Letter | Published:

Genomic characterization of metastatic breast cancers

Abstract

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.

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Data availability

Data are available at https://www.ebi.ac.uk/ega/ (EGAS00001003290). In addition, the data and scripts used to generate the figures are accessible at https://github.com/gustaveroussy/mBC_WES_Fabrice_Andre_2019, or on request from the corresponding author.

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Acknowledgements

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.

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.

Correspondence to Fabrice André.

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

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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.
Extended Data Fig. 1: Study workflow.
Extended Data Fig. 2: Outcome of patients with HR+/HER2 mBCs according to the presence of mutational signatures S10, S13 and S17.
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.
Extended Data Fig. 4: Prediction of tumour neoantigens according to mutational processes.

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