Abstract Hereditary breast and ovarian cancer syndrome (HBOC) is principally caused by germline mutations in BRCA1 and BRCA2 . However, most women with HBOC are undiagnosed, and some patients meeting clinical criteria for HBOC will have no identifiable mutation after genetic testing. Here, we deploy a lasso-based model to combine serum miRNA profiles with clinical data to identify women at elevated risk for ovarian cancer among a population of 1831 individuals enrolled in an institutional biobank. The miRNA and metadata variables are mapped to two-dimensional space using lasso, after which a linear classification model is trained to estimate “ BRCA ness” and long-term risk of cancer. After tenfold cross-validation, the method offers a BRCA prediction AUC score of 0.98 (95% CI 0.94–1.0) and generalizes across subgroups stratified by age, cancer history, and racial/ethnic group. To demonstrate the clinical relevance of this phenotype, we use the lasso-based model to assess 5-year ovarian cancer risk among an independent cohort of 1044 subjects agnostic to genetic testing results enrolled in a randomized clinical trial. In this unselected population, the output of the lasso-based model strongly correlates to the log 5-year relative risk of ovarian cancer (R = 0.93, 95% CI 0.83–0.97, p < 0.0001). When the model was used to predict future onset of ovarian cancer directly, the AUC offered was AUC = 0.75 (95% CI 0.70–0.78). Together, these data suggest the proposed model is a predictor of future ovarian cancer risk.
Webber et al. (Tue,) studied this question.