Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

  2. 2.

    Hoadley, K. A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

  3. 3.

    Ahmed, A. A. et al. Driver mutations in TP53 are ubiquitous in high grade serous carcinoma of the ovary. J. Pathol. 221, 49–56 (2010).

  4. 4.

    Vaughan, S. et al. Rethinking ovarian cancer: recommendations for improving outcomes. Nat. Rev. Cancer 11, 719–725 (2011).

  5. 5.

    Fong, P. C. et al. Poly(ADP)-ribose polymerase inhibition: frequent durable responses in BRCA carrier ovarian cancer correlating with platinum-free interval. J. Clin. Oncol. 28, 2512–2519 (2010).

  6. 6.

    Gelmon, K. A. et al. Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: a phase 2, multicentre, open-label, non-randomised study. Lancet Oncol. 12, 852–861 (2011).

  7. 7.

    Swisher, E. M. et al. Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial. Lancet Oncol. 18, 75–87 (2017).

  8. 8.

    The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  9. 9.

    Etemadmoghadam, D. et al. Integrated genome-wide DNA copy number and expression analysis identifies distinct mechanisms of primary chemoresistance in ovarian carcinomas. Clin. Cancer Res. 15, 1417–1427 (2009).

  10. 10.

    Verhaak, R. G. et al. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J. Clin. Invest. 123, 517–525 (2013).

  11. 11.

    Chen, G. M. et al. Consensus on molecular subtypes of ovarian cancer. Preprint at https://biorxiv.org/content/early/2017/07/12/162685 (2017).

  12. 12.

    Patch, A.-M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015).

  13. 13.

    Wang, Y. K. et al. Genomic consequences of aberrant DNA repair mechanisms stratify ovarian cancer histotypes. Nat. Genet. 49, 856–865 (2017).

  14. 14.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  15. 15.

    Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

  16. 16.

    Goranova, T. et al. Safety and utility of image-guided research biopsies in relapsed high-grade serous ovarian carcinoma-experience of the BriTROC consortium. Br. J. Cancer 116, 1294–1301 (2017).

  17. 17.

    Campbell, P. J. et al. Pan-cancer analysis of whole genomes. Preprint at https://biorxiv.org/content/early/2017/07/12/162784 (2017).

  18. 18.

    Murnane, J. P. Telomere dysfunction and chromosome instability. Mutat. Res. 730, 28–36 (2012).

  19. 19.

    Korbel, J. O. & Campbell, P. J. Criteria for inference of chromothripsis in cancer genomes. Cell 152, 1226–1236 (2013).

  20. 20.

    Ng, C. K. et al. The role of tandem duplicator phenotype in tumour evolution in high-grade serous ovarian cancer. J. Pathol. 226, 703–712 (2012).

  21. 21.

    Menghi, F. et al. The tandem duplicator phenotype as a distinct genomic configuration in cancer. Proc. Natl Acad. Sci. USA 113, E2373–E2382 (2016).

  22. 22.

    Lee, M. et al. Comparative analysis of whole genome sequencing-based telomere length measurement techniques. Methods 114, 4–15 (2017).

  23. 23.

    Zakov, S., Kinsella, M. & Bafna, V. An algorithmic approach for breakage–fusion–bridge detection in tumor genomes. Proc. Natl Acad. Sci. USA 110, 5546–5551 (2013).

  24. 24.

    Knauf, J. A. et al. Oncogenic RAS induces accelerated transition through G2/M and promotes defects in the G2 DNA damage and mitotic spindle checkpoints. J. Biol. Chem. 281, 3800–3809 (2006).

  25. 25.

    Saavedra, H. I., Fukasawa, K., Conn, C. W. & Stambrook, P. J. MAPK mediates RAS-induced chromosome instability. J. Biol. Chem. 274, 38083–38090 (1999).

  26. 26.

    Popova, T. et al. Ovarian cancers harboring inactivating mutations in CDK12 display a distinct genomic instability pattern characterized by large tandem duplications. Cancer Res. 76, 1882–1891 (2016).

  27. 27.

    Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

  28. 28.

    Berenjeno, I. M. et al. Oncogenic PIK3CA induces centrosome amplification and tolerance to genome doubling. Nat. Commun. 8, 1773 (2017).

  29. 29.

    Govind, S. K. et al. ShatterProof: operational detection and quantification of chromothripsis. BMC Bioinforma. 15, 78 (2014).

  30. 30.

    Malhotra, A. et al. Breakpoint profiling of 64 cancer genomes reveals numerous complex rearrangements spawned by homology-independent mechanisms. Genome Res. 23, 762–776 (2013).

  31. 31.

    Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472 (2018).

  32. 32.

    Etemadmoghadam, D. et al. Synthetic lethality between CCNE1 amplification and loss of BRCA1. Proc. Natl Acad. Sci. USA 110, 19489–19494 (2013).

  33. 33.

    Candido Dos Reis, F. J. et al. Germline mutation in BRCA1 or BRCA2 and ten-year survival for women diagnosed with epithelial ovarian cancer. Clin. Cancer Res. 21, 652–657 (2015).

  34. 34.

    Norquist, B. M. et al. Mutations in homologous recombination genes and outcomes in ovarian carcinoma patients in GOG 218: an NRG oncology/gynecologic oncology group study. Clin. Cancer Res. 24, 777–783 (2018).

  35. 35.

    Schwarz, R. F. et al. Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Med. 12, e1001789 (2015).

  36. 36.

    Walton, J. B. et al. CRISPR/Cas9-derived models of ovarian high grade serous carcinoma targeting Brca1, Pten and Nf1, and correlation with platinum sensitivity. Sci. Rep. 7, 16827 (2017).

