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Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Nature Geneticsvolume 50pages682692 (2018) | Download Citation


Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials.

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The authors thank those men with prostate cancer and the subjects who have donated their time and their samples to the Cambridge, Oxford, The Institute of Cancer Research, John Hopkins and University of Tampere BioMediTech Biorepositories for this study. We also acknowledge support of the research staff in S4 who so carefully curated the samples and the follow-up data (J. Burge, M. Corcoran, A. George and S. Stearn). We thank M. Stratton for discussions when setting up the CR-UK Prostate Cancer ICGC Project. We acknowledge support from Cancer Research UK C5047/A14835/A22530/A17528, C309/A11566, C368/A6743, A368/A7990, C14303/A17197 (Z.K.-J., S. Merson, N.C., S.E., D.L., T. Dadaev, M.A., E.B., J.B., G.A., P.W., B.A.-L., D.S.B., C.S.C., R.A.E.), the Dallaglio Foundation (CR-UK Prostate Cancer ICGC Project and Pan Prostate Cancer Group), PC-UK/Movember (Z.K.-J.), the NIHR support to The Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust (Z.K.-J., N.D., S. Merson, N.C., S.E., D.L., T. Dadaev, S. Thomas, M.A., E.B., C.F., N.L., D.N., V.K., N.A., P.K., C.O., D.C., A.T., E.M., E.R., T. Dudderidge, S. Hazell, J.B., G.A., P.W., B.A.-L., D.S.B., C.S.C., R.A.E.), Cancer Research UK funding to The Institute of Cancer Research and the Royal Marsden NHS Foundation Trust CRUK Centre, the National Cancer Research Institute (National Institute of Health Research (NIHR) Collaborative Study: “Prostate Cancer: Mechanisms of Progression and Treatment (PROMPT)” (grant G0500966/75466) (D.E.N., V.G.), the Li Ka Shing Foundation (D.C.W., D.J.W.) and the Academy of Finland and Cancer Society of Finland (G.S.B.). We thank the National Institute for Health Research, Hutchison Whampoa Limited, University of Cambridge and the Human Research Tissue Bank (Addenbrooke’s Hospital), which is supported by the NIHR Cambridge Biomedical Research Centre; The Core Facilities at the Cancer Research UK Cambridge Institute, Orchid and Cancer Research UK, D. Holland from the Infrastructure Management Team, and P. Clapham from the Informatics Systems Group at the Wellcome Trust Sanger Institute. D.M.B. is supported by Orchid. C.V.’s academic time was supported by the NIHR Oxford Biomedical Research Centre (Molecular Diagnostics Theme/Multimodal Pathology sub-theme). We also acknowledge support from the Bob Champion Cancer Trust, The Masonic Charitable Foundation successor to The Grand Charity, The King Family and the Stephen Hargrave Trust (C.S.C., D.S.B.). P.W. is a Cancer Research Life Fellow. We acknowledge core facilities provided by CRUK funding to the CRUK ICR Centre, the CRUK Cancer Therapeutics Unit and support for canSAR C35696/A23187 (P.W., G.A.).

Author information

Author notes

  1. Full lists of members and affiliations appear in the Supplementary Note.

  2. These authors contributed equally: David C. Wedge, Gunes Gundem and Thomas Mitchell.

  3. These authors jointly supervised this work: Bissan Al-Lazikani, Paul Workman, Andrew G. Lynch, G. Steven Bova, Christopher S. Foster, Daniel S. Brewer, David E. Neal, Colin S. Cooper and Rosalind A. Eeles.


  1. Oxford Big Data Institute, University of Oxford, Oxford, UK

    • David C. Wedge
    • , Dan J. Woodcock
    •  & Stefan Dentro
  2. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK

    • David C. Wedge
    • , Gunes Gundem
    • , Thomas Mitchell
    • , Inigo Martincorena
    • , Mohammed Ghori
    • , Jorge Zamora
    • , Adam Butler
    • , Ludmil B. Alexandrov
    • , Peter Van Loo
    • , Stefan Dentro
    • , Barbara Kremeyer
    • , Daniel Leongamornlert
    • , Stuart McLaren
    • , Keiran Raine
    • , David Jones
    • , Andrew Menzies
    • , Lucy Stebbings
    • , Jon Teague
    • , Andrew Futreal
    •  & Ultan McDermott
  3. Oxford NIHR Biomedical Research Centre, Oxford, UK

    • David C. Wedge
    •  & Clare Verrill
  4. Memorial Sloan-Kettering Cancer Center, New York, NY, USA

    • Gunes Gundem
    •  & Niedzica Camacho
  5. Department of Urology, Addenbrooke’s Hospital, Cambridge, UK

