Yu, Herbert

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with few known risk factors and biomarkers. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers, usually in small samples. In this study, we evaluated associations of circulating protein levels and PDAC risk using genetic instruments. Methods: To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative trait loci. Results: We observed associations between predicted concentrations of 38 proteins and PDAC risk at an FDR of < 0.05, including 23 of those proteins that showed an association even after Bonferroni correction. These include the protein encoded by ABO, which has been implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with PDAC risk after adjusting for previously reported PDAC risk variants (OR ranged from 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL15 production. Conclusions: We identified 38 candidates of protein biomarkers for PDAC risk. Impact: This study identifies novel protein biomarker candidates for PDAC, which if validated by additional studies, may contribute to the etiologic understanding of PDAC development.

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 Background: Melanoma cases may exist in pancreatic cancer kindreds, whereas there is increased risk of pancreatic cancer in familial melanoma. The two cancers may share genetic susceptibility variants in common. Methods: Three dbGaP (datasets in Genotypes and Phenotypes)-deposited GWAS (genome-wide association study) datasets (MD Anderson melanoma, PanScan 1, and PanScan 2 for pancreatic cancer) were used. Thirty-seven melanoma susceptibility variants in 22 genomic regions from published GWAS, plus melanoma-related genes and pathways were examined for pancreatic cancer risk in the PanScan datasets. Conversely, nine known pancreatic cancer susceptibility variants were examined for melanoma risk in the MD Anderson dataset. Results: In the PanScan data, initial associations were found with melanoma susceptibility variants in NCOA6 [rs4911442; OR, 1.32; 95% confidence interval (CI), 1.03–1.70; P = 0.03], YWHAZP5 (rs17119461; OR, 2.62; 95% CI, 1.08–6.35; P = 0.03), and YWHAZP5 (rs17119490; OR, 2.62; 95% CI, 1.08–6.34; P = 0.03), TYRP1 (P = 0.04), and IFNA13 (P = 0.04). In the melanoma dataset, two pancreatic cancer susceptibility variants were associated: NR5A2 (rs12029406; OR, 1.39; 95% CI, 1.01–1.92; P = 0.04) and CLPTM1L-TERT (rs401681; OR, 1.16; 95% CI, 1.01–1.34; P = 0.04). None of these associations remained significant after correcting for multiple comparisons. Conclusion: Reported variants of melanoma genes and pathways do not play a role in pancreatic cancer predisposition. Reciprocally, pancreatic cancer susceptibility variants are not associated with melanoma risk. Impact: Known melanoma-related genes and pathways, as well as GWAS-derived susceptibility variants of melanoma and pancreatic cancer, do not explain the shared genetic etiology of these two cancers. Cancer Epidemiol Biomarkers Prev; 23(6); 1121–4. ©2014 AACR.