Citations

Abstract Objective Genome-wide association studies (GWAS) identify associations of individual SNPs with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants has been shown for many disorders to be a powerful tool in discovering novel networks of susceptibility genes. Design We have conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product (sARTP) method to identify novel gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDA) in 9,040 cases and 12,495 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes identified in PDA-associated gene sets and pathways. Results We identified 14 gene sets and pathways associated with PDA at FDR < 0.05. Five of the strongest signals (P-value ≤ 1.3x10-5 Bonferroni corrected) were in genes from KEGG maturity onset diabetes of the young, Reactome regulation of beta cell development, Biocarta role of epidermal growth factor (EGF) receptor transactivation by G-protein-coupled receptors in cardiac hypertrophy pathway, Nikolsky breast cancer chr17q11-q21 amplicon gene set, and Pujana ATM Pearson correlation coefficient (PCC) network gene set. We identified and validated rs876493 and three correlated SNPs (PGAP3) and rs3124737 (CASP7) as eQTLs in two normal derived pancreas tissue datasets. Conclusion Our agnostic gene set and pathway analysis integrated with functional annotation and eQTL analysis provides novel insight into genes and pathways that may be biologically relevant for risk of PDA. Citation Format: Naomi Walsh, Han Zhang, Paula L. Hyland, Qi Yang, Evelina Mocci, Mingfeng Zhang, Erica J. Childs, Zhaoming Wang, Stephen Chanock, Patricia Hartge, Robert Hoover, Peter Kraft, Donghui Li, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Laufey T. Amundadottir, Kai Yu, Alison P. Klein, Rachael Z. Stolzenberg-Solomon. Large pathway and gene set analysis of GWAS data identifies novel associations for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 242.

Abstract Objective: To elucidate the genetic architecture of gene expression in pancreatic tissues. Design: We performed expression quantitative trait locus (eQTL) and allele specific expression (ASE) analyses using RNA-sequence data and 1000 Genomes (1000G) imputed GWAS genotypes from 95 fresh frozen histologically normal pancreatic tissue samples. Data from 115 pancreatic tumor-derived tissue samples from The Cancer Genome Atlas (TCGA) was included for comparison. Results: We identified 38,615 cis-eQTLs (corresponding to 484 Genes) in histologically normal tissues and 39,713 cis-eQTL (corresponding to 237 Genes) in tumor tissues (FDR<0.1), with the strongest effects seen near transcriptional start sites (TSS). Approximately 23% and 42% of genes with significant cis-eQTLs (eGenes) appeared to be specific for tumor and normal derived tissues, respectively. Significant enrichment of cis-eQTL variants was noted in noncoding regulatory regions marked by modified histones, DNAse hypersensitivity and bound transcription factors, in particular for pancreatic tissues (1.53-3.12 fold, P≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 in the ABO gene (rs687289) was associated with ABO expression in histologically normal (P=5.8x10-8) and tumor-derived (P=8.3x10-5) pancreatic tissues. The high linkage disequilibrium (LD) between this variant and the O blood group generating deletion variant in exon 6 of ABO suggested that nonsense-mediated decay (NMD) of the “O” mRNA could explain the eQTL. However, knock-down of crucial NMD regulators did not influence decay of the ABO “O” mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. Conclusions: We have identified cis-eQTLs representing potential functional regulatory variants in the pancreas and generated a rich dataset for further studies on gene expression and regulation in pancreatic tissues. Citation Format: Laufey T. Amundadottir, Soren Lykke Andresen, Wenming Xiao, Jason Hoskins, Ashley Jermusyk, Lauren Rost, Irene Collins, Jinping Jia, Michael Mobaraki, Bin Zhu, Robert Kurtz, Hemang Parikh, Lei Song, Meredith Yeager, Torben Jensen, William Bamlet, Nilanjan Chatterjee, Brian Wolpin, Jill Smith, Sara Olson, Gloria Petersen, Jianxin Shi, Mingfeng Zhang. Analysis of cis-eQTLs in normal and tumor-derived pancreatic tissues reveals functional insights, including for the 9q34.1 ABO pancreatic cancer risk locus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1442. doi:10.1158/1538-7445.AM2017-1442

