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

• 批准号: 10301898
• 负责人: Benjamin Raphael
• 金额: $ 33.81万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 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/10301898
• 关键词: Address; Algorithms; Area; Biological; Cancer Patient; Cancerous; Cells; Clinical; Cloud Computing; Code; Collection; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Development; Drug resistance; Epigenetic Process; Evolution; Gene Expression; Genome; Genome Data Analysis Center; Genome Data Analysis Network; Genomics; 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; Untranslated RNA; Work; analysis pipeline; cancer cell; cancer diagnosis; cancer genome; cancer genomics; cancer therapy; cancer type; cell stroma; 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; spatiotemporal; transcriptome; transcriptomics; treatment strategy; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor progression; whole genome

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 癌症测序项目已经证明，肿瘤是非常异质的， 这反映了促进肿瘤生长的细胞机制中的扰动的巨大多样性 和转移。来自同一类型癌症的不同患者的肿瘤具有不同的 基因组、表观基因组和转录组畸变的集合。此外，一个肿瘤从一个 单个患者是包括正常细胞、基质和多个亚群的细胞类型的混合物 癌细胞。我们建议建立一个基因组数据分析中心（GDAC）， 应用新的计算方法来解决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， 多种测序技术--包括批量、单细胞和空间测序 技术-并利用路径和相互作用网络的先验知识来解释 临床表型和告知治疗策略。GDAC将执行路径和网络 基因组表征数据的整合，肿瘤微环境的空间分析， 肿瘤内异质性、肿瘤演变和网络重新布线的时间分析。这些 分析将使癌症基因组表征工作能够更精确地转化为 临床实用性为更大比例的癌症患者。GDAC将继续进行 PI对当前基因组数据分析网络（GDAN）和以前的基因组数据分析网络（GDAN）的贡献 癌症基因组图谱（TCGA）和国际癌症基因组联盟（International Cancer Genome Consortium） （ICGC）项目。