Whole genomes redefine the mutational landscape of pancreatic cancer

Journal name:
Nature
Volume:
518,
Pages:
495–501
Date published:
DOI:
doi:10.1038/nature14169
Received
Accepted
Published online

Abstract

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

At a glance

Figures

  1. Mutations in key genes and pathways in pancreatic cancer.
    Figure 1: Mutations in key genes and pathways in pancreatic cancer.

    The upper panel shows non-silent single nucleotide variants and small insertions or deletions. The central matrix shows: non-silent mutations (blue), copy number changes (amplification (>5 copies) represented in red and loss represented in green) and genes affected by structural variants (SV, yellow). Pathogenic germline variants are highlighted with asterisk (

  2. Subtypes of pancreatic cancer.
    Figure 2: Subtypes of pancreatic cancer.

    a, Subgroups of PDAC based on the frequency and distribution of structural rearrangements. Representative tumours of each group are shown. The coloured outer rings are chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency (proportion of the B allele to the total quantity of both alleles). The inner lines depict chromosome structural rearrangements. b, The contribution of the BRCA mutational signature within each tumour ranked by prevalence (red bars). Unstable tumours are associated with a high BRCA mutation signature and deleterious mutations in BRCA pathway genes. The dagger () symbol indicates predicted only as possibly damaging by Polyphen2.

  3. Putative biomarkers of platinum and PARP inhibitor responsiveness.
    Figure 3: Putative biomarkers of platinum and PARP inhibitor responsiveness.

    a, A Venn diagram showing the overlap of surrogate measures of defects in DNA maintenance (unstable genomes and BRCA mutational signature), with mutations in BRCA pathway genes. Of a total of 24 patients (24%), 10 have both unstable genomes and the BRCA mutational signature. The majority of patients with mutations in BRCA pathway genes (9) are within this intersect, however 2 have the mutational signature, but are classified either as scattered (n = 1) or locally rearranged (n = 1). GL, germline; S, somatic. b, Individual tumours are ranked based on their BRCA mutational signature burden, with the diameter of each circle representing the number of structural variants in each. Those encircled by a solid line have mutations in BRCA pathway genes. Responders and non-responders to platinum-based therapy are indicated with solid lines for patients and broken lines for patient-derived xenografts (PDX).

  4. Responses to platinum therapy.
    Figure 4: Responses to platinum therapy.

    a, ICGC_0006: one of two patients who had an exceptional response to platinum-based therapy. Treatment of the recurrence with FOLFOX resulted in an exceptional response with recanalization of the portal vein which was previously obliterated by tumour and resolution of the mass with complete normalization of CA19.9 levels. b, Platinum responsiveness in patient-derived xenografts. Curves represent relative tumour volume (0.5 × length × width2, y axis) over time (days, x axis). Arrows indicate drug treatment. Responses remain stable for >210 days. Error bars indicate standard error of the mean.

  5. Summary of structural rearrangements.
    Extended Data Fig. 1: Summary of structural rearrangements.

    a, Histogram showing the number of events verified in silico or by orthogonal sequencing methods (Methods). In total 7,228 of the 11,868 events identified (61%) were verified, the others remain untested. These included 5,666 events which contained multiple lines of evidence (qSV category 1: discordant pairs, soft clipping on both sides and split read evidence, Methods) thus were considered verified. Of these events 2,463 events were also verified by orthogonal sequencing methods (SOLiD long mate pair or PCR amplicon sequencing) or the event was associated with a copy number change which was determined using SNP arrays. The remaining 1,562 events were verified using orthogonal sequencing methods or the event was associated with a copy number change (qSV category 2 and 3, Methods). b, Histogram showing the number of structural rearrangements in each pancreatic cancer. 100 PDACs were sequenced using HiSeq paired-end whole-genome sequencing. Structural rearrangements were identified and classified into 8 categories (deletions, duplications, tandem duplications, foldback inversions, amplified inversions, inversions, intra-chromosomal and inter-chromosomal translocations, Methods). The number and type of event for each patient is shown. PDAC shows a high degree of heterogeneity in both the number and types of events per patient. The structural rearrangements were used to classify the tumours into four categories (stable, locally rearranged, scattered and unstable, Methods).

  6. Distribution of structural variant breakpoints within each patient.
    Extended Data Fig. 2: Distribution of structural variant breakpoints within each patient.

    The 100 patients are plotted along the x axis. The upper plot shows the number of structural rearrangements (y axis) in each tumour. The lower plot shows which chromosomes (y axis) harbour clusters of breakpoints. The distribution of breakpoints (events per Mb) within each chromosome for each sample was evaluated using two methods to identify clusters of rearrangements or chromosomes which contain a large number of events. Method 1: chromosomes with a significant cluster of events were determined by a goodness-of-fit test against the expected exponential distribution (with a significance threshold of <0.0001). Chromosomes which pass these criteria are coloured blue. Method 2: chromosomes were identified which contain significantly more events per Mb than other chromosomes for that patient. Chromosomes were deemed to harbour a high number of events if they had a mutation rate per Mb which exceeds 1.5 times the length of the interquartile range from the 75th percentile of the chromosome counts for each patient. Chromosomes which pass these criteria are coloured orange. Chromosomes which pass both tests they are coloured red. These criteria show that the unstable tumours which contain many events often have significant clusters of events. In contrast locally rearranged tumours are associated with both clusters of events and a high number of events within that chromosome when compared to other chromosomes.

