Systematic, network-based characterization of therapeutic target inhibitors

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

大量被鉴定为肿瘤关键依赖蛋白的靶点，要么属于药效学特性欠佳的难治靶点，要么缺乏临床相关的小分子抑制剂。因此，若能开发出可完全泛化的方法，以系统高效的方式对肿瘤背景特异性蛋白活性抑制剂进行优先级排序，将具备重要的转化应用价值。遗憾的是，传统生化实验无法以系统性、覆盖全蛋白质组的方式直接测定抑制剂对蛋白活性的影响。本研究提出OncoLead——一种基于网络的全新方法，可系统地对任意治疗相关靶点的候选抑制剂进行优先级排序。体外与体内验证实验结果证实，OncoLead分析不仅能够复现已知的抑制剂，还可对MYC、STAT3等难治靶点的新型背景特异性抑制剂进行优先级排序。本研究借助OncoLead构建了首张无偏倚的药物/调控因子相互作用图谱，该图谱涵盖了调控癌症相关转录因子活性的化合物，在精准医学领域具备应用潜力。