Genomic analyses identify molecular subtypes of pancreatic cancer (original) (raw)

Accession codes

Primary accessions

ArrayExpress

Gene Expression Omnibus

Data deposits

All DNA sequencing and RNA-seq data have been deposited in the European Genome-phenome Archive (EGA): accession code EGAS00001000154. All gene expression, genotyping, and methylome data used in this study has been deposited in the NCBI Gene Expression Omnibus (GEO) under accession codes GSE49149 and GSE36924. Mouse cell line expression data are available in the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-4415.

Change history

A present address was added for author R.G.

References

  1. Rahib, L. et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 74, 2913–2921 (2014)
    Google Scholar
  2. Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015)
    Google Scholar
  3. Jones, S. et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806 (2008)
    Google Scholar
  4. Wang, L. et al. Whole-exome sequencing of human pancreatic cancers and characterization of genomic instability caused by MLH1 haploinsufficiency and complete deficiency. Genome Res. 22, 208–219 (2012)
    Google Scholar
  5. Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012)
    Google Scholar
  6. Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep . 3, 246–259 (2013)
    Google Scholar
  7. Nones, K. et al. Genome-wide DNA methylation patterns in pancreatic ductal adenocarcinoma reveal epigenetic deregulation of SLIT-ROBO, ITGA2 and MET signaling. Int. J. Cancer 135, 1110–1118 (2014)
    Google Scholar
  8. Nones, K. et al. Genomic catastrophes frequently arise in esophageal adenocarcinoma and drive tumorigenesis. Nature Commun . 5, 5224 (2014)
    Google Scholar
  9. Patch, A. M. et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 521, 489–494 (2015)
    Google Scholar
  10. The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014)
  11. Bechara, E. G., Sebestyen, E., Bernardis, I., Eyras, E. & Valcarcel, J. RBM5, 6, and 10 differentially regulate NUMB alternative splicing to control cancer cell proliferation. Mol. Cell 52, 720–733 (2013)
    Google Scholar
  12. Scott, L. M. & Rebel, V. I. Acquired mutations that affect pre-mRNA splicing in hematologic malignancies and solid tumors. J. Natl. Cancer Inst. 105, 1540–1549 (2013)
    Google Scholar
  13. Maguire, S. L. et al. SF3B1 mutations constitute a novel therapeutic target in breast cancer. J. Pathol. 235, 571–580 (2015)
    Google Scholar
  14. Horn, S. et al. Mind bomb 1 is required for pancreatic β-cell formation. Proc. Natl Acad. Sci. USA 109, 7356–7361 (2012)
    Google Scholar
  15. Scaltriti, M. et al. Cyclin E amplification/overexpression is a mechanism of trastuzumab resistance in HER2+ breast cancer patients. Proc. Natl Acad. Sci. USA 108, 3761–3766 (2011)
    Google Scholar
  16. Shain, A. H., Salari, K., Giacomini, C. P. & Pollack, J. R. Integrative genomic and functional profiling of the pancreatic cancer genome. BMC Genomics 14, 624 (2013)
    Google Scholar
  17. Tubio, J. M. et al. Mobile DNA in cancer. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes. Science 345, 1251343 (2014)
    Google Scholar
  18. 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)
    Google Scholar
  19. Brody, J. R. et al. Adenosquamous carcinoma of the pancreas harbors KRAS2, DPC4 and TP53 molecular alterations similar to pancreatic ductal adenocarcinoma. Mod. Pathol. 22, 651–659 (2009)
    Google Scholar
  20. Engelmann, D. & Putzer, B. M. Emerging from the shade of p53 mutants: N-terminally truncated variants of the p53 family in EMT signaling and cancer progression. Sci. Signal. 7, re9 (2014)
