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Landscape of somatic mutations in 560 breast cancer whole-genome sequences

An Author Correction to this article was published on 18 January 2019

This article has been updated

Abstract

We analysed whole-genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. We found that 93 protein-coding cancer genes carried probable driver mutations. Some non-coding regions exhibited high mutation frequencies, but most have distinctive structural features probably causing elevated mutation rates and do not contain driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed twelve base substitution and six rearrangement signatures. Three rearrangement signatures, characterized by tandem duplications or deletions, appear associated with defective homologous-recombination-based DNA repair: one with deficient BRCA1 function, another with deficient BRCA1 or BRCA2 function, the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operating, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

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Figure 1: Cohort and catalogue of somatic mutations in 560 breast cancers.
Figure 2: Non-coding analyses of breast cancer genomes.
Figure 3: Extraction and contributions of base substitution signatures in 560 breast cancers.
Figure 4: Additional characteristics of base substitution signatures and novel rearrangement signatures in 560 breast cancers.
Figure 5: Integrative analysis of rearrangement signatures.

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Accession codes

Data deposits

Raw data have been submitted to the European-Genome Phenome Archive under the overarching accession number EGAS00001001178 (please see Supplementary Notes for breakdown by data type). Somatic variants have been deposited at the International Cancer Genome Consortium Data Portal (https://dcc.icgc.org/).

Change history

  • 18 January 2019

    In the Methods section of this Article, 'greater than' should have been 'less than' in the sentence 'Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance that was at least 10 times greater than the whole-genome average for the individual sample.'. The Article has not been corrected.

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Acknowledgements

This work has been funded through the ICGC Breast Cancer Working group by the Breast Cancer Somatic Genetics Study (BASIS), a European research project funded by the European Community’s Seventh Framework Programme (FP7/2010-2014) under the grant agreement number 242006; the Triple Negative project funded by the Wellcome Trust (grant reference 077012/Z/05/Z) and the HER2+ project funded by Institut National du Cancer (INCa) in France (grant numbers 226-2009, 02-2011, 41-2012, 144-2008, 06-2012). The ICGC Asian Breast Cancer Project was funded through a grant of the Korean Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (A111218-SC01). Personally funded by grants above: F.G.R.-G., S.M., K.R., S.M. were funded by BASIS. Recruitment was performed under the auspices of the ICGC breast cancer projects run by the UK, France and Korea. For contributions towards instruments, specimens and collections: Tayside Tissue Bank (funded by CRUK, University of Dundee, Chief Scientist Office & Breast Cancer Campaign), Asan Bio-Resource Center of the Korea Biobank Network, Seoul, South Korea, OSBREAC consortium, The Icelandic Centre for Research (RANNIS), The Swedish Cancer Society and the Swedish Research Council, and Fondation Jean Dausset-Centre d’Etudes du polymorphisme humain. Icelandic Cancer Registry, The Brisbane Breast Bank (The University of Queensland, The Royal Brisbane and Women’s Hospital and QIMR Berghofer), Breast Cancer Tissue and Data Bank at KCL and NIHR Biomedical Research Centre at Guy’s and St Thomas’s Hospitals. Breakthrough Breast Cancer and Cancer Research UK Experimental Cancer Medicine Centre at KCL. For pathology review: The Mouse Genome Project and Department of Pathology, Cambridge University Hospitals NHS Foundation Trust for microscopes. A. Richardson, A. Ehinger, A. Vincent-Salomon, C. Van Deurzen, C. Purdie, D. Larsimont, D. Giri, D. Grabau, E. Provenzano, G. MacGrogan, G. Van den Eynden, I. Treilleux, J. E. Brock, J. Jacquemier, J. Reis-Filho, L. Arnould, L. Jones, M. van de Vijver, Ø. Garred, R. Salgado, S. Pinder, S. R. Lakhani, T. Sauer, V. Barbashina. Illumina UK Ltd for input on optimization of sequencing throughout this project. Wellcome Trust Sanger Institute Sequencing Core Facility, Core IT Facility and Cancer Genome Project Core IT team and Cancer Genome Project Core Laboratory team for general support. Personal funding: S.N.-Z. is a Wellcome Beit Fellow and personally funded by a Wellcome Trust Intermediate Fellowship (WT100183MA). L.B.A. is supported through a J. Robert Oppenheimer Fellowship at Los Alamos National Laboratory. A.L.R. is partially supported by the Dana-Farber/Harvard Cancer Center SPORE in Breast Cancer (NIH/NCI 5 P50 CA168504-02). D.G. was supported by the EU-FP7-SUPPRESSTEM project. A.S. was supported by Cancer Genomics Netherlands through a grant from the Netherlands Organisation of Scientific research (NWO). M.S. was supported by the EU-FP7-DDR response project. C.S. and C.D. are supported by a grant from the Breast Cancer Research Foundation. E.B. was funded by EMBL. C.S. is funded by FNRS (Fonds National de la Recherche Scientifique). S.J.J. is supported by Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Republic Korea (NRF 2011-0030105). G.K. is supported by National Research Foundation of Korea (NRF) grants funded by the Korean government (NRF 2015R1A2A1A10052578). J.F. received funding from an ERC Advanced grant (no. 322737). For general contribution and administrative support: Fondation Synergie Lyon Cancer in France. J. G. Jonasson, Department of Pathology, University Hospital & Faculty of Medicine, University of Iceland. K. Ferguson, Tissue Bank Manager, Brisbane Breast Bank and The Breast Unit, The Royal Brisbane and Women's Hospital, Brisbane, Australia. The Oslo Breast Cancer Consortium of Norway (OSBREAC). Angelo Paradiso, IRCCS Istituto Tumori “Giovanni Paolo II”, Bari Italy. A. Vines for administratively supporting to identifying the samples, organizing the bank, and sending out the samples. M. Schlooz-Vries, J. Tol, H. van Laarhoven, F. Sweep, P. Bult in Nijmegen for contributions in Nijmegen. This research used resources provided by the Los Alamos National Laboratory Institutional Computing Program, which is supported by the US Department of Energy National Nuclear Security Administration under contract no. DE-AC52-06NA25396. Research performed at Los Alamos National Laboratory was carried out under the auspices of the National Nuclear Security Administration of the United States Department of Energy. N. Miller (in memoriam) for her contribution in setting up the clinical database. Finally, we would like to acknowledge all members of the ICGC Breast Cancer Working Group and ICGC Asian Breast Cancer Project.

