Skip to main content

Thank you for visiting 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.

Genomic characterization of metastatic breast cancers

An Author Correction to this article was published on 16 July 2019

This article has been updated


Metastasis is the main cause of death for patients with breast cancer. Many studies have characterized the genomic landscape of breast cancer during its early stages. However, there is evidence that genomic alterations are acquired during the evolution of cancers from their early to late stages, and that the genomic landscape of early cancers is not representative of that of lethal cancers1,2,3,4,5,6,7. Here we investigated the landscape of somatic alterations in 617 metastatic breast cancers. Nine driver genes (TP53, ESR1, GATA3, KMT2C, NCOR1, AKT1, NF1, RIC8A and RB1) were more frequently mutated in metastatic breast cancers that expressed hormone receptors (oestrogen and/or progesterone receptors; HR+) but did not have high levels of HER2 (HER2; n = 381), when compared to early breast cancers from The Cancer Genome Atlas. In addition, 18 amplicons were more frequently observed in HR+/HER2 metastatic breast cancers. These cancers showed an increase in mutational signatures S2, S3, S10, S13 and S17. Among the gene alterations that were enriched in HR+/HER2 metastatic breast cancers, mutations in TP53, RB1 and NF1, together with S10, S13 and S17, were associated with poor outcome. Metastatic triple-negative breast cancers showed an increase in the frequency of somatic biallelic loss-of-function mutations in genes related to homologous recombination DNA repair, compared to early triple-negative breast cancers (7% versus 2%). Finally, metastatic breast cancers showed an increase in mutational burden and clonal diversity compared to early breast cancers. Thus, the genomic landscape of metastatic breast cancer is enriched in clinically relevant genomic alterations and is more complex than that of early breast cancer. The identification of genomic alterations associated with poor outcome will allow earlier and better selection of patients who require the use of treatments that are still in clinical trials. The genetic complexity observed in advanced breast cancer suggests that such treatments should be introduced as early as possible in the disease course.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Repertoire of somatic genetic alterations of metastatic breast cancers.
Fig. 2: Comparison with eBCs from the TCGA cohort revealed new significantly mutated genes in mBCs.
Fig. 3: Mutational processes and HRD in mBCs.
Fig. 4: Tumour mutational burden and clonal diversity in mBC.

Data availability

Data are available at (EGAS00001003290). In addition, the data and scripts used to generate the figures are accessible at, or on request from the corresponding author.

Change history

  • 16 July 2019

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


  1. 1.

    Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    ADS  CAS  Article  Google Scholar 

  2. 2.

    Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    ADS  CAS  Article  Google Scholar 

  3. 3.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  Article  Google Scholar 

  4. 4.

    Yates, L. R. et al. Genomic evolution of breast cancer metastasis and relapse. Cancer Cell 32, 169–184 (2017).

    CAS  Article  Google Scholar 

  5. 5.

    Ng, C. K. Y. et al. Genetic heterogeneity in therapy-naïve synchronous primary breast cancers and their metastases. Clin. Cancer Res. 23, 4402–4415 (2017).

    CAS  Article  Google Scholar 

  6. 6.

    Yu, H. A. et al. Analysis of tumor specimens at the time of acquired resistance to EGFR-TKI therapy in 155 patients with EGFR-mutant lung cancers. Clin. Cancer Res. 19, 2240–2247 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Gramza, A. W., Corless, C. L. & Heinrich, M. C. Resistance to tyrosine kinase inhibitors in gastrointestinal stromal tumors. Clin. Cancer Res. 15, 7510–7518 (2009).

    CAS  Article  Google Scholar 

  8. 8.

    Robinson, D. R. et al. Activating ESR1 mutations in hormone-resistant metastatic breast cancer. Nat. Genet. 45, 1446–1451 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Fumagalli, D. et al. Somatic mutation, copy number and transcriptomic profiles of primary and matched metastatic estrogen receptor-positive breast cancers. Ann. Oncol. 27, 1860–1866 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Lefebvre, C. et al. Mutational profile of metastatic breast cancers: a retrospective analysis. PLoS Med. 13, e1002201 (2016).

    Article  Google Scholar 

  11. 11.

    Brastianos, P. K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).

    CAS  Article  Google Scholar 

  12. 12.

    Brown, D. et al. Phylogenetic analysis of metastatic progression in breast cancer using somatic mutations and copy number aberrations. Nat. Commun. 8, 14944 (2017).

