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• W2531210961 abstract "Pancreatic cancer is molecularly diverse, with few effective therapies. Increased mutation burden and defective DNA repair are associated with response to immune checkpoint inhibitors in several other cancer types. We interrogated 385 pancreatic cancer genomes to define hypermutation and its causes. Mutational signatures inferring defects in DNA repair were enriched in those with the highest mutation burdens. Mismatch repair deficiency was identified in 1% of tumors harboring different mechanisms of somatic inactivation of MLH1 and MSH2. Defining mutation load in individual pancreatic cancers and the optimal assay for patient selection may inform clinical trial design for immunotherapy in pancreatic cancer. Pancreatic cancer is molecularly diverse, with few effective therapies. Increased mutation burden and defective DNA repair are associated with response to immune checkpoint inhibitors in several other cancer types. We interrogated 385 pancreatic cancer genomes to define hypermutation and its causes. Mutational signatures inferring defects in DNA repair were enriched in those with the highest mutation burdens. Mismatch repair deficiency was identified in 1% of tumors harboring different mechanisms of somatic inactivation of MLH1 and MSH2. Defining mutation load in individual pancreatic cancers and the optimal assay for patient selection may inform clinical trial design for immunotherapy in pancreatic cancer. Pancreatic ductal adenocarcinoma has a 5-year survival of <5%, with therapies offering only incremental benefit,1Vogelzang N.J. et al.J Clin Oncol. 2012; 30: 88-109Crossref PubMed Scopus (85) Google Scholar potentially due to the diversity of its genomic landscape.2Bailey P. et al.Nature. 2016; 531: 47-52Crossref PubMed Scopus (1973) Google Scholar, 3Biankin A.V. et al.Nature. 2012; 491: 399-405Crossref PubMed Scopus (1379) Google Scholar, 4Waddell N. et al.Nature. 2015; 518: 495-501Crossref PubMed Scopus (1466) Google Scholar Recent reports link high mutation burden with response to immune checkpoint inhibitors in several cancer types.5Le D.T. et al.N Engl J Med. 2015; 372: 2509-2520Crossref PubMed Scopus (6099) Google Scholar Defining tumors that are hypermutated with an increased mutation burden and understanding the underlying mechanisms in pancreatic cancer has the potential to advance therapeutic development, particularly for immunotherapeutic strategies. Whole genome sequencing (WGS, n = 180) and whole exome sequencing (n = 205) of 385 unselected predominantly sporadic pancreatic ductal adenocarcinoma (Supplementary Table 1) defined a mean mutation load of 1.8 and 1.1 mutation per megabase (Mb), respectively (Supplementary Table 2). Outlier analysis identified 20 tumors with the highest mutation burden (5.2%, 15 WGS and 5 exome) (Table 1 and Supplementary Figure 1A), 5 of which were considered extreme outliers and classified as hypermutated as they contained ≥12 somatic mutations/Mb, the defined threshold for hypermutation in colorectal cancer.6Cancer Genome Atlas NetworkNature. 2012; 487: 330-337Crossref PubMed Scopus (5894) Google Scholar Immunohistochemistry for mismatch repair (MMR) proteins (MSH2, MSH6, MLH1, and PMS2) identified 4 MMR-deficient tumors, all of which were hypermutated (n = 180, Figure 1).Table 1Clinical and Histologic Features and Proposed Etiology for Highly Mutated Pancreatic Ductal Adenocarcinoma Tumors (n = 20)Sample IDPersonal and family history of malignancyHistologyMutation load, mutations/MbIHC resultMSIsensor scoreKRAS mutationPredominant mutation signature (mutations/Mb)SV subtype (no. of events)Proposed etiologyHypermutation (extreme outliers) ICGC_0076aSample sequenced by WGS, other samples by exome sequencing.NoneMixed signet ring, mucinous and papillary adenocarcinoma38.55Absent MLH1 and PMS228.3p.G12VMMR (18.3)Scattered (131)MMR deficiency: >280 kb somatic homozygous deletion over MSH2. ICGC_0297aSample sequenced by WGS, other samples by exome sequencing.NoneUndifferentiated adenocarcinoma60.62Absent MSH2 and MSH627.33WTMMR (33.4)Scattered (75)MMR deficiency: Somatic MLH1 promoter hypermethylation. ICGC_0548aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, moderately differentiated30.13Absent MSH2 and MSH617.47WTMMR (16.6)Stable (49)MMR deficiency: >27 kb somatic inversion rearrangement disrupting