Pathway and Network Integration of Cancer Genomics and Clinical Data

癌症基因组学和临床数据的通路和网络整合

基本信息

批准号：
9765287
负责人：
Benjamin Raphael
金额：
$ 31.59万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-15 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9765287
关键词：
Address Algorithms Biological Catalogs Clinical Clinical Data Cloud Computing Competence Complex Computer Analysis Computer software Data Data Set Databases Drug resistance Ecosystem Epigenetic Process Event Exhibits Gene Expression Alteration Genes Genome Genome Data Analysis Center Genomics Human Imagery International Internet Intervention Knowledge Link Literature Malignant Neoplasms Measurement Methods Molecular Mutation Neoplasm Metastasis Nucleotides Outcome Pathway Analysis Pathway interactions Pattern Pharmaceutical Preparations Phenotype Resolution Sampling Source System The Cancer Genome Atlas Translations Work analysis pipeline automated analysis cancer diagnosis cancer genome cancer genomics cancer therapy clinical application clinical phenotype clinical predictors cohort combinatorial data integration data to knowledge data warehouse dosage empowered epigenomics exceptional responders genome sequencing genomic aberrations genomic data human-in-the-loop individual patient novel novel strategies novel therapeutics open source response transcriptomics treatment strategy tumor growth tumor progression

项目摘要

PROJECT SUMMARY Cancer genomics projects have successfully cataloged many of the frequent genomic, epigenetic, and gene expression alterations that drive cancer progression. However, these initial projects have also demonstrated that both the types and targets of genomic aberrations are incredibly heterogeneous, reflecting the large diversity of perturbations in the cellular machinery that promote tumor growth and metastasis. As cancer sequencing efforts expand to determine the molecular basis of additional phenotypes such as drug resistance or exceptional responders, novel methods to integrate data from multiple genomic characterization platforms across combinations of alterations in pathways and interaction networks are essential. We propose to build a Genome Data Analysis Center (GDAC) focused on pathway analysis. Our GDAC will integrate data from multiple genome characterization platforms, and use several computational approaches to identify combinations of genomic aberrations and downstream expression changes that distinguish clinical phenotypes. We will employ algorithms that utilize information about known pathways and/or biological interaction networks, as well as other approaches that analyze statistical patterns of mutual exclusivity and co-occurrence between alterations and clinical variables. We will combine the discovered pathways with knowledge of drugs and their targets to identify novel interventions in individual patients. Finally, we will augment the computational analyses with a web platform for interactive visualization and annotation of discovered pathways. This human-in-the-loop system will accelerate the annotation of mutations, pathways, and interventions and provide a dynamic ecosystem linking cancer genomics datasets to new and existing literature. By combining rigorous computational and statistical approaches with human-in-the-loop annotation, the proposed GDAC will facilitate the translation of multi-platform genome characterization data to clinical application.

项目摘要癌症基因组学项目已成功分类许多频繁的基因组，表观遗传和基因驱动癌症进展的表达改变。但是，这些初始项目也证明了基因组畸变的类型和靶标都是异质的，反映了较大的促进肿瘤生长和转移的细胞机制中扰动的多样性。作为癌症测序工作扩展以确定其他表型的分子基础，例如耐药性或杰出的响应者，是整合来自多个基因组表征平台的数据的新方法在途径和相互作用网络的变化中，至关重要。我们建议建造一个基因组数据分析中心（GDAC）着重于途径分析。我们的GDAC将整合来自多个基因组表征平台，并使用多种计算方法来识别基因组畸变和下游表达变化的组合，区分临床表型。我们将采用有关已知途径和/或生物学的信息的算法交互网络以及其他分析相互排他性统计模式和的方法变化和临床变量之间的同时出现。我们将结合发现的途径对药物及其靶标的知识，以确定个别患者的新干预措施。最后，我们会的使用网络平台来增强计算分析，以进行交互式可视化和注释发现的途径。这种人类在循环系统将加速突变，途径，和干预措施，并提供一个动态的生态系统，将癌症基因组学数据集连接到新的和现有文学。通过将严格的计算方法和统计方法与人类的注释相结合，拟议的GDAC将促进多平台基因组表征数据转换为临床应用。