Olson, Sara H.

Background The association among gallbladder disease, cholecystectomy, and pancreatic cancer is unclear. Moreover, time interval between gallbladder disease or cholecystectomy and pancreatic cancer diagnosis is not considered in most previous studies. Aim To quantify the association among gallbladder disease, cholecystectomy, and pancreatic cancer, considering time since first diagnosis of gallbladder disease or cholecystectomy. Methods We used data from nine case-control studies within the Pancreatic Cancer Case-Control Consortium, including 5760 cases of adenocarcinoma of the exocrine pancreas and 8437 controls. We estimated pooled odds ratios and the corresponding 95% confidence intervals by estimating study-specific odds ratios through multivariable unconditional logistic regression models, and then pooling the obtained estimates using fixed-effects models. Results Compared with patients with no history of gallbladder disease, the pooled odds ratio of pancreatic cancer was 1.69 (95% confidence interval, 1.51–1.88) for patients reporting a history of gallbladder disease. The odds ratio was 4.90 (95% confidence interval, 3.45–6.97) for gallbladder disease diagnosed <2 years before pancreatic cancer diagnosis and 1.11 (95% confidence interval, 0.96–1.29) when ≥2 years elapsed. The pooled odds ratio was 1.64 (95% confidence interval, 1.43–1.89) for patients who underwent cholecystectomy, as compared to those without cholecystectomy. The odds ratio was 7.00 (95% confidence interval, 4.13–11.86) for a surgery <2 years before pancreatic cancer diagnosis and 1.28 (95% confidence interval, 1.08–1.53) for a surgery ≥2 years before. Conclusions There appears to be no long-term effect of gallbladder disease on pancreatic cancer risk, and at most a modest one for cholecystectomy. The strong short-term association can be explained by diagnostic bias and reverse causation.

Abstract Background The etiological basis of glioma is poorly understood. We have used genetic markers in a Mendelian Randomisation (MR) framework to examine if lifestyle, cardiometabolic and inflammatory factors influence the risk of glioma. This methodology reduces bias from confounding and is not affected by reverse causation. Methods We identified genetic instruments for 37 potentially modifiable risk factors and evaluated their association with glioma risk using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. We used the estimated odds ratio of glioma associated with each of the genetically defined traits to infer evidence for a causal relationship with the following exposures: lifestyle and dietary factors (height, plasma IGF-1, blood carnitine, blood methionine, blood selenium, blood zinc, circulating adiponectin, circulating carotenoids, iron status, serum calcium, vitamin [A1, B12, B6, E and 25-hydroxyvitamin D], fatty acids levels [mono-unsaturated, omega-3 and omega-6] and circulating fetuin-A); cardiometabolic factors (birth weight, HDL cholesterol, LDL cholesterol, total cholesterol, total triglycerides, basal metabolic rate, body fat percentage, body mass index, fasting glucose, fasting proinsulin, HbA1C levels, diastolic and systolic blood pressure, waist circumference, waist-to-hip ratio) were included; inflammatory factors (C-reactive protein (CRP), plasma IL-6 sRa and serum IgE). Results After correction for the testing of multiple potential risk factors and excluding associations driven by one single nucleotide polymorphism (SNP) no significant association with glioma risk was observed (i.e. PCorrected > 0.05). Conclusions This study did not provide evidence supporting any of the 37 factors examined as having a significant influence on glioma risk.

Abstract BACKGROUND: Approximately 5% of gliomas occur in individuals with a family history of glioma, and first-degree relatives of brain tumor cases have a two-fold increase in risk of brain tumor. Recent somatic characterization has shown that tumors from familial cases are indistinguishable from sporadic cases, suggesting that familial cases may arise through similar mechanisms of gliomagenesis, and therefore may be associated with common variants as well as rare mutations. In this analysis, we assessed whether previously identified common risk variants are associated with familial glioma. METHODS: Data were obtained from the Glioma International Case Control (GICC) and Gliogene studies for 448 cases with reported family history, 4,405 cases without reported family history, and 3,288 controls. We assessed 25 risk loci previously identified by glioma GWAS, and odds ratios (OR) and 95% confidence intervals (95%CI) were calculated using an additive genetic logistic regression model adjusted for age, sex, and the first two principal components for familial cases versus unaffected controls, and non-familial cases versus controls. Results were considered significant at p&lt;0.002 (Bonferroni correction for 25 tests). RESULTS: Significant associations were detected at 5/25 loci, including TERT, EGFR, CCDC26, CDKN2B, and RTEL1. The strongest association was at rs55705857 (CCDC26, OR=2.7, p=7.49x10-17). For GBM (222 familial cases), significant associations were detected at 6/26 loci (TERT, EGFR, CDKN2B, TP53 and RTEL1), while in non-GBM (205 familial cases) significant associations were detected at 3/25 loci (LRIG1, CCDC26, PHLDB1). These SNPs were further examined using a case-only approach comparing familial to non-familial cases, and there was no significant difference in allele frequencies by family history status. There was a strong correlation between log(OR) for familial cases only versus non-familial cases (adjusted R2=0.88). CONCLUSIONS: In this analysis we identified a significant association between familial glioma and five common risk loci previously identified by glioma GWAS. This provides further evidence of shared pathways of genetic risk and gliomagenesis between familial and non-familial glioma. Further exploration is necessary to determine the overall contribution of common genetic variation to risk of familial glioma. Citation Format: Quinn T. Ostrom, Georgina Armstrong, Christopher I. Amos, Jonine L. Bernstein, Elizabeth B. Claus, Jeanette E. Eckel-Passow, Dora Il'yasova, Christoffer Johansen, Daniel H. Lachance, Rose K. Lai, Ryan T. Merrell, Sara H. Olson, Joellen H. Schildkraut, Sanjay S. Shete, Richard S. Houlston, Robert B. Jenkins, Margaret R. Wrensch, Beatrice Melin, Jill S. Barnholtz-Sloan, Melissa L. Bondy. Previously identified common glioma risk SNPs are associated with familial glioma [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 4173.