    Google Scholar
  21. Hale, M. A. et al. The homeodomain protein PDX1 is required at mid-pancreatic development for the formation of the exocrine pancreas. Dev. Biol. 286, 225–237 (2005)
    Google Scholar
  22. von Figura, G., Morris, J. P. IV, Wright, C. V. & Hebrok, M. Nr5a2 maintains acinar cell differentiation and constrains oncogenic Kras-mediated pancreatic neoplastic initiation. Gut 63, 656–664 (2014)
    Google Scholar
  23. Hale, M. A. et al. The nuclear hormone receptor family member NR5A2 controls aspects of multipotent progenitor cell formation and acinar differentiation during pancreatic organogenesis. Development 141, 3123–3133 (2014)
    Google Scholar
  24. Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015)
    Google Scholar
  25. Weissmueller, S. et al. Mutant p53 drives pancreatic cancer metastasis through cell-autonomous PDGF receptor β signaling. Cell 157, 382–394 (2014)
    Google Scholar
  26. Miller, B. W. et al. Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy. EMBO Mol. Med. 7, 1063–1076 (2015)
    Google Scholar
  27. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nature Med. 17, 500–503 (2011)
    Google Scholar
  28. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nature Genet. 47, 1168–1178 (2015)
    Google Scholar
  29. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov . 2, 401–404 (2012)
    Google Scholar
  30. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)
    Google Scholar
  31. Gonzalez-Perez, A. et al. IntOGen-mutations identifies cancer drivers across tumor types. Nature Methods 10, 1081–1082 (2013)
    Google Scholar
  32. Leiserson, M. D. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature Genet. 47, 106–114 (2015)
    Google Scholar
  33. Razick, S., Magklaras, G. & Donaldson, I. M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008)
    Google Scholar
  34. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011)
    Google Scholar
  35. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010)
    Google Scholar
  36. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013)
    Google Scholar
  37. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014)
    Google Scholar
  38. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)
    Google Scholar
  39. Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012)
    Google Scholar
  40. Kuehn, H., Liberzon, A., Reich, M. & Mesirov, J. P. Using GenePattern for gene expression analysis. Current Protoc. Bioinformatics Chapter 7, Unit 7 12, (2008)
    Google Scholar
  41. Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010)
    Google Scholar
  42. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014)
  43. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013)
    Google Scholar
  44. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008)
    Google Scholar
  45. Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24, 719–720 (2008)
    Google Scholar
  46. Fang, H. & Gough, J. The ‘dnet’ approach promotes emerging research on cancer patient survival. Genome Med . 6, 64 (2014)
    Google Scholar
  47. Wu, G. & Stein, L. A network module-based method for identifying cancer prognostic signatures. Genome Biol. 13, R112 (2012)
    Google Scholar
  48. Du, P., Kibbe, W. A. & Lin, S. M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008)
    Google Scholar
  49. Tan, E. H. et al . Functions of TAp63 and p53 in restraining the development of metastatic cancer. Oncogene 25, 3325–3333 (2014)
    Google Scholar
  50. Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Commun . 4, 2612 (2013)
    Google Scholar