    • Thomas Mitchell
    • , Vincent Gnanapragasam
    •  & Nimish C. Shah
  6. Uro-Oncology Research Group, Cancer Research UK, Cambridge Institute, Cambridge, UK

    • Thomas Mitchell
    • , Charlie E. Massie
    • , Steve Hawkins
    •  & David E. Neal
  7. Molecular Diagnostics and Therapeutics Group, University College London, London, UK

    • Hayley Whitaker
    • , Jonathan Kay
    •  & Hayley Luxton
  8. The Institute of Cancer Research, London, UK

    • Zsofia Kote-Jarai
    • , Sue Merson
    • , Niedzica Camacho
    • , Sandra Edwards
    • , Daniel Leongamornlert
    • , Tokhir Dadaev
    • , Mahbubl Ahmed
    • , Elizabeth Bancroft
    • , Johann de Bono
    • , Gerhardt Attard
    • , Paul Workman
    • , Bissan Al-Lazikani
    • , Daniel S. Brewer
    • , Colin S. Cooper
    •  & Rosalind A. Eeles
  9. Cancer Genomics, The Francis Crick Institute, London, UK

    • Peter Van Loo
    •  & Stefan Dentro
  10. Early Detection Programme, Cancer Research UK Cambridge Centre, Department of Oncology, University of Cambridge, Cambridge, UK

    • Charlie E. Massie
  11. Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

    • Anne Y. Warren
    •  & William Howat
  12. Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK

    • Clare Verrill
    • , Adam Lambert
    • , Katalin Karaszi
    • , Luke Marsden
    • , Lucy Matthews
    • , Pelvender Gill
    •  & Freddie C. Hamdy
  13. Centre for Molecular Oncology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    • Dan M. Berney
    •  & Yong-Jie Lu
  14. Royal Marsden NHS Foundation Trust, London and Sutton, UK

    • Nening Dennis
    • , Sarah Thomas
    • , Elizabeth Bancroft
    • , Cyril Fisher
    • , Naomi Livni
    • , David Nicol
    • , Vincent Khoo
    • , Nicholas Van As
    • , Pardeep Kumar
    • , Christopher Ogden
    • , Declan Cahill
    • , Alan Thompson
    • , Erik Mayer
    • , Edward Rowe
    • , Tim Dudderidge
    • , Steven Hazell
    •  & Rosalind A. Eeles
  15. Statistics and Computational Biology Laboratory, Cancer Research UK Cambridge Institute, Cambridge, UK

    • Valeria Bo
    • , Simon Tavaré
    •  & Andrew G. Lynch
  16. The Chinese University of Hong Kong, Shatin, Hong Kong, China

    • Anthony Ng
  17. Second Military Medical University, Shanghai, China

    • Yongwei Yu
    •  & Hongwei Zhang
  18. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK

    • Douglas F. Easton
  19. Norwich Medical School, University of East Anglia, Norwich, UK

    • Christopher Greenman
    • , Daniel S. Brewer
    •  & Colin S. Cooper
  20. Department of Surgical Oncology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK

    • Vincent Gnanapragasam
    •  & David E. Neal
  21. St George’s Healthcare NHS Trust, London, UK

    • Cathy Corbishley
  22. Johns Hopkins School of Medicine, Baltimore, MD, USA

    • William Isaacs
    •  & G. Steven Bova
  23. Institute of Biosciences and Medical Technology, BioMediTech, University of Tampere and Fimlab Laboratories, Tampere University Hospital, Tampere, Finland

    • Tapio Visakorpi
    •  & G. Steven Bova
  24. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    • Michael Fraser
    •  & Robert G. Bristow
  25. Ontario Institute for Cancer Research, Toronto, ON, Canada

    • Paul C. Boutros
  26. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    • Paul C. Boutros
    •  & Robert G. Bristow
  27. Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada

    • Paul C. Boutros
  28. Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada

    • Robert G. Bristow
  29. cBio Center, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA

    • Chris Sander
  30. School of Mathematics and Statistics/School of Medicine, University of St. Andrews, Fife, UK

