Illuminating understudied druggable proteins using pan-cancer proteogenomics data

项目摘要 激酶、G蛋白偶联受体和离子通道家族中的蛋白质经常参与 疾病的发病机制，是开发治疗药物的良好候选者。事实上，41%的FDA- 批准的药物靶向这些家族中的蛋白质。然而，这些蛋白质家族中的每一个都具有许多不同的结构。 成员，对此知之甚少。更好地了解这些“黑暗”成员可能会为 治疗疾病的新方法。利用现有的大型组学数据集可以是一个很好的起点， 产生新的假说的功能和表型关联的未充分研究的蛋白质。近日 NCI的临床蛋白质组学肿瘤分析联盟（CPTAC）计划已经表征了1,000多个原发性肿瘤。 肿瘤覆盖10种癌症类型，使用多个组学平台。虽然以前的大规模组学数据集 CPTAC数据集中于基因组和转录组数据，还整合了基于质谱（MS）的 蛋白质组学和磷酸蛋白质组学。我们的同事和我们在CPTAC联盟发表的研究报告 证明了这些蛋白质基因组学数据集作为加强现有 知识，确定新的生物学见解，并产生治疗假设。这个目标 应用是使用CPTAC泛癌症蛋白质基因组学数据来阐明未充分研究的可药用蛋白质。我们 我们将通过两个具体目标来实现这一目标。目标1是建立在我们建立的多组学数据基础上的 分析门户网站LinkedIn。我们将把LinkedOmics扩展成一个知识库LinkedOmicsKB， 从协调的CPTAC泛癌症蛋白基因组学数据中获得的信息将被组织成基因- 以网页为中心，具有易于浏览的部分和有效的可视化效果。目标2是基于我们以前的 报告说，蛋白质分析数据比mRNA分析数据更接近基因功能。我们 将使用CPTAC泛癌蛋白质组学数据对未充分研究的药物进行功能预测， 蛋白质，然后通过实验验证选定的预测。数据、可视化和预测结果 将被整合到灯塔，照亮可药用基因组的知识门户网站 (IDG)计划，以加速我们对IDG合格的未充分研究的蛋白质的理解。

Project Summary Proteins in the families of kinases, G protein coupled receptors, and ion channels frequently contribute to disease pathogenesis and are good candidates for the development of therapeutics. In fact, 41% of the FDA- approved drugs target proteins in these families. However, each of these protein families has a number of members about which very little is known. Better understanding of these ‘dark’ members may pave the way to new methods for treating diseases. Utilizing existing large omics datasets can be a great starting point to generate new hypotheses on the function and phenotype association of the understudied proteins. Recently, the NCI’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) program has characterized over 1,000 primary tumors covering 10 cancer types using multiple omics platforms. While previous large-scale omics datasets have focused on genomic and transcriptomic data, the CPTAC data also integrate mass spectrometry (MS)-based proteomics and phosphoproteomics. Published studies by our colleagues and us in the CPTAC consortium have demonstrated the value of these proteogenomics datasets as a comprehensive resource for reinforcing existing knowledge, identifying new biological insights, and generating therapeutic hypotheses. The goal of this application is to illuminate understudied druggable proteins using CPTAC pan-cancer proteogenomics data. We will achieve this goal by addressing two specific Aims. Aim 1 is built upon our established multi-omics data analysis portal LinkedOmics. We will extend LinkedOmics into a knowledgebase, LinkedOmicsKB, in which information derived from harmonized CPTAC pan-cancer proteogenomics data will be organized into gene- centric web pages with easily browsable sections and effective visualizations. Aim 2 is based on our previous report that protein profiling data is much more closely aligned with gene function than mRNA profiling data. We will use CPTAC pan-cancer proteomics data to make function predictions for the understudied druggable proteins, followed by experimental validation of selected predictions. Data, visualization, and prediction results from both Aims will be integrated into Pharos, the knowledge portal of the Illuminating the Druggable Genome (IDG) program, to accelerate our understanding of IDG-eligible understudied proteins.