Epigenome-wide Association Study of Breast Cancer Using Prospectively Collected Sister Study Samples

Breast cancer is the most common cancer in women and the leading cause of cancer mortality for women worldwide (1). According to estimates for 2011, there are an estimated 230480 incident cases and 40000 deaths from the disease each year in the United States (2). The Gail model, based on known risk factors including age and reproductive, medical, and family history, is the best breast cancer risk prediction method currently available for populations, but its predictive accuracy for individuals is only about 58.0% to 59.0% (3,4). Rare inherited mutations of the breast cancer susceptibility genes BRCA1 and BRCA2 are strongly associated with familial breast cancer, but together only account for 5% to 10% of breast cancers in the United States (5). Recent genome-wide association studies (GWASs) have identified common polymorphisms associated with breast cancer risk. A panel of 10 such SNPs have a predictive accuracy of 59.7% (4). Thus the known environmental and common genetic risk factors for breast cancer have limited use in predicting a woman’s risk of disease (4–6). Epigenetic modifications, including DNA methylation, are increasingly recognized as important determinants of gene transcriptional regulation that have both heritable and acquired characteristics (7). Aberrant DNA methylation patterns are among the earliest and most common events in carcinogenesis (8), and genome-wide methylation profiling has recently been extended to retrospectively collected blood samples in case–control studies of ovarian, bladder, and head and neck cancers (9–11). But unlike genotype, which remains constant, epigenetic modifications may differ from cell to cell, over time, and with exposure, making the results of case–control studies subject to a number of potential biases (12). In this study we used genomic arrays of 27578 CpGs and prospectively collected blood samples from women in the Sister Study cohort to compare women who subsequently developed breast cancer to those who remained cancer-free. We use these data to address two questions: Does the epigenetic profile differ between women who subsequently develop cancer vs those who do not? Within the case subject group, is there an effect of time to diagnosis—the interval between blood draw and clinical diagnosis of disease? In addition, we examined the predictive power of blood methylation compared with the Gail model and GWAS single nucleotide polymorphisms (SNPs).