    • Andrew G. Lynch
  31. University of Liverpool, Liverpool, UK

    • Christopher S. Foster
  32. HCA Laboratories, London, UK

    • Christopher S. Foster
  33. Earlham Institute, Norwich, UK

    • Daniel S. Brewer


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  1. CAMCAP Study Group

    1. The TCGA Consortium


      R.A.E., C.S.C., D.E.N., D.S.B., C.S.F., G.S.B., A.G.L., P.W., B.A.-L., D.C.W., F.C.H. and D.F.E. designed the study. R.A.E., C.S.C., D.C.W. and D.S.B. wrote the paper. M.F., P.C.B., R.G.B. and all other authors contributed to revisions. Z.K.-J., H.W., C.E.M., D.E.N., V.G., A.G.L., R.A.E., F.C.H., G.S.B., A.Y.W., C.S.F., C.V., D.M.B., N.D., S. Merson, S. Hawkins, W.H., Y.-J.L., A.L., J.K., K.K., H.L., L. Marsden, S.E., L. Matthews, A.N., Y.Y., H.Z., S.T., E.B., C.F., N.L., S. Hazell, D.N., P.G., V.K., N.V.A., P.K., C.O., D.C., A.T., E.M., E.R., T. Dudderidge, C.C., W.I., T.V. and N.C.S. coordinated sample collection, pathology review and processing. D.C.W., G.G., T.M., I.M., D.J.W., D.S.B., M.G., J.Z., A.B., L.B.A., S.D., B.K., N.C., V.B., D.L., S. McLaren, T. Dadaev, M.A., S.T., C.G., K.R., D.J., A.M., L.S., J.T., A.F., P.V.L. and U.M. supported, directed and performed the analyses. C.S. and the TCGA, J.d.B. and G.A. provided data for the meta-analysis. D.F.E., A.G.L., G.S.B., C.S.F., D.S.B., D.E.N., C.S.C. and R.A.E. are joint principal investigators for the CR-UK Prostate Cancer ICGC Project.

      Competing interests

      The authors declare no competing interests.

      Corresponding authors

      Correspondence to David C. Wedge or Rosalind A. Eeles.

      Integrated supplementary information

      1. Supplementary Figure 1 RNA expression of novel driver genes.

        Data are taken from the CamCaP study, which includes 40 samples from this study. All genes identified as novel within this study and bearing structural variants in at least two samples are shown. P-values are from two-sided Wilcoxon rank sum test.

      2. Supplementary Figure 2 Heterogeneity of point substitutions and CNAs.

        Each dot represents a different sample, colored by sample type. The x-axis shows the amount of each genome that has subclonal CNA divided by the total amount of each genome that is copy-number aberrant, and the y-axis shows the fraction of point substitutions that are subclonal. Contour lines were calculated using R package kde2d.

      3. Supplementary Figure 3 Multiple driver mutations in APC.

        (a) Mutations in the APC gene in PD14713a are mutually exclusive, indicating that they have occurred in separate subclonal populations. Each blue or yellow string represents a forward or backward read, with somatic variants shown in red. Mutations in the left-hand and right-hand red-boxed regions never occur on the same reads. (b) PD14713a is haploid on chromosome 5q, so the mutual exclusivity of APC mutations cannot be explained by occurrence in different chromosome copies. Purple lines show total copy number, and blue lines show minor allele specific copy number, called by the Battenberg algorithm.

      4. Supplementary Figure 4 Prognostic biomarkers.

        (a) Kaplan-Meier plot of the number of mutational processes detected vs. time to biochemical recurrence (n = 89 independent prostate cancer patients who had a prostatectomy; all primary TURP samples (3) and metastatic samples (20) were excluded from this analysis). Two-sided log rank test P value is indicated. (b) Correlation between the number of processes detected and the number of substitutions detected. There was a significant positive correlation (r = 0.31, P = 0.0027; two-sided Spearman’s correlation, n = 10, 42, 25, 10, and 2 biologically independent samples for 1, 2, 3, 4, and 5 mutational processes detected, respectively.). The number of mutational signatures identified in a cancer was negatively correlated with time to biochemical recurrence in prostatectomy patients (P = 0.014, HR = 3.0; Cox proportional hazards model on number of processes greater than three). The number of substitutions detected was also an independent prognostic biomarker (P = 0.031, HR = 1.0005; Cox proportional hazards model).

      5. Supplementary Figure 5 Analysis pipeline to identify the prostate disease network.

        Mutated proteins do not function in isolation but rather through interacting within pathways and complex networks which propagate the disease causing effect. These wider pathways and networks can provide additional avenues for therapy. To identify the prostate cancer disease network, we performed the following steps. The 71 protein products of the 73 genes identified in the study were used to seed a search in the canSAR interactome to identify direct protein-protein interactions, yielding a much larger list B. The proteins in list B were then individually expanded and only proteins enriched for interaction with the prostate disease proteins retained (see Online Methods for statistical detail). Iterative culling of the network, retaining only the most significant intreractors, finally yielded 156 proteins that are either prostate proteins or proteins that significantly interact with them. This list was then submitted to canSAR druggability and actionability analysis as described in the Online Methods.

      6. Supplementary Figure 6 dN/dS analysis.

        QQ plot of the P-values derived from dN/dS analysis shows no sign of inflation. Four genes (of 20,184) had P-values = 0 and are not shown on this plot.

