TRAFIKK: systematic prediction and mechanistic interpretation of anticancer drug synergies

Abstract

Effective drug combination therapies can improve cancer treatment, yet the mechanistic basis of drug synergy remains poorly understood. Most computational approaches prioritize predictive accuracy over molecular mechanistic interpretability, providing hence limited insights into how synergistic effects emerge across signalling contexts. We developed Trafikk, a molecular-signalling network-based framework that simulates drug perturbations in cell line-specific computational models to mirror functional outcomes in experimental combination screens. Across two independent large-scale datasets, Trafikk identified synergistic combinations with >77% recall. Functional response predictions revealed both conserved and context-dependent mechanisms. While AKT-MEK co-inhibition consistently disrupted coordinated survival and apoptotic signalling in 742 cell lines, PI3K-BCL2 synergy arose through distinct death programs shaped by cell-context-specific network constraints. Trafikk combines predictive performance with mechanistic interpretability, capturing how and why drug synergy emerges across cellular contexts. Source code, installation instructions and usage tutorial are freely available at https://github.com/druglogics/trafikk. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/723755v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@9f09f0org.highwire.dtl.DTLVardef@1456ef5org.highwire.dtl.DTLVardef@177b11forg.highwire.dtl.DTLVardef@10d7435_HPS_FORMAT_FIGEXP M_FIG C_FIG