Center for Bioinformatics and Computational Biology, University of Maryland Institute of Advanced Computer Science (UMIACS) & Department of Computer Science, University of Maryland, College Park, MD, 20742, USA.
Cancer Data Science Lab, National Cancer Institute, National Institute of Health, Bethesda, MD, 20892, USA.
Nat Commun. 2018 Jun 29;9(1):2546. doi: 10.1038/s41467-018-04647-1.
While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.
虽然合成致死性 (SL) 在开发有效的癌症疗法方面具有广阔的前景，但通过实验筛选发现的 SL 候选物通常具有有限的转化价值。在这里，我们提出了一种数据驱动的方法 ISLE（临床相关合成致死性的鉴定），该方法通过挖掘 TCGA 队列，从给定的实验室筛选的 SL 候选物集中鉴定出最有可能具有临床相关性的 SL 相互作用（cSLi）。我们首先通过对大规模药物反应筛选的基准测试和通过预测在小鼠异种移植模型中的药物疗效来验证 ISLE。然后，我们通过新的筛选实验对一组选定的预测 cSLi 进行实验测试，验证它们在缺氧与常氧条件下预测的特定环境敏感性，并证明 cSLi 在预测协同药物组合方面的实用性。我们表明，cSLi 可以成功预测患者的药物治疗反应，并提供患者分层特征。因此，ISLE 补充了现有的基于可操作突变的精确癌症治疗方法，为扩大其应用范围至整个基因组提供了机会。
