Michigan Center for Translational Cancer Proteogenomics

密歇根转化癌症蛋白质组学中心

• 批准号: 10440158
• 负责人: Saravana Mohan Dhanasekaran
• 金额: $ 76.88万
• 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/10440158
• 关键词: Address; Adoption; Algorithms; Area; Benchmarking; Biological; Biological Assay; Biological Markers; Cancer Biology; Cell Communication; Cells; Cellularity; Clinical; Clonal Expansion; 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; Plant Roots; 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; 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; novel; novel strategies; oncology program; precision oncology; programs; protein biomarkers; proteogenomics; search engine; specific biomarkers; success; 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.

摘要