MSH2. ICGC_0328aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma16.63Normal3.2p.G12DUnknown (11.9)Scattered (110)Cell line with signature: etiology unknown. ICGC_00901 FDR, father CRCDuctal adenocarcinoma, moderately differentiated12.9Absent MSH2 and MSH60.21p.G12CNANAMMR deficiency: somatic MSH2 splice site c.2006G>A.Highly mutated tumors ICGC_0054aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, poorly differentiated6.52Normal0.01p.G12VHR deficiency (1.3)Unstable (310)HR deficiency: no germline or somatic cause found. ICGC_0290aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, poorly differentiated6.54Not available0.07p.G12VHR deficiency (3.1)Unstable (558)HR deficiency: Germline BRCA2 mutation c.7180A>T, p.A2394*. Somatic CN-LOH. ICGC_0215aSample sequenced by WGS, other samples by exome sequencing.2 FDR lung cancer, 2 FDR prostate cancer. Previous CRC and melanomaDuctal adenocarcinoma, moderately differentiated6.27Normal0.01p.G12VHR deficiency (1.9)Scattered (111)HR deficiency: Germline ATM mutation c.7539_7540delAT, p.Y2514*. Somatic CN-LOH. ICGC_0324NoneDuctal adenocarcinoma, moderately differentiated6.24Normal0p.G12DNANAUndefined ICGC_0034aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, poorly differentiated6.09Normal4.02p.G12DHR deficiency (3.4)Unstable (366)HR deficiency: Germline BRCA2 mutation c.5237_5238insT, p.N1747*. Somatic CN-LOH. ICGC_0131aSample sequenced by WGS, other samples by exome sequencing.Lung cancer after PCDuctal adenocarcinoma, moderately differentiated5.63Normal0p.G12DT>G at TT sites (3.0)Focal (147)T>G at TT sites signature: etiology potentially associated with DNA oxidation ICGC_0006aSample sequenced by WGS, other samples by exome sequencing.1 FDR, father lung cancerAdenocarcinoma arising from IPMN, moderately differentiated5.29Normal0.01p.G12DHR deficiency (1.2)Unstable (211)HR deficiency: Somatic BRCA2 c.5351dupA, p.N1784KfsTer3. Somatic CN-LOH. ICGC_0321aSample sequenced by WGS, other samples by exome sequencing.2 FDR, mother and cousin breast cancerDuctal adenocarcinoma, poorly differentiated4.79Not available0p.G12DHR deficiency (2.1)Unstable (286)HR deficiency: Germline BRCA2 c.6699delT, p.F2234LfsTer7. Somatic CN loss- 1 copy. ICGC_0309aSample sequenced by WGS, other samples by exome sequencing.NoneAdenocarcinoma arising from IPMN, moderately differentiated4.74Normal0.03p.G12VT>G at TT sites (3.1)Unstable (232)T>G at TT sites signature: etiology potentially associated with DNA oxidation ICGC_0005aSample sequenced by WGS, other samples by exome sequencing.1 FDR, mother CRCDuctal adenocarcinoma, poorly differentiated4.72Not available1p.G12VHR deficiency (1.1)Focal (95)HR deficiency: No germline or somatic cause found. ICGC_0016aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, poorly differentiated4.61Normal3.03p.G12VHR deficiency (1.7)Unstable (447)HR deficiency: potentially linked to Somatic RPA1 c.273G>T, p.R91S ICGC_00461 FDR, brother PCDuctal adenocarcinoma, poorly differentiated4.3Normal0p.Q61HNANAUndefined GARV_0668aSample sequenced by WGS, other samples by exome sequencing.NoneDuctal adenocarcinoma, poorly differentiated4.3Not available2.19p.G12VHR deficiency (1.6)Unstable (464)HR deficiency: Germline BRCA2 c.7068_7069delTC, p.L2357VfsTer2. Somatic CN loss - 1 copy. ICGC_0291NoneDuctal adenocarcinoma, well differentiated3.84Not available0.03p.G12RNANAHR deficiency: Somatic BRCA2 c.7283T>A, p.L2428*. ICGC_0256NoneDuctal adenocarcinoma, poorly differentiated3.72Not available0.06p.G12DNANAUndefinedCRC, colorectal cancer; FDR, first-degree relative; IHC, immunohistochemistry; IPMN, intraductal papillary mucinous neoplasm; CN-LOH, copy neutral loss of heterozygosity; CN, copy number; PC, pancreatic cancer; NA, not applicable to exome data.a Sample sequenced by WGS, other samples by exome sequencing. Open table in a new tab CRC, colorectal cancer; FDR, first-degree relative; IHC, immunohistochemistry; IPMN, intraductal papillary mucinous neoplasm; CN-LOH, copy neutral loss of heterozygosity; CN, copy number; PC, pancreatic cancer; NA, not applicable to exome data. KRAS mutation status and histopathologic characteristics have been associated with MMR-deficient pancreatic tumors.7Goggins M. et al.Am J Pathol. 