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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 Deborah 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. Funding support was from: National Health and Medical Research Council of Australia (NHMRC; 631701, 535903, 427601); Queensland Government (NIRAP); University of Queensland; 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; Cancer Research UK Glasgow Centre Program, A18076; Avner Nahmani Pancreatic Cancer Research 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; Wellcome Trust Senior Investigator Award 103721/Z/14/Z; Cancer Research UK Programme Grant C29717/A17263; Cancer Research UK Programme Grant A12481; Pancreatic Cancer UK; The Howat Foundation; University of Glasgow; European Research Council Starting Grant, 311301, Italian Ministry of University and Research (Cancer Genome Project FIRB RBAP10AHJB), Associazione Italiana Ricerca Cancro (n.12182) , Fondazione Italiana Malattie Pancreas – Ministry of Health (CUP_J33G13000210001), European Community Grant FP7 Cam-Pac, grant agreement number 602783.

Author information

Author notes

  1. Robert Grützmann
    Present address: † Present address: Universitätsklinikum Erlangen, Department of Surgery, 91054 Erlangen, Germany.,
  2. Robert L. Sutherland: Deceased.

Authors and Affiliations

  1. Queensland Centre for Medical Genomics, Institute for Molecular Bioscience, The University of Queensland, St Lucia, Brisbane, Queensland 4072, Australia
    Peter Bailey, Katia Nones, Ann-Marie Patch, David K. Miller, Angelika N. Christ, Tim J. C. Bruxner, Michael C. Quinn, Craig Nourse, Ivon Harliwong, Senel Idrisoglu, Suzanne Manning, Ehsan Nourbakhsh, Shivangi Wani, Lynn Fink, Oliver Holmes, Matthew J. Anderson, Stephen Kazakoff, Conrad Leonard, Felicity Newell, Nick Waddell, Scott Wood, Qinying Xu, Peter J. Wilson, Nicole Cloonan, Karin S. Kassahn, Darrin Taylor, Kelly Quek, Alan Robertson, John V. Pearson, Nicola Waddell & Sean M. Grimmond
  2. Wolfson Wohl Cancer Research Centre, Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Bearsden, Glasgow, G61 1BD, UK
    Peter Bailey, David K. Chang, Craig Nourse, Laura Mincarelli, Luis N. Sanchez, Lisa Evers, Marc D. Jones, Kim Moran-Jones, Nigel B. Jamieson, Janet S. Graham, Elizabeth A. Musgrove, Ulla-Maja Hagbo Bailey, Oliver Hofmann, Andrew V. Biankin & Sean M. Grimmond
  3. The Kinghorn Cancer Centre, 370 Victoria St, Darlinghurst, and the Cancer Research Program, Garvan Institute of Medical Research, 384 Victoria St, Darlinghurst, Sydney, 2010, New South Wales, Australia
    David K. Chang, Amber L. Johns, David K. Miller, Venessa Chin, Jianmin Wu, Mark Pinese, Mark J. Cowley, Marc D. Jones, Emily K. Colvin, Adnan M. Nagrial, Emily S. Humphrey, Lorraine A. Chantrill, Amanda Mawson, Jeremy Humphris, Angela Chou, Marina Pajic, Christopher J. Scarlett, Andreia V. Pinho, Marc Giry-Laterriere, Ilse Rooman, James G. Kench, Jessica A. Lovell, Christopher W. Toon, Karin Oien, Robert L. Sutherland, Anthony J. Gill & Andrew V. Biankin
  4. Department of Surgery, Bankstown Hospital, Eldridge Road, Bankstown, Sydney, 2200, New South Wales, Australia
    David K. Chang & Andrew V. Biankin
  5. South Western Sydney Clinical School, Faculty of Medicine, University of New South Wales, Liverpool, 2170, New South Wales, Australia
    David K. Chang, Neil D. Merrett & Andrew V. Biankin
  6. QIMR Berghofer Medical Research Institute, Herston, 4006, Queensland, Australia
    Katia Nones, Ann-Marie Patch, Michael C. Quinn, Shivangi Wani, Oliver Holmes, Stephen Kazakoff, Conrad Leonard, Scott Wood, Qinying Xu, Nicole Cloonan, John V. Pearson & Nicola Waddell
  7. Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030, Texas, USA
    Marie-Claude Gingras, Donna M. Munzy, David A. Wheeler & Richard A. Gibbs
  8. Michael DeBakey Department of Surgery, Baylor College of Medicine, Houston, 77030, Texas, USA
    Marie-Claude Gingras, Donna M. Munzy, David A. Wheeler & Richard A. Gibbs
  9. Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, 77030, Texas, USA
    Marie-Claude Gingras
  10. Department of Human Genetics, University of Utah, Salt Lake City, 84112, Utah, USA
    L. Charles Murtaugh
  11. Genetic and Molecular Pathology, SA Pathology, Adelaide, 5000, South Australia, Australia
    Karin S. Kassahn
