TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data

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 癌症治疗的机制和反应。 MD 安德森癌症中心一直是该领域的领导者 实施这种基于抗体的技术，可以评估大量蛋白质标记 以经济有效、灵敏且高通量的方式提取样品。该平台目前评估约 300 蛋白质标记物，涵盖所有主要信号通路和大多数药物靶点。它的实用性得到了证明 通过 The Cancer 选择其作为表征 > 10,000 个患者样本的唯一平台 基因组图谱（TCGA）；最近它被指定为两个 NCI 基因组表征之一 中心，并将表征来自正在进行的 NCI 计划和其他联盟的多达约 10,000 个样本 项目。对于 TCGA 项目，申请人建立了癌症蛋白质组图谱 (TCPA)，这是一个网络平台，用于 可视化和分析 RPPA 数据，该数据在全球拥有超过 5,000 个用户的社区。长期目标 旨在提高功能蛋白质组学影响癌症研究和相关技术开发的能力 治疗策略。当前的目标是通过添加新功能和扩展 TCPA 的范围 数据集，并增强和改进其现有的分析能力。工作关系已 旨在将 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）以及其他 ITCR 项目。一支经验丰富的多学科团队已经组建 追求四个具体目标： 目标#1。开发一个开源、一体化的处理软件包 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、质量控制和数据标准化。由此产生的程序将被导出到其他 RPPA 设施。 目标#2。扩展和增强我们现有的网络平台，用于分析患者队列 RPPA 数据。网络 平台将覆盖其他患者群体，整合其他类型的分子/临床数据，并提供 基于路径/网络的分析。目标#3。构建用户友好、交互式、开放的网络分析平台 细胞系 RPPA 数据。这项工作将收集和编译超过 1,500 个细胞系的 RPPA 数据，并开发一个网络 与目标 #2 平行的平台。目标#4。促进 TCPA 并与用户社区积极互动。这个努力 将提供文档、实践研讨会和错误修复，并构建 Web API 以与其他人交互 工具。预期成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，从而可以流畅地探索和分析高质量的蛋白质组 丰富上下文中的数据。该项目很重要，因为它将大大提高研究的质量和可重复性 来自重要联合体项目的 RPPA 数据；大大减少生物医学研究人员在研究中面临的障碍 挖掘复杂的功能蛋白质组数据；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；并直接促进精准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.