  7. The stable subtype in pancreatic ductal adenocarcinoma.
    Extended Data Fig. 3: The stable subtype in pancreatic ductal adenocarcinoma.

    The 20 stable tumours are shown using circos. The coloured outer ring represents the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired sequencing and the legend indicates the type of rearrangement. Stable tumours contained less than 50 structural rearrangements in each tumour.

  8. The locally rearranged subtype in pancreatic ductal adenocarcinoma.
    Extended Data Fig. 4: The locally rearranged subtype in pancreatic ductal adenocarcinoma.

    The 30 locally rearranged tumours are shown using circos. The coloured outer rings represent the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole-genome paired sequencing and the legend indicates the type of rearrangement. In the locally rearranged subtype over 25% of the structural rearrangements are clustered on one of few chromosomes.

  9. Example of evidence for chromothripsis in a pancreatic ductal adenocarcinoma (ICGC_0109).
    Extended Data Fig. 5: Example of evidence for chromothripsis in a pancreatic ductal adenocarcinoma (ICGC_0109).

    Upper plot is a density plot showing a concentration of break-points on chromosome 5. Next panel shows the structural rearrangements which are coloured as presented in the legend. The lower panels show copy number, logR ratio and B allele frequency derived from SNP arrays. This chromosome showed a complex localization of events similar to chromothripsis. Copy number profile and structural rearrangements suggest a shattering of chromosome 5 with a high concentration of structural rearrangements, switches in copy number state and retention of heterozygosity, which are characteristics of a chromothriptic event.

  10. Example of evidence for breakage-fusion-bridge (BFB) in a pancreatic ductal adenocarcinoma (ICGC_0042).
    Extended Data Fig. 6: Example of evidence for breakage-fusion-bridge (BFB) in a pancreatic ductal adenocarcinoma (ICGC_0042).

    Upper plot is a density plot showing a concentration of break-points on chromosome 5. Next panel shows the structural rearrangements which are coloured as presented in the legend. The lower panels show copy number, logR ratio and B allele frequency derived from SNP arrays. This chromosome showed a complex localization of events similar to BFB. Copy number profile suggests loss of telomeric q arm and a high concentration of structural rearrangements suggesting a series of BFB cycles, with multiple inversions mapped to the amplified regions.

  11. The scattered subtype in pancreatic ductal adenocarcinoma.
    Extended Data Fig. 7: The scattered subtype in pancreatic ductal adenocarcinoma.

    The 36 tumours classified as scattered are shown using circos. The coloured outer rings represent the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next shows the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired end sequencing. The legend indicates the type of rearrangement. The scattered tumours contained 50–200 structural rearrangements in each tumour.

  12. The unstable subtype in pancreatic ductal adenocarcinoma.
    Extended Data Fig. 8: The unstable subtype in pancreatic ductal adenocarcinoma.

    The 14 unstable tumours are shown using circos. The coloured outer rings are chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired sequencing and the legend indicates the type of rearrangement. The unstable tumours contained a large degree of genomic instability and harboured over 200 structural rearrangements in each tumour which were predominantly intra-chromosomal rearrangements evenly distributed through the genome.

  13. RAD51 foci formation in a primary culture of genomically unstable PDAC.
    Extended Data Fig. 9: RAD51 foci formation in a primary culture of genomically unstable PDAC.

    a, RAD51 and geminin fluorescence in untreated cells derived from an unstable pancreatic tumour with a somatic mutation in the RPA1 gene (ICGC_0016). Primary culture of ICGC_0016 consists of eGFP+ mouse stromal and eGFP tumour cells. b, Upper panel: irradiated unstable pancreatic cancer cells (ICGC_0016), middle panel: HR-competent (TKCC-07) and lower panel: HR-deficient (Capan-1) pancreatic tumour cells. Cells were irradiated in vitro with 10Gy, and 6 h post-irradiation examined by immunofluorescence microscopy. eGFP negative tumour cells from ICGC_0016 readily form RAD51 foci following induction of DNA damage. TKCC-07 is a pancreas cancer cell line generated from a homologous recombination (HR) pathway competent patient-derived xenograft and served as a positive control for staining and RAD51 foci formation after DNA damage. Capan-1 cells which are HR-deficient do not form RAD51 foci. c, RAD51 score (percentage of geminin positive cells that have RAD51 foci) in examined pancreatic tumour cells.

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Gene Expression Omnibus

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Author information

  1. These authors jointly supervised this work.

    • Andrew V. Biankin &
    • Sean M. Grimmond
  2. Deceased.

