A BRCA1-mutation associated DNA methylation signature in blood cells predicts sporadic breast cancer incidence and survival

BACKGROUND: BRCA1 mutation carriers have an 85% risk of developing breast cancer but the risk of developing non-hereditary breast cancer is difficult to assess. Our objective is to test whether a DNA methylation (DNAme) signature derived from BRCA1 mutation carriers is able to predict non-hereditary breast cancer. METHODS: In a case/control setting (72 BRCA1 mutation carriers and 72 BRCA1/2 wild type controls) blood cell DNA samples were profiled on the Illumina 27 k methylation array. Using the Elastic Net classification algorithm, a BRCA1-mutation DNAme signature was derived and tested in two cohorts: (1) The NSHD (19 breast cancers developed within 12 years after sample donation and 77 controls) and (2) the UKCTOCS trial (119 oestrogen receptor positive breast cancers developed within 5 years after sample donation and 122 controls). RESULTS: We found that our blood-based BRCA1-mutation DNAme signature applied to blood cell DNA from women in the NSHD resulted in a receiver operating characteristics (ROC) area under the curve (AUC) of 0.65 (95% CI 0.51 to 0.78, P = 0.02) which did not validate in buccal cells from the same individuals. Applying the signature in blood DNA from UKCTOCS volunteers resulted in AUC of 0.57 (95% CI 0.50 to 0.64; P = 0.03) and is independent of family history or any other known risk factors. Importantly the BRCA1-mutation DNAme signature was able to predict breast cancer mortality (AUC = 0.67; 95% CI 0.51 to 0.83; P = 0.02). We also found that the 1,074 CpGs which are hypermethylated in BRCA1 mutation carriers are significantly enriched for stem cell polycomb group target genes (P <10(-20)). CONCLUSIONS: A DNAme signature derived from BRCA1 carriers is able to predict breast cancer risk and death years in advance of diagnosis. Future studies may need to focus on DNAme profiles in epithelial cells in order to reach the AUC thresholds required of preventative measures or early detection strategies.