Abstract 246: An integrative genetic epidemiologic approach to identify susceptibility genes for post-menopausal breast cancer
Authors: Moslehi Roxana , Zeinomar Nur , Stagnar Cristy , Tsao Hui-Shien , Dzutsev Amiran 
Published: August 2018

Abstract
Background: Up to 30% of common (peri- and post-menopausal) breast cancer cases in the general population are believed to be due to segregation of mutations and variations with low to medium risk [i.e., 1.5&lt; relative risk &lt;20] for breast cancer. The majority of these genes remain to be identified. We used a hypothesis-driven integrative genetic epidemiologic approach to test the association of post-menopausal breast cancer with Nucleotide Excision Repair (NER) genes using several gene expression microarray and genome-wide association studies (GWAS) datasets. Methods: As per inclusion criteria, all datasets contained Caucasian cases with invasive ductal breast carcinoma diagnosed post-menopause (≥50 years of age) and healthy controls (with no personal history of breast cancer). Raw data from four gene expression microarray datasets (GSE5764, GSE10797, GSE10780, E-TABM-276) were used for pathway and meta-analysis. Raw data from three GWAS datasets, the Cancer Genetic Markers of Susceptibility (CGEMS) (1089 cases, 1093 controls), a nested case-control (351 cases, 884 controls) within National Surgical Adjuvant Breast and Bowel Project (NSABP), and a nested case-control (465 cases and 1394 controls) within the Women's Health Initiative (WHI) Hormone Therapy Trials, were analyzed using logistic regression models adjusting for covariates. Linkage disequilibrium patterns and deviations from Hardy-Weinberg Equilibrium (HWE) were assessed for single nucleotide polymorphisms (SNPs) within all candidate genes. Individual haplotypes and diplotypes were determined for all subjects. Odd ratios (OR) and 95% confidence intervals (CI) were calculated. Results: Individual analysis of gene expression microarray datasets and meta-analysis of GSE10780 and E-TABM-276 identified significant up-regulation of several NER genes including ERCC6 (P=7.95*10-6) and ERCC8 (P=4.67*10-6). Increased risk of breast cancer was found in association with variations in ERCC6 in all GWAS datasets. ERCC6 SNPs were in strong LD in all datasets with no deviations from HWE. In CGEMS, increased risk of breast cancer was found with ERCC6 rs4253082 (OR=1.32, 95%CI: 1.09-1.61, P=0.005), rs3750749 (OR=1.40, 95%CI: 1.07-1.8, P=0.01), and rs3750751 (OR=2.91, 95%CI: 1.05-8.06, P=0.04). In NSABP, increased risk of breast cancer was found with ERCC6 rs2229760 (OR=1.37, 95%CI: 1.02-1.83, P=0.03). In WHI, increased risk of breast cancer was found with ERCC6 rs1012553 (OR=1.35, 95%CI: 1.07-1.71, P=0.01) and rs2228528 (OR=1.29, 95%CI: 1.01-1.66, P=0.04). Gene-gene interaction analysis was significant for interactions between ERCC6 and several NER genes, including between specific diplotypes in ERCC6 and ERCC8 (Pinteraction=0.0009 in CGEMS). Conclusion: Genes involved in the NER pathway, including ERCC6 and ERCC8, may be associated with low to medium risk for susceptibility to post-menopausal breast cancer.
Citation Format: Roxana Moslehi, Nur Zeinomar, Cristy Stagnar, Hui-Shien Tsao, Amiran Dzutsev. An integrative genetic epidemiologic approach to identify susceptibility genes for post-menopausal breast 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 246.