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.

项目摘要癌症测序项目表明肿瘤是异质的，反映了促进肿瘤生长的细胞机械中扰动的大量多样性和转移。来自不同类型癌症患者的肿瘤患有潜水基因组，表观基因组和转录组畸变的收集。而且，来自单个患者是细胞类型的混合物，包括正常细胞，基质和多个亚群取消细胞。我们提出了一个基因组数据分析中心（GDAC），该中心将发展并应用新颖的计算方法来应对肿瘤间和肿瘤内的挑战异质性。该GDAC将整合来自多个基因组表征平台的数据，多个测序技术 - 包括散装，单细胞和空间测序技术 - 并利用途径和交互网络的先验知识来解释临床表型和信息治疗策略。 GDAC将执行途径和网络基因组表征数据的整合，肿瘤微环境的空间分析和肿瘤内异质性，肿瘤演化和网络重新布置的临时分析。这些分析将使癌症基因组表征工作更精确地翻译成癌症患者较大的临床实用性。 GDAC将继续进行 PI对当前基因组数据分析网络（GDAN）和以前的贡献癌症基因组图集（TCGA）和国际癌症基因组联盟的努力（ICGC）项目。