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. This evidence concerns the gene ESR1 and breast cancer.