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Genomic analyses identify molecular subtypes of pancreatic cancer

Nature volume 531, pages 47–52 (2016)Cite this article

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This article has been updated

Abstract

Integrated genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing. Expression analysis defined 4 subtypes: (1) squamous; (2) pancreatic progenitor; (3) immunogenic; and (4) aberrantly differentiated endocrine exocrine (ADEX) that correlate with histopathological characteristics. Squamous tumours are enriched for TP53 and KDM6A mutations, upregulation of the TP63∆N transcriptional network, hypermethylation of pancreatic endodermal cell-fate determining genes and have a poor prognosis. Pancreatic progenitor tumours preferentially express genes involved in early pancreatic development (FOXA2/3, PDX1 and MNX1). ADEX tumours displayed upregulation of genes that regulate networks involved in KRAS activation, exocrine (NR5A2 and RBPJL), and endocrine differentiation (NEUROD1 and NKX2-2). Immunogenic tumours contained upregulated immune networks including pathways involved in acquired immune suppression. These data infer differences in the molecular evolution of pancreatic cancer subtypes and identify opportunities for therapeutic development.

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Figure 1: Molecular classes and transcriptional networks defining PDAC.
Figure 2: Molecular characterization of the squamous class.
Figure 3: Immune pathways in PDAC.
Figure 4: Gain of function TP53 mutations and loss of TAp63 regulate key GPs associated with the squamous class.

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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

  • 02 March 2016

    A present address was added for author R.G.

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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

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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 to Andrew 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.

Supplementary information

Supplementary Tables

This file contains Supplementary Tables 1-13 (XLSX 21586 kb)

Supplementary Tables

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Supplementary Information

This file contains a list of the participants and their affiliations for the Australian Pancreatic Cancer Genome Initiative. (PDF 105 kb)

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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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