Abstract 1591: Large-scale transcriptome-wide association study (TWAS) identifies novel candidate susceptibility genes for pancreatic cancer
Authors: Jun Zhong, Ashley Jermusyk, Lang Wu, Jason W. Hoskins, Irene Collins, Mingfeng Zhang, Song Lei, Charles C. Chung, Tongwu Zhang, Wenming Xiao, Rachael Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Xiao-Ou Shu, Stephen J. Chanock, Sara Olson, Nilanjan Chatterjee, Jill Smith, Jianxin Shi, Peter Kraft, Gloria M. Petersen, Wei Zheng, Laufey T. Amundadottir
Journal: Cancer Research (2019)

Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in United States with a 5-year survival rate of only 8%. Inherited predisposition plays an important role in PDAC risk. Rare, moderately to highly penetrant mutations in hereditary cancer and pancreatitis genes, identified in families with a high incidence of disease, account for a small fraction of cases. At the other end of the spectrum, common risk variants of low penetrance have been discovered through genome-wide association studies (GWAS). Currently, over 20 common PDAC risk signals have been identified through GWAS in populations of European ancestry. However, these loci explain only a small fraction of genetic heritability for PDAC, and the causal genes for the large majority of these are unknown. Most susceptibility alleles discovered through GWAS are located in noncoding regions of the genome and are likely to function through allele specific regulation of gene expression. Transcriptome-wide association studies integrate expression quantitative trait loci (eQTL) with GWAS data to discover genes whose cis-regulated expression is associated with complex traits. To identify novel susceptibility loci and likely causal genes, we applied this approach to PDAC. Firstly, we used two reference datasets generated from histologically normal pancreatic tissue samples from individuals of European ancestry, the DCEG/LTG (n=95) and Genotype-Tissue Expression (GTEx v7, n=174), to build robust predictors for gene expression (n=2,872-5,906; r2&amp;gt;0.01), using 5 different models (Top1, Lasso, Enet, Blup and Bslmm) in FUSION and PrediXcan. Gene expression predictors (n=2,043-21,422) were also generated by Enet in 48 different tissues (n=74-421 samples) from 608 individuals of European ancestry (GTEx). We then applied these predictor models to PDAC GWAS summary statistics from 9,040 PC cases and 12,496 controls. We discovered 24 genes whose genetically predicted expression is significantly associated with PDAC risk (FDR &amp;lt; 0.05), including 13 candidate genes at 10 novel loci (CELA3B, SMC2, SMC2-AS1, RP11-80H5.9, SMUG1, BTBD6, RCCD1, PNMT, CDK12, PGAP3, SUPT4H1, RP11-888D10.3 and PGPEP1) and 11 at 5 known risk loci (TERT, CLPTM1L, ZDHHC11B, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, BCAR1 and TMEM170A). Pathway enrichment analysis for genes differentially expressed in the top vs. bottom quartile of CELA3B expression highlighted higher expression of inflammatory and immune response genes in both the DCEG/LTG and GTEx pancreatic transcriptome datasets, indicating that low expression of this gene may be associated with an inflammatory state in the pancreas. Our findings showcase the utility of integrating multiple datasets to generate hypotheses and provide novel clues for the genetic mechanisms underlying pancreatic cancer risk. Citation Format: Jun Zhong, Ashley Jermusyk, Lang Wu, Jason W. Hoskins, Irene Collins, Mingfeng Zhang, Song Lei, Charles C. Chung, Tongwu Zhang, Wenming Xiao, Rachael Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Xiao-Ou Shu, Stephen J. Chanock, Sara Olson, Nilanjan Chatterjee, Jill Smith, Jianxin Shi, Peter Kraft, Gloria M. Petersen, Wei Zheng, Laufey T. Amundadottir. Large-scale transcriptome-wide association study (TWAS) identifies novel candidate susceptibility genes for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1591.