    • Robert L. Sutherland

Affiliations

  1. Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia

    • Nicola Waddell,
    • Ann-Marie Patch,
    • Karin S. Kassahn,
    • Peter Bailey,
    • David Miller,
    • Katia Nones,
    • Kelly Quek,
    • Michael C. J. Quinn,
    • Alan J. Robertson,
    • Muhammad Z. H. Fadlullah,
    • Tim J. C. Bruxner,
    • Angelika N. Christ,
    • Ivon Harliwong,
    • Senel Idrisoglu,
    • Suzanne Manning,
    • Craig Nourse,
    • Ehsan Nourbakhsh,
    • Shivangi Wani,
    • Peter J. Wilson,
    • Emma Markham,
    • Nicole Cloonan,
    • Matthew J. Anderson,
    • J. Lynn Fink,
    • Oliver Holmes,
    • Stephen H. Kazakoff,
    • Conrad Leonard,
    • Felicity Newell,
    • Barsha Poudel,
    • Sarah Song,
    • Darrin Taylor,
    • Nick Waddell,
    • Scott Wood,
    • Qinying Xu,
    • John V. Pearson &
    • Sean M. Grimmond
  2. QIMR Berghofer Medical Research Institute, Herston Road, Brisbane 4006, Australia

    • Nicola Waddell,
    • Nicole Cloonan &
    • John V. Pearson
  3. The Kinghorn Cancer Centre, Cancer Division, Garvan Institute of Medical Research, University of New South Wales, 384 Victoria St, Darlinghurst, Sydney, New South Wales 2010, Australia

    • Marina Pajic,
    • David K. Chang,
    • Amber L. Johns,
    • Jianmin Wu,
    • Mark Pinese,
    • Mark J. Cowley,
    • Hong C. Lee,
    • Marc D. Jones,
    • Adnan M. Nagrial,
    • Jeremy Humphris,
    • Lorraine A. Chantrill,
    • Venessa Chin,
    • Angela M. Steinmann,
    • Amanda Mawson,
    • Emily S. Humphrey,
    • Emily K. Colvin,
    • Angela Chou,
    • Christopher J. Scarlett,
    • Andreia V. Pinho,
    • Marc Giry-Laterriere,
    • Ilse Rooman,
    • James G. Kench,
    • Jessica A. Pettitt,
    • Christopher Toon,
    • Anthony J. Gill &
    • Andrew V. Biankin
  4. St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, New South Wales 2010, Australia

    • Marina Pajic
  5. Department of Surgery, Bankstown Hospital, Eldridge Road, Bankstown, Sydney, New South Wales 2200, Australia

    • David K. Chang,
    • Neil D. Merrett &
    • Andrew V. Biankin
  6. South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, New South Wales 2170, Australia

    • David K. Chang &
    • Andrew V. Biankin
  7. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, Glasgow G61 1BD, UK

    • David K. Chang,
    • Peter Bailey,
    • Craig Nourse,
    • Marc D. Jones,
    • Nigel B. Jamieson,
    • Janet S. Graham,
    • Elizabeth A. Musgrove,
    • Andrew V. Biankin &
    • Sean M. Grimmond
  8. Department of Anatomical Pathology, St Vincent’s Hospital, Sydney, New South Wales 2010, Australia

    • Angela Chou
  9. School of Environmental & Life Sciences, University of Newcastle, Ourimbah, New South Wales 2258, Australia

    • Christopher J. Scarlett
  10. Department of Surgery, Royal North Shore Hospital, St Leonards, Sydney, New South Wales 2065, Australia

    • Jaswinder S. Samra
  11. University of Sydney, Sydney, New South Wales 2006, Australia

    • Jaswinder S. Samra,
    • James G. Kench &
    • Anthony J. Gill
  12. Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Camperdown, New South Wales 2050, Australia

    • James G. Kench
  13. School of Medicine, University of Western Sydney, Penrith, New South Wales 2175, Australia

    • Neil D. Merrett
  14. Department of Surgery, Fremantle Hospital, Alma Street, Fremantle, Western Australia 6160, Australia

    • Krishna Epari
  15. Department of Gastroenterology, Royal Adelaide Hospital, North Terrace, Adelaide, South Australia 5000, Australia

    • Nam Q. Nguyen
  16. Department of Surgery, Princess Alexandra Hospital, Ipswich Rd, Woollongabba, Queensland 4102, Australia

    • Andrew Barbour
  17. School of Surgery M507, University of Western Australia, 35 Stirling Highway, Nedlands 6009, Australia

    • Nikolajs Zeps
  18. St John of God Pathology, 12 Salvado Rd, Subiaco, Western Australia 6008, Australia

    • Nikolajs Zeps
  19. Bendat Family Comprehensive Cancer Centre, St John of God Subiaco Hospital, Subiaco, Western Australia 6008, Australia