  12. School of Biological Sciences, The University of Adelaide, Adelaide, 5000, South Australia, Australia
    Karin S. Kassahn
  13. Harvard Chan Bioinformatics Core, Harvard T. H. Chan School of Public Health, Boston, 02115, Massachusetts, USA
    Lorena Pantano & Oliver Hofmann
  14. Macarthur Cancer Therapy Centre, Campbelltown Hospital, 2560, New South Wales, Australia
    Lorraine A. Chantrill
  15. Department of Pathology. SydPath, St Vincent’s Hospital, Sydney, 2010, NSW, Australia
    Angela Chou
  16. St Vincent’s Clinical School, Faculty of Medicine, University of New South Wales, New South Wales 2052, Australia
    Marina Pajic
  17. School of Environmental & Life Sciences, University of Newcastle, Ourimbah, 2258, New South Wales, Australia
    Christopher J. Scarlett
  18. Department of Surgery, Royal North Shore Hospital, St Leonards, Sydney, 2065, New South Wales, Australia
    Jaswinder S. Samra
  19. University of Sydney, Sydney, 2006, New South Wales, Australia
    Jaswinder S. Samra, James G. Kench & Anthony J. Gill
  20. Tissue Pathology and Diagnostic Oncology, Royal Prince Alfred Hospital, Camperdown, 2050, New South Wales, Australia
    James G. Kench
  21. School of Medicine, University of Western Sydney, Penrith, 2175, New South Wales, Australia
    Neil D. Merrett
  22. Fiona Stanley Hospital, Robin Warren Drive, Murdoch, 6150, Western Australia, Australia
    Krishna Epari
  23. Department of Gastroenterology, Royal Adelaide Hospital, North Terrace, Adelaide, 5000, South Australia, Australia
    Nam Q. Nguyen
  24. Department of Surgery, Princess Alexandra Hospital, Ipswich Rd, Woollongabba, 4102, Queensland, Australia
    Andrew Barbour
  25. School of Surgery M507, University of Western Australia, 35 Stirling Hwy, Nedlands 6009, Australia and St John of God Pathology, 12 Salvado Rd, Subiaco, 6008, Western Australia, Australia
    Nikolajs Zeps
  26. 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
  27. West of Scotland Pancreatic Unit, Glasgow Royal Infirmary, Glasgow, G31 2ER, UK
    Nigel B. Jamieson & Andrew V. Biankin
  28. Department of Medical Oncology, Beatson West of Scotland Cancer Centre, 1053 Great Western Road, Glasgow, G12 0YN, UK
    Janet S. Graham
  29. Department of Pathology, Southern General Hospital, Greater Glasgow & Clyde NHS, Glasgow, G51 4TF, UK
    Fraser Duthie & Karin Oien
  30. Pathology Department, GGC Bio-repository, Southern General Hospital, 1345 Govan Road, Glasgow, G51 4TY, UK
    Jane Hair
  31. Department of Surgery, TU Dresden, Fetscherstr. 74, Dresden, 01307, Germany
    Robert Grützmann & Christian Pilarsky
  32. Departments of Pathology and Translational Molecular Pathology, UT MD Anderson Cancer Center, Houston, 77030, Texas, USA
    Anirban Maitra
  33. The David M. Rubenstein Pancreatic Cancer Research Center and Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
    Christine A. Iacobuzio-Donahue
  34. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA
    Christopher L. Wolfgang, Richard A. Morgan, James R. Eshleman & Ralph H. Hruban
  35. Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, The Johns Hopkins University School of Medicine, Baltimore, 21231, Maryland, USA
    Christopher L. Wolfgang
  36. ARC-Net Applied Research on Cancer Centre, University and Hospital Trust of Verona, Verona, 37134, Italy
    Rita T. Lawlor, Vincenzo Corbo, Borislav Rusev & Aldo Scarpa
  37. Department of Pathology and Diagnostics, University of Verona, Verona, 37134, Italy
    Rita T. Lawlor, Paola Capelli & Aldo Scarpa
  38. Department of Surgery, Pancreas Institute, University and Hospital Trust of Verona, Verona, 37134, Italy
    Claudio Bassi & Roberto Salvia
  39. Department of Medical Oncology, Comprehensive Cancer Centre, University and Hospital Trust of Verona, Verona, 37134, Italy
    Giampaolo Tortora
  40. Mayo Clinic, Rochester, 55905, Minnesota, USA
    Debabrata Mukhopadhyay & Gloria M. Petersen
  41. Elkins Pancreas Center, Baylor College of Medicine, One Baylor Plaza, MS226, Houston, 77030-3411, Texas, USA
    William E. Fisher
  42. Cancer Research UK Beatson Institute, Glasgow, G61 1BD, UK
    Saadia A. Karim, Jennifer P. Morton & Owen J. Sansom
  43. Institute for Cancer Science, University of Glasgow, Glasgow, G12 8QQ, UK
    Owen J. Sansom
  44. University of Melbourne, Parkville, Victoria, 3010, Australia
    Sean M. Grimmond

