The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.

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The BriTROC-1 study was funded by Ovarian Cancer Action (to I.A.M. and J.D.B., grant number 006). We acknowledge funding and support from Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694), the Universities of Cambridge and Glasgow, National Institute for Health Research Cambridge and Imperial Biomedical Research Centres, National Cancer Research Network, the Experimental Cancer Medicine Centres at participating sites, the Beatson Endowment Fund and Hutchison Whampoa Limited. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the Biorepository, Bioinformatics, Histopathology and Genomics Core Facilities of the Cancer Research UK Cambridge Institute and the Pathology Core at the Cancer Research UK Beatson Institute for technical support; members of the PCAWG Evolution and Heterogeneity Working Group for the consensus copy number analysis, the PCAWG Structural Variation Working Group for the consensus structural variants and the PCAWG Technical Working Group for annotating driver mutations in the 112 PCAWG-OV samples.

Author information

Author notes

  1. These authors contributed equally: Geoff Macintyre, Teodora E. Goranova.


  1. Cancer Research UK Cambridge Institute, Cambridge, UK

    • Geoff Macintyre
    • , Teodora E. Goranova
    • , Dilrini De Silva
    • , Anna M. Piskorz
    • , Matthew Eldridge
    • , Anna Supernat
    • , Florian Markowetz
    •  & James D. Brenton
  2. Institute of Cancer Sciences, University of Glasgow, Glasgow, UK

    • Darren Ennis
    • , Suzanne Dowson
    • , Craig Nourse
    • , Laura Mincarelli
    • , Luis Navarro Sanchez
    • , Oliver Hofmann
    •  & Iain A. McNeish
  3. VU University Medical Center, Amsterdam, the Netherlands

    • Daoud Sie
    •  & Bauke Ylstra
  4. Cancer Research UK Clinical Trials Unit, Institute of Cancer Sciences, University of Glasgow, Glasgow, UK

    • Liz-Anne Lewsley
    • , Aishah Hanif
    • , Cheryl Wilson
    •  & James Paul
  5. Beatson West of Scotland Cancer Centre, Glasgow, UK

    • Rosalind M. Glasspool
    •  & Iain A. McNeish
  6. Barts Cancer Institute, Queen Mary University of London, London, UK

    • Michelle Lockley
  7. University College London Hospital, London, UK

    • Michelle Lockley
  8. Barts Health NHS Trust, London, UK

    • Elly Brockbank
  9. Guy’s Hospital, London, UK

    • Ana Montes
  10. Bristol Cancer Institute, Bristol, UK

    • Axel Walther
  11. City Hospital, Birmingham, UK

    • Sudha Sundar
  12. Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, St Mary’s Hospital, Manchester, UK

    • Richard Edmondson
  13. Department of Obstetrics and Gynaecology, Manchester Academic Health Science Centre, St Mary’s Hospital, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK

    • Richard Edmondson
  14. St James’s Institute of Oncology, Leeds, UK

    • Geoff D. Hall
  15. The Christie Hospital, Manchester, UK

    • Andrew Clamp
  16. Nicola Murray Centre for Ovarian Cancer Research, MRC IGMM, University of Edinburgh, Edinburgh, UK

    • Charlie Gourley
  17. Mount Vernon Cancer Centre, Northwood, UK

    • Marcia Hall
  18. Division of Cancer and Ovarian Cancer Action Research Centre, Department of Surgery and Cancer, Imperial College, London, UK

    • Christina Fotopoulou
    • , Hani Gabra
    •  & Iain A. McNeish
  19. Early Clinical Development, IMED Biotech Unit, AstraZeneca, Cambridge, UK

    • Hani Gabra
  20. Department of Pathology, Queen Elizabeth University Hospital, Glasgow, UK

    • David Millan
    • , Aoisha Hoyle
    •  & Gareth Bryson
  21. Addenbrooke’s Hospital, Cambridge, UK

    • Mercedes Jimenez-Linan
    • , Luiza Moore
    •  & James D. Brenton
  22. Centre for Cancer Research, University of Melbourne, Melbourne, Victoria, Australia

    • Oliver Hofmann
  23. Department of Oncology, University of Cambridge, Cambridge, UK

    • James D. Brenton


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G.M., T.E.G., F.M., I.A.M. and J.D.B. conceptualized the study; S.D., R.M.G., M.L., E.B., A.M., A.W., S.S., R.E., G.D.H., A.C., C.G., M.H., C.F., H.G., D.M., A.Ho., G.B., I.A.M. and J.D.B. collected samples; T.E.G., D.E., A.M.P., L.-A.L., A.Ha., C.W., C.N., L.Mi., L.N.S., M.J.-L., L.Mo., A.S. and J.P. performed experiments; G.M., T.E.G., D.D.S., M.E., D.S., B.Y., O.H. and F.M. performed data analysis; G.M., D.D.S. and F.M. developed the methodology and software; G.M., T.E.G., D.D.S., F.M., I.A.M. and J.D.B. wrote the manuscript.

Competing interests

The following authors declare competing interests: C.G. has the following personal interests: Roche, AstraZeneca, Tesaro, Clovis, Foundation One, Nucana, received research funding from: AstraZeneca, Novartis, Aprea, Nucana, Tesaro and is a named co-inventor on five patents (issued: PCT/US2012/040805; pending: PCT/GB2013/053202, 1409479.1, 1409476.7 and 1409478.3). H.G. is employed by AstraZeneca. I.A.M. has the following personal interest: Clovis Oncology. J.D.B. is a cofounder and shareholder of Inivata Ltd (a cancer genomics company that commercializes ctDNA analysis). All other authors declare no competing interests.