Abstract Introduction: Obesity and diabetes are two major modifiable risk factors for pancreatic cancer. Genetic variation affecting the risk of obesity- and diabetes-related pancreatic cancer have yet to be comprehensively investigated at the genome-wide level. Aims: To identify novel gene-environment interactions (GxE) of obesity/diabetes on pancreatic cancer risk. Methods: We conducted a multi-stage genome-wide gene-diabetes and gene-obesity interaction study using GWAS data of 7095 cases and 10710 controls from Pancreatic Cancer Consortium (PanScan I, PanScan II and PanScan III) and Pancreatic Cancer Case Control Consortium (PanC4). About 870,000 SNPs, having MAF ≥ 0.005 and having been genotyped in at least one study, were analyzed. Missing genotypes were imputed using IMPUTE2 and the Haplotype Reference Consortium (HRC) reference panel. Case-control (CC) and case-only (CO) methods were used for SNP-level GxE analysis. Fixed-, random- and joint-effect models built in “rareGE” R package were used for gene-based GxE analysis. About 20,000 genes that have at least one SNP genotyped were included in the analysis. Age, gender and principal components accounting for subpopulation structure were adjusted in both SNP-level and gene-level analyses. Obesity was defined as body mass index ≥30 mg/m2. Diabetics with <3-year duration were excluded for gene-diabetes interaction analysis. The PanScan I, II, III and PanC4 datasets were analyzed separately followed by meta-analysis of the summary statistics. Fixed-effect and Fisher’s p-value based meta-analysis was applied for SNP-level and gene-level GxE analysis, respectively. Results: No genome-wide significant (P<5 x 10-8 ) interaction at the SNP level was detected in any of the PanScan or PanC4 datasets or in the meta-analysis. A significant interaction of diabetes and FAM63A (family with sequence similarity 63 member A) gene (P<2.5 x 10-6) was observed in the meta-analysis (PFixed= 3.4 x 10-5, PRandom= 1.9 x 10-6, PJoint= 6.8 x 10-7) . This interaction showed nominal significance in all individual dataset analyses. The corresponding P values for this interaction were 4.2 x 10-2, 3.8 x 10-2 and 6.8 x 10-2 in PanScan I, 2.8 x 10-2, 3.9 x 10-2 and 6.5 x 10-2 9.1 x 10-3, 2.2 x 10-4 and 8.8 x 10-6 in PanScan III, and 3.1 x 10-3, 3.4 x 10-3 and 8.8 x 10-3 in PanC4, respectively. Conclusions: In the largest genome-wide GxE analysis for pancreatic cancer to date, we identified a significant diabetes interacting gene in pancreatic cancer, which was consistently replicated in 4 individual GWAS studies. This observation identifies FAM63A as a candidate for future functional studies to understand its role in pancreatic cancer etiology. Further increasing the sample size may unveil additional genetic loci that contribute to the susceptibility to pancreatic cancer via G x E. Citation Format: Hongwei Tang, Lai Jiang, Laufey T. Amundadottir, Harvey A. Risch, Rachael S. Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Gloria Petersen, Nilanjan Chatterjee, Donghui Li, Peter Kraft, Peng Wei. Genomewide gene-diabetes and gene-obesity interaction scan in 7095 cases and 10710 controls from Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case Control Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1295. doi:10.1158/1538-7445.AM2017-1295

