A network-based strategy for prioritizing hits from chemical screening data by leveraging genetic, epigenetic and transcriptional datasets. Homo sapiens

Small molecule screens are widely used to prioritize compounds for development of pharmaceuticals and to reveal pathways altered in biological processes. However, interpreting the results of these screens is very challenging since in almost all cases, the compounds are highly promiscuous. Here we present a network-based strategy for analyzing molecular screening data. We report a screen for kinase inhibitors that synergize with gemcitabine, the first-line chemotherapy treatment for pancreatic cancer. The eight kinase inhibitors that emerge from the screen target a total of 140 kinases, and these kinases show little overlap with previously detected genetic modifiers of gemcitabine toxicity. Using the SAMNet algorithm, we link the chemical and genetic modifiers of gemcitabine toxicity to 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 is able to recapitulate known gemcitabine response pathways including DNA damage repair, control of cellular growth and the EMT pathway. We query the network downstream of putative kinase inhibitor targets and in addition to identifying known gemcitabine synergizers STAT3, NFKB2 and AKT1, we propose novel candidate targets for gemcitabine chemoresistance, including ETS transcription factors (ELK1, ELK3) and the adaptor protein NCK1. Our work suggests that a subset of the “off-target kinases” are directly involved in the cellular response to gemcitabine by modulating the activity of known and proposed chemosensitizing genes. Overall design: 4 RNAseq samples and 2 DNaseI-hypersensitivity samples in the PANC1 human cell line

小分子筛选（Small molecule screens）被广泛用于遴选药物开发候选化合物，并揭示生物过程中发生异常的信号通路。然而，此类筛选结果的解读极具挑战性，因为在绝大多数情况下，受试化合物均具有高度的靶点混杂性。本研究提出一种基于网络的分子筛选数据分析策略。本研究报道了一项筛选可与吉西他滨（gemcitabine，胰腺癌一线化疗用药）产生协同作用的激酶抑制剂的实验。本次筛选获得的8种激酶抑制剂共计靶向140种激酶，且这些激酶与此前已发现的吉西他滨毒性遗传修饰因子几乎无重叠。本研究借助SAMNet算法（SAMNet），将吉西他滨毒性的化学与遗传修饰因子，与我们通过DNaseI测序（DNaseI-Seq）和RNA测序（RNA-Seq）测得的吉西他滨诱导的转录与表观遗传变化关联起来。SAMNet采用约束优化算法，通过少量蛋白-蛋白相互作用与蛋白-DNA相互作用，将来自这些互补数据集的基因进行关联。所得网络能够复现已知的吉西他滨应答通路，包括DNA损伤修复、细胞生长调控以及上皮间质转化（EMT）通路。我们对推定的激酶抑制剂靶点下游的网络进行检索，除了已报道的吉西他滨协同作用因子STAT3、NFKB2与AKT1之外，还提出了全新的吉西他滨化疗耐药候选靶点，包括ETS转录因子家族（ELK1、ELK3）以及接头蛋白NCK1。本研究表明，部分"脱靶激酶"可通过调控已报道及本研究提出的化疗增敏基因的活性，直接参与细胞对吉西他滨的应答过程。实验整体设计：以人类PANC1细胞系为模型，共设置4组RNA测序（RNA-Seq）样本与2组DNaseI超敏位点测序样本。