Chatterjee, Nilanjan

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast 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: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women <50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [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 962.

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>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 < 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.

Limited epidemiological evidence suggests that the etiology of hormone receptor positive (HR+) breast cancer may differ by levels of histologic grade and proliferation. We pooled risk factor and pathology data on 5,905 HR+ breast cancer cases and 26,281 controls from 11 epidemiological studies. Proliferation was determined by centralized automated measures of KI67 in tissue microarrays. Odds ratios (OR), 95% confidence intervals (CI) and p‐values for case–case and case–control comparisons for risk factors in relation to levels of grade and quartiles (Q1–Q4) of KI67 were estimated using polytomous logistic regression models. Case–case comparisons showed associations between nulliparity and high KI67 [OR (95% CI) for Q4 vs. Q1 = 1.54 (1.22, 1.95)]; obesity and high grade [grade 3 vs. 1 = 1.68 (1.31, 2.16)] and current use of combined hormone therapy (HT) and low grade [grade 3 vs. 1 = 0.27 (0.16, 0.44)] tumors. In case–control comparisons, nulliparity was associated with elevated risk of tumors with high but not low levels of proliferation [1.43 (1.14, 1.81) for KI67 Q4 vs. 0.83 (0.60, 1.14) for KI67 Q1]; obesity among women ≥50 years with high but not low grade tumors [1.55 (1.17, 2.06) for grade 3 vs. 0.88 (0.66, 1.16) for grade 1] and HT with low but not high grade tumors [3.07 (2.22, 4.23) for grade 1 vs. 0.85 (0.55, 1.30) for grade 3]. Menarcheal age and family history were similarly associated with HR+ tumors of different grade or KI67 levels. These findings provide insights into the etiologic heterogeneity of HR+ tumors.

Abstract Objective: To elucidate the genetic architecture of gene expression in pancreatic tissues. Design: We performed expression quantitative trait locus (eQTL) and allele specific expression (ASE) analyses using RNA-sequence data and 1000 Genomes (1000G) imputed GWAS genotypes from 95 fresh frozen histologically normal pancreatic tissue samples. Data from 115 pancreatic tumor-derived tissue samples from The Cancer Genome Atlas (TCGA) was included for comparison. Results: We identified 38,615 cis-eQTLs (corresponding to 484 Genes) in histologically normal tissues and 39,713 cis-eQTL (corresponding to 237 Genes) in tumor tissues (FDR<0.1), with the strongest effects seen near transcriptional start sites (TSS). Approximately 23% and 42% of genes with significant cis-eQTLs (eGenes) appeared to be specific for tumor and normal derived tissues, respectively. Significant enrichment of cis-eQTL variants was noted in noncoding regulatory regions marked by modified histones, DNAse hypersensitivity and bound transcription factors, in particular for pancreatic tissues (1.53-3.12 fold, P≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 in the ABO gene (rs687289) was associated with ABO expression in histologically normal (P=5.8x10-8) and tumor-derived (P=8.3x10-5) pancreatic tissues. The high linkage disequilibrium (LD) between this variant and the O blood group generating deletion variant in exon 6 of ABO suggested that nonsense-mediated decay (NMD) of the “O” mRNA could explain the eQTL. However, knock-down of crucial NMD regulators did not influence decay of the ABO “O” mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. Conclusions: We have identified cis-eQTLs representing potential functional regulatory variants in the pancreas and generated a rich dataset for further studies on gene expression and regulation in pancreatic tissues. Citation Format: Laufey T. Amundadottir, Soren Lykke Andresen, Wenming Xiao, Jason Hoskins, Ashley Jermusyk, Lauren Rost, Irene Collins, Jinping Jia, Michael Mobaraki, Bin Zhu, Robert Kurtz, Hemang Parikh, Lei Song, Meredith Yeager, Torben Jensen, William Bamlet, Nilanjan Chatterjee, Brian Wolpin, Jill Smith, Sara Olson, Gloria Petersen, Jianxin Shi, Mingfeng Zhang. Analysis of cis-eQTLs in normal and tumor-derived pancreatic tissues reveals functional insights, including for the 9q34.1 ABO pancreatic cancer risk locus [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1442. doi:10.1158/1538-7445.AM2017-1442

