iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

项目摘要 通过将基于MS的蛋白质组学与基因组学、表观基因组学和 转录组学，蛋白质组学具有比个体更好地解释癌症复杂性的巨大潜力 ‘OMES。在过去的10年里，临床蛋白质组学肿瘤分析联盟(CPTAC)进行了 10种癌症类型的1,500个肿瘤的全面蛋白质基因组学特征。这些研究没有 不仅产生对不同癌症类型的新的生物学和临床见解，而且还产生有价值的数据集和 可供广大科学界进一步使用的计算工具。CPTAC的下一阶段 该计划寻求将目前的成功扩展到更多癌症类型和专注于 与临床相关的问题。我们的综合蛋白质组数据分析中心(IPGDAC)是目前 CPTAC资助了PGDAC。我们参与了CPTAC所有癌症类型的研究，并发挥了 在几种癌症类型的数据分析中处于领先地位。此应用程序旨在继续并增强我们的 对CPTAC计划下一阶段的贡献。我们PGDAC的首要目标是加快 将癌症蛋白质组数据转化为更好地理解癌症生物学和改善癌症 治疗。我们将继续开发和改进我们的计算工具、工作流程和Web门户 已经成功地用于基于序列和基于路径/网络的CPTAC研究 蛋白质组数据集成。此外，我们还将解决翻译后修改中未满足的需求 通过使用基于蛋白质序列和自然语言的深度学习技术进行(PTM)相关分析 改进PTM多肽鉴定，预测基因组变异对PTM的影响，并将PTM位点连接到 现有的知识。使用我们团队的独特工具和尖端的统计推理和机器学习 算法，我们将对CPTAC研究中的蛋白质组数据进行综合分析：1)创建 为每个患者的肿瘤提供全面的分子和细胞肖像；2)鉴定和表征分子 和肿瘤微环境/免疫亚型；3)使用 蛋白质组学数据；4)揭示癌症表型的分子机制；5)开发预测模型 以了解患者的预后和治疗反应。我们的PGDAC为CPTAC网络带来了完全集成的、 在RFA指定的所有关键领域拥有专业知识的完全建立的计划。我们有一个经过验证的 在计算蛋白质组学领域的领导地位和在CPTAC网络中的成功合作的记录， 我们希望通过这个项目广泛地推动这一领域的发展。

Project Summary By combining mass spectrometry (MS)-based proteomics with genomics, epigenomics, and transcriptomics, proteogenomics holds great potential to better illuminate cancer complexities than individual ‘omes. During the past 10 years, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) has performed comprehensive proteogenomic characterization of >1,500 tumors across 10 cancer types. These studies not only yield novel biological and clinical insights into different cancer types but also produce valuable datasets and computational tools that can be further used by the broad scientific community. The next phase of the CPTAC program seeks to expand the current success to more cancer types and translational research focusing on clinically relevant questions. Our integrative proteogenomic data analysis center (iPGDAC) is one of the current CPTAC funded PGDACs. We have participated in the studies of all CPTAC cancer types and have played a leading role in data analysis for several cancer types. This application seeks to continue and enhance our contribution to the next phase of the CPTAC program. The overarching goal of our PGDAC is to accelerate the translation of cancer proteogenomic data into better understanding of cancer biology and improved cancer treatment. We will continue developing and improving our computing tools, workflows, and web portals that have already been successfully used in the CPTAC studies for sequence-based and pathway/network-based proteogenomic data integration. In addition, we will address unmet needs in post-translational modification (PTM)-related analyses by using protein sequence and natural language-based deep learning techniques to improve PTM peptide identification, to predict genomic variant impact on PTMs, and to connect PTM sites to existing knowledge. Using unique tools from our team and cutting edge statistical inference and machine learning algorithms, we will perform integrated analysis on proteogenomic data from the CPTAC studies to: 1) create a comprehensive molecular and cellular portrait for each patient’s tumor; 2) identify and characterize molecular and tumor microenvironment/immune subtypes; 3) prioritize functional genomic aberrations using proteogenomic data; 4) reveal molecular mechanisms of cancer phenotypes; and 5) develop predictive models for patient prognosis and treatment response. Our PGDAC brings to the CPTAC network a fully integrated, completely established program with expertise in all the critical areas specified by the RFA. We have a proven track record of leadership in computational proteogenomics and successful collaboration in the CPTAC network, and we expect to broadly advance the field through this project.