The Cancer Proteome Atlas: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data

总结/摘要 反相蛋白质芯片（RPPA）为研究分子生物学提供了一种强有力的功能蛋白质组学方法。 机制和对癌症治疗的反应。MD安德森癌症中心是实施 这种基于抗体的技术，可以评估大量样本中的许多蛋白质标记物， 成本有效、灵敏和高通量的方式。该平台目前评估约500种蛋白质标记物， 涵盖了所有主要的信号通路和大多数药物靶点。它的实用性是通过它的选择证明的 作为通过TCGA对约8，000例患者样本和> 1，000个细胞系进行蛋白质组学表征的平台 通过CCLE，并在2015年被指定为两个NCI基因组表征中心之一。这是一 批准的癌症治疗评估平台网站，用于样品表征，从而实现 多项有效的临床试验。在ITCR的支持下，我们开发了一个主要的生物信息学资源， RPPA数据的分析、可视化和传播，癌症蛋白质组图谱（TCPA）， 一个在全球拥有超过80，000名用户的社区。目前的目标是改进数据质量控制， 现有的分析能力，并通过增加新的功能和数据集来扩大TCPA的范围。 我们已经建立了工作关系，将TCPA与其他广泛使用的生物信息学资源联系起来。作为 作为一个经验丰富的多学科团队，我们将追求四个具体目标：目标1。开发一个用户友好的多功能一体化 用于处理RPPA数据的软件管道。我们将改进质量控制和批次效果调整步骤 的RPPA数据处理，增强流水线的性能和结果的交互性，并提供 一个用户友好的，通用的软件包，以科学界。目标2。扩大和加强我们现有的 用于分析RPPA数据的网络平台。我们将扩大RPPA数据的范围，纳入其他类型的 分子数据，特别是蛋白质组数据，并增强分析和可视化能力。目标3。构建 一个用户友好的交互式网络平台，用于分析来自异种移植物、PDX和动物的癌症RPPA数据 模型我们将收集和汇编超过10，000个此类样本的RPPA数据，并开发相关的可视化和 分析模块目标4。促进TCPA和与用户社区的积极互动。我们将加强 RPPA数据存储库，并将其作为标准参考数据库进行推广，提供文档，动手操作 研讨会和错误修复，并构建用于与其他工具交互的Web API。预期的结果是 专用的、全面的生物信息学资源，完全集成了RPPA数据生成、分析 传播和用户反馈，允许流畅地探索和分析高质量的蛋白质组数据， 丰富的上下文。该项目很重要，因为它将大大提高RPPA的质量和重现性 来自重要联盟项目的数据;大大减少挖掘复杂功能蛋白质组的障碍 数据;作为一个中心，将高质量的基于RPPA的蛋白质组学数据整合到其他广泛使用的 生物信息资源，并直接促进蛋白质标记物的发展，用于精确的癌症医学。

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 is 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 ~500 protein markers, covering all major signaling pathways and most drug targets. Its utility was demonstrated through its selection as the platform for proteomic characterization of ~8,000 patient samples through TCGA and >1,000 cell lines through CCLE, and its designation as one of two NCI Genome Characterization Centers in 2015. It is an approved Cancer Therapy Evaluation Platform site for sample characterization, leading to the implementation of multiple effective clinical trials. With ITCR support, we have developed a major bioinformatics resource dedicated to the analysis, visualization, and dissemination of RPPA data, The Cancer Proteome Atlas (TCPA), which has a community of >80,000 users worldwide. The current objective is to improve the data quality control, to enhance the existing analytic capabilities, and to expand the scope of TCPA by adding new functionalities and datasets. We have formed working relationships to link TCPA with other widely used bioinformatics resources. As an experienced, multidisciplinary team, we will pursue four specific aims: Aim #1. Develop a user-friendly, all-in-one software pipeline for processing RPPA data. We will improve quality control and batch effects adjustment steps of RPPA data processing, enhance the performance of the pipeline and interactivity of the results, and provide a user-friendly, general software package to the scientific community. Aim #2. Expand and enhance our existing web platforms for the analysis of RPPA data. We will extend the scope of RPPA data, incorporate other types of molecular data, especially proteomic data, and enhance the analytic and visualization capabilities. Aim #3. Build a user-friendly, interactive web platform for the analysis of cancer RPPA data from xenograft, PDX, and animal models. We will collect and compile RPPA data of >10,000 such samples and develop related visualization and analytic modules. Aim #4. Promote TCPA and active interaction with the user community. We will enhance the RPPA data repository and promote it as a standard reference database, provide documentation, hands-on workshops, and bug fixes, and build web APIs for interaction with other tools. The expected outcome is a dedicated, comprehensive bioinformatics resource that fully integrates RPPA data generation, analysis, dissemination, and user feedback, allowing for fluent exploration and analysis of high-quality proteomic data in rich contexts. The project is important because it will greatly enhance the quality and reproducibility of RPPA data from important consortium projects; substantially reduce barriers in mining complex functional proteomic data; serve as a hub for integrating high-quality RPPA-based proteomics data into other widely used bioinformatic resources, and directly facilitate the development of protein markers for precision cancer medicine.