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Whole-genome landscape of pancreatic neuroendocrine tumours

A Corrigendum to this article was published on 27 September 2017


The diagnosis of pancreatic neuroendocrine tumours (PanNETs) is increasing owing to more sensitive detection methods, and this increase is creating challenges for clinical management. We performed whole-genome sequencing of 102 primary PanNETs and defined the genomic events that characterize their pathogenesis. Here we describe the mutational signatures they harbour, including a deficiency in G:C > T:A base excision repair due to inactivation of MUTYH, which encodes a DNA glycosylase. Clinically sporadic PanNETs contain a larger-than-expected proportion of germline mutations, including previously unreported mutations in the DNA repair genes MUTYH, CHEK2 and BRCA2. Together with mutations in MEN1 and VHL, these mutations occur in 17% of patients. Somatic mutations, including point mutations and gene fusions, were commonly found in genes involved in four main pathways: chromatin remodelling, DNA damage repair, activation of mTOR signalling (including previously undescribed EWSR1 gene fusions), and telomere maintenance. In addition, our gene expression analyses identified a subgroup of tumours associated with hypoxia and HIF signalling.

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Figure 1: Mutational signatures in pancreatic neuroendocrine tumours.
Figure 2: EWSR1 gene fusions in pancreatic neuroendocrine tumours.
Figure 3: Mutational processes in pancreatic neuroendocrine tumours.
Figure 4: Core pathways in PanNETs.


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We thank E. Missiaglia, S. Beghelli, N. Sperandio, G. Bonizzato, S. Grimaldi, F. Pisani, C. Cantù, G. Zamboni and P. Merlini for assistance at the ARC-Net Research Centre and Verona University; 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; D. Gwynne and D. Stetner for support at the Queensland Centre for Medical Genomics; and The Kinghorn Centre for Clinical Genomics for genome sequencing of validation samples. Funding support was from: Italian Ministry of Research (Cancer Genome Project FIRB RBAP10AHJB); Associazione Italiana Ricerca Cancro (AIRC n. 12182); Fondazione Italiana Malattie Pancreas – Ministero Salute (CUP_J33G13000210001); National Health and Medical Research Council of Australia (NHMRC; 631701, 535903, CDF 1112113, PRF 1025427, SRF 455857, 535903); The Queensland State Government Smart State National and International Research Alliances Program (NIRAP); Institute for Molecular Bioscience/University of Queensland; The Royal Australasian College of Physicians, Sidney Catalyst, NHMRC, Pancare Australia; 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; R.T. Hall Trust; Petre Foundation; Philip Hemstritch Foundation; Gastroenterological Society of Australia (GESA Senior Research Fellowship); Royal Australasian College of Surgeons (RACS); Royal Australasian College of Physicians (RACP); Royal College of Pathologists of Australasia (RCPA); QIMR Berghofer Medical Research; The Keith Boden Fellowship (K.N.); NHGRI U54 HG003273; CPRIT grant RP101353-P7; Wellcome Trust Senior Investigator Award (103721/Z/14/Z); CRUK Programme (C29717/A17263 and C29717/A18484); CRUK Glasgow Centre (C596/A18076); CRUK Clinical Training Award (C596/A20921); Pancreatic Cancer UK Future Research Leaders Fund; The Howat Foundation; and the University of Glasgow.

Author information

Authors and Affiliations




Biospecimens were collected at affiliated hospitals and processed at each biospecimen core resource centre. Investigator contributions are as follows: A.S., D.K.C., Nicola W., A.V.B., S.M.G. (concept and design); A.S., D.K.C., Nicola W., A.V.B., S.M.G. (project leaders); A.S., D.K.C., K.N., V.Co., Nicola W., A.V.B., S.M.G. (writing team); K.N., A.-M.P., P.B., R.T.L., A.L.J., B.R., S.C., M.C.J.Q, P.J.W., S.H.N., I.D., A.P.D.T, M.V.D., L.L., A.Mal., M.M., M.D.J., J.Hu., L.A.C., V.Ch., A.M.N., M.Pa., M.Pi., C.J.S., A.P., I.R., C.T., V.Ch, A.Maw., E.S.H., E.K.C., A.C., J.A.L., N.B.J., F.D., M.C.G., J.S.S., N.D.M., K.E., N.Q.N., N.Z., M.Fal., M.Fas., G.B., S.P., W.E.F., A. Malp., A. Maw., G.V.B., D.A.W., R.A.G., E.A.M., A.B., C.B., G.T., P.P., A.V.B. (sample collection, processing, quality control & clinical annotation); A.S., D.K.C., J.G.K., A.J.G, A.V.B. (clinico-pathological analyses and interpretation); V.L.J.W., B.A.L. (colon sample collection and clinical annotation); A.S., B.R., I.C., P.C., J.G.K., M.Fas., A.J.G (pathology assessment); V.Co., D.K.M., M.Sc., M.Si., D.A., C.V., T.J.C.B., A.N.C., I.H., S.I., S.McL., C.N., E.N., E.A., S.Be., M.Si. (sequencing); O.H., R.A.D., L.M.S.L., M.L., H.A.P., R.R.R., J.V.P. (telomere analysis); K.N., A.M.P., P.B., R.T.L., A.L.J., A.Maf., S.Ba., K.O.S., S.S., M.C.J.Q., P.J.W., M.J.A., J.L.F., F.N., Nick W., O.H., S.H.K., C.L., S.W., Q.X., J.W., M.Pi., M.C., J.V.P., Nicola W., S.M.G. (bioinformatics); K.K.K, J.Ha. (protein modelling); JLH., K.K.K (functional validation of CHEK2 variants); A.P.D.T. (revision of fusion cases and FISH analysis); A.S., D.K.C., K.N., V.Co., P.B., P.P., N.B.J., F.D., Nicola W., S.M.G., A.V.B. (data interpretation). All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Aldo Scarpa, Andrew V. Biankin or Sean M. Grimmond.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks S. Chanock and the other anonymous reviewer(s) for their contribution to the peer review of this work.

