• A Corrigendum to this article was published on 27 October 2017

This article has been updated

Abstract

Somatic rearrangements contribute to the mutagenized landscape of cancer genomes. Here, we systematically interrogated rearrangements in 560 breast cancers by using a piecewise constant fitting approach. We identified 33 hotspots of large (>100 kb) tandem duplications, a mutational signature associated with homologous-recombination-repair deficiency. Notably, these tandem-duplication hotspots were enriched in breast cancer germline susceptibility loci (odds ratio (OR) = 4.28) and breast-specific 'super-enhancer' regulatory elements (OR = 3.54). These hotspots may be sites of selective susceptibility to double-strand-break damage due to high transcriptional activity or, through incrementally increasing copy number, may be sites of secondary selective pressure. The transcriptomic consequences ranged from strong individual oncogene effects to weak but quantifiable multigene expression effects. We thus present a somatic-rearrangement mutational process affecting coding sequences and noncoding regulatory elements and contributing a continuum of driver consequences, from modest to strong effects, thereby supporting a polygenic model of cancer development.

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Change history

  • 13 February 2017

    In the version of this article initially published online, in the Methods section, under subheading "Rearrangement signatures," the statement "Putative regions of clustered rearrangements were identified as having an average inter-rearrangement distance at least ten times greater than the whole-genome average for the individual sample" should have read "ten times less than." The error has been corrected in the print, PDF and HTML versions of this article.

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Acknowledgements

Data used in this analysis were 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 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 nos. 226-2009, 02-2011, 41-2012, 144-2008 and 06-2012). J.W.M.M. received funding for this project through an ERC Advanced grant (no. 322737). G.K. is supported by National Research Foundation of Korea grants (NRF 2015R1A2A1A10052578). The ICGC Asian Breast Cancer Project was funded through a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (A111218-SC01). D.G. is supported by the EU-FP7-SUPPRESSTEM project. S.N.-Z. is funded by a Wellcome Trust Intermediate Fellowship (WT100183MA) and is supported as a Wellcome Beit Fellow.

Author information

Affiliations

  1. Wellcome Trust Sanger Institute, Cambridge, UK.

    • Dominik Glodzik
    • , Sandro Morganella
    • , Helen Davies
    • , Yilong Li
    • , Xueqing Zou
    • , Javier Diez-Perez
    • , Ludmil B Alexandrov
    • , Keiran Raine
    • , Peter J Campbell
    • , Michael R Stratton
    •  & Serena Nik-Zainal
  2. The University of Queensland: UQ Centre for Clinical Research and School of Medicine, Brisbane, Queensland, Australia.

    • Peter T Simpson
    •  & Sunil R Lakhani
  3. Department of Clinical Sciences Lund, Division of Oncology and Pathology, Lund University, Lund, Sweden.

    • Johan Staaf
  4. Theoretical Biology and Biophysics (T-6), Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

    • Ludmil B Alexandrov
  5. Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico, USA.

    • Ludmil B Alexandrov
  6. Department of Medical Oncology, Erasmus MC Cancer Institute and Cancer Genomics Netherlands, Erasmus University Medical Center, Rotterdam, the Netherlands.

    • Marcel Smid
    • , Hendrik G Stunnenberg
    •  & John W M Martens
  7. Department of Molecular Biology, Faculties of Science and Medicine, Radboud University, Nijmegen, the Netherlands.

    • Arie B Brinkman
  8. Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Norwegian Radiumhospital, Oslo, Norway.

    • Inga Hansine Rye
    • , Hege Russnes
    •  & Anne-Lise Børresen-Dale
  9. K.G. Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway.

    • Inga Hansine Rye
    • , Hege Russnes
    •  & Anne-Lise Børresen-Dale
  10. Department of Pathology, Ninewells Hospital & Medical School, Dundee, UK.

    • Colin A Purdie
    •  & Alastair M Thompson
  11. Pathology Queensland, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia.

    • Sunil R Lakhani
  12. Department of Breast Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Alastair M Thompson
  13. European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridgeshire, UK.

    • Ewan Birney
  14. Department of Pathology, Academic Medical Center, Amsterdam, the Netherlands.

    • Marc J van de Vijver
  15. Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Andrea L Richardson
  16. Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Andrea L Richardson
  17. Department of Pathology, College of Medicine, Hanyang University, Seoul, South Korea.

    • Gu Kong
  18. Equipe Erable, INRIA Grenoble-Rhône-Alpes, Montbonnot-Saint Martin, France.

    • Alain Viari
  19. Synergie Lyon Cancer, Centre Léon Bérard, Lyon, France.

    • Alain Viari
  20. Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.

    • Douglas Easton
  21. Department of Biochemistry, University of Cambridge, Cambridge, UK.

    • Gerard Evan
  22. East Anglian Medical Genetics Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

    • Serena Nik-Zainal

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Contributions

D.G. and S.N.-Z. designed the study, analyzed data and wrote the manuscript. M.R.S., P.J.C., D.E. and G.E. contributed to idea development. D.G. and S.M. performed all statistical analyses. H.D., S.M., J.D.-P., J.S., M.S. and X.Z. performed curation and contributed to analyses. M.S. contributed to curation and analysis of transcriptomic data. Y.L. and L.B.A. contributed to analysis. C.A.P., P.T.S., S.R.L., I.H.R. and H.R. contributed pathology assessment and/or samples and FISH analyses. K.R. contributed IT expertise. A.B.B., A.M.T., E.B., H.G.S., M.J.v.d.V., J.W.M.M., A.-L.B.-D., A.L.R., G.K. and A.V. contributed samples, clinical data collection and intellectual input to the project. All authors discussed the results and commented on the manuscript.

Competing interests

D.G. and S.N.-Z. are inventors on a patent application relating to the use of hotspots as breast and ovarian cancer diagnostics.

Corresponding author

Correspondence to Serena Nik-Zainal.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–12 and Supplementary Note

Excel files

  1. 1.

    Supplementary Table 1

    Hotspots of rearrangement signatures RS1 and RS3 identified through a PCF-based method. (a) Description of headers. (b) Summary of hotspots.

  2. 2.

    Supplementary Table 2

    Genomic consequences of RS1 and RS3 duplications (related to Fig. 4). Numbers of duplications and transections of genomic elements, separately for RS1 and RS3, inside and outside of the hotspots.

  3. 3.

    Supplementary Table 3

    Hotspots of other rearrangement signatures (RS2, RS4, RS5, RS6) identified through PCF-based method. (a) Description of headers. (b) Summary of hotspots.

  4. 4.

    Supplementary Table 4

    Genomic features of the RS1 hotspots. Comparison with the rest of tandem-duplicated genome with respect to: breast cancer susceptibility SNPs, breast tissue super-enhancers, non-breast super-enhancers, known oncogenes, promoters, enhancers, broad fragile sites, narrow fragile sites. (a) Description of headers. (b) Associations.

  5. 5.

    Supplementary Table 5

    Modeling the effects of RS1 tandem duplications on gene expression. Rows, coefficients used in the regression models. Columns, experiments with different sets of genes. In the table we show the fitted values of regression coefficients.

  6. 6.

    Supplementary Table 6

    Hotspots of rearrangement signatures RS1 and RS3 identified through PCF-based method in ovarian tumors. (a) Description of headers. (b) Summary of hotspots.

About this article

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DOI

https://doi.org/10.1038/ng.3771

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