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

癌症蛋白质组图谱：功能性癌症蛋白质组数据的综合生物信息学资源

• 批准号: 10653202
• 负责人: Rehan Akbani
• 金额: $ 78.67万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653202
• 关键词: Acceleration; Activities of Daily Living; Animal Model; Antibodies; Apoptosis; Atlases; Biological Markers; Cancer Center; Cell Cycle; Cell Line; Clinical; Clinical Trials; Communities; Complex; Computer software; DNA Damage; Data; Data Set; Databases; Dedications; Development; Documentation; Drug Targeting; Educational workshop; FRAP1 gene; Feedback; Functional disorder; Generations; Genome; Goals; Human; Immune; Individual; Internet; Journals; Knowledge; Letters; Link; Literature; MAP Kinase Gene; Malignant Neoplasms; Memorial Sloan-Kettering Cancer Center; Mining; Mission; Molecular; Outcome; PIK3CG gene; Paper; Patients; Performance; Phase; Protein Array; Proteins; Proteome; Proteomics; Public Health; Quality Control; Reference Standards; Reproducibility; Research Personnel; Resistance; Sampling; Signal Pathway; Site; Software Engineering; Synapses; Technology; The Cancer Genome Atlas; Therapeutic; Therapy Evaluation; Transforming Growth Factor beta; Translations; United States National Institutes of Health; University of Texas M D Anderson Cancer Center; Visualization; Writing; Xenograft Model; anticancer research; bioinformatics resource; biomarker validation; burden of illness; cBioPortal; cancer proteomics; cancer therapy; computerized data processing; cost effective; data quality; data repository; disability; experience; improved; multidisciplinary; patient derived xenograft model; precision oncology; prognostic; protein biomarkers; success; therapy resistant; tool; treatment response; user-friendly; web platform

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.

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。安德森癌症中心是实施这一计划的领先者 这种基于抗体的技术可以在大量样本中评估许多蛋白质标记物 经济高效、灵敏、高吞吐量的方式。该平台目前评估~500个蛋白质标记物， 覆盖所有主要信号通路和大多数药物靶点。它的实用性通过它的选择得到了证明。 作为通过TCGA和&gt；1000细胞系对约8,000名患者样本进行蛋白质组分析的平台 通过CCLE，并于2015年被指定为两个NCI基因组表征中心之一。这是一个 批准用于样本表征的癌症治疗评估平台网站，导致实施 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 为了分析、可视化和传播RPPA数据，癌症蛋白质组图谱(TCPA)，它有 在全球拥有80,000名用户的社区。目前的目标是改善数据质量控制，以增强 现有的分析能力，并通过添加新的功能和数据集来扩展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。作为一种 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一体机 用于处理RPPA数据的软件管道。完善质量控制和批次效应调整步骤 加强区域合作伙伴关系数据处理，加强渠道的性能和成果的互动性，并提供 一个面向科学界的用户友好的通用软件包。目标2.扩展和增强我们现有的 用于分析RPPA数据的Web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、PDX和动物的癌症RPPA数据 模特们。我们将收集和编辑10,000个这样的样本的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、实际操作 研讨会和错误修复，以及构建用于与其他工具交互的Web API。预期的结果是 专门的、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 丰富的背景。该项目很重要，因为它将大大提高RPPA的质量和再现性 来自重要联盟项目的数据；大大减少了挖掘复杂功能蛋白质组的障碍 数据；充当将基于RPPA的高质量蛋白质组学数据集成到其他广泛使用的 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。