1998; 152: 1501-1507PubMed Google Scholar Of the 4 MMR-deficient tumors in our cohort, 2 were KRAS wild-type; 3 had undifferentiated to moderately differentiated histology and one had a signet-ring component. These features were not predictive of MMR deficiency in our cohort, as 11 additional non−MMR-deficient tumors had a signet-ring cell component or colloid morphology, and 131 of 347 assessable tumors had poorly or undifferentiated histology. Mutational signature analysis can detect MMR deficiency indirectly based on the pattern of somatic mutations.8Alexandrov L.B. et al.Nature. 2013; 500: 415-421Crossref PubMed Scopus (6213) Google Scholar An MMR-deficient signature dominated the MMR-deficient tumors (with WGS), and was minimal in MMR intact tumors (Supplementary Figure 1). In addition, microsatellite instability (MSI), a hallmark of MMR deficiency in colorectal cancer, was detected in all three MMR deficient tumors with WGS using MSIsensor9Niu B. Ye K. et al.Bioinformatics. 2014; 30: 1015-1016Crossref PubMed Scopus (294) Google Scholar (Supplementary Table 2). MSI was not identified for the fourth MMR deficient sample potentially due to the reduced number of microsatellite loci in exome data. The underlying causes of MMR deficiency in the 4 cases were private somatic events. For 2 cases, MSH2 was disrupted by different structural rearrangements, 1 case contained a missense MSH2 mutation and the last, methylation of the MLH1 promoter (Figure 1). The missense mutation caused an MSH2 splice acceptor site mutation that alters the same nucleotide results in a pathogenic skipping of exon 13 in germline studies.10Thompson B.A. et al.Nat Genet. 2014; 46: 107-115Crossref PubMed Scopus (346) Google Scholar Hypermethylation of the MLH1 promoter is the predominant mechanism of MSI in sporadic colon cancer.11Boland C.R. et al.Gastroenterology. 2010; 138: 2073-2087 e3Abstract Full Text Full Text PDF PubMed Scopus (1359) Google Scholar The remaining hypermutated tumor contained an intact MMR pathway, and was a cell line (ATCC, CRL-2551) with an unidentified mutational signature, therefore the high mutation burden in this sample may be the result of long-term cell culture. The 15 samples (11 WGS and 4 exome) identified in the outlier analysis with high mutation burden, but not hypermutated (∼4 to 12 mutations/Mb) contained no evidence of MMR deficiency. Mutational signature analysis of the WGS samples indicated homologous recombination (HR) repair deficiency as the most substantial (range, 1.0–3.4 mutations/Mb) contributor to the mutation burden for 8 WGS mutation load outlier tumors. In support of a HR defect4Waddell N. et al.Nature. 2015; 518: 495-501Crossref PubMed Scopus (1466) Google Scholar; 7 of these tumors contained high levels of genomic instability with >200 structural variants and mutations in genes involved in HR were present for 6 of 8 cases (Supplementary Table 2). In addition, 1 case that had undergone exome sequencing had a somatic BRCA2 nonsense mutation that likely contributed to HR deficiency in this case. A mutational signature associated with T>G mutations at TT sites previously described in other cancers, including esophageal cancer12Nones K. Waddell N. Wayte N. et al.Nat Commun. 2014; : 5Google Scholar was the major contributor (>3 mutations/Mb) in 2 samples. For these 2 and the remaining 4 cases, no potential causative event could be identified. Although germline defects in MMR genes are well reported in pancreatic cancer13Grant R.C. Selander I. et al.Gastroenterology. 2015; 148: 556-564Abstract Full Text Full Text PDF PubMed Scopus (211) Google Scholar in our cohort, they did not contribute to MMR deficiency even in those with familial pancreatic cancer or a personal or family history of Lynch-related tumors. A germline truncating variant was detected in PMS2 in 1 case, but did not have loss of the second allele, had normal immunohistochemistry staining and did not display a MMR mutational signature (Supplementary Table 2). MMR deficiency is important in the evolution in a small, but meaningful proportion of pancreatic cancers with a prevalence of 1% (4 of 385) in our cohort. This is consistent with recent studies using the Bethesda polymerase chain reaction panel,14Laghi L. et al.PLoS One. 2012; 7: e46002Crossref PubMed Scopus (55) Google Scholar and with previous estimates of MSI prevalence of 2%−3%.15Nakata B. et al.Clin Cancer Res. 2002; 8: 2536-2540PubMed Google Scholar However, in tumors with low epithelial content that underwent exome sequencing, the sensitivity of somatic mutation detection is reduced, which will affect mutation burden and signature