    ADS  Article  Google Scholar 

  13. 13.

    Savas, P. et al. The subclonal architecture of metastatic breast cancer: results from a prospective community-based rapid autopsy program “CASCADE”. PLoS Med. 13, e1002204 (2016).

    Article  Google Scholar 

  14. 14.

    Murtaza, M. et al. Multifocal clonal evolution characterized using circulating tumour DNA in a case of metastatic breast cancer. Nat. Commun. 6, 8760 (2015).

    ADS  Article  Google Scholar 

  15. 15.

    Schrijver, W. A. M. E. et al. Mutation profiling of key cancer genes in primary breast cancers and their distant metastases. Cancer Res. 78, 3112–3121 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    De Mattos-Arruda, L. et al. Genetic heterogeneity and actionable mutations in HER2-positive primary breast cancers and their brain metastases. Oncotarget 9, 20617–20630 (2018).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Razavi, P. et al. The genomic landscape of endocrine-resistant advanced breast cancers. Cancer Cell 34, 427–438 (2018).

    CAS  Article  Google Scholar 

  18. 18.

    Nayar, U. et al. Acquired HER2 mutations in ER+ metastatic breast cancer confer resistance to estrogen receptor-directed therapies. Nat. Genet. 51, 207–216 (2019).

    Article  Google Scholar 

  19. 19.

    Li, Z. et al. Loss of the FAT1 tumor suppressor promotes resistance to CDK4/6 inhibitors via the hippo pathway. Cancer Cell 34, 893–905 (2018).

    Article  Google Scholar 

  20. 20.

    Knudsen, E. S. & Wang, J. Y. J. Targeting the RB-pathway in cancer therapy. Clin. Cancer Res. 16, 1094–1099 (2010).

    CAS  Article  Google Scholar 

  21. 21.

    Lock, R. et al. Cotargeting MNK and MEK kinases induces the regression of NF1-mutant cancers. J. Clin. Invest. 126, 2181–2190 (2016).

    Article  Google Scholar 

  22. 22.

    Gala, K. et al. KMT2C mediates the estrogen dependence of breast cancer through regulation of ERα enhancer function. Oncogene 37, 4692–4710 (2018).

    CAS  Article  Google Scholar 

  23. 23.

    Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. (2017).

    Article  PubMed  Google Scholar 

  24. 24.

    Law, E. K. et al. The DNA cytosine deaminase APOBEC3B promotes tamoxifen resistance in ER-positive breast cancer. Sci. Adv. 2, e1601737 (2016).

    ADS  Article  Google Scholar 

  25. 25.

    Popova, T. et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res. 72, 5454–5462 (2012).

    CAS  Article  Google Scholar 

  26. 26.

    Riaz, N. et al. Pan-cancer analysis of bi-allelic alterations in homologous recombination DNA repair genes. Nat. Commun. 8, 857 (2017).

    ADS  Article  Google Scholar 

  27. 27.

    Polak, P. et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat. Genet. 49, 1476–1486 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Edwards, S. L. et al. Resistance to therapy caused by intragenic deletion in BRCA2. Nature 451, 1111–1115 (2008).

    ADS  CAS  Article  Google Scholar 

  29. 29.

    Sakai, W. et al. Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mutated cancers. Nature 451, 1116–1120 (2008).

    ADS  CAS  Article  Google Scholar 

  30. 30.

    Lee, J. Y. et al. Lobular carcinomas in situ display intralesion genetic heterogeneity and clonal evolution in the progression to invasive lobular carcinoma. Clin. Cancer Res. 25, 674–686 (2019).

    Article  Google Scholar 

  31. 31.

    André, F. et al. Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: a multicentre, prospective trial (SAFIR01/UNICANCER). Lancet Oncol. 15, 267–274 (2014).

    Article  Google Scholar 

  32. 32.

    Massard, C. et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 7, 586–595 (2017).

    CAS  Article  Google Scholar 

  33. 33.

    Le Tourneau, C. et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 16, 1324–1334 (2015).

    Article  Google Scholar 

  34. 34.

    Hortobagyi, G. N. et al. Ribociclib as first-line therapy for HR-positive, advanced breast cancer. N. Engl. J. Med. 375, 1738–1748 (2016).

    CAS  Article  Google Scholar 

  35. 35.

