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. 2016 Dec 27;13(12):e1002201.
doi: 10.1371/journal.pmed.1002201. eCollection 2016 Dec.

Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis

Celine Lefebvre  1 , Thomas Bachelot  2 , Thomas Filleron  3 , Marion Pedrero  1 , Mario Campone  4 , Jean-Charles Soria  1   5   6   7 , Christophe Massard  7 , Christelle Lévy  8 , Monica Arnedos  5 , Magali Lacroix-Triki  1 , Julie Garrabey  9 , Yannick Boursin  10 , Marc Deloger  10 , Yu Fu  1 , Frédéric Commo  1 , Véronique Scott  1 , Ludovic Lacroix  1   11 , Maria Vittoria Dieci  12   13 , Maud Kamal  14 , Véronique Diéras  14 , Anthony Gonçalves  15 , Jean-Marc Ferrerro  16 , Gilles Romieu  17 , Laurence Vanlemmens  18 , Marie-Ange Mouret Reynier  19 , Jean-Christophe Théry  20 , Fanny Le Du  21 , Séverine Guiu  22 , Florence Dalenc  23 , Gilles Clapisson  24 , Hervé Bonnefoi  25 , Marta Jimenez  9 , Christophe Le Tourneau  14   26 , Fabrice André  1   5   6
Affiliations

Mutational Profile of Metastatic Breast Cancers: A Retrospective Analysis

Celine Lefebvre et al. PLoS Med. .

Abstract

Background: Major advances have been achieved in the characterization of early breast cancer (eBC) genomic profiles. Metastatic breast cancer (mBC) is associated with poor outcomes, yet limited information is available on the genomic profile of this disease. This study aims to decipher mutational profiles of mBC using next-generation sequencing.

Methods and findings: Whole-exome sequencing was performed on 216 tumor-blood pairs from mBC patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA, or Molecular Screening for Cancer Treatment Optimization (MOSCATO) prospective trials. Mutational profiles from 772 primary breast tumors from The Cancer Genome Atlas (TCGA) were used as a reference for comparing primary and mBC mutational profiles. Twelve genes (TP53, PIK3CA, GATA3, ESR1, MAP3K1, CDH1, AKT1, MAP2K4, RB1, PTEN, CBFB, and CDKN2A) were identified as significantly mutated in mBC (false discovery rate [FDR] < 0.1). Eight genes (ESR1, FSIP2, FRAS1, OSBPL3, EDC4, PALB2, IGFN1, and AGRN) were more frequently mutated in mBC as compared to eBC (FDR < 0.01). ESR1 was identified both as a driver and as a metastatic gene (n = 22, odds ratio = 29, 95% CI [9-155], p = 1.2e-12) and also presented with focal amplification (n = 9) for a total of 31 mBCs with either ESR1 mutation or amplification, including 27 hormone receptor positive (HR+) and HER2 negative (HER2-) mBCs (19%). HR+/HER2- mBC presented a high prevalence of mutations on genes located on the mechanistic target of rapamycin (mTOR) pathway (TSC1 and TSC2) as compared to HR+/HER2- eBC (respectively 6% and 0.7%, p = 0.0004). Other actionable genes were more frequently mutated in HR+ mBC, including ERBB4 (n = 8), NOTCH3 (n = 7), and ALK (n = 7). Analysis of mutational signatures revealed a significant increase in APOBEC-mediated mutagenesis in HR+/HER2- metastatic tumors as compared to primary TCGA samples (p < 2e-16). The main limitations of this study include the absence of bone metastases and the size of the cohort, which might not have allowed the identification of rare mutations and their effect on survival.

Conclusions: This work reports the results of the analysis of the first large-scale study on mutation profiles of mBC. This study revealed genomic alterations and mutational signatures involved in the resistance to therapies, including actionable mutations.

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Conflict of interest statement

I have read the journal's policy and the authors of this manuscript have the following competing interests: TB is a board member for Roche, Novartis, Pfizer, and AstraZeneca, and received non-financial support from Roche, Novartis, and AstraZeneca. TB also received grants from Roche and Novartis.

Figures

Fig 1
Fig 1. Driver gene mutations in metastatic breast cancers.
The top panel shows the synonymous and nonsynonymous mutation rates (number of mutations) per patient according to the molecular subtype of the metastasis. HR, hormone receptor; ND, not determined. The bottom panel shows the significantly mutated genes according to MutSig analysis at FDR < 0.1. Amplifications and deletions correspond to the thresholded values from the Gistic2 output (respectively +2 and −2 values).
Fig 2
Fig 2. Genes more frequently mutated in mBC as compared to eBC (TCGA).
The axes show the odds ratio calculated as the ratio of gene frequencies (x-axis) and the −log10 of the FDR of a Fisher exact test (y-axis) comparing the gene frequencies in metastatic versus primary tumors. The size of the points is proportional to the mutation frequency of the gene in the metastatic cohort. Highlighted points correspond to FDR < 0.01 or to significantly mutated genes.
Fig 3
Fig 3. OS according to the presence of a mutation in one of the eight genes enriched in mBC as compared to eBC at FDR < 0.01.
No mutation = mBC patients with tumors with no somatic mutation in the eight genes; mutation = mBC patients with tumors carrying at least one somatic mutation in the eight genes.
Fig 4
Fig 4. Somatic mutations of genes TSC1, TSC2, ERBB4, and NOTCH3 in mBC (from cBioPortal).
Green dots represent missense mutations, while black dots represent truncating mutations.
Fig 5
Fig 5. COSMIC mutational signature contribution in mBC.
DNA DSBR, DNA double-strand break-repair by homologous recombination; DNA MMR, DNA mismatch repair.
Fig 6
Fig 6. Distribution of the number of mutations according to mutational signatures in HR+/HER2− metastatic and primary (TCGA) breast tumors.
See this image and copyright information in PMC

References

    1. Ellis MJ, Ding L, Shen D, Luo J, Suman VJ, Wallis JW, et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature. 2012;486(7403):353–60. PubMed Central PMCID: PMCPMC3383766. 10.1038/nature11143 - DOI - PMC - PubMed
    1. The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70. 10.1038/nature11412 - DOI - PMC - PubMed
    1. Alexandrov LB, Nik-Zainal S, Wedge DC, Campbell PJ, Stratton MR. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 2013;3(1):246–59. PubMed Central PMCID: PMCPMC3588146. 10.1016/j.celrep.2012.12.008 - DOI - PMC - PubMed
    1. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92. 10.1056/NEJMoa1113205 - DOI - PMC - PubMed
    1. Haffner MC, Mosbruger T, Esopi DM, Fedor H, Heaphy CM, Walker DA, et al. Tracking the clonal origin of lethal prostate cancer. J Clin Invest. 2013;123(11):4918–22. PubMed Central PMCID: PMC3809798. 10.1172/JCI70354 - DOI - PMC - PubMed