Multiscale Computational Oncology Research Core

多尺度计算肿瘤学研究核心

基本信息

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
项目类别：
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

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

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项目摘要

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.

多尺度计算肿瘤研究核心（M核） - 摘要多尺度计算肿瘤研究核心（M核）提供计算肿瘤学从最初的实验设计和功率研究到正在进行的数据生成和分析到数据的专业知识单细胞和基因组数据的沉积和共享。核心负责人Fortig博士是单细胞多的领导者用于癌症应用的OMIC，特别关注翻译胰腺癌研究应用。 M核将支持所有三个三态胰腺腺癌TBEL中的高通量数据分析（TRI-PACT）中心项目和协作项目。方法将应用于桥梁空间和临时尺度以鉴定细胞固有机制的固有机制发展为侵入性癌症。这些将扩展以分析介导恶性肿瘤发展的细胞外部，细胞间相互作用和肿瘤起始，结合了来自Tri-Pact的新的三维成像技术。其中许多方法最近是由核心领导者开发的，新算法将专门开发以满足 TBEL需要并提供跨项目的协同作用。研究项目将进行新的单细胞测试动态空间过程和新型成像技术的方法以及计算协作项目将从M核资源中受益。核心中的标准化将统一数据以实现元生物模型与人类前体病变，项目的生物学机制之间的分析，以及通过较宽的TBEL中的交叉疾病分析与预科剂相关的新型机制财团与协调和数据管理中心（CDMC）合作。核心负责人将参加所有常规中心会议，并与TBEL CDMC紧密合作，以确保足够新研究方向的计算资源。核心将与TBEL CDMC合作以协助数据标准和协调，数据验证，数据包装以及总体数据沉积，数据和软件共享和基因组数据共享。总而言之，M核将支持整体TBEL研究通过提供计算方法来表征恶性倡议机制的目标和发展为侵袭性肿瘤；分析空间和临时数据以推断相互作用和串扰带有基质细胞和微环境中其他细胞的恶性细胞；并执行集成通过鉴定共享机制来促进三契约和联盟协同作用的分析。