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Authors and Affiliations

Authors

Contributions

S.N.-Z., M.R.S. designed the study, analysed data and wrote the manuscript. H.D., J.S., M. Ramakrishna, D.G., X.Z. performed curation of data and contributed towards genomic and copy number analyses. M.S., A.B.B., M.R.A., O.C.L., A.L., M. Ringner, contributed towards curation and analysis of non-genomic data (transcriptomic, miRNA, methylation). I.M., L.B.A., D.C.W., P.V.L., S. Morganella, Y.S.J., contributed towards specialist analyses. G.T., G.K., A.L.R., A-L.B.-D., J.W.M.M., M.J.v.d.V., H.G.S., E.B., A. Borg., A.V., P.A.F., P.J.C., designed the study, drove the consortium and provided samples. S.Martin was the project coordinator. S.McL., S.O.M., K.R., contributed operationally. S.-M.A., S.B., J.E.B., A.Brooks., C.D., L.D., A.F., J.A.F., G.K.J.H., S.J.J., H.-Y.K., T.A.K., S.K., H.J.L., J.-Y.L., I.P., X.P., C.A.P., F.G.R.-G., G.R., A.M.S., P.T.S., O.A.S., S.T., I.T., G.G.V.d.E., P.V., A.V.-S., L.Y., C.C., L.v.V., A.T., S.K., B.K.T.T., J.J., N.t.U., C.S., P.N.S., S.V.L., S.R.L., J.E.E., A.M.T contributed pathology assessment and/or samples. A. Butler., S.D., M.G., D.R.J., Y.L., A.M., V.M., K.R., R.S., L.S., J.T. contributed IT processing and management expertise. All authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Serena Nik-Zainal, Alain Viari, Gu Kong or Michael R. Stratton.

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Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Landscape of driver mutations.

a, Summary of subtypes of cohort of 560 breast cancers. b, Driver mutations by mutation type. c, Distribution of rearrangements throughout the genome. Black line represents background rearrangement density (calculation based on rearrangement breakpoints in intergenic regions only). Red lines represent frequency of rearrangement within breast cancer genes.