Authors

  1. Peter Bailey
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  2. David K. Chang
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  3. Katia Nones
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  4. Amber L. Johns
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  5. Ann-Marie Patch
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  6. Marie-Claude Gingras
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  7. David K. Miller
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  8. Angelika N. Christ
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  9. Tim J. C. Bruxner
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  10. Michael C. Quinn
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  11. Craig Nourse
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  12. L. Charles Murtaugh
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  13. Ivon Harliwong
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  14. Senel Idrisoglu
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  15. Suzanne Manning
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  16. Ehsan Nourbakhsh
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  17. Shivangi Wani
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  18. Lynn Fink
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  19. Oliver Holmes
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  20. Venessa Chin
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  21. Matthew J. Anderson
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  22. Stephen Kazakoff
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  23. Conrad Leonard
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  24. Felicity Newell
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  25. Nick Waddell
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  26. Scott Wood
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  27. Qinying Xu
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  28. Peter J. Wilson
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  29. Nicole Cloonan
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  30. Karin S. Kassahn
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  31. Darrin Taylor
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  32. Kelly Quek
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  34. Lorena Pantano
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  35. Laura Mincarelli
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  36. Luis N. Sanchez
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  37. Lisa Evers
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  38. Jianmin Wu
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  39. Mark Pinese
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  40. Mark J. Cowley
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  41. Marc D. Jones
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  43. Adnan M. Nagrial
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  45. Lorraine A. Chantrill
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  49. Marina Pajic
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  50. Christopher J. Scarlett
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  51. Andreia V. Pinho
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  52. Marc Giry-Laterriere
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  53. Ilse Rooman
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  54. Jaswinder S. Samra
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  55. James G. Kench
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  56. Jessica A. Lovell
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  57. Neil D. Merrett
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  58. Christopher W. Toon
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  59. Krishna Epari
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  60. Nam Q. Nguyen
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  61. Andrew Barbour
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  62. Nikolajs Zeps
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  64. Nigel B. Jamieson
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  67. Karin Oien
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  68. Jane Hair
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  69. Robert Grützmann
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  70. Anirban Maitra
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  71. Christine A. Iacobuzio-Donahue
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  72. Christopher L. Wolfgang
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  74. Rita T. Lawlor
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  81. Debabrata Mukhopadhyay
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  86. James R. Eshleman
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  87. Ralph H. Hruban
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  98. Richard A. Gibbs
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  99. John V. Pearson
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  100. Nicola Waddell
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  101. Andrew V. Biankin
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  102. Sean M. Grimmond
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Consortia

