Jacobs, Eric J.

PURPOSE This study assessed the joint association of pathogenic variants (PVs) in breast cancer (BC) predisposition genes and polygenic risk scores (PRS) with BC in the general population. METHODS A total of 26,798 non-Hispanic white BC cases and 26,127 controls from predominately population-based studies in the Cancer Risk Estimates Related to Susceptibility consortium were evaluated for PVs in BRCA1, BRCA2, ATM, CHEK2, PALB2, BARD1, BRIP1, CDH1, and NF1. PRS based on 105 common variants were created using effect estimates from BC genome-wide association studies; the performance of an overall BC PRS and estrogen receptor–specific PRS were evaluated. The odds of BC based on the PVs and PRS were estimated using penalized logistic regression. The results were combined with age-specific incidence rates to estimate 5-year and lifetime absolute risks of BC across percentiles of PRS by PV status and first-degree family history of BC. RESULTS The estimated lifetime risks of BC among general-population noncarriers, based on 10th and 90th percentiles of PRS, were 9.1%-23.9% and 6.7%-18.2% for women with or without first-degree relatives with BC, respectively. Taking PRS into account, more than 95% of BRCA1, BRCA2, and PALB2 carriers had > 20% lifetime risks of BC, whereas, respectively, 52.5% and 69.7% of ATM and CHEK2 carriers without first-degree relatives with BC, and 78.8% and 89.9% of those with a first-degree relative with BC had > 20% risk. CONCLUSION PRS facilitates personalization of BC risk among carriers of PVs in predisposition genes. Incorporating PRS into BC risk estimation may help identify > 30% of CHEK2 and nearly half of ATM carriers below the 20% lifetime risk threshold, suggesting the addition of PRS may prevent overscreening and enable more personalized risk management approaches.

Abstract Genome wide association studies (GWAS) in 9,013 pancreatic cancer patients and 12,452 controls of European ancestry have discovered over 20 risk loci in the human genome. Here, we fine mapped one such locus on chromosome 16q23.1 in the vicinity of two Chymotrypsinogen precursor genes, CTRB1 and CTRB2. We fine-mapped this locus to rs72802365 (P=2.51x10-17, OR=1.36, 95% CI=1.31-1.40) and identified colocalization (PP=87%) with aberrant exon 5→7 CTRB2 splicing in pancreatic tissues. Imputation of a 584 bp insertion/deletion variant overlapping exon 6 of CTRB2 into the GWAS datasets resulted in a highly significant association with pancreatic cancer risk (P=2.83x10-16, OR=1.36, 95% CI=1.31-1.42), indicating that it may underlie this signal. Exon skipping attributable to the deletion (risk) allele introduces a premature stop codon in exon 7 of CTRB2, yielding a truncated chymotrypsin B2 protein that lacks chymotrypsin activity, is poorly secreted, and accumulates intracellularly in the endoplasmic reticulum (ER). The intracellular accumulation of a nonfunctional chymotrypsinogen B2 protein may lead to ER stress and pancreatic inflammation, which in turn, may explain the increased pancreatic cancer risk in carriers of CTRB2 exon 6 deletion alleles. Citation Format: Ashley Jermusyk, Jun Zhong, Naomi Gordon, Katelyn Connelly, Sumeth Perera, Ehssan Abdolalizadeh, Tongwu Zhang, Aidan O'Brien, Jason Hoskins, Irene Collins, Daina Eiser, Chen Yuan, Harvey Risch, Eric J. Jacobs, Donghui Li, Mengmeng Du, Rachael Stolzenberg-Solomon, Alison P. Klein, Jill P. Smith, Brian M. Wolpin, Stephen J. Chanock, Jianxin Shi, Gloria M. Petersen, Christopher Westlake, Laufey T. Amundadottir. An exception to the rule: A coding functional variant at a pancreatic cancer GWAS locus [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr IA-11.

Abstract Background: Whether circulating polyunsaturated fatty acid (PUFA) levels are associated with pancreatic cancer risk is uncertain. Mendelian randomization (MR) represents a study design using genetic instruments to better characterize the relationship between exposure and outcome. Methods: We utilized data from genome-wide association studies within the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case–Control Consortium, involving approximately 9,269 cases and 12,530 controls of European descent, to evaluate associations between pancreatic cancer risk and genetically predicted plasma n-6 PUFA levels. Conventional MR analyses were performed using individual-level and summary-level data. Results: Using genetic instruments, we did not find evidence of associations between genetically predicted plasma n-6 PUFA levels and pancreatic cancer risk [estimates per one SD increase in each PUFA-specific weighted genetic score using summary statistics: linoleic acid odds ratio (OR) = 1.00, 95% confidence interval (CI) = 0.98–1.02; arachidonic acid OR = 1.00, 95% CI = 0.99–1.01; and dihomo-gamma-linolenic acid OR = 0.95, 95% CI = 0.87–1.02]. The OR estimates remained virtually unchanged after adjustment for covariates, using individual-level data or summary statistics, or stratification by age and sex. Conclusions: Our results suggest that variations of genetically determined plasma n-6 PUFA levels are not associated with pancreatic cancer risk. Impact: These results suggest that modifying n-6 PUFA levels through food sources or supplementation may not influence risk of pancreatic cancer.