Extended Data Figure 2 Rearrangements in oncogenes.

a, Variation in rearrangement and copy number events affecting ESR1. Clear amplification in top panel, transection of ESR1 in middle panel and focused tandem duplication events in bottom panel. b, Predicted outcomes of some rearrangements affecting ETV6. Red crosses indicate exons deleted as a result of rearrangements within the ETV6 genes, black dotted lines indicate rearrangement break points resulting in fusions between ETV6 and ERC, WNK1, ATP2B1 or LRP6. ETV6 domains indicated are: N-terminal (NT) pointed domain and E26 transformation-specific DNA binding domain (ETS).

Extended Data Figure 3 Recurrent non-coding events in breast cancers.

a, Manhattan plot demonstrating sites with most significant P values as identified by binning analysis. Purple highlighted sites were also detected by the method seeking recurrence when partitioned by genomic features. b, Locus at chr11 65 Mb, which was identified by independent analyses as being more mutated than expected by chance. Bottom, a rearrangement hotspot analysis identified this region as a tandem duplication hotspot, with nested tandem duplications noted at this site. Partitioning the genome into different regulatory elements, an analysis of substitutions and indels identified lncRNAs MALAT1 and NEAT1 (topmost panels) with significant P values.

Extended Data Figure 4 Copy number analyses.

a, Frequency of copy number aberrations across the cohort. Chromosome position along x axis, frequency of copy number gains (red) and losses (green) y axis. b, Identification of focal recurrent copy number gains by the GISTIC method (Supplementary Methods). c, Identification of focal recurrent copy number losses by the GISTIC method. d, Heatmap of GISTIC regions following unsupervised hierarchical clustering. Five cluster groups are noted and relationships with expression subtype (basal, red; luminal B, light blue; luminal A, dark blue), immunohistopathology status (ER, PR, HER2 status; black, positive), abrogation of BRCA1 (red) and BRCA2 (blue) (whether germline, somatic or through promoter hypermethylation), driver mutations (black, positive), HRD index (top 25% or lowest 25%; black, positive).

Extended Data Figure 5 miRNA analyses.

Hierarchical clustering of the most variant miRNAs using complete linkage and Euclidean distance. miRNA clusters were assigned using the partitioning algorithm using recursive thresholding (PART) method. Five main patient clusters were revealed. The horizontal annotation bars show (from top to bottom): PART cluster group, PAM50 mRNA expression subtype, GISTIC cluster, rearrangement cluster, lymphocyte infiltration score and histological grade. The heatmap shows clustered and centred miRNA expression data (log2 transformed). Details on colour coding of the annotation bars are presented below the heatmap.

Extended Data Figure 6 Rearrangement cluster groups and associated features.

a, Overall survival (OS) by rearrangement cluster group. b, Age of diagnosis. c, Tumour grade. d, Menopausal status. e, ER status. f, Immune response metagene panel. g, Lymphocytic infiltration score.

Extended Data Figure 7 Contrasting tandem duplication phenotypes.

Contrasting tandem duplication phenotypes of two breast cancers using chromosome X. Copy number (y axis) depicted as black dots. Lines represent rearrangements breakpoints (green, tandem duplications; pink, deletions; blue, inversions; black, translocations with partner breakpoint provided). Top, PD4841a has numerous large tandem duplications (>100 kb, rearrangement signature 1), whereas PD4833a has many short tandem duplications (<10 kb, rearrangement signature 3) appearing as ‘single’ lines in its plot.

Extended Data Figure 8 Hotspots of tandem duplications.

A tandem duplication hotspot occurring in six different patients.

Extended Data Figure 9 Rearrangement breakpoint junctions.

a, Breakpoint features of rearrangements in 560 breast cancers by rearrangement signature. b, Breakpoint features in BRCA and non-BRCA cancers.

Extended Data Figure 10 Signatures of focal hypermutation.

a, Kataegis and alternative kataegis occurring at the same locus (ERBB2 amplicon in PD13164a). Copy number (y axis) depicted as black dots. Lines represent rearrangements breakpoints (green, tandem duplications; pink, deletions; blue, inversions). Top, an ~10 Mb region including the ERBB2 locus. Middle, zoomed-in tenfold to an ~1 Mb window highlighting co-occurrence of rearrangement breakpoints, with copy number changes and three different kataegis loci. Bottom, demonstrates kataegis loci in more detail. log10 intermutation distance on y axis. Black arrow, kataegis; blue arrows, alternative kataegis. b, Sequence context of kataegis and alternative kataegis identified in this data set.

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Nik-Zainal, S., Davies, H., Staaf, J. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016). https://doi.org/10.1038/nature17676

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