      Supplementary information

      1. Supplementary Text and Figures

        Supplementary Figures 1–6 and Supplementary Note

      2. Reporting Summary

      3. Supplementary Table 1

        Summary of sample specific genetic aberrations. Worksheet 1 reports the number of genetic aberrations identified in each gene this study, separated by type: essential splice site, frameshift, inframe, missense, nonsense, silent, stop-lost, homozygous deletion, rearrangement (n = 112 biologically independent samples). We also report the number of samples with multiple hits resulting in homozygous loss. Worksheet 2 reports the number of genetic aberrations identified in each gene across the joint dataset (n = 930 biologically independent samples). Worksheet 3 reports the samples within our dataset that bear homozygous losses in each gene. Worksheet 4 contains results of dNdScv analysis to identify coding drivers. Novel drivers are shown with red text. Significant q-values (FDR < 0.1, Benjamini- Hochberg) from analyzing either missense SNVs or all SNVs and indels are shown with green shading. Effect sizes (dN/dS values) are reported separately for missense, nonsense, essential splice site and indel variants (n = 930 biologically independent samples). Worksheet 5 contains results of NBR analysis to identify noncoding drivers in samples within this study. Significant regions (FDR < 0.1, Benjamini-Hochberg) are shown with blue/purple shading (n = 112 biologically independent samples).

      4. Supplementary Table 2

        Classification of driver genes. Genes were identified in our study using several methods, detailed in the last column: dN/dS; enrichment for SVs or CNAs in ETS+ or ETS- cancers; enrichment for truncating mutations or homozygous deletions, clinical correlation. From a PubMed literature search, prior evidence for each gene being a driver of prostate cancer was classified as ‘low’ if the gene has not been previously reported as playing a role in prostate cancer tumorigenesis or progression. Prior evidence was classified as ‘medium’ for genes reported previously as playing a role in prostate carcinogenesis or progression but currently lacking statistical support based on genetic alterations. Evidence considered included presence of multiple genetic alterations, SNP associations, and known cancer genes in other tissues. The high confidence genes are those that are widely accepted to represent cancer genes and to be altered in prostate cancer. In each case, there are two or more of the following: statistical verification of higher incidence, biological experiments, clinical correlations, confirmation in multiple studies, recognition as cancer genes in other cancer types. In the last column, novel driver genes are classified as ‘tumor suppressor’ or ‘oncogene’ based on the predominant functional effect of the observed mutations. dN/dS, non-synonymous: synonymous ratio, calculated for all SNVs and indels; dN/dS (missense), non-synonymous: synonymous ratio calculated for missense SNVs only; SV, structural variant; CNA, copy number aberration; SNV, single nucleotide variant; indel, small insertion/deletion; ETS, E26 transformation-specific.

      5. Supplementary Table 3

        Mutational signature analysis. For each sample, the number of somatic mutations attributed to each signature is shown in worksheet 1. Worksheet 2 reports the number of mutations assigned to each signature, broken down into temporal categories (early, late, clonal, subclonal) as described in Online Methods

      6. Supplementary Table 4

        Survival analyses. Cox regression model results for the presence of recurrent in genes with time to biochemical recurrence after prostatectomy as endpoint. Multivariate analysis was performed taking into account cofactors Gleason (6–9), PSA at prostatectomy, and pathological T- stage (T2, T3). Clinical information was available for 89 prostatectomy samples with WGS data, with a median follow up of 1,108 days in which biochemical recurrence occurred in 26 patients. Red background shading indicates features that have a significant association with outcome in both univariate analyses after Benjamini-Hochberg multiple testing correction and multivariate analyses. Dark grey shading indicates features that are only significant without multiple testing correction. Light grey shading indicates features that are significant in univariate but not in multivariate analyses.

      7. Supplementary Table 5

        Druggability analysis. Genes identified using the CanSAR software. Genes are colorcoded: bright green, target of an approved drug; dark green, target of an investigational drug; yellow, target that is being investigated chemically; red, no chemical information in public databases but predicted to be druggable using our structure-based method.

      8. Supplementary Table 6

        Drug sensitivity data. Of the drugs identified through CanSAR analysis, 18 are reported in the Genomics of Drug Sensitivity in Cancer database. Of these, 5 showed significant effect on growth inhibition, and the remaining 13 drugs showed weak activity in at least one cell line.

      9. Supplementary Table 7

        Clinical and molecular details of cancers subject to DNA sequencing. Clinical characteristics of each participant are reported, including age, Gleason score, T stage, PSA and primary/metastatic. The order of samples in Fig. 1 and Fig. 2 are in the columns fig1_order and fig2_order.

      10. Supplementary Table 8

        Details of samples included and excluded from the subclonal analysis

      11. Supplementary Table 9

        Software versions used to align genomes and call variants

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