Ursu Oana, Gosline Sara J C, Beeharry Neil, Fink Lauren, Bhattacharjee Vikram, Huang Shao-Shan Carol, Zhou Yan, Yen Tim, Fraenkel Ernest
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS One. 2017 Oct 12;12(10):e0185650. doi: 10.1371/journal.pone.0185650. eCollection 2017.
Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitizing agents.
小分子筛选被广泛用于确定药物研发的优先级。然而，确定这些分子所靶向的通路具有挑战性，因为这些化合物往往具有多效性。我们提出了一种网络策略，该策略考虑小分子的多药理学特性，以便为其更广泛的作用模式生成假设。我们报告了一项针对激酶抑制剂的筛选，这些抑制剂可提高吉西他滨（胰腺癌的一线化疗药物）的疗效。出现了八种已知可影响201种激酶的激酶抑制剂，其中只有三种激酶先前被确定为吉西他滨毒性的调节剂。在这项工作中，我们使用SAMNet算法来识别将这些激酶与吉西他滨毒性的遗传修饰因子与我们使用DNaseI-seq和RNA-seq测量的吉西他滨诱导的转录和表观遗传变化联系起来的通路。SAMNet使用一种约束优化算法，通过一小组蛋白质-蛋白质和蛋白质-DNA相互作用将来自这些互补数据集的基因连接起来。由此产生的网络概括了包括DNA修复、细胞增殖和上皮-间质转化在内的已知通路。我们使用该网络来预测在吉西他滨反应中起重要作用的基因，包括六个已被证明可改变胰腺癌中吉西他滨疗效的基因和十个新的候选基因。我们的工作揭示了多药理学在这些化学增敏剂活性中的重要作用。
