Breast cancer is the most common cancer and the second leading cause of cancer-related death among women in the U.S. Known breast cancer risk factors include age, race/ethnicity, reproductive factors, and benign breast disease. Family history of breast cancer and hereditary cancer syndromes, such as BRCA1/BRCA2 mutations, confer the strongest risk for this disease. Although there have been a number of genome-wide association studies (GWAS) to identify genetic predictors of breast cancer, most have focused on high-risk cohorts of women with a strong family history rather than population-based cohorts and few have looked at genetic predictors based upon breast cancer subtypes. For example, BRCA1 mutation carriers tend to develop estrogen receptor (ER)-negative breast cancers, whereas the majority of breast tumors in BRCA2 mutation carriers are ER-positive. The purpose of this algorithm is to identify breast cancer subtypes based upon tumor hormone receptor (HR) status. There are currently FDA-approved drugs, such as tamoxifen, which have been shown to reduce the incidence of ER-positive breast cancer by up to 50-65% among high-risk women. Identifying cohorts of women who are more likely to benefit from anti-estrogen therapy may lead to a more precision medicine approach to breast cancer prevention strategies.
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Thursday, June 28, 2018
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Ning Shang, George Hripcsak, Chunhua Weng, Wendy K. Chung, Katherine Crew. Breast Cancer. PheKB; 2018 Available from: https://phekb.org/phenotype/1052