Network profiling analysis for generating modulator-dependent gene networks

Construction and analysis of molecular networks for each cancer patient is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Systems biology enables us to reconstruct a gene network within a cell from gene expression data. However, from a collection of gene expression data for clinical samples, traditional methods including Bayesian networks can only provide an “averaged” network across the samples. Therefore, these methods do not find patient-specific varying structures of molecular networks during a change of cancer characteristic. Here we develop a novel statistical method, NetworkProfiler, which aims to infer modulator-dependent gene regulatory networks from a collection of gene expression data. NetworkProfiler builds a sequence of gene networks with the change of a specific cancer characteristic such as cancer progression by reordering the samples according to the specified cancer characteristic. We applied NetworkProfiler to microarray gene expression data of 762 cancer cell lines and extracted system changes related to epithelial-mesenchymal transition (EMT). NetworkProfiler identified 25 regulators for E-cadherin, an EMT maker, from 1732 candidate regulators and about half of them are supported by literature. Also, EMT-dependent regulations for gene sets of adhesion, migration, and metastasis were predicted by NetworkProfiler.