Identifying synthetic lethal targets in metastatic triple negative breast cancer

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Although precision oncology has been defined to date by the use of genomic biomarkers for prediction of response to treatment and prognostication of outcomes, a large fraction of patients with cancer still do not benefit from these therapies to date. The broad improvement in RNAseq technologies and obtaining bulk transcriptomic data has created another source of potential biomarkers that can improve prediction and prognostication. A computational method recently developed by Eytan Ruppin et al. demonstrated effective prediction of therapeutic targets by leveraging synthetic lethality networks using the predicted transcriptome. The primary aim of this project is to evaluate, using a clinical trial, the feasibility of applying this bioinformatics approach to patients with metastatic triple-negative breast cancer on third-line treatment. In addition, exploratory biological and clinical measures will be obtained to determine efficacy of predicted treatment and clinical outcomes.