Australian Pancreatic Cancer Genome Initiative

Contributions

Investigator contributions are as follows: P.J.B., J.V.P., N.W., A.V.B., S.M.G. (concept and design); P.J.B., D.A.W., R.A.G., A.S., D.K.C., J.V.P., N.W., A.V.B., S.M.G. (project leaders); P.J.B., D.K.C., A.V.B., S.M.G. (writing team); D.K.M., A.N.C., T.J.C.B., C.N., K.N., S.W., D.M.M., N.W., L.E., L.M., L.S., S.M.G., I.H., S.I., S.M., E.N., K.Q., S.M.G. (genomics); P.J.B., D.K.M., K.S.K., N.W., P.J.W., O. H., A.M.P., F.N., O.H., C.L., D.T., S.W., Q.X., K.N., N.C., M.Q., M.A., A.R., M.G., S.K., K.Q., L.P., J.M., M.C., L.C.M., O.S., L.F., U.B., N.W., J.V.P., S.M.G. (data analysis); D.K.C., A.L.J., A.M.N., A.M., A.V.P., C.W.T., E.K.C., E.S.H., I.R., M.G., J.H., J.A.L., K.E., L.A.C., M.D.J., A.J.G., N.Q.N., A.B., N.Z., C.P., R.G., J.R.E., R.H.H., A.M., C.A.I., C.L.W., B.R., V.C., P.C., C.B., R.S., G.T., D.M., G.M.P., J.H., M.P., J.W., V.C., C.J.S., J.G.K., R.T.L., N.D.M., N.B.J., J.S.G., J.D.S., R.A.M., J.H., S.A.K., K.M., R.L.S., A.V.B. (sample acquisition and processing, clinical annotation, interpretation and analysis); A.J.G., A.C., R.H.H., F.D., K.O., A.S., W.F., J.G.K., C.T. (pathology assessment).

Corresponding authors

Correspondence toAndrew V. Biankin or Sean M. Grimmond.

Ethics declarations

Competing interests

R.H.H. receives royalty payments from Myriad Genetics for the PALB2 invention.

Additional information

A list of authors and affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Mutational landscape of PC.

a, Barplot representing the somatic mutation rate for each of the 456 samples included in this analysis.b, Non-silent mutations (blue), amplifications (≥8 copies, red), deletions (purple) and structural variants (SV, green) ranked in order of exclusivity. c, Significantly mutated genes identified by OncodriverFM. An asterisk denotes a significantly mutated gene identified by both MutSigCV and OncodriverFM. d, PC mutation functional interaction (FI) sub-network identified by the ReactomeFI cytoscape plugin. Mutated genes are indicated as coloured circles and linker genes (that is, genes not significantly mutated but highly connected to mutated genes in the network) indicated as coloured diamonds. Different node colours indicate different network clusters or closely interconnected genes. P values represent FDR < 0.05. Pathways significantly enriched in the identified FI sub-network are shown in the accompanying bar graph. Linker genes were not included in the enrichment analysis. Pie chart representing significantly altered genes and pathways in PC.

Extended Data Figure 2 Selected genomic events in PC.

a, Lollipop plots showing the type and location of mutations in the RNA processing genes RBM10, SF3B1 and U2AF1 and the tumour suppressor TP53.In each plot, mutations observed across multiple cancers (top plot; PanCancer) are compared with those observed in the current study (bottom plot; PDAC). Significant recurrent mutations are labelled above the relevant lollipop. b, Regions of copy number alteration showing concordant gene expression changes. For each of the indicated chromosomes, significant GISTIC peaks are shown at their respective genomic locations (x axis) as grey bars. Each gene is represented by a dot at its specific chromosomal coordinate, with blue representing concordant copy number loss and gene downregulation and red representing concordant copy number amplification (copy number ≥ 8) and gene upregulation. Significance of concordant copy number/expression change is measured as a value of −log10 (_q_-value) times the sign of the direction of change. Dotted lines represent a significance threshold of −log10 (_q_-value = 0.05) times the sign of the direction of change. Genes showing concordant copy number/expression changes and overlapping GISTIC peaks are listed above the plot. Asterisk denotes known PC oncogenes showing amplification but non-significant concordant copy number/expression change.

