Tran Hai Joey, Speyer Gil, Kiefer Jeff, Kim Seungchan
Integrated Cancer Genomics Division, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA.
Department of Electrical and Computer Engineering, Roy G. Perry College of Engineering, Prairie View A&M University, Prairie View, TX, 77446, USA.
BMC Bioinformatics. 2017 May 31;18(Suppl 7):252. doi: 10.1186/s12859-017-1642-8.
Genomic analysis of drug response can provide unique insights into therapies that can be used to match the "right drug to the right patient." However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results.
In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to "explain" the relation between a drug and a mediator. RESULTS: We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator.
We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.
药物反应的基因组分析能够为可用于实现“正确的药物匹配正确的患者”的疗法提供独特见解。然而，利用基因组数据发现此类治疗见解的过程并不简单，是一个正在积极研究的领域。EDDY（差异依赖性评估）是一种用于检测差异统计依赖性的统计检验，是利用基因组数据识别差异遗传依赖性的一种方法。EDDY已与癌症治疗反应门户（CTRP，一个包含400多种小分子药物反应测量数据的数据集）以及癌症细胞系百科全书（CCLE）中的细胞系RNAseq数据结合使用，以寻找潜在的药物 - 介导物对。介导物被确定为在药物敏感和药物不敏感细胞系的注释途径内遗传统计依赖性显示出显著变化的基因，其结果以公共网站门户（EDDY - CTRP）的形式呈现。然而，目前药物 - 介导物对的可解释性阻碍了对这些潜在有价值结果的进一步探索。
在本研究中，我们通过构建基于STITCH和STRING相互作用数据库中的蛋白质与药物相互作用的证据网络来应对这一挑战。STITCH和STRING是姊妹数据库，分别编目已知和预测的药物 - 蛋白质相互作用以及蛋白质 - 蛋白质相互作用。利用这两个数据库，我们开发了一种构建证据网络的方法来“解释”药物与介导物之间的关系。
我们将这种方法应用于在EDDY - CTRP分析中发现的药物 - 介导物关系，并为约70%的药物 - 介导物对确定了证据网络，其中大多数介导物并非该药物已知的直接靶点。构建的证据网络使研究人员能够将药物 - 介导物对与当前的研究和知识相结合。通过证据网络，我们能够通过将药物和介导物与与药物和介导物相关的基因联系起来，提高EDDY - CTRP结果的可解释性。
我们预计这些证据网络将有助于为EDDY - CTRP结果提供信息，并增强对药物敏感性的重要见解的产生，从而改善精准医学应用。
