TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data

TCPA：功能性癌症蛋白质组数据的综合生物信息学资源

• 批准号: 9184861
• 负责人: Han Liang
• 金额: $ 79.63万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9184861
• 关键词: Activities of Daily Living; Algorithms; Antibodies; Apoptosis; Atlases; Bioinformatics; Biological Assay; Biological Markers; Cancer Center; Cell Cycle; Cell Line; Clinical; Clinical Data; Communities; Complement; Complex; Computer software; DNA Damage; Data; Data Collection; Data Set; Databases; Development; Documentation; Drug Targeting; Educational workshop; Experimental Designs; FRAP1 gene; Face; Feedback; Functional disorder; Generations; Genome; Goals; Housing; Human; Imagery; Immune; Informatics; Internet; Knowledge; Letters; Link; Literature; MAP Kinase Gene; Malignant Neoplasms; Mining; Mission; Molecular; Network-based; Outcome; Paper; Pathway interactions; Patients; Phase; Process; Protein Array; Proteins; Proteome; Proteomics; Public Health; Quality Control; Reproducibility; Research Personnel; Resistance; Resources; Sampling; Signal Pathway; Site; Software Engineering; Technology; The Cancer Genome Atlas; Therapeutic; Transforming Growth Factor beta; Translations; United States National Institutes of Health; University of Texas M D Anderson Cancer Center; Work; Writing; anticancer research; base; burden of illness; cancer genome; cancer proteomics; cancer therapy; cohort; computerized data processing; cost effective; disability; drug sensitivity; experience; genome browser; improved; multidisciplinary; open source; precision oncology; prognostic; programs; protein biomarkers; research and development; response; success; technology validation; tool; treatment response; user-friendly

SUMMARY/ABSTRACT Reverse-phase protein arrays (RPPAs) offer a powerful functional proteomic approach to investigate molecular mechanisms and response to therapy in cancer. MD Anderson Cancer Center has been a leader in the implementation of this antibody-based technology that can assess many protein markers across large numbers of samples in a cost-effective, sensitive and high-throughput manner. The platform currently assesses ~300 protein markers, covering all major signaling pathways and most drug targets. Its utility was demonstrated through its selection as the sole platform for characterizing >10,000 patient samples through The Cancer Genome Atlas (TCGA); and recently it has been designated as one of two NCI Genome Characterization Centers, and will characterize up to ~10,000 samples from ongoing NCI initiatives and other consortium projects. For TCGA project, the applicants built The Cancer Proteome Atlas (TCPA), a web platform for visualizing and analyzing RPPA data, which has a community of >5,000 users worldwide. The long-term goal is to promote the ability of functional proteomics to impact cancer research and the development of relevant therapeutic strategies. The current objective is to expand the scope of TCPA by adding new functionalities and datasets, and to enhance and improve its existing analytic capabilities. Working relationships have been formed to link TCPA with other widely used bioinformatic resources (e.g., cBio, UCSC Genome Browsers, Firehose and Synpase) and other ITCR projects. An experienced, multidisciplinary team has been assembled to pursue four specific aims: Aim #1. Develop an open source, all-in-one software package for processing RPPA data. This effort will standardize each informatic step for RPPA data generation including experimental design, quality control, and data normalization. The resultant program will be exported to other RPPA facilities. Aim #2. Expand and enhance our existing web platform for the analysis of patient-cohort RPPA data. The web platform will cover other patient cohorts, incorporate other types of molecular/clinical data, and provide pathway/network-based analytics. Aim #3. Build a user-friendly, interactive, open web platform for the analysis of cell line RPPA data. This effort will collect and compile RPPA data of >1,500 cell lines, and develop a web platform parallel to Aim #2. Aim #4. Promote TCPA and active interaction with the user community. This effort will provide documentation, hands-on workshops, and bug fixes, and build web APIs for interaction with other tools. The expected outcome is the first, dedicated bioinformatic resource that fully integrates RPPA data generation, analysis and user feedback, allowing for fluent exploration and analysis of high-quality proteomic data in a rich context. The project is important because it will greatly enhance the quality and reproducibility of RPPA data from important consortium projects; substantially reduce barriers biomedical researchers face in mining complex functional proteomic data; serve as a hub for integrating proteomic data into other widely used bioinformatic resources; and directly facilitate development of protein markers for precision cancer medicine.

摘要/摘要 反相蛋白阵列（RPPA）提供了一种强大的功能蛋白质组学方法来研究分子 机制和对癌症治疗的反应。 MD安德森癌症中心一直是 实施基于抗体的技术，可以评估大量蛋白质标记 以具有成本效益，敏感和高通量方式的样品。该平台目前评估〜300 蛋白质标记物，涵盖所有主要的信号通路和大多数药物靶标。展示了它的实用性 通过选择作为通过癌症来表征10,000个患者样品的唯一平台 基因组地图集（​​TCGA）;最近它被指定为两个NCI基因组表征之一 中心，并将从正在进行的NCI计划和其他财团中的多达约10,000个样本来表征 项目。对于TCGA项目，申请人建造了Cancer Proteome Atlas（TCPA），这是一个用于 可视化和分析RPPA数据，该数据在全球范围内拥有> 5,000个用户的社区。长期目标 是促进功能蛋白质组学影响癌症研究和相关发展的能力 治疗策略。当前的目标是通过添加新功能和 数据集，并增强和提高其现有的分析功能。工作关​​系已经 形成的是将TCPA与其他广泛使用的生物信息学资源联系起来（例如，CBIO，UCSC基因组浏览器， Firehose和Synpase）和其他ITCR项目。一支经验丰富的多学科团队已经组装 追求四个具体目标：目标＃1。开发一个用于处理的开源，多合一的软件包 RPPA数据。这项工作将标准化RPPA数据生成的每个信息步骤，包括实验 设计，质量控制和数据归一化。结果计划将出口到其他RPPA设施。 目标＃2。扩展和增强我们现有的Web平台，以分析患者-Cohort RPPA数据。网络 平台将涵盖其他患者队列，结合其他类型的分子/临床数据，并提供 途径/基于网络的分析。目标＃3。构建一个用户友好，交互式，开放的网络平台进行分析 细胞系RPPA数据。这项工作将收集和编译> 1,500个单元线的RPPA数据，并开发网络 平台与AIM＃2平行。目标＃4。促进TCPA和与用户社区的积极互动。这项努力 将提供文档，实践研讨会和错误修复，并构建Web API，以与其他 工具。预期的结果是第一个完全集成了RPPA数据的专用生物信息学资源 产生，分析和用户反馈，允许对高质量蛋白质组学的流利探索和分析 在丰富的环境中的数据。该项目很重要，因为它将大大提高 来自重要财团项目的RPPA数据；实质上减少了生物医学研究人员面临的障碍 挖掘复杂的功能蛋白质组学数据；用作将蛋白质组学数据集成到其他广泛使用的枢纽 生物信息学资源；并直接促进蛋白质标志物的精确癌症医学的发展。