Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

Abstract

BackgroundAltered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality.MethodsWe report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases.ResultsWe observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets.ConclusionsOur study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

On Different Aspects of Network Analysis in Systems Biology

“…Deciphering GRNs from rapidly growing microarray expression databases has been shown to be a very promising approach e.g. in cancer research [16,17]. Many tools are emerging and have been used for constructing, inferring and analyzing such GRNs.…”

Pheno2GRN

“…Phenotype to genotype research has produced informatics resources such as the Phenoscape Knowledgebase (Phenoscape KB; http://kb.phenoscape.org) [14] and the Phenotype-Genotype Integrator (PhenGenI; www.ncbi.nlm.nih.gov/gap/phegeni) [20]; researchers using these resources have been able to verify key phenotype-genotype relationships [19]. Many tools also exist for the reverse engineering of GRNs from microarray datasets [15][25] [16]. Networks obtained from reverse engineered microarrays or next generation sequencing (NGS) datasets constitute a big knowledge opportunity for studying the connections between intracellular systems, metabolic interaction models, and transcriptomics [7].…”

Supervised method for construction of microRNA-mRNA networks: Application in cardiac tissue aging dataset

“…However, it has been shown that including other kinds of information leads to biologically more accurate results [5]. Of great interest is the Machine Learning category, under which are included algorithms based on Random Forest (GENIE3 [6]), Support Vector Machines (SVM) (SIRENE [7]) and Neural Networks (ENFRN [8]), because by construction these methods can embed a priori knowledge.…”