Gayvert Kaitlyn M, Dardenne Etienne, Cheung Cynthia, Boland Mary Regina, Lorberbaum Tal, Wanjala Jackline, Chen Yu, Rubin Mark A, Tatonetti Nicholas P, Rickman David S, Elemento Olivier
Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA; Institute for Precision Medicine, Weill Cornell Medical College, New York, NY 10021, USA; Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA.
Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY 10021, USA.
Cell Rep. 2016 Jun 14;15(11):2348-56. doi: 10.1016/j.celrep.2016.05.037. Epub 2016 Jun 2.
Mutations in transcription factor (TF) genes are frequently observed in tumors, often leading to aberrant transcriptional activity. Unfortunately, TFs are often considered undruggable due to the absence of targetable enzymatic activity. To address this problem, we developed CRAFTT, a computational drug-repositioning approach for targeting TF activity. CRAFTT combines ChIP-seq with drug-induced expression profiling to identify small molecules that can specifically perturb TF activity. Application to ENCODE ChIP-seq datasets revealed known drug-TF interactions, and a global drug-protein network analysis supported these predictions. Application of CRAFTT to ERG, a pro-invasive, frequently overexpressed oncogenic TF, predicted that dexamethasone would inhibit ERG activity. Dexamethasone significantly decreased cell invasion and migration in an ERG-dependent manner. Furthermore, analysis of electronic medical record data indicates a protective role for dexamethasone against prostate cancer. Altogether, our method provides a broadly applicable strategy for identifying drugs that specifically modulate TF activity.
转录因子(TF)基因的突变在肿瘤中经常被观察到,这往往会导致异常的转录活性。不幸的是,由于缺乏可靶向的酶活性,转录因子通常被认为是不可成药的。为了解决这个问题,我们开发了CRAFTT,一种用于靶向转录因子活性的计算药物重新定位方法。CRAFTT将染色质免疫沉淀测序(ChIP-seq)与药物诱导的表达谱分析相结合,以识别能够特异性干扰转录因子活性的小分子。应用于ENCODE ChIP-seq数据集揭示了已知的药物-转录因子相互作用,全局药物-蛋白质网络分析支持了这些预测。将CRAFTT应用于ERG(一种促侵袭、经常过度表达的致癌转录因子),预测地塞米松会抑制ERG活性。地塞米松以ERG依赖的方式显著降低细胞侵袭和迁移。此外,电子病历数据分析表明地塞米松对前列腺癌具有保护作用。总之,我们的方法为识别特异性调节转录因子活性的药物提供了一种广泛适用的策略。