    • Nikolajs Zeps
  20. Academic Unit of Surgery, School of Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow Royal Infirmary, Glasgow G4 OSF, UK

    • Nigel B. Jamieson
  21. West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow G31 2ER, UK

    • Nigel B. Jamieson
  22. Department of Medical Oncology, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow G12 0YN, UK

    • Janet S. Graham
  23. Norlux Neuro-Oncology Laboratory, CRP-Santé Luxembourg, 84 Val Fleuri, L-1526, Luxembourg

    • Simone P. Niclou
  24. Norlux Neuro-Oncology, Department of Biomedicine, University of Bergen, Jonas Lies vei 91, N-5019 Bergen, Norway

    • Rolf Bjerkvig
  25. Departments of Surgery and Pathology, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany

    • Robert Grützmann,
    • Daniela Aust &
    • Christian Pilarsky
  26. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA

    • Ralph H. Hruban,
    • Richard A. Morgan &
    • James R. Eshleman
  27. Departments of Pathology and Translational Molecular Pathology, University of Texas MD Anderson Cancer Center, Houston Texas 77030, USA

    • Anirban Maitra
  28. The David M. Rubenstein Pancreatic Cancer Research Center and Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA

    • Christine A. Iacobuzio-Donahue
  29. Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, the Johns Hopkins University School of Medicine, Baltimore, Maryland 21231, USA

    • Christopher L. Wolfgang
  30. ARC-NET Centre for Applied Research on Cancer, University and Hospital Trust of Verona, Verona 37134, Italy

    • Rita T. Lawlor,
    • Vincenzo Corbo &
    • Aldo Scarpa
  31. Department of Pathology and Diagnostics, University of Verona, Verona 37134, Italy

    • Rita T. Lawlor,
    • Giuseppe Zamboni &
    • Aldo Scarpa
  32. Department of Surgery and Oncology, Pancreas Institute, University and Hospital Trust of Verona, Verona 37134, Italy

    • Claudio Bassi &
    • Massimo Falconi
  33. Departments of Surgery and Pathology, Ospedale Sacro Cuore Don Calabria Negrar, Verona 37024, Italy

    • Massimo Falconi &
    • Giuseppe Zamboni
  34. Department of Oncology, University and Hospital Trust of Verona, Verona 37134, Italy

    • Giampaolo Tortora
  35. Division of Hematology and Oncology, University of California, San Francisco, California 94122, USA

    • Margaret A. Tempero
  36. The Kinghorn Cancer Centre, Garvan Institute of Medical Research, 370 Victoria Street, Darlinghurst, Sydney, New South Wales 2010, Australia.

    • Andrew V. Biankin,
    • Amber L. Johns,
    • Amanda Mawson,
    • David K. Chang,
    • Christopher J. Scarlett,
    • Mary-Anne L. Brancato,
    • Sarah J. Rowe,
    • Skye H. Simpson,
    • Mona Martyn-Smith,
    • Michelle T. Thomas,
    • Lorraine A. Chantrill,
    • Venessa T. Chin,
    • Angela Chou,
    • Mark J. Cowley,
    • Jeremy L. Humphris,
    • Marc D. Jones,
    • R. Scott Mead,
    • Adnan M. Nagrial,
    • Marina Pajic,
    • Jessica Pettit,
    • Mark Pinese,
    • Ilse Rooman,
    • Jianmin Wu,
    • Jiang Tao,
    • Renee DiPietro,
    • Clare Watson,
    • Angela Steinmann,
    • Hong Ching Lee,
    • Rachel Wong,
    • Andreia V. Pinho,
    • Marc Giry-Laterriere,
    • Roger J. Daly,
    • Elizabeth A. Musgrove &
    • Robert L. Sutherland
  37. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, Glasgow, Scotland G61 1BD, UK.

    • Andrew V. Biankin,
    • David K. Chang,
    • Marc D. Jones,
    • Sean M. Grimmond,
    • Elizabeth A. Musgrove,
    • Craig Nourse,
    • Nigel B. Jamieson,
    • Janet S. Graham &
    • Karen Oien
  38. Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, University of Queensland, St Lucia, Queensland 4072, Australia.

    • Sean M. Grimmond,
    • Nicola Waddell,
    • Karin S. Kassahn,
    • David K. Miller,
    • Peter J. Wilson,
    • Ann-Marie Patch,
    • Sarah Song,
    • Ivon Harliwong,
    • Senel Idrisoglu,
    • Craig Nourse,
    • Ehsan Nourbakhsh,
    • Suzanne Manning,
    • Shivangi Wani,
    • Milena Gongora,
    • Matthew Anderson,
    • Oliver Holmes,
    • Conrad Leonard,
    • Darrin Taylor,
    • Scott Wood,
    • Christina Xu,
    • Katia Nones,
    • J. Lynn Fink,
    • Angelika Christ,
    • Tim Bruxner,
    • Nicole Cloonan,
    • Felicity Newell,
    • John V. Pearson,
    • Peter Bailey,
    • Michael Quinn,
    • Shivashankar Nagaraj,
    • Stephen Kazakoff,
    • Nick Waddell,
    • Keerthana Krisnan,
    • Kelly Quek,
    • David Wood &
    • Muhammad Z. H. Fadlullah
  39. Royal North Shore Hospital, Westbourne Street, St Leonards, New South Wales 2065, Australia.