  37. 37.

    Gerstung, M. et al. The evolutionary history of 2,658 cancers. Preprint at https://www.biorxiv.org/content/early/2017/08/30/161562 (2017).

  38. 38.

    Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 486, 346–52 (2012).

  39. 39.

    The Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

  40. 40.

    Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).

  41. 41.

    Piskorz, A. M. et al. Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. Ann. Oncol. 27, 532–539 (2016).

  42. 42.

    Scheinin, I. et al. DNA copy number analysis of fresh and formalin-fixed specimens by shallow whole-genome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res. 24, 2022–2032 (2014).

  43. 43.

    Macintyre, G., Ylstra, B. & Brenton, J. D. Sequencing structural variants in cancer for precision therapeutics. Trends Genet. 32, 530–542 (2016).

  44. 44.

    bcbio-nextgen. https://github.com/bcbio/bcbio-nextgen. Revision 7969a67b (2017).

  45. 45.

    Lai, Z. et al. VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res. 44, e108 (2016).

  46. 46.

    Koboldt, D. C. et al. VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 25, 2283–2285 (2009).

  47. 47.

    Garrison E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at https://arxiv.org/abs/1207.3907 (2012).

  48. 48.

    Jones, D. et al. cgpCaVEManWrapper: simple execution of CaVEMan in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinforma. 56, 15.10.1–15.10.18 (2016).

  49. 49.

    Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

  50. 50.

    Schumacher, S. https://doi.org/10.7303/syn1710464.2 (2015).

  51. 51.

    Van Loo, P. et al. Allele-specific copy number analysis of tumors. Proc. Natl Acad. Sci. USA 107, 16910–16915 (2010).

  52. 52.

    Al-Kateb, H., Nguyen, T. T., Steger-May, K. & Pfeifer, J. D. Identification of major factors associated with failed clinical molecular oncology testing performed by next generation sequencing (NGS). Mol. Oncol. 9, 1737–1743 (2015).

  53. 53.

    Grün, B. & Leisch, F. FlexMix version 2: finite mixtures with concomitant variables and varying and constant parameters. J. Stat. Soft. 28, 1–35 (2008).

  54. 54.

    Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinforma. 11, 367 (2010).

  55. 55.

    Brunet, J.-P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 4164–4169 (2004).

  56. 56.

    Huebschmann, D., Gu, Z. & Schlesner, M. YAPSA: yet another package for signature analysis. R package v.1.2.0 (2015). http://bioconductor.org/packages/YAPSA/

  57. 57.

    Farmery, J. H. R., Smith, M. L., NIHR BioResource - Rare Diseases & Lynch, A. G. Telomerecat: a ploidy-agnostic method for estimating telomere length from whole genome sequencing data. Sci. Rep. 8, 1300 (2018).

  58. 58.

    Kim, S. ppcor: partial and semi-partial (part) correlation. R package v.1.1 https://CRAN.R-project.org/package=ppcor (2015).

  59. 59.

    Gehring, J. S., Fischer, B., Lawrence, M. & Huber, W. SomaticSignatures: inferring mutational signatures from single-nucleotide variants. Bioinformatics 31, 3673–3675 (2015).

  60. 60.

    Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

  61. 61.

    Harrell, F. E. Hmisc: Harrell miscellaneous. R package v.4.0-0 http://cran.r-project.org/web/packages/Hmisc (2016).

  62. 62.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

  63. 63.

    Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).

  64. 64.

    Yu, G. & He, Q. Y. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Mol. Biosyst. 12, 477–479 (2016).

  65. 65.

    Therneau, T. M. & Grambsch, P. M. Modeling Survival Data: Extending the Cox Model. (Springer, New York,NY, 2000).

  66. 66.

    Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. NbClust: an R package for determining the relevant number of clusters in a data set. J. Stat. Soft. 61, 1–36 (2014).

Download references

Acknowledgements

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.

Affiliations

  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

Authors

  1. Search for Geoff Macintyre in:

  2. Search for Teodora E. Goranova in:

  3. Search for Dilrini De Silva in:

  4. Search for Darren Ennis in:

  5. Search for Anna M. Piskorz in:

  6. Search for Matthew Eldridge in:

  7. Search for Daoud Sie in:

  8. Search for Liz-Anne Lewsley in:

  9. Search for Aishah Hanif in:

  10. Search for Cheryl Wilson in:

  11. Search for Suzanne Dowson in:

  12. Search for Rosalind M. Glasspool in:

  13. Search for Michelle Lockley in:

  14. Search for Elly Brockbank in:

  15. Search for Ana Montes in:

  16. Search for Axel Walther in:

  17. Search for Sudha Sundar in:

  18. Search for Richard Edmondson in:

  19. Search for Geoff D. Hall in:

  20. Search for Andrew Clamp in:

  21. Search for Charlie Gourley in:

  22. Search for Marcia Hall in:

  23. Search for Christina Fotopoulou in:

  24. Search for Hani Gabra in:

  25. Search for James Paul in:

  26. Search for Anna Supernat in:

  27. Search for David Millan in:

  28. Search for Aoisha Hoyle in:

  29. Search for Gareth Bryson in:

  30. Search for Craig Nourse in:

  31. Search for Laura Mincarelli in:

  32. Search for Luis Navarro Sanchez in:

  33. Search for Bauke Ylstra in:

  34. Search for Mercedes Jimenez-Linan in:

  35. Search for Luiza Moore in:

  36. Search for Oliver Hofmann in:

  37. Search for Florian Markowetz in:

  38. Search for Iain A. McNeish in:

  39. Search for James D. Brenton in:

Contributions

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

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41588-018-0179-8

Further reading