Extended Data Figure 3 Classification of PC into 4 classes.

a, Unsupervised classification of PC RNAseq using NMF. Solutions are shown for k = 2 to k = 7 classes. A peak cophenetic correlation is observed for k = 4 classes. b, Silhouette information for k = 4 classes. c–e, Boxplots representing QPURE, stromal signature scores and immune signature scores stratified by class. Boxplots are annotated by a Kruskall–Wallis P value. For comparisons the following sample sizes were used: ADEX (n = 16); immunogenic (n = 25); squamous (n = 25); and pancreatic progenitor (n = 30). f, Heatmap showing differential gene expression between classes. Samples with positive silhouette widths were retained for ‘sam’ analysis. g, Heatmap showing overlap of the 4 classes identified in the current study and Collisson et al. classification27.

Extended Data Figure 4 Identification of 4 robust PC classes in 232 PCs with mixed low and high cellularity.

a, Unsupervised classification of PC expression array data representing 232 samples using NMF. Solutions are shown for k = 2 to k = 7 classes. b, Silhouette information for k = 4 classes. c, Heatmap showing differential gene expression between classes. d, Boxplots representing QPURE, stromal signature scores and immune signature scores stratified by class. e, Boxplots representing ADEX, pancreatic progenitor, squamous and immunogenic signature scores defined using the RNA-seq PC set stratified by class. Boxplots in d and e are annotated by a Kruskall–Wallis P value. For comparisons the following sample sizes were used: ADEX (n = 49); immunogenic (n = 67); squamous (n = 71); and pancreatic progenitor (n = 45).

Extended Data Figure 5 Characterization of PC subtypes.

a, Heatmap showing the statistical significance of correlations observed between the expressions of genes significantly expressed in each PC class and gene programmes identified by WGCNA. Pearson correlations and Student’s asymptotic P values are provided in each cell. b, Principal component analysis (PCA) using methylation data. Plot showing pairwise comparisons of samples distributed along the identified principle components (PC). Adjacent non-tumorous pancreatic samples represented as green points cluster as a distinct group. PC samples represented by points coloured brown (ADEX), blue (squamous), orange (pancreatic progenitor) and red (immunogenic) cluster together. c, Venn diagram showing the number of common and unique genes differentially methylated in the indicated PC subtypes when compared to adjacent non-tumorous pancreas. It is observed that distinct subsets of genes are differentially methylated in the 4 PC subtypes. d, Heatmap showing genes that are significantly methylated between tumours comprising the squamous class and all other classes. Methylation values for the same genes in adjacent non-tumorous pancreas are also shown. e–h, Plots showing regulation of gene expression by methylation. Hyper- or hypomethylation of the indicated probe is associated with either the concordant downregulation or upregulation of the indicated gene. Pearson correlation and adjusted P values are provided for each gene methylation comparison. Boxplot colours designate class: ADEX (brown); immunogenic (red); squamous (blue); and pancreatic progenitor (orange). Single letter designations representing the first letter of each class are provided under the relevant boxes in each plot.

Extended Data Figure 6 Core gene programmes (GP) defining the squamous class.

Each panel shows from left to right: (i) a heatmap representing the genes in the specified gene programme most correlated with the indicated PC class with tumours ranked according to their gene programme module eigengene values (MEs) (PC classes are designated by colour as follows: ADEX (brown); pancreatic progenitor (orange); immunogenic (red); and squamous (blue)); (ii) Kaplan–Meier analysis comparing survival of patients having either high or low gene programme MEs; (iii) pathways significantly enriched in a given GP functional interaction (FI) sub-network defined by the ReactomeFI Cytoscape plugin. P values represent FDR < 0.05.

Extended Data Figure 7 Gene programme defining the pancreatic progenitor class.

a, Panel showing from left to right: (i) a heatmap representing the genes in GP1 most correlated with the pancreatic progenitor class with tumours ranked according to their GP1 module eigengene values (MEs); (ii) Kaplan–Meier analysis comparing survival of patients having either high or low GP1 MEs; (iii) pathways significantly enriched in a GP1 FI sub-network defined by the ReactomeFI Cytoscape plugin. P values represent FDR <0.05. b, Network diagram depicting pathways significantly enriched in GP1 (FDR <0.0001). Different node colours indicate different network clusters or closely interconnected genes.

