用于精准癌症治疗探索的保守合成致死相互作用网络。
A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.
作者信息
Srivas Rohith, Shen John Paul, Yang Chih Cheng, Sun Su Ming, Li Jianfeng, Gross Andrew M, Jensen James, Licon Katherine, Bojorquez-Gomez Ana, Klepper Kristin, Huang Justin, Pekin Daniel, Xu Jia L, Yeerna Huwate, Sivaganesh Vignesh, Kollenstart Leonie, van Attikum Haico, Aza-Blanc Pedro, Sobol Robert W, Ideker Trey
机构信息
Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA; The Cancer Cell Map Initiative.
Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; The Cancer Cell Map Initiative.
出版信息
Mol Cell. 2016 Aug 4;63(3):514-25. doi: 10.1016/j.molcel.2016.06.022. Epub 2016 Jul 21.
An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.
一种新兴的癌症治疗策略是通过利用肿瘤驱动突变与特定药物靶点之间的相互作用，在肿瘤中诱导选择性致死。在此，我们采用多物种方法开发了与癌症治疗相关的合成致死相互作用资源。首先，我们在酵母中筛选了人类肿瘤抑制基因（TSG）的直系同源基因与编码药物靶点的基因之间在多种基因毒性环境下的约169,000种潜在相互作用。在最强信号的指导下，我们在HeLa细胞中评估了数千种TSG-药物组合，从而构建了保守的合成致死相互作用网络。对这些网络的分析表明，跨环境的相互作用稳定性和共享基因功能增加了在人类癌细胞中观察到相互作用的可能性。利用这些规则，我们对约10^5种人类TSG-药物组合进行了优先级排序，以供未来跟进。我们基于细胞和/或患者存活率验证了相互作用，包括拓扑异构酶与RAD17以及检查点激酶与BLM之间的相互作用。
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