A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.
A chemical-genetic interaction map of small molecules using high-throughput imaging in cancer cells.
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DOI：
10.15252/msb.20156400
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发表时间：
2015-12-23
影响因子：
9.9
通讯作者：
Boutros M
中科院分区：
文献类型：
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作者：
Breinig M;Klein FA;Huber W;Boutros M
小分子通常影响多个靶标，引起脱靶效应，并诱导基因型特异性反应。化学遗传学，小分子效应的基因型依赖性在广泛的表型谱中的映射可以识别新的作用机制。它还可以揭示意想不到的效果，从而降低小分子开发管道的高损耗率。在这里，我们使用高含量筛选和图像分析来测量1，280种具有生物活性的化合物对在关键致癌信号通路中含有激活或失活突变的同基因癌细胞系中复杂表型的影响。使用多参数化学-遗传相互作用分析，我们观察到超过193种化合物的表型基因-药物相互作用，其中许多影响细胞生长以外的表型。我们创建了一个名为药物遗传学表型纲要（PGPC）的资源，它可以探索药物的作用模式，检测潜在的脱靶效应，并生成关于药物组合和协同作用的假设。例如，我们证明MEK抑制剂可以增强临床使用的抗酗酒药物双硫仑的活力效应，并表明EGFR抑制剂tyrphostin AG 555对蛋白酶体具有脱靶活性。总之，这项研究表明，如何结合不同遗传背景的多参数表型可以用来预测其他的作用机制，并重新定位临床使用的药物。
Small molecules often affect multiple targets, elicit off‐target effects, and induce genotype‐specific responses. Chemical genetics, the mapping of the genotype dependence of a small molecule's effects across a broad spectrum of phenotypes can identify novel mechanisms of action. It can also reveal unanticipated effects and could thereby reduce high attrition rates of small molecule development pipelines. Here, we used high‐content screening and image analysis to measure effects of 1,280 pharmacologically active compounds on complex phenotypes in isogenic cancer cell lines which harbor activating or inactivating mutations in key oncogenic signaling pathways. Using multiparametric chemical–genetic interaction analysis, we observed phenotypic gene–drug interactions for more than 193 compounds, with many affecting phenotypes other than cell growth. We created a resource termed the Pharmacogenetic Phenome Compendium (PGPC), which enables exploration of drug mode of action, detection of potential off‐target effects, and the generation of hypotheses on drug combinations and synergism. For example, we demonstrate that MEK inhibitors amplify the viability effect of the clinically used anti‐alcoholism drug disulfiram and show that the EGFR inhibitor tyrphostin AG555 has off‐target activity on the proteasome. Taken together, this study demonstrates how combining multiparametric phenotyping in different genetic backgrounds can be used to predict additional mechanisms of action and to reposition clinically used drugs.