TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data

摘要/摘要 反相蛋白质阵列(RPPA)为研究分子提供了一种强大的功能蛋白质组学方法 癌症治疗的机制和反应。安德森癌症中心一直是该领域的领导者。 这项基于抗体的技术的实施可以大量评估许多蛋白质标记物 以具有成本效益的、灵敏的和高通量的方式对样品进行分析。该平台目前评估约300 蛋白质标记物，覆盖所有主要信号通路和大多数药物靶点。它的作用是被证明的 通过选择它作为唯一的平台，通过癌症表征&gt；10,000个患者样本 基因组图谱(TCGA)；最近被指定为两个NCI基因组特征之一 中心，并将从正在进行的NCI倡议和其他财团中鉴定多达10,000个样本 项目。对于TCGA项目，申请者建立了癌症蛋白质组图谱(TCPA)，这是一个用于 可视化和分析RPPA数据，该公司在全球拥有5,000名用户。长期目标 是推动功能蛋白质组学影响癌症的研究和相关研究的发展 治疗策略。目前的目标是通过增加新的功能和 数据集，并增强和改进其现有的分析能力。工作关系一直是 形成为将TCPA与其他广泛使用的生物信息资源(例如，cBio，UCSC基因组浏览器， FireHose和Synpase)和其他ITCR项目。已经组建了一支经验丰富的多学科团队 追求四个具体目标：目标1.开发用于处理的开源、一体化软件包 RPPA数据。这项工作将标准化RPPA数据生成的每个信息步骤，包括试验性步骤 设计、质量控制和数据标准化。由此产生的程序将被输出到RPPA的其他设施。 目标#2.扩展和增强我们现有的用于分析患者队列RPPA数据的网络平台。网络 平台将覆盖其他患者队列，纳入其他类型的分子/临床数据，并提供 基于路径/网络的分析。目标#3.为分析建立一个用户友好、交互、开放的网络平台 细胞系RPPA数据。这项工作将收集和汇编1,500个细胞系的RPPA数据，并开发一个网络 与目标2、目标4平行的平台。促进TCPA和与用户社区的积极互动。这一努力 将提供文档、实践研讨会和错误修复，并构建用于与其他 工具。预期结果是第一个完全集成RPPA数据的专用生物信息资源 生成、分析和用户反馈，允许流畅地探索和分析高质量的蛋白质组 丰富的上下文中的数据。该项目很重要，因为它将极大地提高 来自重要联盟项目的RPPA数据；大大减少了生物医学研究人员在 挖掘复杂的蛋白质组功能数据；作为将蛋白质组数据整合到其他广泛使用的数据中的枢纽 生物信息资源；并直接促进精确癌症医学蛋白质标记物的开发。

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.