    • Jaswinder S. Samra,
    • Anthony J. Gill,
    • Nick Pavlakis,
    • Alex Guminski &
    • Christopher Toon
  40. Bankstown Hospital, Eldridge Road, Bankstown, New South Wales 2200, Australia.

    • Ray Asghari,
    • Neil D. Merrett,
    • Darren Pavey &
    • Amitabha Das
  41. Liverpool Hospital, Elizabeth Street, Liverpool, New South Wales 2170, Australia.

    • Peter H. Cosman,
    • Kasim Ismail &
    • Chelsie O’Connnor
  42. Westmead Hospital, Hawkesbury and Darcy Roads, Westmead, New South Wales 2145, Australia.

    • Vincent W. Lam Duncan McLeod,
    • Henry C. Pleass,
    • Arthur Richardson &
    • Virginia James
  43. Royal Prince Alfred Hospital, Missenden Road, Camperdown, New South Wales 2050, Australia.

    • James G. Kench,
    • Caroline L. Cooper,
    • David Joseph,
    • Charbel Sandroussi,
    • Michael Crawford &
    • James Gallagher
  44. Fremantle Hospital, Alma Street, Fremantle, Western Australia 6959, Australia.

    • Michael Texler,
    • Cindy Forest,
    • Andrew Laycock,
    • Krishna P. Epari,
    • Mo Ballal,
    • David R. Fletcher &
    • Sanjay Mukhedkar
  45. Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia 6009, Australia.

    • Nigel A. Spry,
    • Bastiaan DeBoer &
    • Ming Chai
  46. St John of God Healthcare, 12 Salvado Road, Subiaco, Western Australia 6008, Australia.

    • Nikolajs Zeps,
    • Maria Beilin &
    • Kynan Feeney
  47. Royal Adelaide Hospital, North Terrace, Adelaide, South Australia 5000, Australia.

    • Nan Q. Nguyen,
    • Andrew R. Ruszkiewicz,
    • Chris Worthley,
    • Chuan P. Tan &
    • Tamara Debrencini
  48. Flinders Medical Centre, Flinders Drive, Bedford Park, South Australia 5042, Australia.

    • John Chen,
    • Mark E. Brooke-Smith &
    • Virginia Papangelis
  49. Greenslopes Private Hospital, Newdegate Street, Greenslopes, Queensland 4120, Australia.

    • Henry Tang &
    • Andrew P. Barbour
  50. Envoi Pathology, 1/49 Butterfield Street, Herston, Queensland 4006, Australia.

    • Andrew D. Clouston &
    • Patrick Martin
  51. Princess Alexandria Hospital, 237 Ipswich Road, Woolloongabba, Queensland 4102, Australia.

    • Thomas J. O’Rourke,
    • Amy Chiang,
    • Jonathan W. Fawcett,
    • Kellee Slater,
    • Shinn Yeung,
    • Michael Hatzifotis &
    • Peter Hodgkinson
  52. Austin Hospital, 145 Studley Road, Heidelberg, Victoria 3084, Australia.

    • Christopher Christophi,
    • Mehrdad Nikfarjam &
    • Angela Mountain
  53. Victorian Cancer Biobank, 1 Rathdown Street, Carlton, Victoria 3053, Australia.

    • $affiliationAuthor
  54. Johns Hopkins Medical Institute, 600 North Wolfe Street, Baltimore, Maryland 21287, USA.

    • James R. Eshleman,
    • Ralph H. Hruban,
    • Anirban Maitra,
    • Christine A. Iacobuzio-Donahue,
    • Richard D. Schulick,
    • Christopher L. Wolfgang,
    • Richard A Morgan &
    • Mary Hodgin
  55. ARC-NET Center for Applied Research on Cancer, University of Verona, Via dell’Artigliere, 19 37129 Verona, Province of Verona, Italy.

    • Aldo Scarpa,
    • Rita T. Lawlor,
    • Stefania Beghelli,
    • Vincenzo Corbo,
    • Maria Scardoni &
    • Claudio Bassi
  56. University of California, San Francisco, 500 Parnassus Avenue, San Francisco, California 94122, USA.

    • Margaret A. Tempero
  57. Greater Glasgow and Clyde National Health Service, 1053 Great Western Road, Glasgow G12 0YN, UK.

    • Andrew V. Biankin,
    • David K. Chang,
    • Nigel B. Jamieson,
    • Janet S. Graham,
    • Karen Oien &
    • Jane Hair

Consortia

  1. Australian Pancreatic Cancer Genome Initiative

    • Victorian Cancer Biobank,
  2. (Participants are arranged by institution.)