    Tripathy, D. et al. Ribociclib plus endocrine therapy for premenopausal women with hormone-receptor-positive, advanced breast cancer (MONALEESA-7): a randomised phase 3 trial. Lancet Oncol. 19, 904–915 (2018).

    CAS  Article  Google Scholar 

  36. 36.

    Slamon, D. J. et al. Phase III randomized study of ribociclib and fulvestrant in hormone receptor-positive, human epidermal growth factor receptor 2-negative advanced breast cancer: MONALEESA-3. J. Clin. Oncol. 36, 2465–2472 (2018).

    CAS  Article  Google Scholar 

  37. 37.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  Article  Google Scholar 

  38. 38.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  Article  Google Scholar 

  39. 39.

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

    CAS  Article  Google Scholar 

  40. 40.

    Saunders, C. T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    CAS  Article  Google Scholar 

  41. 41.

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

    CAS  Article  Google Scholar 

  42. 42.

    Gao, J. et al. 3D clusters of somatic mutations in cancer reveal numerous rare mutations as functional targets. Genome Med. 9, 4 (2017).

    Article  Google Scholar 

  43. 43.

    Costello, M. et al. Discovery and characterization of artifactual mutations in deep coverage targeted capture sequencing data due to oxidative DNA damage during sample preparation. Nucleic Acids Res. 41, e67 (2013).

    Article  Google Scholar 

  44. 44.

    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).

    Article  Google Scholar 

  45. 45.

    Riester, M. et al. PureCN: copy number calling and SNV classification using targeted short read sequencing. Source Code Biol. Med. 11, 13 (2016).

    Article  Google Scholar 

  46. 46.

    Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).

    CAS  Article  Google Scholar 

  47. 47.

    Schröder, J. et al. Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads. Bioinformatics 30, 1064–1072 (2014).

    Article  Google Scholar 

  48. 48.

    Newman, A. M. et al. Integrated digital error suppression for improved detection of circulating tumor DNA. Nat. Biotechnol. 34, 547–555 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

    ADS  Article  Google Scholar 

  50. 50.

    Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    ADS  CAS  Article  Google Scholar 

  51. 51.

    Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

    Article  Google Scholar 

  52. 52.

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

    CAS  Article  Google Scholar 

  53. 53.

    Roth, A. et al. PyClone: statistical inference of clonal population structure in cancer. Nat. Methods 11, 396–398 (2014).

    CAS  Article  Google Scholar 

  54. 54.

    Hundal, J. et al. pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).

    Article  Google Scholar 

  55. 55.

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

    ADS  CAS  Article  Google Scholar 

  56. 56.

    Piscuoglio, S. et al. The genomic landscape of male breast cancers. Clin. Cancer Res. 22, 4045–4056 (2016).

    CAS  Article  Google Scholar 

  57. 57.

    Leiserson, M. D., Wu, H. T., Vandin, F. & Raphael, B. J. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 16, 160 (2015).

    Article  Google Scholar 

Download references


The SAFIR02 trial is supported by Fondation ARC. Sequencing was supported by French NCI (INCA), Breast Cancer Research Foundation, Operation Parrain Chercheurs, RHU MyProbe (ANR) and Odyssea. S.P. is supported by the Swiss National Science Foundation (Ambizione grant number PZ00P3_168165). F.B. and D.B. are supported by SIRIC, label Ligue EL2016, Ruban Rose and Fondation Groupe EDF. We thank all patients who consented to enter the study, and all investigators and their teams.

Reviewer information

Nature thanks Peter Campbell and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information




F.B. designed the study, is the principal investigator (PI) of the PERMED trial and contributed to writing of the paper. C.K.Y.N. and S.P. designed the study, supervised bioinformatics pipelines, generated data from Variant Call Format (VCF) and contributed to writing of the paper. A.P. is the PI of the RUBY trial, designed whole-genome analyses and acquired samples in the SAFIR02 trial. N.D. ran the whole-exome and whole-genome sequencing profiling. N.C. created libraries from PERMED samples. J.C.S. is the PI of the MOSCATO and MATCH-R trials and provided samples from these trials. A.T.D. and Y.A. generated VCF files from raw whole-exome sequencing data and whole-genome sequencing data. M.K. provided DNA from the SHIVA trial. S.G. generated libraries from the PERMED trial. G.M. supervised A.T.D. and Y.A. M.J. is the project manager of the SAFIR01, SAFIR02 and RUBY trials. S.D. and B.V. contributed to writing of the paper. M. Chaffanet and D.B. acquired samples from PERMED and supervised the production of libraries from samples. T.B., M. Campone, C.L., H.B. and F.D. acquired samples for the SAFIR01 and SAFIR02 trials. A.J. is the project manager of the SAFIR02 trial and centralized collected samples and data. M.R.D.F. generated data from VCF files under the supervision of C.K.Y.N. and S.P. N.B. is the translational research head of the MONALEESA trial. T.F. ran all statistical analyses related to outcomes and contributed to the writing. C.L.T. is the PI of the SHIVA trial and helped to design the study. F.A. designed the study, is the PI of the SAFIR01 and SAFIR02 trials, and contributed to the writing. All authors approved the final manuscript and contributed to critical revisions of its intellectual content.

