Uncovering Molecular Mechanisms of Drug Resistance via Network-Constrained Common Structure Identification.

Abstract

Uncovering mechanisms of acquired drug resistance has garnered increasing attention worldwide as drug resistance reduces antibiotic and chemotherapy effectiveness. Most bioinformatics studies have elucidated these mechanisms based on differentially expressed gene (DEG) analysis. However, considering the associated complex network of biological systems, the specific molecular interactions must also be studied to obtain a complete understanding of the mechanisms related to drug resistance. Accordingly, by analyzing sample-specific gene networks, we sought to elucidate mechanisms of acquired drug resistance of cells based on molecular interactions between genes. In the current study, we focus on gefitinib and erlotinib and characterized cell lines based on their sensitivity. We also consider CRISPR knockout screening of the target gene, epidermal growth factor receptor (EGFR), as a characteristic of cells. Subsequently, we constructed a drug sensitivity-CRISPR knockout screen-specific gene network. To identify the molecular mechanisms of drug resistance from the multiple large-scale networks, we proposed a novel computational method, designated network-constrained sparse common component analysis (NetSCCA), that extracts common structures of multiple networks characterizing molecular interaction in drug-sensitive and drug-resistant cell lines. We then applied NetSCCA to multilayer networks of candidate drug-response genes to identify common structures of the regulatory system in drug-sensitive and EGFR-dependent cells, and drug-resistant and EGFR-independent cells. NetSCCA identified crucial common targets and regulator genes that dominate multiple networks in drug-sensitive and drug-resistant cell lines, respectively. Our analysis for common structure identification based on NetSCCA has the capacity to characterize the molecular interplay between genes and crucial markers related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on DEG analysis. The biological mechanisms associated with gefitinib and erlotinib sensitivity of identified genes were verified through the literature. We expect that the proposed method will serve as a useful tool for uncovering not only drug resistance mechanisms but also complex biological systems based on massive genomic data sets.