  3. Garvan Institute of Medical Research

    • Andrew V. Biankin,
    • Amber L. Johns,
    • Amanda Mawson,
    • David K. Chang,
    • Christopher J. Scarlett,
    • Mary-Anne L. Brancato,
    • Sarah J. Rowe,
    • Skye H. Simpson,
    • Mona Martyn-Smith,
    • Michelle T. Thomas,
    • Lorraine A. Chantrill,
    • Venessa T. Chin,
    • Angela Chou,
    • Mark J. Cowley,
    • Jeremy L. Humphris,
    • Marc D. Jones,
    • R. Scott Mead,
    • Adnan M. Nagrial,
    • Marina Pajic,
    • Jessica Pettit,
    • Mark Pinese,
    • Ilse Rooman,
    • Jianmin Wu,
    • Jiang Tao,
    • Renee DiPietro,
    • Clare Watson,
    • Angela Steinmann,
    • Hong Ching Lee,
    • Rachel Wong,
    • Andreia V. Pinho,
    • Marc Giry-Laterriere,
    • Roger J. Daly,
    • Elizabeth A. Musgrove &
    • Robert L. Sutherland
  4. Queensland Centre for Medical Genomics / Institute for Molecular Biosciences

    • Sean M. Grimmond,
    • Nicola Waddell,
    • Karin S. Kassahn,
    • David K. Miller,
    • Peter J. Wilson,
    • Ann-Marie Patch,
    • Sarah Song,
    • Ivon Harliwong,
    • Senel Idrisoglu,
    • Craig Nourse,
    • Ehsan Nourbakhsh,
    • Suzanne Manning,
    • Shivangi Wani,
    • Milena Gongora,
    • Matthew Anderson,
    • Oliver Holmes,
    • Conrad Leonard,
    • Darrin Taylor,
    • Scott Wood,
    • Christina Xu,
    • Katia Nones,
    • J. Lynn Fink,
    • Angelika Christ,
    • Tim Bruxner,
    • Nicole Cloonan,
    • Felicity Newell,
    • John V. Pearson,
    • Peter Bailey,
    • Michael Quinn,
    • Shivashankar Nagaraj,
    • Stephen Kazakoff,
    • Nick Waddell,
    • Keerthana Krisnan,
    • Kelly Quek,
    • David Wood &
    • Muhammad Z. H. Fadlullah
  5. Royal North Shore Hospital

    • Jaswinder S. Samra,
    • Anthony J. Gill,
    • Nick Pavlakis,
    • Alex Guminski &
    • Christopher Toon
  6. Bankstown Hospital

    • Ray Asghari,
    • Neil D. Merrett,
    • Darren Pavey &
    • Amitabha Das
  7. Liverpool Hospital

    • Peter H. Cosman,
    • Kasim Ismail &
    • Chelsie O’Connnor
  8. Westmead Hospital

    • Vincent W. Lam Duncan McLeod,
    • Henry C. Pleass,
    • Arthur Richardson &
    • Virginia James
  9. Royal Prince Alfred Hospital

    • James G. Kench,
    • Caroline L. Cooper,
    • David Joseph,
    • Charbel Sandroussi,
    • Michael Crawford &
    • James Gallagher
  10. Fremantle Hospital

    • Michael Texler,
    • Cindy Forest,
    • Andrew Laycock,
    • Krishna P. Epari,
    • Mo Ballal,
    • David R. Fletcher &
    • Sanjay Mukhedkar
  11. Sir Charles Gairdner Hospital

    • Nigel A. Spry,
    • Bastiaan DeBoer &
    • Ming Chai
  12. St John of God Healthcare

    • Nikolajs Zeps,
    • Maria Beilin &
    • Kynan Feeney
  13. Royal Adelaide Hospital

    • Nan Q. Nguyen,
    • Andrew R. Ruszkiewicz,
    • Chris Worthley,
    • Chuan P. Tan &
    • Tamara Debrencini
  14. Flinders Medical Centre

    • John Chen,
    • Mark E. Brooke-Smith &
    • Virginia Papangelis
  15. Greenslopes Private Hospital

    • Henry Tang &
    • Andrew P. Barbour
  16. Envoi Pathology

    • Andrew D. Clouston &
    • Patrick Martin
  17. Princess Alexandria Hospital

    • Thomas J. O’Rourke,
    • Amy Chiang,
    • Jonathan W. Fawcett,
    • Kellee Slater,
    • Shinn Yeung,
    • Michael Hatzifotis &
    • Peter Hodgkinson
  18. Austin Hospital

    • Christopher Christophi,
    • Mehrdad Nikfarjam &
    • Angela Mountain
  19. Victorian Cancer Biobank

  20. Johns Hopkins Medical Institutes

    • James R. Eshleman,
    • Ralph H. Hruban,
    • Anirban Maitra,
    • Christine A. Iacobuzio-Donahue,
    • Richard D. Schulick,
    • Christopher L. Wolfgang,
    • Richard A Morgan &
    • Mary Hodgin
  21. ARC-Net Centre for Applied Research on Cancer