Abstract Introduction: Pancreatic cancer is the seventh leading cause of cancer death worldwide with pancreatic ductal adenocarcinoma (PDAC) being the most common subtype (>90%). Inherited genetic changes and cigarette smoking are established independent risk factors of PDAC. Methods: We conducted a genome-wide gene by smoking interaction analysis of PDAC risk among 7,937 PDAC cases and 12,389 controls of European ancestry from the Pancreatic Cancer Cohort (PanScan) and Pancreatic Cancer Case-Control Consortia (PanC4) with complete genotyping and smoking data. Over 6,769,447 SNPs met our inclusion criteria of imputation score >0.5 and MAF >0.05 after imputation to phase 3 1000G and were included in our analysis. We evaluated the gene-environment interactions using the empirical Bayes method which allows data-adaptive relaxation of the gene-environment independence assumption. The logistic regression model was adjusted for age, sex, top principal components for each phase, study and study region (PanScan only). The analysis was conducted separately for the PanScan and PanC4 GWAS studies, using a score test to efficiently incorporate expected SNP dosages. The estimates were then combined using a fixed-effect meta-analysis. We examined expression quantitative trait locus using data from the NIH Genotype-Tissue Expression (GTEx) database. We considered a P-value < 5.0 × 10−8 statistically significant. Results: We found a genome-wide significant qualitative interaction with the SNP rs1818613 by smoking status (P-value <5 × 10−9) where, compared to the G allele, the T allele was associated with a reduced risk PDAC in never smokers (per T allele OR, 0.87; 95% CI, 0.82-0.93), had no association in former smokers (OR, 1.00; 95% CI, 0.91-1.07) and was associated with an increased risk in current smokers (OR, 1.25; 95% CI, 1.12-1.40). This SNP is located on chromosome 2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) gene and upstream of the cyclin T2 (CCNT2) gene. More than 40 additional SNPs within 100Kbp and in high LD (r2 > 0.8) with rs1818613 provided significant evidence of interaction by smoking status for PDAC risk. rs1818613 was associated with differential expression of TMEM163 in several tissues: for T allele compared with the G allele there was increased expression in the heart, pituitary, whole blood, and esophagus-muscularis and decreased expression in the testis tissues (P-value < 3 × 10−11). It was also associated with decreased CCNT2 expression in the tibial nerve tissue (P-value = 1.9 × 10−9). Conclusions: Our work supports that the genetic variation in the region, in conjunction with cigarette smoking, influences PDAC risk. Future studies are needed to explore this association in other smoking-related cancers and to understand the biological mechanism of such association. Citation Format: Prosenjit Kundu, Evelina Mocci, William Wheeler, Laufey T. Amundadottir, Donghui Li, Eric J. Jacobs, Gloria M. Petersen, Brian M. Wolpin, Harvey A. Risch, Peter Kraft, Nilanjan Chatterjee, Alison P. Klein, Rachael Z. Stolzenberg-Solomon, The PanScan and Panc4 consortia. Genome-wide interaction scan identifies gene by smoking interaction at 2q21.3 for pancreatic cancer risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1213.

Abstract Background: Regular use of nonsteroidal anti-inflammatory drugs (NSAID) is associated with lower risk of colorectal cancer. Genome-wide interaction analysis on single variants (G × E) has identified several SNPs that may interact with NSAIDs to confer colorectal cancer risk, but variations in gene expression levels may also modify the effect of NSAID use. Therefore, we tested interactions between NSAID use and predicted gene expression levels in relation to colorectal cancer risk. Methods: Genetically predicted gene expressions were tested for interaction with NSAID use on colorectal cancer risk among 19,258 colorectal cancer cases and 18,597 controls from 21 observational studies. A Mixed Score Test for Interactions (MiSTi) approach was used to jointly assess G × E effects which are modeled via fixed interaction effects of the weighted burden within each gene set (burden) and residual G × E effects (variance). A false discovery rate (FDR) at 0.2 was applied to correct for multiple testing. Results: Among the 4,840 genes tested, genetically predicted expression levels of four genes modified the effect of any NSAID use on colorectal cancer risk, including DPP10 (PG×E = 1.96 × 10−4), KRT16 (PG×E = 2.3 × 10−4), CD14 (PG×E = 9.38 × 10−4), and CYP27A1 (PG×E = 1.44 × 10−3). There was a significant interaction between expression level of RP11-89N17 and regular use of aspirin only on colorectal cancer risk (PG×E = 3.23 × 10−5). No interactions were observed between predicted gene expression and nonaspirin NSAID use at FDR < 0.2. Conclusions: By incorporating functional information, we discovered several novel genes that interacted with NSAID use. Impact: These findings provide preliminary support that could help understand the chemopreventive mechanisms of NSAIDs on colorectal cancer.

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 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.