Childs, Erica J.

Abstract Introduction: Identifying families with an underlying inherited cancer predisposition is a major goal of cancer prevention efforts. Mendelian risk models have been developed to better predict the risk associated with a pathogenic variant of developing breast/ovarian cancer (with BRCAPRO), and the risk of developing pancreatic cancer (PANCPRO). Given that pathogenic variants involving BRCA2 and BRCA1 predispose to all three of these cancers, we developed a joint risk model to capture shared susceptibility. Methods: We expanded the existing framework for PANCPRO and BRCAPRO to model underlying risk of pancreatic, breast, and ovarian cancer. We then validated this model on three datasets each reflecting the target populations for these models, including patients referred to a high-risk genetics clinic, patients with multiple family members with pancreatic cancer who underwent genetic testing for BRCA1/2, as well as a prospective registry of pancreatic cancer families. The new model is designated BRCAPANCPRO. Results: BRCAPANCPRO yielded good discrimination for differentiating BRCA1 and BRCA2 carriers from non-carriers (AUC = 0.84, 95% CI: 0.78, 0.87) and was reasonably well-calibrated for predicting future risk of pancreatic cancer (observed-to-expected (O/E) ratio = 0.82 [0.69, 0.94]). In these families, BRCAPANCPRO outperformed both the prior BRCAPRO and PANCPRO models. Discussion: The BRCAPANCPRO model improves the identification of families with a pathogenic variant in BRCA2 or BRCA1, particularly for families where there is the co-occurrence of pancreatic cancer in addition to breast and/or ovarian cancer. The model also improves the prediction of pancreatic cancer among families with a pancreatic cancer family history, with or without breast and/or ovarian cancer. Citation Format: Amanda L. Blackford, Erica J. Childs, Nancy Porter, Gloria Petersen, Kari Rabe, Steven Gallinger, Ayelet Borgida, Sapna Syngal, Ann Schwartz, Michele Cote, Ralph Hruban, Michael Goggins, Alison P. Klein, Giovanni Parmigiani. A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 885.

Abstract Background: Obesity and diabetes are major modifiable risk factors for pancreatic cancer. Interactions between genetic variants and diabetes/obesity have not previously been comprehensively investigated in pancreatic cancer at the genome-wide level. Methods: We conducted a gene–environment interaction (GxE) analysis including 8,255 cases and 11,900 controls from four pancreatic cancer genome-wide association study (GWAS) datasets (Pancreatic Cancer Cohort Consortium I–III and Pancreatic Cancer Case Control Consortium). Obesity (body mass index ≥30 kg/m2) and diabetes (duration ≥3 years) were the environmental variables of interest. Approximately 870,000 SNPs (minor allele frequency ≥0.005, genotyped in at least one dataset) were analyzed. Case–control (CC), case-only (CO), and joint-effect test methods were used for SNP-level GxE analysis. As a complementary approach, gene-based GxE analysis was also performed. Age, sex, study site, and principal components accounting for population substructure were included as covariates. Meta-analysis was applied to combine individual GWAS summary statistics. Results: No genome-wide significant interactions (departures from a log-additive odds model) with diabetes or obesity were detected at the SNP level by the CC or CO approaches. The joint-effect test detected numerous genome-wide significant GxE signals in the GWAS main effects top hit regions, but the significance diminished after adjusting for the GWAS top hits. In the gene-based analysis, a significant interaction of diabetes with variants in the FAM63A (family with sequence similarity 63 member A) gene (significance threshold P < 1.25 × 10−6) was observed in the meta-analysis (PGxE = 1.2 ×10−6, PJoint = 4.2 ×10−7). Conclusions: This analysis did not find significant GxE interactions at the SNP level but found one significant interaction with diabetes at the gene level. A larger sample size might unveil additional genetic factors via GxE scans. Impact: This study may contribute to discovering the mechanism of diabetes-associated pancreatic cancer.

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

Abstract Background: Pancreatic cancer is the fourth-leading cause of cancer death in both men and women in the United States. The currently identified common susceptibility loci account for a small fraction of estimated heritability. We sought to estimate overall heritability of pancreatic cancer and partition the heritability by variant frequencies and functional annotations. Methods: Analysis using the genome-based restricted maximum likelihood method (GREML) was conducted on Pancreatic Cancer Case-Control Consortium (PanC4) genome-wide association study (GWAS) data from 3,568 pancreatic cancer cases and 3,363 controls of European Ancestry. Results: Applying linkage disequilibrium- and minor allele frequency-stratified GREML (GREML-LDMS) method to imputed GWAS data, we estimated the overall heritability of pancreatic cancer to be 21.2% (SE = 4.8%). Across the functional groups (intronic, intergenic, coding, and regulatory variants), intronic variants account for most of the estimated heritability (12.4%). Previously identified GWAS loci explained 4.1% of the total phenotypic variation of pancreatic cancer. Mutations in hereditary pancreatic cancer susceptibility genes are present in 4% to 10% of patients with pancreatic cancer, yet our GREML-LDMS results suggested these regions explain only 0.4% of total phenotypic variance for pancreatic cancer. Conclusions: Although higher than previous studies, our estimated 21.2% overall heritability may still be downwardly biased due to the inherent limitation that the contribution of rare variants in genes with a substantive overall impact on disease are not captured when applying these commonly used methods to imputed GWAS data. Impact: Our work demonstrated the importance of rare and common variants in pancreatic cancer risk.

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 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 < 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.

Abstract Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genomes of 638 patients with familial pancreatic cancer and the tumor exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A, and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types. Significance: The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer. Cancer Discov; 6(2); 166–75. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 109