CRA1501 Background: African American (AA) women are disproportionately affected by early-onset and triple-negative breast cancer (TNBC). One explanation for these disparities may be a higher frequency of inherited mutations among AA women in genes in DNA repair pathways, including BRCA1 and BRCA2. Using targeted genomic capture and next generation sequencing (NGS), we screened DNA from AA women with breast cancer for mutations in all 18 known breast cancer genes. Methods: A total of 249 unrelated AA women with breast cancer were ascertained through the Cancer Risk Clinic at The University of Chicago. Genomic DNA was extracted from peripheral blood and 3 micrograms were used for targeted capture and sequencing. Average read depth across the 1.4 MB targeted region was 320-fold. Sequence reads were aligned and all classes of variants identified: point mutations, small insertions and deletions, and large genomic rearrangements. Only unambiguously damaging mutations were called: stops, complete genomic deletions, and missenses demonstrated experimentally to cause loss of protein function. Variants were validated by PCR or Taqman analysis. Results: Fifty-six of 249 subjects (22%) carried at least one loss-of-function mutation, distributed among BRCA1 (n=26), BRCA2 (n=20), CHEK2 (n=3), PALB2 (n=3), ATM (n=5), and PTEN (n=1). The majority of mutations were unique. Damaging mutations were carried by 30% of patients with TNBC, 27% of patients diagnosed at age ≤45, 49% with a second breast primary, and 30% with a family history of either breast or ovarian cancer in any close relative. Conclusions: We present the first comprehensive screen of all known breast cancer susceptibility genes among AA women using NGS. Mutation carrier frequencies are >25% for major subsets of patients defined by tumor or host characteristics. These high carrier frequencies suggest the importance of screening for mutations in all breast cancer genes in all AA breast cancer patients diagnosed at a young age, with a family history, or with TNBC as a way to identify at-risk family members for life-saving interventions.
Assessment of Subnetwork Detection Methods for Breast Cancer
