权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Pathway, Network and Spatiotemporal Integration of Cancer Genomics Data

癌症基因组数据的路径、网络和时空整合

基本信息

批准号：
10704174
负责人：
Benjamin Raphael
金额：
$ 30.11万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-22 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10704174
关键词：
Address Algorithms Area Biological Cancer Patient Cancerous Cells Clinical Cloud Computing Code Collaborations Collection Communities Complex Computer software Computing Methodologies Data Development Drug resistance Epigenetic Process Evolution Gene Expression Genome Genome Data Analysis Center Genome Data Analysis Network Genomics Inter-tumoral heterogeneity International Intervention Knowledge Malignant Neoplasms Measurement Molecular Mutation Neoplasm Metastasis Normal Cell Nucleotides Outcome Pathway Analysis Pathway interactions Patients Phenotype Phylogeny Proteins Research Signal Transduction Technology The Cancer Genome Atlas Time Translations Tumor Promotion Untranslated RNA Work alternative treatment analysis pipeline cancer cell cancer diagnosis cancer genome cancer genomics cancer therapy cancer type cell type clinical phenotype clinical predictors cohort data integration epigenetic profiling epigenomics exceptional responders exome sequencing experience genome sequencing genomic data neoplastic cell novel novel strategies novel therapeutics open source response single cell sequencing single-cell RNA sequencing spatiotemporal transcriptome transcriptomic profiling transcriptomics treatment strategy tumor tumor growth tumor heterogeneity tumor microenvironment tumor progression whole genome

项目摘要

PROJECT SUMMARY Cancer sequencing projects have demonstrated that tumors are tremendously heterogeneous, reflecting the large diversity of perturbations in the cellular machinery that promote tumor growth and metastasis. Tumors from different patients with the same type of cancer have a diverse collection of genomic, epigenomic, and transcriptomic aberrations. Moreover, a tumor from a single patient is a mixture of cell types including normal cells, stroma, and multiple subpopulations of cancerous cells. We propose a Genome Data Analysis Center (GDAC) that will develop and apply novel computational approaches to address the challenges of inter-tumor and intra-tumor heterogeneity. This GDAC will integrate data from multiple genome characterization platforms, multiple sequencing technologies -- including bulk, single-cell, and spatial sequencing technologies – and leverage prior knowledge of pathways and interaction networks to explain clinical phenotypes and inform treatment strategies. The GDAC will perform pathway and network integration of genome characterization data, spatial analysis of tumor microenvironment, and temporal analysis of intra-tumor heterogeneity, tumor evolution, and network rewiring. These analyses will enable more precise translation of cancer genome characterization efforts into clinical utility for a larger fraction of cancer patients. The GDAC will continue the ongoing contributions of the PIs to the current Genome Data Analysis Network (GDAN) and previous efforts in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects.

项目总结