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&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 &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.

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.

Glioma incidence is highest in non‐Hispanic Whites, and to date, glioma genome‐wide association studies (GWAS) to date have only included European ancestry (EA) populations. African Americans and Hispanics in the US have varying proportions of EA, African (AA) and Native American ancestries (NAA). It is unknown if identified GWAS loci or increased EA is associated with increased glioma risk. We assessed whether EA was associated with glioma in African Americans and Hispanics. Data were obtained for 832 cases and 675 controls from the Glioma International Case–Control Study and GliomaSE Case–Control Study previously estimated to have <80% EA, or self‐identify as non‐White. We estimated global and local ancestry using fastStructure and RFMix, respectively, using 1,000 genomes project reference populations. Within groups with ≥40% AA (AFR ≥0.4 ), and ≥15% NAA (AMR ≥0.15 ), genome‐wide association between local EA and glioma was evaluated using logistic regression conditioned on global EA for all gliomas. We identified two regions (7q21.11, p = 6.36 × 10 −4 ; 11p11.12, p = 7.0 × 10 −4 ) associated with increased EA, and one associated with decreased EA (20p12.13, p = 0.0026) in AFR ≥0.4 . In addition, we identified a peak at rs1620291 ( p = 4.36 × 10 −6 ) in 7q21.3. Among AMR ≥0.15 , we found an association between increased EA in one region (12q24.21, p = 8.38 × 10 −4 ), and decreased EA in two regions (8q24.21, p = 0. 0010; 20q13.33, p = 6.36 × 10 −4 ). No other significant associations were identified. This analysis identified an association between glioma and two regions previously identified in EA populations (8q24.21, 20q13.33) and four novel regions (7q21.11, 11p11.12, 12q24.21 and 20p12.13). The identifications of novel association with EA suggest regions to target for future genetic association studies.

Abstract Genome-wide association studies (GWAS) have so far identified 25 loci associated with glioma risk, with most showing specificity for either glioblastoma (GBM) or non-GBM tumors. The majority of these GWAS susceptibility variants reside in noncoding regions and the causal genes underlying the associations are largely unknown. Here we performed a transcriptome-wide association study to search for novel risk loci and candidate causal genes at known GWAS loci using Genotype-Tissue Expression Project (GTEx) data to predict cis-predicted gene expression in relation to GBM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and 5,820 non-GBM) and 18,169 controls. Imposing a Bonferroni-corrected significance level of P &lt; 5.69 × 10−6, we identified 31 genes, including GALNT6 at 12q13.33, as a candidate novel risk locus for GBM (mean Z = 4.43; P = 5.68 × 10−6). GALNT6 resides at least 55 Mb away from any previously identified glioma risk variant, while all other 30 significantly associated genes were located within 1 Mb of known GWAS-identified loci and were not significant after conditioning on the known GWAS-identified variants. These data identify a novel locus (GALNT6 at 12q13.33) and 30 genes at 12 known glioma risk loci associated with glioma risk, providing further insights into glioma tumorigenesis. Significance: This study identifies new genes associated with glioma risk, increasing understanding of how these tumors develop.

As a follow‐up to genome‐wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well‐known ovarian cancer risk factors and their interactions with 28 genome‐wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case–control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10−4). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46–0.60) compared to 0.71 (95%CI = 0.66–0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1–5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene–environment analysis in ovarian cancer.

Abstract Background: There have been few studies of sufficient size to address the relationship between glioma risk and the use of aspirin or NSAIDs, and results have been conflicting. The purpose of this study was to examine the associations between glioma and aspirin/NSAID use, and to aggregate these findings with prior published studies using meta-analysis. Methods: The Glioma International Case-Control Study (GICC) consists of 4,533 glioma cases and 4,171 controls recruited from 2010 to 2013. Interviews were conducted using a standardized questionnaire to obtain information on aspirin/NSAID use. We examined history of regular use for ≥6 months and duration-response. Restricted maximum likelihood meta-regression models were used to aggregate site-specific estimates, and to combine GICC estimates with previously published studies. Results: A history of daily aspirin use for ≥6 months was associated with a 38% lower glioma risk, compared with not having a history of daily use [adjusted meta-OR = 0.62; 95% confidence interval (CI), 0.54–0.70]. There was a significant duration-response trend (P = 1.67 × 10−17), with lower ORs for increasing duration of aspirin use. Duration-response trends were not observed for NSAID use. In the meta-analysis aggregating GICC data with five previous studies, there was a marginally significant association between use of aspirin and glioma (mOR = 0.84; 95% CI, 0.70–1.02), but no association for NSAID use. Conclusions: Our study suggests that aspirin may be associated with a reduced risk of glioma. Impact: These results imply that aspirin use may be associated with decreased glioma risk. Further research examining the association between aspirin use and glioma risk is warranted.