Pathway and Network Integration of Cancer Genomics and Clinical Data

项目总结 癌症基因组学项目已经成功地对许多常见的基因组、表观遗传学和基因进行了编目 导致癌症进展的表情改变。然而，这些最初的项目也证明了 基因组异常的类型和目标都是令人难以置信的异质性，反映了 促进肿瘤生长和转移的细胞机制中的扰动的多样性。作为癌症 测序工作扩大以确定其他表型的分子基础，如耐药性 或卓越的响应者，集成来自多个基因组表征平台的数据的新方法 各种途径和相互作用网络的改变是必不可少的。我们建议建立一个 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 区分临床的基因组异常和下游表达变化的组合 表型。我们将使用利用已知路径和/或生物信息的算法 交互网络，以及其他分析相互排他性和 改变和临床变量之间的共同发生。我们将把已发现的路径与 了解药物及其靶点，以确定针对个别患者的新干预措施。最后，我们会 使用Web平台增强计算分析，以实现交互式可视化和注释 被发现的路径。这种人在环路中的系统将加速对突变、路径、 并提供一个动态生态系统，将癌症基因组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将有助于将多平台基因组特征数据转化为临床数据 申请。

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.