Network perturbation by recurrent regulatory variants in cancer

Abstract

Cancer driving genes have been identified as recurrently affected by variants that alter protein-coding sequences. However, a majority of cancer variants arise in noncoding regions, and some of them are thought to play a critical role through transcriptional perturbation. Here we identified putative transcriptional driver genes based on combinatorial variant recurrence in cis-regulatory regions. The identified genes showed high connectivity in the cancer type-specific transcription regulatory network, with high outdegree and many downstream genes, highlighting their causative role during tumorigenesis. In the protein interactome, the identified transcriptional drivers were not as highly connected as coding driver genes but appeared to form a network module centered on the coding drivers. The coding and regulatory variants associated via these interactions between the coding and transcriptional drivers showed exclusive and complementary occurrence patterns across tumor samples. Transcriptional cancer drivers may act through an extensive perturbation of the regulatory network and by altering protein network modules through interactions with coding driver genes.

An integrative multi-omics network-based approach identifies key regulators for breast cancer

“…Several previous studies have constructed regulatory networks to unravel potential mechanisms for complex diseases by using integrative approaches. For example, Jang et al [15] used the chromatin interactome and protein interactome for combinatorial regulatory variants to find driving genes in breast and liver cancers. Hsiao et al [16] performed a network analysis on the gene level and the gene set level.…”

Recurrence-associated gene signature in patients with stage I non-small-cell lung cancer

“…2 C). We further assessed complementary recurrence patterns of interacting 4 genes, as described in the previous method 10 , for each pair of genes. We calculated variant complementarity, which is the frequency of variants for the gene set of interacting pairs.…”

How to interpret and integrate multi-omics data at systems level

“…This network analysis is capable of extending or validating the existent network biology. One of our recent studies proposed that the extension of transcriptional drivers using both of physical and functional interactome networks successfully identified known coding drivers in cancer (Jang et al 2017).…”