A list of participants and their affiliations is provided in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Flow chart of the experiments performed on 160 PanNETs.

The chart shows the workflow of analyses conducted on the discovery set of 98 PanNETs and on the validation set of an additional 62 PanNETs and 1 colorectal cancer. CNA, copy-number analysis.

Extended Data Figure 2 Five mutation signatures in pancreatic neuroendocrine tumours.

a, Stability plot indicates there are five mutation signatures (>0.9). b, The profile of the five mutational signatures (A–E) and what function has been assigned to these signatures (MUTYH, APOBEC, BRCA, Age and ‘Signature 5’).

Extended Data Figure 3 Validation of the novel signature in additional MUTYH carriers.

a, Four PanNet samples, three of which harboured a pathogenic MUTYH germline variant, and a colon tumour with a pathogenic MUTYH mutation underwent WGS to validate the association of MUTYH biallelic inactivation with the MUTYH mutation signature. b, Family pedigree of the patient with colon cancer. The 64-year-old male patient with colon cancer was identified as a candidate for MUTYH mutation analysis owing to the presence of two synchronous cancers in the proximal colon, each arising in a contiguous tubulovillous adenoma, as well as approximately 50 adenomatous polyps predominantly in the caecum and ascending colon. The index patient’s brother presented with colorectal cancer at 45 years of age and his sister presented with colorectal cancer at 64 years of age and with breast cancer at 59 years of age. The index patient’s son had polyps removed at 36 years of age. Mutation signature analysis was performed using the 98 discovery PanNET samples and the colon and 4 PanNET validation samples. c, Stability plot showing the solution for the five mutational signatures (>0.75). d, The profile of the five mutational signatures (A–E) and what function has been assigned to these signatures (MUTYH, APOBEC, BRCA, Age and ‘Signature 5’). e, The contribution of each signature (mutations per Mb) and proportion of the signatures in each tumour are shown.

Extended Data Figure 4 Structural rearrangements in pancreatic neuroendocrine tumours.

a, Top, the number and type of somatic structural rearrangements in each tumour. Bottom, tumours with more events tended to have longer telomeres. b, Two methods were used to determine clusters of somatic structural rearrangement breakpoints. Orange squares, chromosomes with a significant cluster of events as determined by a goodness-of-fit test against the expected distribution (P < 0.0001, Kolmogorov–Smirnov test). Blue squares, chromosomes deemed to harbour a high number of breakpoints because they had a chromosomal breakpoint per Mb rate that exceeded the 75th percentile of the chromosomal breakpoint per Mb rate for the cohort by five times the interquartile range. Red squares, chromosomes for which both of these criteria were met. Clusters of events were reviewed and nine tumours were found to harbour regions of chromothripsis. c, Recurrent chromothripsis for chromosome 11 was detected in four tumours. The chromothripsis event caused loss of the MEN1 gene locus in two of these samples.

Extended Data Figure 5 Functional analysis of CHEK2 variants.

a, CHEK2 structure indicating the positions of the germline variants. Mutations are highlighted by rendering as magenta sticks with protein domains coloured as indicated in the adjacent keys. The model includes a superimposed phosphopeptide (red). b, A summary of the CHEK2 variants and their predicted impact on protein structure. To functionally test the CHEK2 variants, a panel of FLAG–CHEK2 constructs encoding P85L, ∆77–82, D177H and E282K was generated. c, d, FLAG western blot of transfected HEK293T whole cell lysates (c) or anti-FLAG immunoprecipitates (d) showed that, compared to the wild type, there was normal expression of P85L but reduced expression of ∆77–82, D177H and E282K. e, Assessment of kinase activity of CHEK2 variants. Immunoprecipitated proteins were incubated either with GST alone (−) or with GST–CDC25C amino acids 200–256 (+) in the presence of γ-P32 ATP. Input and kinase activity were assessed by film radiography (top) and coomassie staining (bottom). Immunoprecipitates of ∆77–82, D177H and E282K had significantly reduced kinase activity in terms of both autophosphorylation and phosphorylation of CDC25C whereas the activity of P85L was normal. f, Quantification of expression levels by western blotting expressed as a fraction of wild type. Data points represent independent experiments. Error bars are mean ± s.e.m. g, h, Quantification of kinase activity. P32 counts for CDC25C (g) and CHEK2 (h) bands were scintillation counted. Corresponding bands from untransfected controls were used for background subtraction. Background-corrected P32 counts per minute were then standardized to wild type for each experiment. Data points represent independent experiments. Error bars are mean ± s.e.m. i, j, Quantification of kinase activity relative to protein expression. Kinase activity (from i and j) was standardized to protein expression level (from f). D177H was not analysed in this manner owing to its very low expression level. Error bars are mean ± s.e.m. Once the low expression level of ∆77–82 is taken into account, it is evident that the expressed protein retains normal kinase activity. On the other hand, E282K is kinase defective even after adjusting for its reduced expression. D177H expression is so low that it is not possible to reliably correct kinase activity for relative expression level, so it is unclear whether D177H is kinase dead as well as unstable. Data are summarized in Supplementary Table 16.