    • Aldo Scarpa,
    • Rita T. Lawlor,
    • Stefania Beghelli,
    • Vincenzo Corbo,
    • Maria Scardoni &
    • Claudio Bassi
  22. University of California, San Francisco

    • Margaret A. Tempero
  23. University of Glasgow

    • Andrew V. Biankin,
    • Sean M. Grimmond,
    • David K. Chang,
    • Elizabeth A. Musgrove,
    • Marc D. Jones,
    • Craig Nourse,
    • Nigel B. Jamieson &
    • Janet S. Graham
  24. Greater Glasgow & Clyde National Health Service

    • Andrew V. Biankin,
    • David K. Chang,
    • Nigel B. Jamieson,
    • Janet S. Graham,
    • Karen Oien &
    • Jane Hair

Contributions

Biospecimens were collected at affiliated hospitals and processed at each biospecimen core resource centre. Data generation and analyses were performed by the Queensland Centre for Medical Genomics. Investigator contributions are as follows: A.V.B. and S.M.G. (concept and design); S.M.G., J.V.P. N.W., A.V.B. (project leaders); N.W., S.M.G., D.K.C., A.V.B. (writing team); J.V.P., S.M.G., N.W., A.L.J., P.B., S.S., K.S.K., Nk.W., P.J.W., A.M.P., F.N., B.P., E.M., O.H., J.L.F., C.L., D.T., S.W., Q.X., K.N., N.C., M.C.J.Q., M.J.A., M.Z.H.F., A.J.R., S.K., K.Q., M.Pi., H.C.L., M.J.C. and J.W. (bioinformatics); M.Pa., C.J.S., D.K.C., E.S.H., A.M.N., A.C., A.S., C.S., A.V.P., I.R., A.M.S., S.P.N., R. B. (preclinical testing); A.L.J., M.D.J., M.P., C.J.S., C.T., A.M.N., V.T.C., L.A.C., J.S.S., D.K.C., V.C., A.S., C.S., A.J.G., J.A.L., I.R., A.V.P., E.A.M. (sample processing and quality control); A.J.G., J.G.K., C.T., G.Z., A.S., D.A. R.H.H., A.M., C.A.I-D., A.S. (pathology assessment); A.L.J., L.A.C., A.J.G., A.C., R.S.M., C.B., M.F., G.T., J.S.S., J.G.K., C.T., K.E., N.Q.N., N.Z., H.W., N.B.J., J.S.G, R.G., C.P., R.G., C.L.W., R.A.M., R.T.L., M.F., G.Z., G.T., M.A.T., A.P.G.I., J.R.E., R.H.H., A.M., C.A.I-D., A.S. (sample collection and clinical annotation); D.M., T.J.C.B., A.N.C., I.H., S.I., S.M., C.N., E.N., S.W. (sequencing). All authors have read and approved the final manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

BAM files and associated metadata in XML format have been uploaded to the European Genome-phenome Archive (EGA; http://www.ebi.ac.uk/ega) under accession number EGAS00001000154. All SNP array data is available via GEO (GSE61502). For more information about Australian Pancreatic Cancer Genome Initiative, see (http://www.pancreaticcancer.net.au/apgi/collaborators).

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Summary of structural rearrangements. (142 KB)

    a, Histogram showing the number of events verified in silico or by orthogonal sequencing methods (Methods). In total 7,228 of the 11,868 events identified (61%) were verified, the others remain untested. These included 5,666 events which contained multiple lines of evidence (qSV category 1: discordant pairs, soft clipping on both sides and split read evidence, Methods) thus were considered verified. Of these events 2,463 events were also verified by orthogonal sequencing methods (SOLiD long mate pair or PCR amplicon sequencing) or the event was associated with a copy number change which was determined using SNP arrays. The remaining 1,562 events were verified using orthogonal sequencing methods or the event was associated with a copy number change (qSV category 2 and 3, Methods). b, Histogram showing the number of structural rearrangements in each pancreatic cancer. 100 PDACs were sequenced using HiSeq paired-end whole-genome sequencing. Structural rearrangements were identified and classified into 8 categories (deletions, duplications, tandem duplications, foldback inversions, amplified inversions, inversions, intra-chromosomal and inter-chromosomal translocations, Methods). The number and type of event for each patient is shown. PDAC shows a high degree of heterogeneity in both the number and types of events per patient. The structural rearrangements were used to classify the tumours into four categories (stable, locally rearranged, scattered and unstable, Methods).

