iPGDAC, An Integrative Proteogenomic Data Analysis Center for CPTAC

iPGDAC，CPTAC 综合蛋白质组数据分析中心

• 批准号: 10440591
• 负责人: Bing Zhang
• 金额: $ 89.67万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10440591
• 关键词: Address; Amino Acid Sequence; Area; Base Sequence; Benchmarking; Biological; Biological Assay; Biological Markers; Biological Models; Cancer Biology; Center for Translational Science Activities; Clinical; Clinical Data; Clinical Trials; Collaborations; Communities; Data; Data Analyses; Data Set; Disease; Funding; Genomics; Goals; Human; Immunophenotyping; Individual; Knowledge; Leadership; Malignant Neoplasms; Mass Spectrum Analysis; Molecular; Network-based; Pathway Analysis; Pathway interactions; Patients; Peptides; Phase; Phenotype; Play; Portraits; Post-Translational Modification Site; Post-Translational Protein Processing; Prognosis; Proteins; Proteomics; Publishing; Quality Control; Resources; Role; Sampling; Specific qualifier value; Techniques; Translational Research; Translations; Work; base; cancer diagnosis; cancer therapy; cancer type; candidate identification; candidate marker; clinical center; clinical translation; clinically relevant; computerized data processing; computerized tools; data integration; data quality; deep learning; epigenomics; functional genomics; genetic variant; genomic aberrations; genomic predictors; improved; insight; machine learning algorithm; natural language; novel; patient prognosis; predictive modeling; programs; protein biomarkers; proteogenomics; success; targeted biomarker; tool; tool development; transcriptomics; treatment response; tumor; tumor-immune system interactions; verification and validation; web portal; working group

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.

项目摘要 通过将基于质谱（MS）的蛋白质组学与基因组学、表观基因组学和 转录组学，蛋白质基因组学具有很大的潜力，更好地阐明癌症的复杂性比个人 来吧在过去的10年里，临床蛋白质组学肿瘤分析联盟（CPTAC）进行了 10种癌症类型中超过1,500种肿瘤的全面蛋白基因组学表征。这些研究不 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， 这些计算工具可以被广泛的科学界进一步使用。CPTAC的下一阶段 该计划旨在将目前的成功扩大到更多的癌症类型和转化研究，重点是 临床相关问题。我们的整合蛋白基因组数据分析中心（iPGDAC）是目前 CPTAC资助了PGDAC。我们参与了所有CPTAC癌症类型的研究， 在几种癌症类型的数据分析中发挥主导作用。此应用程序旨在继续和加强我们的 为CPTAC计划的下一阶段做出贡献。我们的PGDAC的首要目标是加快 将癌症蛋白基因组学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、工作流程和门户网站， 已经成功地用于基于序列和基于途径/网络的CPTAC研究 蛋白质组学数据集成。此外，我们将解决翻译后修饰中未满足的需求 （PTM）相关分析，通过使用蛋白质序列和基于自然语言的深度学习技术， 改进PTM肽鉴定，以预测基因组变体对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以及尖端的统计推断和机器学习 算法，我们将对来自CPTAC研究的蛋白质基因组数据进行综合分析，以：1）创建一个 为每个患者的肿瘤提供全面的分子和细胞画像; 2）识别和表征分子 和肿瘤微环境/免疫亚型; 3）使用 蛋白基因组学数据; 4）揭示癌症表型的分子机制; 5）开发预测模型 用于患者预后和治疗反应。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整计划。我们有着良好 在计算蛋白质基因组学领域的领导地位以及在CPTAC网络中的成功合作记录， 我们希望通过这个项目广泛地推进这一领域。