Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755 USA.
Program in Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, 03755 USA.
Sci Rep. 2016 Jun 30;6:29228. doi: 10.1038/srep29228.
The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.
后基因组时代产生了大量高通量癌症数据，这些数据来自于包括下一代测序和微阵列在内的各种基因组技术。例如，从 GWAS 和测序项目中分别生成的大量种系变异和体细胞突变数据，为提供这些遗传异常的系统级视图提供了很大的希望。在这项研究中，我们使用基于网络的方法分析了来自大型数据库的公开可用的 GWAS、体细胞突变和药物靶标数据，该方法将有向边信息纳入随机网络假设检验过程中。我们表明，在功能相互作用组的背景下，这三类疾病相关节点表现出非随机的拓扑特征。具体来说，我们表明药物靶标往往位于体细胞突变和疾病易感性种系变异的上游。此外，我们通过利用方向性和路径长度信息，引入了一种新的方法来测量药物靶标、体细胞突变和疾病易感性基因之间的层次关系。总的来说，我们的结果提供了对这些节点类之间内在关系的新见解，从而加深了我们对癌症的理解。此外，我们的结果与 GWAS 和体细胞突变节点的治疗可操作性的现有知识一致，同时从全局网络角度展示了节点类之间的关系。
