PARP inhibitor therapy than do treated patients with wild-type BRCA tumours (BRCA1/2-mutant classes: germline carrier somatic heterozygous (hazard ratio = 0.39, 0.21–0.72, P = 0.003); germline carrier somatic biallelic (hazard ratio = 0.5, 0.35–0.72, P = 2 × 10−4); somatic heterozygous LoF (hazard ratio = 0.5, 0.26–0.95, P = 0.03); and somatic LoF biallelic (hazard ratio = 0.34, 0.16–0.72, P = 0.005)).

Supplementary information

Supplementary Information

A guide to Supplementary Tables 1-6

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-6 – see Supplementary Information document for a full guide.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jonsson, P., Bandlamudi, C., Cheng, M.L. et al. Tumour lineage shapes BRCA-mediated phenotypes. Nature 571, 576–579 (2019). https://doi.org/10.1038/s41586-019-1382-1

Download citation

Further reading


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.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing