Multiscale Computational Oncology Research Core

多尺度计算肿瘤学研究核心

• 批准号: 10518940
• 负责人: Elana Judith Fertig
• 金额: $ 24.55万
• 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: 2022-09-21 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518940
• 关键词: 3-Dimensional; Algorithms; Animal Model; Atlases; Automobile Driving; Biological; Biological Models; Biology; Cancer Biology; Cells; Clinical; Clinical Research; Coculture Techniques; Collaborations; Computer Analysis; Computer software; Computing Methodologies; Data; Data Analyses; Data Science; Data Set; Development; Disease; Ensure; Evolution; Experimental Designs; Fee-for-Service Plans; Gene Expression; Generations; Genetic Heterogeneity; Genetic Transcription; Genomics; Goals; Heterogeneity; Human; Imaging technology; Immune; Lead; Lesion; Machine Learning; Malignant Neoplasms; Malignant neoplasm of pancreas; Mediating; Meta-Analysis; Methods; Modeling; Molecular; Mucinous Neoplasm; Oncogenic; Oncology; Organoids; Outcome; Pancreas; Pancreatic Adenocarcinoma; Pancreatic Intraepithelial Neoplasia; Papillary; Pre-Clinical Model; Premalignant Cell; Process; Proteomics; Regulation; Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Resources; Sampling; Scientist; Standardization; Stromal Cells; T cell receptor repertoire sequencing; Techniques; Technology; Three-Dimensional Imaging; Validation; algorithm development; analysis pipeline; anticancer research; cancer cell; cancer initiation; cancer invasiveness; cancer type; cell behavior; computerized tools; computing resources; data harmonization; data management; data sharing; data standards; data submission; data wrangling; exome sequencing; experience; experimental study; flexibility; genomic data; high dimensionality; high throughput analysis; improved; interoperability; meetings; method development; multiple omics; novel; novel imaging technology; novel therapeutic intervention; premalignant; programs; single cell technology; single-cell RNA sequencing; synergism; technology development; tool; transcriptomics; transfer learning; tumor; tumor initiation; tumor microenvironment

MULTISCALE COMPUTATIONAL ONCOLOGY RESEARCH CORE (M-CORE) – Abstract The Multiscale Computational Oncology Research Core (M-CORE) provides computational oncology expertise from initial experimental design and power studies to ongoing data generation and analysis to data deposition and sharing for single-cell and genomics data. Core Leader Dr. Fertig is a leader in single-cell multi- omics for applications to cancer, with a particular focus on translational pancreatic cancer research applications. The M-CORE will support high-throughput data analysis in all three Tri-State Pancreatic Adenocarcinoma TBEL (Tri-PACT) Center Projects and Collaborative Projects. Methods will be applied to bridge spatial and temporal scales to identify cell intrinsic mechanisms underlying pre-malignant lesions progression into invasive cancers. These will be extended to analyze cell extrinsic, intercellular interactions that mediate malignancy development and tumor initiation incorporating new three-dimensional imaging technologies from Tri-PACT. Many of these methods were recently developed by the Core leader, and new algorithms will be developed specifically to meet the TBEL needs and provide synergy across Projects. Research Projects will be testbeds for new single-cell methods for dynamic spatial processes and novel imaging technologies, and computational Collaborative Projects will benefit from M-CORE resources. Standardization in the Core will harmonize data to enable meta- analyses between biological models and human precursor lesions, biological mechanisms from the Projects, and novel mechanisms associated with precancer through cross-disease analysis in the broader TBEL consortium in collaboration with the Coordinating and Data Management Center (CDMC). The Core Leader will participate in all regular Center meetings and collaborate closely with the TBEL CDMC to ensure ample computational resources for new research directions. The Core will partner with the TBEL CDMC to assist with data standards and harmonization, data validation, data wrangling, and overall data deposition, data and software sharing, and genomic data sharing. In summary, the M-CORE will support the overall TBEL research objectives by providing computational methods to characterize the mechanisms of malignancy initiation and progression into invasive tumors; to analyze spatial and temporal data to infer interactions and crosstalk between pre-malignant cells with stromal cells, and other cells in the microenvironment; and to perform integrative analyses to promote Tri-PACT and Consortium synergy by identifying shared mechanisms.

多尺度计算肿瘤学研究核心(M-CORE) -摘要