Abstract B065: A framework for transcriptome-wide association studies in breast cancer in diverse study populations

Abstract

Abstract The relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations. Traditional genome-wide association studies (GWAS) have interrogated survival associations in cohorts of predominantly European ancestries but are often underpowered due to subtype heterogeneity in breast cancer and a wide range of clinical covariates. Furthermore, these analyses tend to detect loci in non-coding regions, which require follow-up functional studies to interpret. Recent work in transcriptome-wide association studies (TWAS) has shown increased power in detecting functionally-relevant, trait-associated loci by leveraging information from expression quantitative trait loci (eQTLs) in external reference panels in relevant tissues. However, race-specific reference panels for TWAS may be needed to draw correct inference in large, ethnically-heterogeneous cohorts, and such panels for breast cancer are lacking. In this work, we provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled for women self-identifying as African American. Using Nanostring expression data from CBCS, we perform an eQTL analysis for 417 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. We use these models to impute expression in held-out samples from the CBCS and in The Cancer Genome Atlas, using a permutation method to assess predictive performance accounting for sampling variability. We find that these race-stratified expression models are not always applicable across race, depending on the imputation cohort. Furthermore, their predictive performance varies by breast cancer subtypes. Lastly, we conduct a small-scale TWAS for breast cancer mortality in CBCS (N = 3,828), controlling for covariates used in previous GWAS for breast cancer survival. At an false discovery rate-adjusted P-value less than 0.1, adjusting for self-reported race, we find hazardous associations near CAPN13 (2p23.1), VAV3 (1p13.3), and BLK (8p23.1) and a protective association near SERPINB5 (18q21.33) in TWAS that are underpowered in GWAS. This approach shows increased power for detection of survival-associated genomic loci, demonstrating the relative strength of TWAS over GWAS. A carefully implemented TWAS is an efficient alternative to GWAS for understanding the genetic architecture underpinning breast cancer outcomes in diverse human populations and across biologically distinct tumor types. Citation Format: Arjun Bhattacharya, Montserrat García-Closas, Andrew F. Olshan, Charles M. Perou, Melissa A. Troester, Michael I Love. A framework for transcriptome-wide association studies in breast cancer in diverse study populations [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr B065.