Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes

Overview of SynGeNet drug combination prediction study design. The first step of our method involves generating melanoma genotype-specific protein subnetworks from a source of disease-associated root genes (i.e., significantly co-mutated) from which network flow is propagated across a background network of protein–protein interactions (PPI) using up-regulated gene expression data (e.g., tumor vs. normal samples) via the belief propagation algorithm. Next, drug combinations are predicted using the resulting networks, where drug synergy scores are calculated based on the degree of drug-induced gene signature reversal (i.e., negative gene set enrichment analysis connectivity scores) and the weighted sum of centrality metrics calculated for the combined set drug targets in the network for each drug pair. Finally, predicted drug combinations are ranked according to a final synergy score. Drug predictions were validated in this study in two settings: (i) retrospectively, using Bliss synergy score results from a high-throughput drug screening across melanoma cell lines with different genomic backgrounds, and (ii) prospectively, where a top-ranked drug combination predicted for BRAF-mutant melanoma was selected as a case study for prospective validation using in vitro and in vivo models of BRAF-mutant melanoma, and the mechanistic basis for this drug combination prediction was investigated via RNA-seq gene expression analysis and the subnetwork level and for individual genes determined to be highly centra