Corresponding author

Correspondence to Fabrice André.

Ethics declarations

Competing interests

N.B. is an employee of Novartis. F.A. and T.B. received grants from Novartis. F.A. attended the advisory board and gave talks at events sponsored by Novartis. Gustave Roussy Hospital was compensated for these. M. Campone, F.D. and T.B. received honoraria from Novartis. The RUBY trial is funded by Clovis. The MONALEESA trials are sponsored by Novartis. J.C.S. is an employee and holds stock in AstraZeneca since September 2017. Over the past five years he has received consultancy fees from: AstraZeneca, Astex, Clovis, GSK, GamaMabs, Lilly, MSD, Mission Therapeutics, Merus, Pfizer, PharmaMar, Pierre Fabre, Roche-Genentech, Sanofi, Servier, Symphogen and Takeda. He is also a shareholder in Gritstone.

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 Study workflow.

The main dataset included tumour samples from 617 patients with metastatic breast cancers who were included in six precision medicine trials. The aim was to generate the genomic landscape of metastatic breast cancers. We then explored the clinical relevance of gene alterations that were enriched in mBCs. To address these questions, we used the main dataset to test the prognostic values of these gene alterations; we used genomic data from the MONALEESA2, 3 and 7 trials to test the efficacy of CDK4 inhibitors in RB1 mutant tumours; we used whole-genome sequencing data from four outlier responders to PARP inhibitors (RUBY trial); and we used data from SAFIR02 to explore the efficacy of MEK inhibitors in patients with NF1 mutations.

Extended Data Fig. 2 Outcome of patients with HR+/HER2 mBCs according to the presence of mutational signatures S10, S13 and S17.

Kaplan–Meier plots of overall survival in patients with HR+/HER2 mBCs, stratified by mutational signatures S10 (S10 = 0, n = 171; S10 > 0, n = 194), S13 (S13 = 0, n = 140; S13 > 0, n = 225) and S17 (S17 = 0, n = 245; S17 > 0, n = 120). HR, hazard ratio according to Cox multivariate analysis (log-rank test, two-sided); 95% CI shown in square brackets.

Extended Data Fig. 3 Detection of LST, signature 3 and indels with microhomology according to the presence of biallelic LOF mutations on genes located in the HR pathway.

Scatter plots of LST and contributions of signature 3 in the HR+/HER2, HER2+ and TNBC cohorts. Each dot represents one tumour and the sizes of the dots are proportion to the number of indels larger than 3 bp with microhomology, a feature of HRD. Tumours associated with biallelic LOF mutations (frameshift, start/stop codon, nonsense and splice sites) in HR-related genes in which one of the alleles lost was a germline variant are shown in purple, and tumours associated with biallelic LOF mutations in which both alleles were lost somatically are shown in red. All cases in the darker pink shaded areas were considered to have HRD, and cases in the lighter pink shaded areas were considered to have HRD if they were LST-high25.

Extended Data Fig. 4 Prediction of tumour neoantigens according to mutational processes.

Scatter plots of the fraction of mutations classified as neoantigens against the number of neoantigens for neoantigens with predicted binding affinity <100 nM (left) and neoantigens with predicted binding affinity <500 nM (right). Above and to the right of the scatter plots are the kernel density plots of the number and fraction of neoantigens, respectively. Each dot represents a sample and is colour-coded according to mutational signature and/or clinical subgroup (see Methods).

Extended Data Table 1 Patient characteristics

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bertucci, F., Ng, C.K.Y., Patsouris, A. et al. Genomic characterization of metastatic breast cancers. Nature 569, 560–564 (2019).

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