analysis. While cognizant of small numbers, immunohistochemistry was the most accurate in defining MMR due to multiple genomic mechanisms of MMR gene inactivation. Multiple methods to define MMR deficiency may be required for clinical trials that aim to recruit MMR-deficient participants to assess the potential efficacy of checkpoint inhibitors or other therapies in pancreatic cancer. Homologous recombination-deficient tumors, and those with a novel signature seen in esophageal cancer had an increased mutation burden, and need further evaluation as potential patient selection markers for clinical trials of checkpoint inhibitor and other therapies that target tumors with a high mutation burden. The authors would like to thank Cathy Axford, Deborah Gwynne, Mary-Anne Brancato, Clare Watson, Michelle Thomas, Gerard Hammond, and Doug Stetner for central coordination of the Australian Pancreatic Cancer Genome Initiative, data management, and quality control; Mona Martyn-Smith, Lisa Braatvedt, Henry Tang, Virginia Papangelis, and Maria Beilin for biospecimen acquisition; and Sonia Grimaldi and Giada Bonizzato of the ARC-Net Biobank for biospecimen acquisition. For a full list of contributors see Australian Pancreatic Cancer Genome Initiative: http://www.pancreaticcancer.net.au/apgi/collaborators. The cohort consisted of 385 patients with histologically verified pancreatic exocrine carcinoma, prospectively recruited between 2006 and 2013 through the Australian Pancreatic Cancer Genome Initiative (www.pancreaticcancer.net.au) as part of the International Cancer Genome Consortium.1Hudson T.J. et al.Nature. 2010; 464: 993-998Crossref PubMed Scopus (1689) Google Scholar Ethical approval was granted at all treating institutions and individual patients provided informed consent upon entry to the study. The clinicopathologic information for the cohort is described in (Supplementary Table 1), and the global mutation profile has previously been reported for some of these tumors (Supplementary Table 2). Tumor and normal DNA were extracted after histologic review from fresh frozen tissue samples collected at the time of surgical resection or biopsy, as described previously.2Biankin A.V. et al.Nature. 2012; 491: 399-405Crossref PubMed Scopus (1513) Google Scholar Tumor cellularity was determined from single-nucleotide polymorphism array data using qpure.3Song S. et al.PLoS One. 2012; 7: e45835Crossref PubMed Scopus (85) Google Scholar Tumors with epithelial content ≥40% underwent WGS lower cellularity tumors underwent whole exome sequencing. DNA from patient-derived pancreas cell lines and matched normal was also extracted. Exome and WGS were performed using paired 100-bp reads on the Illumina HiSeq 2000, as described previously.2Biankin A.V. et al.Nature. 2012; 491: 399-405Crossref PubMed Scopus (1513) Google Scholar, 4Waddell N. et al.Nature. 2015; 518: 495-501Crossref PubMed Scopus (1686) Google Scholar Regions of germline and somatic copy number change were detected using Illumina SNP BeadChips with GAP.5Popova T. et al.Genome Biol. 2009; 10 (R128−R128)Crossref PubMed Scopus (151) Google Scholar Somatic structural variants were identified from WGS reads using the qSV tool.4Waddell N. et al.Nature. 2015; 518: 495-501Crossref PubMed Scopus (1686) Google Scholar, 6Patch A.M. et al.Nature. 2015; 521: 489-494Crossref PubMed Scopus (930) Google Scholar Single nucleotide variants were called using 2 variant callers: qSNP7Kassahn K.S. et al.PLoS One. 2013; 8: e74380Crossref PubMed Scopus (52) Google Scholar and GATK.8McKenna A. et al.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (14755) Google Scholar Mutations identified by both callers or, those that were unique to a caller but verified by an orthogonal sequencing approach, were considered high confidence and used in all subsequent analyses. Small indels (<200 bp) were identified using Pindel9Ye K. et al.Bioinformatics. 