Huang Chen-Tsung, Hsieh Chiao-Hui, Chung Yun-Hsien, Oyang Yen-Jen, Huang Hsuan-Cheng, Juan Hsueh-Fen
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan.
Institute of Molecular and Cellular Biology, National Taiwan University, Taipei 10617, Taiwan.
iScience. 2019 May 31;15:291-306. doi: 10.1016/j.isci.2019.04.039. Epub 2019 May 4.
Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.
癌症是一种复杂的疾病，其生存依赖于致癌突变和非突变基因，因此被称为癌基因成瘾和非癌基因成瘾。人们越来越认识到需要更有效的联合疗法来克服肿瘤学中的耐药性，但大规模鉴定潜在的协同药物仍然具有挑战性。在这里，我们提出了一种基于基因表达的方法，该方法利用从多个细胞系的大量化学和基因扰动实验中推断出的反复扰动-转录调控关系，来产生联合疗法的可测试假设。这些转录水平的反复出现与已知的化合物-蛋白质靶点对应物不同，在外部数据集中具有可重复性，并且与小分子敏感性相关。我们应用这些反复出现的关系来预测癌症的协同药物对，并在体外实验中证实了两种意想不到的药物组合。我们的结果证实了一种基于基因表达的组合药物筛选策略，作为一种针对复杂疾病中非突变基因的方法。
