Abstract
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.
Highlights
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
Despite the popularity of networks, there have been few studies that have applied network-based analysis to study potential relationships between cancer-related germline variants detected from genome-wide association studies (GWAS), somatic mutations found in tumors, and gene products targeted by known therapeutics
If two node classes are considered for example, DTNs and GWAS gene nodes (GGNs), we start by calculating the number of existing geodesics that exist from DTNs to GGNs and vice versa to determine if there are any differences in reachability from one node class to another
Summary
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. 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. GWASs have identified germline variants associated with cancer predisposition and high-throughput sequencing projects have revealed several recurring mutations present in patient tumors In lieu of these rich datasets, organized and integrated analyses are necessary to dissect the relationships between genetic predictors of cancer development and drug treatment response. These studies did not utilize directed edge information in their analysis and did not incorporate somatic mutation data
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