Abstract Introduction: Obesity and diabetes are two major modifiable risk factors for pancreatic cancer. Genetic variation affecting the risk of obesity- and diabetes-related pancreatic cancer have yet to be comprehensively investigated at the genome-wide level. Aims: To identify novel gene-environment interactions (GxE) of obesity/diabetes on pancreatic cancer risk. Methods: We conducted a multi-stage genome-wide gene-diabetes and gene-obesity interaction study using GWAS data of 7095 cases and 10710 controls from Pancreatic Cancer Consortium (PanScan I, PanScan II and PanScan III) and Pancreatic Cancer Case Control Consortium (PanC4). About 870,000 SNPs, having MAF ≥ 0.005 and having been genotyped in at least one study, were analyzed. Missing genotypes were imputed using IMPUTE2 and the Haplotype Reference Consortium (HRC) reference panel. Case-control (CC) and case-only (CO) methods were used for SNP-level GxE analysis. Fixed-, random- and joint-effect models built in “rareGE” R package were used for gene-based GxE analysis. About 20,000 genes that have at least one SNP genotyped were included in the analysis. Age, gender and principal components accounting for subpopulation structure were adjusted in both SNP-level and gene-level analyses. Obesity was defined as body mass index ≥30 mg/m2. Diabetics with <3-year duration were excluded for gene-diabetes interaction analysis. The PanScan I, II, III and PanC4 datasets were analyzed separately followed by meta-analysis of the summary statistics. Fixed-effect and Fisher’s p-value based meta-analysis was applied for SNP-level and gene-level GxE analysis, respectively. Results: No genome-wide significant (P<5 x 10-8 ) interaction at the SNP level was detected in any of the PanScan or PanC4 datasets or in the meta-analysis. A significant interaction of diabetes and FAM63A (family with sequence similarity 63 member A) gene (P<2.5 x 10-6) was observed in the meta-analysis (PFixed= 3.4 x 10-5, PRandom= 1.9 x 10-6, PJoint= 6.8 x 10-7) . This interaction showed nominal significance in all individual dataset analyses. The corresponding P values for this interaction were 4.2 x 10-2, 3.8 x 10-2 and 6.8 x 10-2 in PanScan I, 2.8 x 10-2, 3.9 x 10-2 and 6.5 x 10-2 9.1 x 10-3, 2.2 x 10-4 and 8.8 x 10-6 in PanScan III, and 3.1 x 10-3, 3.4 x 10-3 and 8.8 x 10-3 in PanC4, respectively. Conclusions: In the largest genome-wide GxE analysis for pancreatic cancer to date, we identified a significant diabetes interacting gene in pancreatic cancer, which was consistently replicated in 4 individual GWAS studies. This observation identifies FAM63A as a candidate for future functional studies to understand its role in pancreatic cancer etiology. Further increasing the sample size may unveil additional genetic loci that contribute to the susceptibility to pancreatic cancer via G x E. Citation Format: Hongwei Tang, Lai Jiang, Laufey T. Amundadottir, Harvey A. Risch, Rachael S. Stolzenberg-Solomon, Alison P. Klein, Brian M. Wolpin, Gloria Petersen, Nilanjan Chatterjee, Donghui Li, Peter Kraft, Peng Wei. Genomewide gene-diabetes and gene-obesity interaction scan in 7095 cases and 10710 controls from Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case Control Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1295. doi:10.1158/1538-7445.AM2017-1295

Objective To elucidate the genetic architecture of gene expression in pancreatic tissues. Design We performed expression quantitative trait locus (eQTL) analysis in histologically normal pancreatic tissue samples (n=95) using RNA sequencing and the corresponding 1000 genomes imputed germline genotypes. Data from pancreatic tumour-derived tissue samples (n=115) from The Cancer Genome Atlas were included for comparison. Results We identified 38 615 cis -eQTLs (in 484 genes) in histologically normal tissues and 39 713 cis -eQTL (in 237 genes) in tumour-derived tissues (false discovery rate <0.1), with the strongest effects seen near transcriptional start sites. Approximately 23% and 42% of genes with significant cis -eQTLs appeared to be specific for tumour-derived and normal-derived tissues, respectively. Significant enrichment of cis -eQTL variants was noted in non-coding regulatory regions, in particular for pancreatic tissues (1.53-fold to 3.12-fold, p≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 (rs687289) was associated with ABO expression in histologically normal (p=5.8×10 −8 ) and tumour-derived (p=8.3×10 −5 ) tissues. The high linkage disequilibrium between this variant and the O blood group generating deletion variant in ABO (exon 6) suggested that nonsense-mediated decay (NMD) of the ‘O’ mRNA might explain this finding. However, knockdown of crucial NMD regulators did not influence decay of the ABO ‘O’ mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. Conclusions We have identified cis -eQTLs representing potential functional regulatory variants in the pancreas and generated a rich data set for further studies on gene expression and its regulation in pancreatic tissues.

Terminal duct lobular units (TDLUs) are the predominant source of future breast cancers, and lack of TDLU involution (higher TDLU counts, higher acini count per TDLU and the product of the two) is a breast cancer risk factor. Numerous breast cancer susceptibility single nucleotide polymorphisms (SNPs) have been identified, but whether they are associated with TDLU involution is unknown. In a pooled analysis of 872 women from two studies, we investigated 62 established breast cancer SNPs and relationships with TDLU involution. Poisson regression models with robust variance were used to calculate adjusted per‐allele relative risks (with the non‐breast cancer risk allele as the referent) and 95% confidence intervals between TDLU measures and each SNP. All statistical tests were two‐sided; P < 0.05 was considered statistically significant. Overall, 36 SNPs (58.1%) were related to higher TDLU counts although this was not statistically significant (p = 0.25). Six of the 62 SNPs (9.7%) were nominally associated with at least one TDLU measure: rs616488 (PEX14), rs11242675 (FOXQ1) and rs6001930 (MKL1) were associated with higher TDLU count (p = 0.047, 0.045 and 0.031, respectively); rs1353747 (PDE4D) and rs6472903 (8q21.11) were associated with higher acini count per TDLU (p = 0.007 and 0.027, respectively); and rs1353747 (PDE4D) and rs204247 (RANBP9) were associated with the product of TDLU and acini counts (p = 0.024 and 0.017, respectively). Our findings suggest breast cancer SNPs may not strongly influence TDLU involution. Agnostic genome‐wide association studies of TDLU involution may provide new insights on its biologic underpinnings and breast cancer susceptibility.