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.
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Gene Expression Omnibus
Data deposits
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).
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Acknowledgements
We would like to thank C. Axford, M.-A. Brancato, S. Rowe, M. Thomas, S. Simpson and G. Hammond for central coordination of the Australian Pancreatic Cancer Genome Initiative, data management and quality control; M. Martyn-Smith, L. Braatvedt, H. Tang, V. Papangelis and M. Beilin for biospecimen acquisition; and D. Gwynne for support at the Queensland Centre for Medical Genomics. We also thank M. Hodgins, M. Debeljak and D. Trusty for technical assistance at Johns Hopkins University. N. Sperandio and D. Filippini for technical assistance at Verona University. We acknowledge the following funding support: National Health and Medical Research Council of Australia (NHMRC; 631701, 535903, 427601); Queensland Government (NIRAP); University of Queensland; Institute for Molecular Bioscience; Cancer Research UK (C596/A18076, C29717/A17263); Australian Government: Department of Innovation, Industry, Science and Research (DIISR); Australian Cancer Research Foundation (ACRF); Cancer Council NSW: (SRP06-01, SRP11-01. ICGC); Cancer Institute NSW: (10/ECF/2-26; 06/ECF/1-24; 09/CDF/2-40; 07/CDF/1-03; 10/CRF/1-01, 08/RSA/1-15, 07/CDF/1-28, 10/CDF/2-26,10/FRL/2-03, 06/RSA/1-05, 09/RIG/1-02, 10/TPG/1-04, 11/REG/1-10, 11/CDF/3-26); Garvan Institute of Medical Research; Avner Nahmani Pancreatic Cancer Research Foundation; University of Glasgow; Cancer Research UK; Howat Foundation; R.T. Hall Trust; Petre Foundation; Philip Hemstritch Foundation; Gastroenterological Society of Australia (GESA); American Association for Cancer Research (AACR) Landon Foundation – INNOVATOR Award; Royal Australasian College of Surgeons (RACS); Royal Australasian College of Physicians (RACP); Royal College of Pathologists of Australasia (RCPA); Italian Ministry of Research (Cancer Genome Project FIRB RBAP10AHJB); Associazione Italiana Ricerca Cancro (12182); Fondazione Italiana Malattie Pancreas – Ministero Salute (CUP_J33G13000210001); Wilhelm Sander Stiftung 2009.039.2; National Institutes of Health grant P50 CA62924.
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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.
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Extended data figures and tables
Extended Data Figure 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).
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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.
Extended Data Figure 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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Waddell, N., Pajic, M., Patch, AM. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015). https://doi.org/10.1038/nature14169
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DOI: https://doi.org/10.1038/nature14169