Abstract Background: Many cancers share specific genetic risk factors, including both rare high-penetrance mutations and common SNPs identified through genome-wide association studies (GWAS). However, little is known about the overall shared heritability across cancers. Quantifying the extent to which two distinct cancers share genetic origin will give insights to shared biological mechanisms underlying cancer and inform design for future genetic association studies. Methods: In this study, we estimated the pair-wise genetic correlation between six cancer types (breast, colorectal, lung, ovarian, pancreatic, and prostate) using cancer-specific GWAS summary statistics data based on 66,958 case and 70,665 control subjects of European ancestry. We also estimated genetic correlations between cancers and 14 noncancer diseases and traits. Results: After adjusting for 15 pair-wise genetic correlation tests between cancers, we found significant (P < 0.003) genetic correlations between pancreatic and colorectal cancer (rg = 0.55, P = 0.003), lung and colorectal cancer (rg = 0.31, P = 0.001). We also found suggestive genetic correlations between lung and breast cancer (rg = 0.27, P = 0.009), and colorectal and breast cancer (rg = 0.22, P = 0.01). In contrast, we found no evidence that prostate cancer shared an appreciable proportion of heritability with other cancers. After adjusting for 84 tests studying genetic correlations between cancer types and other traits (Bonferroni-corrected P value: 0.0006), only the genetic correlation between lung cancer and smoking remained significant (rg = 0.41, P = 1.03 × 10−6). We also observed nominally significant genetic correlations between body mass index and all cancers except ovarian cancer. Conclusions: Our results highlight novel genetic correlations and lend support to previous observational studies that have observed links between cancers and risk factors. Impact: This study demonstrates modest genetic correlations between cancers; in particular, breast, colorectal, and lung cancer share some degree of genetic basis. Cancer Epidemiol Biomarkers Prev; 26(9); 1427–35. ©2017 AACR.

Objective To elucidate the genetic architecture of gene expression in pancreatic tissues. Design We performed expression quantitative trait locus (eQTL) analysis in histologically normal pancreatic tissue samples (n=95) using RNA sequencing and the corresponding 1000 genomes imputed germline genotypes. Data from pancreatic tumour-derived tissue samples (n=115) from The Cancer Genome Atlas were included for comparison. Results We identified 38 615 cis -eQTLs (in 484 genes) in histologically normal tissues and 39 713 cis -eQTL (in 237 genes) in tumour-derived tissues (false discovery rate <0.1), with the strongest effects seen near transcriptional start sites. Approximately 23% and 42% of genes with significant cis -eQTLs appeared to be specific for tumour-derived and normal-derived tissues, respectively. Significant enrichment of cis -eQTL variants was noted in non-coding regulatory regions, in particular for pancreatic tissues (1.53-fold to 3.12-fold, p≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 (rs687289) was associated with ABO expression in histologically normal (p=5.8×10 −8 ) and tumour-derived (p=8.3×10 −5 ) tissues. The high linkage disequilibrium between this variant and the O blood group generating deletion variant in ABO (exon 6) suggested that nonsense-mediated decay (NMD) of the ‘O’ mRNA might explain this finding. However, knockdown of crucial NMD regulators did not influence decay of the ABO ‘O’ mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. Conclusions We have identified cis -eQTLs representing potential functional regulatory variants in the pancreas and generated a rich data set for further studies on gene expression and its regulation in pancreatic tissues.

Abstract Individuals from pancreatic cancer families are at increased risk, not only of pancreatic cancer, but also of melanoma, breast, ovarian, and colon cancers. While some of the increased risk may be due to mutations in high-penetrance genes (i.e., BRCA2, PALB2, ATM, p16/CDKN2A or DNA mismatch repair genes), common genetic variants may also be involved. In a high-risk population of cases with either a family history of pancreatic cancer or early-onset pancreatic cancer (diagnosis before the age of 50 years), we examined the role of genetic variants previously associated with risk of pancreatic, breast, ovarian, or prostate cancer. We genotyped 985 cases (79 early-onset cases, 906 cases with a family history of pancreatic cancer) and 877 controls for 215,389 SNPs using the iSelect Collaborative Oncological Gene-Environment Study (iCOGS) array with custom content. Logistic regression was performed using a log-linear additive model. We replicated several previously reported pancreatic cancer susceptibility loci, including recently identified variants on 2p13.3 and 7p13 (2p13.3, rs1486134: OR = 1.36; 95% CI, 1.13–1.63; P = 9.29 × 10−4; 7p13, rs17688601: OR = 0.76; 95% CI, 0.63–0.93; P = 6.59 × 10−3). For the replicated loci, the magnitude of association observed in these high-risk patients was similar to that observed in studies of unselected patients. In addition to the established pancreatic cancer loci, we also found suggestive evidence of association (P &lt; 5 × 10−5) to pancreatic cancer for SNPs at HDAC9 (7p21.1) and COL6A2 (21q22.3). Even in high-risk populations, common variants influence pancreatic cancer susceptibility. Cancer Epidemiol Biomarkers Prev; 25(7); 1185–91. ©2016 AACR.