A workflow to study mechanistic indicators for driver gene prediction with Moonlight

Abstract

Abstract Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.