Illuminating understudied druggable proteins using pan-cancer proteogenomics data

使用泛癌蛋白质组学数据阐明尚未研究的可药物蛋白

• 批准号: 10671574
• 负责人: Bing Zhang
• 金额: $ 48万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-26 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10671574
• 关键词: Acceleration; Address; Biological; Clinical; Clinical Data; Communities; Complex; Computing Methodologies; Darkness; Data; Data Analyses; Data Set; Data Sources; Databases; Decision Making; Development; Disease; Drug Targeting; Eligibility Determination; Exclusion; FDA approved; Family; Family member; G protein coupled receptor kinase; G-Protein-Coupled Receptors; Genes; Genome; Genomics; Goals; Histology; Individual; Intuition; Investigation; Ion Channel; Knowledge; Knowledge Portal; Malignant Neoplasms; Mass Spectrum Analysis; Messenger RNA; Methods; Molecular; Multiomic Data; Normal tissue morphology; Pathogenesis; Pathway interactions; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Play; Post-Translational Protein Processing; Preparation; Primary Neoplasm; Process; Protein Family; Protein Kinase; Proteins; Proteomics; Publishing; Qualifying; Reporting; Research; Research Personnel; Resources; Role; Sampling; Signal Transduction; Standardization; Therapeutic; Time; Tissue Sample; Tumor Tissue; Validation; Visualization; cancer proteomics; cancer type; clinical phenotype; cohort; computing resources; data and analysis portal; flexibility; gene function; genomic data; insight; interest; knowledgebase; member; molecular phenotype; multiple omics; novel; phenotypic data; phosphoproteomics; programs; protein expression; protein profiling; proteogenomics; prototype; therapeutic development; tool; transcriptomics; tumor; web app; web page; web portal

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.

项目摘要 蛋白激酶家族中的蛋白、G蛋白偶联受体和离子通道经常起作用 对疾病发病机制的研究，是治疗学发展的良好候选者。事实上，41%的FDA- 批准的药物针对这些家族中的蛋白质。然而，这些蛋白质家族中的每一个都有许多 对此知之甚少的成员。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能是一个很好的起点 对未被研究的蛋白质的功能和表型关联产生新的假说。最近， NCI的临床蛋白质组肿瘤分析联盟(CPTAC)计划已经确定了1000多名原发肿瘤患者的特征 使用多个组学平台覆盖10种癌症类型的肿瘤。虽然之前的大规模组学数据集已经 CPTAC的数据侧重于基因组和转录组数据，还整合了基于质谱仪(MS)的 蛋白质组学和磷酸蛋白质组学。我们和CPTAC财团的同事发表的研究报告 展示了这些蛋白质组数据集作为强化现有资源的综合资源的价值 知识，识别新的生物学见解，并产生治疗假说。这样做的目的是 应用是利用CPTAC泛癌蛋白质组学数据阐明未被充分研究的可用药蛋白质。我们 将通过解决两个具体目标来实现这一目标。AIM 1建立在我们已建立的多组学数据基础上 分析门户网站LinkedOmics。我们将把LinkedOmics扩展到知识库LinkedOmicsKB中，其中 来自CPTAC泛癌蛋白质组学协调数据的信息将被组织到基因- 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》是基于我们之前的 报告说，蛋白质图谱数据与基因功能的关系比mRNA图谱数据更紧密。我们 将使用CPTAC泛癌症蛋白质组学数据对未被研究的可用药进行功能预测 蛋白质，然后对选定的预测进行实验验证。数据、可视化和预测结果 这两个目标将被整合到Pharos中，Pharos是照亮可药物基因组的知识门户 (IDG)计划，以促进我们对符合IDG资格的未研究蛋白质的理解。