Extended Data Figure 6 Recurrently mutated genes in pancreatic neuroendocrine tumours.

a, The number of SNVs and indels within the genome of each patient (n = 98) is shown in the histogram. The driver plot displays the somatic mutations in key genes or those identified as significantly mutated (Intogen Q < 0.1). SETD2 is also reported, although its Q value was 0.15, as it was recurrently inactivated in six samples and multiple independent deleterious SETD2 mutations were observed in one tumour (a nonsense present at 3%, a missense at 14%, and a frameshift at 11%; only the nonsense is shown but the case is highlighted with a black arrow), suggesting strong selection for SETD2 inactivation in that tumour. b, Somatic mutations in MEN1 are predominantly nonsense mutations or insertions–deletions causing frame shifts and premature protein termination, and occur throughout the protein.

Extended Data Figure 7 Genome characteristics of PanNETs.

Copy number was determined using Illumina SNP arrays in a cohort of 98 PanNETs. a, Copy number events were mainly comprised of whole chromosome arm loss or gain. Cluster analysis of the chromosome arm level copy number state stratified the tumours into four subtypes. Group 1: recurrent pattern of whole chromosomal loss, affecting specific chromosomes (1, 2, 3, 6, 8, 10, 11, 15, 16 and 22); group 2: samples with a limited number of events, many with loss affecting chromosome 11; group 3: polyploid tumours, with gain of all chromosomes; and group 4: aneuploid tumours, containing predominantly whole chromosome gains affecting multiple chromosomes). b, The proportion of bases within the genome affected by copy number change. c, The mutations per Mb (SNPs and small insertion deletions). d, GISTIC analysis showing recurrent gains (red) and losses (blue) of the entire cohort.

Extended Data Figure 8 Telomere length is associated with somatic mutations.

Whole genome sequence data were used to estimate telomere length in PanNETs relative to the matched normal sample. a, Telomere length estimated by whole-genome sequencing correlated with the telomere length calculated from qPCR (R2 = 0.8091). Values are plotted on a log10 scale. be, Boxplots were used to show the association of relative telomere length and DAXX or ATRX and MEN1 mutation status. Mann–Whitney tests were used to determine significant associations (P < 0.05). b, c, Tumours harbouring DAXX or ATRX mutations contain longer telomeres. d, Tumours harbouring MEN1 mutations contain longer telomeres. e, Telomere length is shown in relation to DAXX or ATRX and MEN1 somatic mutations.

Extended Data Figure 9 RNA-seq of PanNET tumours.

Unsupervised clustering, network and gene enrichment analysis for available RNA-seq data identify PanNET subgroups associated with hypoxia and metabolic reprogramming. a, Unsupervised clustering identified three distinct PanNET subgroups (1–3). b, A gene signature defining three expression groups previously described in PanNETs showed enrichment of expression of the intermediate-group genes43 in Group 1 and the metastasis-like PanNET (MLP) genes43 in Group 3. c, Network analysis identified a significant sub-network of genes differentially expressed between Group 3 and other groups (Group 1 and Group 2). Red nodes represent genes upregulated in Group 3 and green nodes represent genes upregulated in other groups. Shaded areas represent network communities. d, Gene enrichment analysis for genes belonging to the sub-network shown in b. e, Heatmap showing the differential expression of genes belong to the identified sub-network. Somatic mutations in some of the recurrently mutated genes are shown (MEN1, DAXX, ATRX and members of the mTOR pathway: DEPDC5, MTOR, PTEN, TSC1 and TSC2).

Extended Data Figure 10 Genomic events associated with outcome.

Kaplan–Meier survival curves. a, b, Tumours harbouring DAXX or ATRX mutations had a poor prognosis in the whole cohort (a) and in the G2 cohort (b). c, Tumours with telomere lengths that were neither short or long had a better prognosis. d, Tumours harbouring mutations in genes that activate the mTOR pathway had a poor prognosis in the G2 cohort (log rank test was used in all instances).

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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 134 kb)

Supplementary Data

This file contains Supplementary Tables 1-16. (XLSX 4587 kb)

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Scarpa, A., Chang, D., Nones, K. et al. Whole-genome landscape of pancreatic neuroendocrine tumours. Nature 543, 65–71 (2017).

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