Pathway and Network Integration of Cancer Genomics and Clinical Data

项目摘要 癌症基因组学项目已经成功地将许多常见的基因组、表观遗传和基因组 基因表达的改变导致癌症进展。然而，这些初步项目也表明， 基因组畸变的类型和靶点都是令人难以置信的异质性，反映了大规模的 促进肿瘤生长和转移的细胞机制中的扰动的多样性。如癌症 测序工作扩展到确定其他表型的分子基础，如耐药性 或特殊反应者，整合来自多个基因组表征平台的数据的新方法 在通路和相互作用网络中的改变的组合是必不可少的。我们提出建设 基因组数据分析中心（GDAC）专注于通路分析。我们的GDAC将整合来自 多个基因组表征平台，并使用几种计算方法来识别 基因组畸变和下游表达变化的组合， 表型我们将采用利用已知途径和/或生物学信息的算法。 互动网络，以及其他方法，分析统计模式的相互排斥， 改变和临床变量之间的共现。我们将联合收割机结合已发现的途径， 了解药物及其靶点，以确定针对个体患者的新型干预措施。最后我们将 通过网络平台增强计算分析，以实现交互式可视化和注释， 发现的路径。这个人在回路系统将加速突变，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与新的和现有的 文学通过将严格的计算和统计方法与人在回路中的注释相结合， 拟议的GDAC将促进多平台基因组表征数据向临床的转化， 应用程序.

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.