Extended Data Figure 8 Gene programmes defining the ADEX class.

a, b, Panel showing from left to right: (i) a heatmap representing the genes in the specified GP most correlated with the ADEX class with tumours ranked according to their GP module eigengene values (MEs); (ii) Kaplan–Meier analysis comparing survival of patients having either high or low GP MEs; (iii) pathways significantly enriched in a GP FI sub-network defined by the ReactomeFI Cytoscape plugin. P values represent FDR <0.05. c, Network diagram depicting pathways significantly enriched in GP9 (FDR <0.0001). Different node colours indicate different network clusters or closely interconnected genes. Genes comprising GP9 are indicated as coloured circles, whereas linker genes (genes not comprising GP9 but forming multiple connections in the network) are indicated as coloured diamonds. d, Network diagram depicting pathways significantly enriched in GP10 (FDR <0.0001). Different node colours indicate different network clusters or closely interconnected genes.

Extended Data Figure 9 Stratification of PC RNASeq data according to Moffitt et al.

a, Heatmap showing the stratification of the PC cohort of the current study using the tumour subtype classifier published in Moffitt et al.28. PCs were classified by consensus clustering using the top 50 weighted genes associated with the basal-like or classical subtypes. b, Boxplots showing the distribution of normal and activated stroma signature scores between the 4 PC classes identified in the current study. Boxplots are annotated by a Kruskall–Wallis P value. A significant difference in activated stroma signature scores was observed between squamous and ADEX tumours P value < 0.01 (_t-_test). Boxplot colours designate class: ADEX (brown); immunogenic (red); squamous (blue); and pancreatic progenitor (orange). c, Plots showing correlation between tumour cellularity, presented as a QPURE score, and either activated or normal stroma signature scores. Plots are annotated with Pearson correlation scores and significance values, with a linear fit represented by a solid line. Sample ICGC_0338, a rare acinar cell carcinoma is highlighted. This sample exhibits near 100% cellularity and has low activated or normal stroma signature scores. d, Principal component analysis (PCA) using methylation data. Plot showing pairwise comparisons of samples distributed along the identified principle components (PC). Adjacent non-tumorous pancreatic samples represented as green points cluster as a distinct group relative to ADEX samples (brown and red points). Rare acinar cell carcinomas (red) cluster with other ADEX samples (brown). All other PC samples are shown as grey points. e, Plot showing the correlation of expression of representative genes expressed in acinar cell carcinoma sample ICGC_0338 compared to the median expression of the same genes across all other ADEX samples. A red shaded region encompasses genes showing high median expression in all other ADEX but low expression in ICGC_0338. A brown shaded region encompasses genes showing high median expression in all other ADEX and correlatively high expression in ICGC_0338. Pearson’s correlation and significance are indicated.

Extended Data Figure 10 Gene programmes defining the immunogenic class.

a–c, Each panel shows from left to right: (i) a heatmap representing the genes in the specified gene programme most correlated with the indicated PC class with tumours ranked according to their gene programme module eigengene values (MEs). PC classes are designated by colour as follows: ADEX (brown); pancreatic progenitor (orange); immunogenic (red); and squamous (blue); (ii) Kaplan–Meier analysis comparing survival of patients having either high or low gene programme MEs; (iii) pathways significantly enriched in a given GP functional interaction (FI) sub-network defined by the ReactomeFI Cytoscape plugin. Corresponding Cytoscape files comprising GP ReactomeFI subnetworks are provided. d, Boxplot of immune gene expression stratified by class. Boxplots are annotated by a Kruskall–Wallis P value and box colours designate class: ADEX (brown); immunogenic (red); squamous (blue); and pancreatic progenitor (orange). Single letter designations representing the first letter of each class are provided under the relevant boxes in each plot.

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Bailey, P., Chang, D., Nones, K. et al. Genomic analyses identify molecular subtypes of pancreatic cancer.Nature 531, 47–52 (2016). https://doi.org/10.1038/nature16965

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