2009; 25: 2865-2871Crossref PubMed Scopus (1391) Google Scholar and each indel was visually inspected in the Integrative Genome Browser. The distribution of the total number of small somatic mutations (coding and noncoding single nucleotide and indel variants) identified per megabase for exome and WGS sequence data were analyzed separately. The group of samples with high mutation load, at the top of each distribution, were defined as the upper distribution outliers for mutations per megabase, that is, ≥75th centile + (1.5× interquartile range). The threshold for detecting outliers in the exome and WGS groups was 3.4 and 4.2 mutations/Mb, respectively. From within the highly mutated set of tumors, hypermutated samples were identified as those with a mutation rate exceeding the thresholds for extreme distribution outliers (≥75th centile + [5× interquartile range]) of 7.4 and 8.1 mutations/Mb for exome and WGS sequencing, respectively. MSIsensor was used to detect microsatellite instability by directly comparing microsatellite repeat lengths between paired normal and tumor sequencing data.10Niu B. et al.Bioinformatics. 2014; 30: 1015-1016Crossref PubMed Scopus (378) Google Scholar A MSIsensor score of >3.5% of somatic microsatellites with repeat length shifts was the detection threshold used to indicate microsatellite instability as published for endometrial cancer.10Niu B. et al.Bioinformatics. 2014; 30: 1015-1016Crossref PubMed Scopus (378) Google Scholar This correlated well with the 5 and 7 microsatellite panels recommended in the Bethesda guidelines.10Niu B. et al.Bioinformatics. 2014; 30: 1015-1016Crossref PubMed Scopus (378) Google Scholar, 11Umar A. et al.J Natl Cancer Inst. 2004; 96: 261-268Crossref PubMed Scopus (2461) Google Scholar Tissue microarrays were constructed using at least three 1-mm formalin-fixed, paraffin-embedded tumor cores. Immunohistochemistry for MSH6 and PMS2 proteins was performed on tissue microarray sections as a screen for MMR deficiency due to MMR proteins forming heterodimers with concordant mismatch repair loss (ie, loss of MLH1 and PMS2 or loss of MSH2 and MSH6).12Hall G. et al.Pathology. 2010; 42: 409-413Abstract Full Text PDF PubMed Scopus (98) Google Scholar Immunohistochemistry on full tumor sections for MSH2, MLH1, MSH6, and PMS2 was performed in those with abnormal staining in core sections. The immunohistochemistry was performed as described previously12Hall G. et al.Pathology. 2010; 42: 409-413Abstract Full Text PDF PubMed Scopus (98) Google Scholar and scored by a senior pathologist. Somatic mutational signatures were extracted from the whole genome sequenced samples using the framework described previously.13Alexandrov L.B. et al.Cell Rep. 2013; 3: 246-259Abstract Full Text Full Text PDF PubMed Scopus (734) Google Scholar High confidence somatic substitutions were classified by the substitution change and sequence context, that is, the type of immediately neighboring bases to the variant. The framework processes the counts of somatic mutations at each context within each sample using non-negative factorization to produce the different signature profiles that are present in the data. The profiles identified were matched against reported signatures from the Cancer of Somatic Mutations in Cancer (http://cancer.sanger.ac.uk/cosmic/signatures). The major contributory signatures, defined as the mutational signature with the highest number of contributing somatic substitution variants, is reported for highly mutated whole genome samples. Bisulfite-converted whole-genome amplified DNA was hybridized to Infinium Human Methylation 450K Beadchips according to the manufacturers protocol (Illumina). Methylation arrays were performed on DNA from 174 pancreatic ductal adenocarcinoma samples, which were compared to DNA from 29 adjacent nonmalignant pancreata. A subset of the methylation data has been published previously.14Nones K. et al.Int J Cancer. 2014; 135: 1110-1118Crossref PubMed Scopus (156) Google Scholar We examined the data for evidence of tumor-specific hypermethylation of the promoter region of MLH1 and MSH2 genes. The methylation array data have been deposited into the International Cancer Genome Consortium data portal (dcc.icgc.org, project PACA-AU). Download .xlsx (.08 MB) Help with xlsx files Supplementary Tables 1 and 2" @default.
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• W2531210961 date "2017-01-01" @default.
• W2531210961 modified "2023-10-05" @default.
• W2531210961 title "Hypermutation In Pancreatic Cancer" @default.
• W2531210961 cites W1781764024 @default.
• W2531210961 cites W1843651808 @default.
• W2531210961 cites W1940241680 @default.
• W2531210961 cites W1987313243 @default.
• W2531210961 cites W2008255210 @default.
• W2531210961 cites W2065180810 @default.
• W2531210961 cites W2087326300 @default.
• W2531210961 cites W2094390470 @default.

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