In summary, we report on i) a computational advance, namely a general RXA framework for phenotype identification based on genomic features, including a rigorous and systematic comparison with earlier approaches in a variety of cancer studies; ii) a cross-study validation based on the notoriously hard problem of predicting ER status in breast cancer; and iii) a clinically relevant application to predicting germline BRCA1 mutations in breast cancer, including extensive bioinformatic analysis to provide biological interpretation for the proposed predictor. Here, ESR1 is linked to breast carcinoma.