Work

Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations

Year: 2026

Type: article

Abstract: Targeted kinase inhibitors often yield transient or incomplete responses because tumour cells buffer pathway blockade through signalling cross-talk, motivating synergistic drug combinations. However, it remains unclear whether synergy typically arises from engaging new signalling programs or from reinforcing the dominant effects of each monotherapy at shared downstream control points. Here we analyse two synergistic inhibitor pairs in AGS gastric adenocarcinoma cells: PI3K inhibition combined with inhibition of either MEK (MAP2K1, MAP2K2) or TAK1 (MAP3K7). Using time-resolved phosphoproteomics and transcriptomics after single-agent and combination treatment, we track signalling and transcriptional rewiring from 30 minutes to 24 h and partition combination responses into monotherapy-like versus combination-specific components. We show that global phosphorylation and gene expression changes under both combinations largely mirror those of the monotherapies, with limited evidence for uniquely engaged kinase programs. Instead, synergy concentrates at pathway convergence nodes, where combined perturbation amplifies downstream regulation of growth and survival circuitry, including transcriptional control through MYC and CREB1. Both combinations produce stronger suppression of MYC-linked proliferative programs and CREB1-associated survival signalling than either single agent, consistent with reinforced constraint on cell-fate decisions. At the transcriptional level, the combinations uniquely repress nucleotide metabolism and aminoacyl-tRNA biosynthesis, consistent with reduced mTOR activity. Together, these results indicate that synergistic growth inhibition can arise primarily from strengthened pathway convergence rather than recruitment of new signalling routes. Our approach provides insights into the molecular mechanisms affected by the PI3Ki-TAK1i and PI3Ki-MEKi combinations and can serve as a flexible framework for dissecting drug combination responses based on multi-omics measurements.

Source: Molecular & Cellular Proteomics

DOI: 10.1016/j.mcpro.2026.101700