Michigan Center for Translational Cancer Proteogenomics

密歇根转化癌症蛋白质组学中心

• 批准号: 10636958
• 负责人: Saravana Mohan Dhanasekaran
• 金额: $ 75.03万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-06 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10636958
• 关键词: Acceleration; Address; Adoption; Algorithms; Area; Benchmarking; Biological; Biological Assay; Biological Markers; Cancer Biology; Cell Communication; Cells; Cellularity; Clinical; Clonal Expansion; Code; Collaborations; Communities; Copy Number Polymorphism; Data; Data Analyses; Data Reporting; Development; Disease; Early Detection Research Network; Ensure; Epigenetic Process; Event; Foundations; Future; Gene Fusion; Generations; Genes; Genetic; Genome; Genomics; Goals; Histologic; Human; Individual; Investigation; Knowledge; Malignant Neoplasms; Measures; Methods; Michigan; Mission; Modeling; Molecular; Mutation; Oncogenic; Oncology; Pathologist; Pathology; Pathway Analysis; Patients; Peptides; Phenotype; Ploidies; Positioning Attribute; Post-Translational Protein Processing; Prognostic Marker; Proteins; Proteome; Proteomics; Quality Control; Recording of previous events; Renaissance; Research; Research Personnel; Resources; SAGA; Speed; Systems Biology; Time; Tissues; Translations; Tumor Biology; United States National Institutes of Health; Universities; Untranslated RNA; Validation; Work; bioinformatics tool; biomarker discovery; biomarker signature; cancer genetics; cancer genome; cancer genomics; cancer subtypes; cancer type; candidate selection; clinical implementation; clinically actionable; clinically relevant; cohort; computer infrastructure; computerized tools; computing resources; data modeling; data visualization; experience; genetic signature; genomic aberrations; improved; individual patient; innovation; insight; inter-institutional; knowledge base; member; molecular subtypes; multidisciplinary; multiple omics; new technology; novel; novel strategies; oncology program; precision oncology; protein biomarkers; proteogenomics; search engine; specific biomarkers; success; technology development; therapeutic target; tool; transcriptome; transcriptomics; translational impact; tumor; tumor microenvironment; whole genome; working group

ABSTRACT This application aims to establish a Proteogenomic Data Analysis Center at the University of Michigan for the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our Center is anchored at the Michigan Center for Translational Pathology and brings together a multi-disciplinary team of leading scientific experts in the foundational areas of proteomics, cancer genomics, immunomics, and integrative systems biology. Our team established the foundations for precision oncology and proteogenomics at the University of Michigan and has a long history of successful inter-institutional collaborations. This positions us well to apply, working in close collaboration with other CPTAC groups, our innovative algorithms, comprehensive computational infrastructure, and expert knowledge to carry out high-impact translational proteogenomics research that is a core mission of the CPTAC. We have developed a balanced approach for integrative proteogenomic analyses, with a blend of both state-of-art and novel pipelines and tools. Our analytics support dual purpose - to perform both cohort-wide and patient centric (personalized) investigations – a unique future and a strength of our proposal. Our experience in support of our real-time precision oncology program and past CPTAC efforts will ensure both the fidelity of detecting diverse proteogenomic cancer driver events and rigorous ascertainment of their biological implications. Both of these features are of paramount importance to understand disease mechanisms and discover prognostic markers and therapeutic targets. Our proposed strategy combines well-established and innovative data analyses and modeling approaches, cognizant of continuing developments in the corresponding areas. In addition, we propose a conceptually novel approach of “integrative cellular network analysis” and advanced data visualization modules, capitalizing on recent advances in single cell and spatial proteogenomics research. These features will refine inference from the bulk tissue omics data in terms of tumor microenvironment, ploidy and cellularity, identification of cell of origin and clonal expansion, cell-cell interactions, distinguishing lineage versus cancer-specific biomarkers, and gene signatures associated with genetic and epigenetic alterations. Such precise and refined integrative analyses across genome and proteome data require advanced bioinformatics tools and stringent quality control measures. Our integrated genome/transcriptome/proteome pipelines – already in wide use by the research community - will be further optimized for speed and accuracy and enhanced with data visualization and report generation capabilities for presenting the findings to cancer biologists in a transparent and readily- interpreted manner. Furthermore, our extensive experience in the area of biomarker discovery and precision oncology, further enhanced through participation of our investigators in the EDRN, SPORE, and other NIH initiatives, puts us in a strong position to drive the biomarker prioritization work to select candidate cancer- specific proteins and peptides for subsequent targeted validation assays.

抽象的 该应用程序旨在在密歇根大学建立一个蛋白质数据分析中心 临床蛋白质组学分析联盟（CPTAC）。我们的中心锚定在密歇根州中心 翻译病理学并汇集了一个由领先的科学专家组成的多学科团队 蛋白质组学，癌症基因组学，免疫学和综合系统生物学的基础区域。我们的团队 在密歇根大学建立了精确肿瘤学和蛋白质组学的基础 成功的机构间合作的悠久历史。这可以很好地申请，在近距离工作 与其他CPTAC组合作，我们的创新算法，全面的计算 基础设施和专家知识，以进行高影响转化的蛋白质组学研究，这是一个 CPTAC的核心任务。我们已经开发了一种平衡的方法来集成保护性基因组 分析，融合了最先进和新颖的管道和工具。我们的分析支持双重目的 - 在整个队列和以患者为中心的（个性化）调查中进行同时进行研究 - 独特的未来和力量 我们的提议。我们支持实时精确肿瘤学计划和过去的CPTAC的经验 努力将确保检测潜水员蛋白质癌驱动器事件和严格的忠诚度 确定其生物学意义。这两个功能对 了解疾病机制并发现原型标记和治疗靶标。我们提出的 策略结合了建立良好和创新的数据分析和建模方法，认识 在相应领域的持续发展。此外，我们提出了一种概念上新颖的方法 “综合蜂窝网络分析”和高级数据可视化模块，利用最近的 单细胞和空间蛋白质组学研究的进展。这些功能将从 散装组织法数据在肿瘤微环境，倍状和细胞性方面，原始细胞的鉴定 以及克隆扩张，细胞 - 细胞相互作用，区分谱系与癌症特异性生物标志物以及 与遗传和表观遗传学改变有关的基因特征。这样的精确和精致的集成 跨基因组和蛋白质组数据的分析需要高级生物信息学工具和严格的质量 控制措施。我们集成的基因组/转录组/蛋白质组管道 - 已经广泛使用 研究社区 - 将进一步优化速度和准确性，并通过数据可视化增强 并报告以透明且很容易地向癌症生物学家提出发现的生成能力 解释的方式。此外，我们在生物标志物发现和精度领域的丰富经验 肿瘤学，通过我们的研究人员参与EDRN，Spore和其他NIH，进一步增强了 倡议，使我们处于强大的位置，以推动生物标志物优先级工作，以选择候选癌症 - 特定的蛋白质和胡椒剂，用于随后的靶向验证测定法。