  2. Extended Data Figure 2: Distribution of structural variant breakpoints within each patient. (181 KB)

    The 100 patients are plotted along the x axis. The upper plot shows the number of structural rearrangements (y axis) in each tumour. The lower plot shows which chromosomes (y axis) harbour clusters of breakpoints. The distribution of breakpoints (events per Mb) within each chromosome for each sample was evaluated using two methods to identify clusters of rearrangements or chromosomes which contain a large number of events. Method 1: chromosomes with a significant cluster of events were determined by a goodness-of-fit test against the expected exponential distribution (with a significance threshold of <0.0001). Chromosomes which pass these criteria are coloured blue. Method 2: chromosomes were identified which contain significantly more events per Mb than other chromosomes for that patient. Chromosomes were deemed to harbour a high number of events if they had a mutation rate per Mb which exceeds 1.5 times the length of the interquartile range from the 75th percentile of the chromosome counts for each patient. Chromosomes which pass these criteria are coloured orange. Chromosomes which pass both tests they are coloured red. These criteria show that the unstable tumours which contain many events often have significant clusters of events. In contrast locally rearranged tumours are associated with both clusters of events and a high number of events within that chromosome when compared to other chromosomes.

  3. Extended Data Figure 3: The stable subtype in pancreatic ductal adenocarcinoma. (1,043 KB)

    The 20 stable tumours are shown using circos. The coloured outer ring represents the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired sequencing and the legend indicates the type of rearrangement. Stable tumours contained less than 50 structural rearrangements in each tumour.

  4. Extended Data Figure 4: The locally rearranged subtype in pancreatic ductal adenocarcinoma. (892 KB)

    The 30 locally rearranged tumours are shown using circos. The coloured outer rings represent the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole-genome paired sequencing and the legend indicates the type of rearrangement. In the locally rearranged subtype over 25% of the structural rearrangements are clustered on one of few chromosomes.

  5. Extended Data Figure 5: Example of evidence for chromothripsis in a pancreatic ductal adenocarcinoma (ICGC_0109). (279 KB)

    Upper plot is a density plot showing a concentration of break-points on chromosome 5. Next panel shows the structural rearrangements which are coloured as presented in the legend. The lower panels show copy number, logR ratio and B allele frequency derived from SNP arrays. This chromosome showed a complex localization of events similar to chromothripsis. Copy number profile and structural rearrangements suggest a shattering of chromosome 5 with a high concentration of structural rearrangements, switches in copy number state and retention of heterozygosity, which are characteristics of a chromothriptic event.

  6. Extended Data Figure 6: Example of evidence for breakage-fusion-bridge (BFB) in a pancreatic ductal adenocarcinoma (ICGC_0042). (221 KB)

    Upper plot is a density plot showing a concentration of break-points on chromosome 5. Next panel shows the structural rearrangements which are coloured as presented in the legend. The lower panels show copy number, logR ratio and B allele frequency derived from SNP arrays. This chromosome showed a complex localization of events similar to BFB. Copy number profile suggests loss of telomeric q arm and a high concentration of structural rearrangements suggesting a series of BFB cycles, with multiple inversions mapped to the amplified regions.

  7. Extended Data Figure 7: The scattered subtype in pancreatic ductal adenocarcinoma. (1,107 KB)

    The 36 tumours classified as scattered are shown using circos. The coloured outer rings represent the chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next shows the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired end sequencing. The legend indicates the type of rearrangement. The scattered tumours contained 50–200 structural rearrangements in each tumour.

  8. Extended Data Figure 8: The unstable subtype in pancreatic ductal adenocarcinoma. (984 KB)

    The 14 unstable tumours are shown using circos. The coloured outer rings are chromosomes, the next ring depicts copy number (red represents gain and green represents loss), the next is the B allele frequency. The inner lines represent chromosome structural rearrangements detected by whole genome paired sequencing and the legend indicates the type of rearrangement. The unstable tumours contained a large degree of genomic instability and harboured over 200 structural rearrangements in each tumour which were predominantly intra-chromosomal rearrangements evenly distributed through the genome.

  9. Extended Data Figure 9: RAD51 foci formation in a primary culture of genomically unstable PDAC. (341 KB)

    a, RAD51 and geminin fluorescence in untreated cells derived from an unstable pancreatic tumour with a somatic mutation in the RPA1 gene (ICGC_0016). Primary culture of ICGC_0016 consists of eGFP+ mouse stromal and eGFP tumour cells. b, Upper panel: irradiated unstable pancreatic cancer cells (ICGC_0016), middle panel: HR-competent (TKCC-07) and lower panel: HR-deficient (Capan-1) pancreatic tumour cells. Cells were irradiated in vitro with 10Gy, and 6 h post-irradiation examined by immunofluorescence microscopy. eGFP negative tumour cells from ICGC_0016 readily form RAD51 foci following induction of DNA damage. TKCC-07 is a pancreas cancer cell line generated from a homologous recombination (HR) pathway competent patient-derived xenograft and served as a positive control for staining and RAD51 foci formation after DNA damage. Capan-1 cells which are HR-deficient do not form RAD51 foci. c, RAD51 score (percentage of geminin positive cells that have RAD51 foci) in examined pancreatic tumour cells.

Supplementary information

Zip files

  1. Supplementary Tables (11.9 MB)

    This zipped file contains Supplementary Tables 1-12.

Additional data