Corresponding authors

Correspondence to Florian Markowetz or Iain A. McNeish or James D. Brenton.

Integrated supplementary information

  1. Supplementary Figure 1 Sample details and workflow.

    a, REMARK diagram of BriTROC-1 samples. b, Analysis workflow. Copy number signatures were initially derived using high-quality samples from 91 patients and then applied to the samples with intermediate quality from 26 more patients; in total, 199 BriTROC-1 samples had copy number signatures assigned. Copy number signature validation was performed across 527 samples from PCAWG-OV and TCGA. The number of samples analyzed from the three cohorts is shown for each analysis. WGS, whole-genome sequencing; Amp. FBI, amplification-associated fold-back inversion; TDP score, tandem duplicator score.

  2. Supplementary Figure 2 Lines of chemotherapy, relapse and survival for BriTROC-1 cases.

    Cleveland dot plot of treatment periods and overall survival for 105 BriTROC-1 patients with clinical data (survival and treatment) available, ranked by overall survival and platinum-sensitive and platinum-resistant relapse. At study entry, patients were classified as having either platinum-sensitive relapse or platinum-resistant relapse based on the time interval between the last platinum chemotherapy and subsequent relapse. Start and stop dates for line 1 of chemotherapy were missing for two patients.

  3. Supplementary Figure 3 Measures for selecting optimal signature number.

    A comparison of signature number (x axis) across four measures for determining optimal signature number. The circle and solid lines represent the results from the BriTROC-1 samples run, whereas the triangles and dotted lines represent results from 1,000 randomly permuted BriTROC-1 matrices (these can be considered a null measure). Here basis refers to the signature-by-component matrix, coefficients refers to the patient-by-signature matrix, and consensus refers to the connectivity matrix of patients clustered by their dominant signature across 1,000 runs. The best fit is the run that showed the lowest objective score across the 1,000 runs. A value of 7 defines the point of stability in the cophenetic, dispersion and silhouette coefficients and is the maximum sparsity achievable above the null model for the basis matrix.

  4. Supplementary Figure 4 Copy number signature exposures across the three cohorts.

    Comparison of copy number signature exposures in the discovery cohort (BriTROC-1) and two independent cohorts (PCAWG-OV and TCGA). Significant differences are highlighted using asterisks (*P < 0.05, **P < 0.01, ***P < 0.001).

  5. Supplementary Figure 5 Overview of copy number feature distributions.

    Separate density distributions are plotted for each copy number feature across all seven copy number signatures. These were generated using a weighted kernel density estimator in R where the copy number features were weighted by their signature exposures for 117 BriTROC-1 cases. The distributions that have highly weighted components (Fig. 3) for each of the feature distributions are colored.

  6. Supplementary Figure 6 Correlation plots of signature exposures with SNV signatures and other genomic features.

    Feature values and SNV signatures (right) are correlated with copy number signature exposure (top). Blue lines represent a linear model fit, and shading around the lines represents the 95% confidence interval. Shaded panels represent results that are significantly correlated (adjusted P < 0.05). Amp FBI, amplification-associated fold-back inversion; TDP score, tandem duplicator score.

  7. Supplementary Figure 7 Differences in exposure between cases with mutations in the genes versus wild-type cases.

    Box plots represent the copy number signature exposures (right) of cases with mutations (mut) in a given gene (top) versus those with wild-type alleles (wt). Box widths are proportional to the number of cases (exact numbers can be found in Fig. 2). Shaded panels indicate significant differences (adjusted P < 0.05; values found in Supplementary Table 6).

  8. Supplementary Figure 8 Differences in exposures between cases with mutated pathways versus wild-type cases.

    Box plots represent the copy number signature exposures (right) of cases with mutations (mut) in a given pathway (top) versus those with wild-type pathways (wt). Box widths are proportional to the number of cases (exact numbers can be found in Fig. 2). Shaded panels indicate significant differences (adjusted P < 0.05; values found in Supplementary Table 7).

  9. Supplementary Figure 9 Mutated genes in specific pathways.

    Bars represent the number of mutated cases for each gene (left) within each pathway (panels) color-coded by mutation type. AMP, amplification; DEL, deletion.

  10. Supplementary Figure 10 Distribution of signature exposures in three groups identified by unsupervised clustering.

    Panels correspond to the three groups of patients identified by unsupervised clustering of exposure vectors for copy number signatures 1–3 and 7. Exposures for all seven signatures are shown for completeness. Box plots show the exposures of the seven signatures for samples in each group. Group 1 is characterized by mixed exposures. Group 2 has high exposure of copy number signature 1. Group 3 has high exposure of copy number signatures 1 and 3.

  11. Supplementary Figure 11 Ploidy and purity correlation between three-star sWGS and matched dWGS data.

    This figure shows the correlation between ploidy and purity estimates for 34 three-star samples derived from sWGS to those derived from 60× WGS using the Battenberg algorithm for copy number calling.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–11 and Supplementary Tables 1–3, 6, 7 and 9–11

  2. Reporting Summary

  3. Supplementary Table 4

    SNVs in BriTROC-1, PCAWG-OV and TCGA samples

  4. Supplementary Table 5

    Amplifications and deletions in BriTROC-1, PCAWG-OV and TCGA samples

  5. Supplementary Table 8

    Summary of mutated cases by gene in each pathway

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