CBNA: A control theory based method for identifying coding and non-coding cancer drivers

Abstract

A key task in cancer genomics research is to identify cancer driver genes. As these genes initialise and progress cancer, understanding them is critical in designing effective cancer interventions. Although there are several methods developed to discover cancer drivers, most of them only identify coding drivers. However, non-coding RNAs can regulate driver mutations to develop cancer. Hence, novel methods are required to reveal both coding and non-coding cancer drivers. In this paper, we develop a novel framework named Controllability based Biological Network Analysis (CBNA) to uncover coding and non-coding cancer drivers (i.e. miRNA cancer drivers). CBNA integrates different genomic data types, including gene expression, gene network, mutation data, and contains a two-stage process: (1) Building a network for a condition (e.g. cancer condition) and (2) Identifying drivers. The application of CBNA to the BRCA dataset demonstrates that it is more effective than the existing methods in detecting coding cancer drivers. In addition, CBNA also predicts 17 miRNA drivers for breast cancer. Some of these predicted miRNA drivers have been validated by literature and the rest can be good candidates for wet-lab validation. We further use CBNA to detect subtype-specific cancer drivers and several predicted drivers have been confirmed to be related to breast cancer subtypes. Another application of CBNA is to discover epithelial-mesenchymal transition (EMT) drivers. Of the predicted EMT drivers, 7 coding and 6 miRNA drivers are in the known EMT gene lists.

Computational methods for cancer driver discovery: A survey

“…In general, network-based methods evaluate the role of genes in biological networks and then combine with the mutation information of genes to predict cancer drivers. There are three methods in this group, including Vinayagam et al 38 , CBNA 18 , and DriverNet 16 . The details of these methods are discussed as below.…”

“…Also inspired by the network controllability, CBNA 18 analyses the controllability of a gene regulatory network to discover cancer drivers. However, the network built by CBNA is a miRNA-TF-mRNA network which consists of microRNAs (miRNAs), Transcription Factors (TFs), and mRNAs.…”

“…Instead of evaluating the controllability of a subset of nodes of a gene network like Vinayagam et al 38 and CBNA 18 , DriverNet 16 considers the influence of mutated genes on other genes in a network.…”

“…Taking these advantages, numerous methods have been proposed to detect cancer driver genes. For example, MutSigCV 13 investigates the significance of mutations in genes to predict cancer drivers, OncodriveFM 14 and OncodriveCLUST 15 evaluate the functional influence and clustering of gene mutations respectively, DriverNet 16 , MEMo 17 , and CBNA 18 examine the role of genes in gene regulatory networks. Due to the large number of the current computational methods for cancer driver discovery, it may take the huge amount of effort for people to find a good resource to know the state-of-the-art methods, and thus a review is necessary and helpful.…”

DriverGroup: A novel method for identifying driver gene groups

“…Network-based methods estimate the role of genes in a biological network and may combine the results with additional information such as mutations to discover cancer drivers. The representatives for the network-based approach are DawnRank (Hou and Ma, 2014), DriverNet (Bashashati et al, 2012), CBNA (Pham et al, 2019), and NBS (Hofree et al, 2013).…”