Identifying synthetic lethal targets in metastatic triple negative breast cancer

尽管迄今为止已经通过使用基因组生物标志物来预测对治疗的反应和对结果的描述来定义精确肿瘤学，但迄今为止，大部分癌症患者仍然没有从这些治疗中受益。RNAseq技术的广泛改进和获得大量转录组学数据创造了另一个潜在的生物标志物来源，可以改善预测和验证。Eytan Ruppin等人最近开发的计算方法通过利用预测的转录组利用合成致死性网络证明了治疗靶点的有效预测。该项目的主要目的是使用临床试验评估将这种生物信息学方法应用于接受三线治疗的转移性三阴性乳腺癌患者的可行性。此外，将获得探索性生物学和临床指标，以确定预测治疗的疗效和临床结局。

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.