Constraint based modeling of drug induced metabolic changes in a cancer cell line.

Abstract

Cancer cells frequently reprogramme their metabolism to support growth and survival, making metabolic pathways attractive targets for therapy. In this study, we investigated the metabolic effects of three kinase inhibitors and their synergistic combinations in the gastric cancer cell line AGS using genome-scale metabolic models and transcriptomic profiling. We applied the tasks inferred from the differential expression (TIDE) algorithm to infer pathway activity changes in the different conditions. We also explored a variant of TIDE that uses task-essential genes to infer metabolic task changes, providing a complementary perspective to the original algorithm. Our results revealed widespread down-regulation of biosynthetic pathways, particularly in amino acid and nucleotide metabolism. Combinatorial treatments induced condition-specific metabolic alterations, including strong synergistic effects in the PI3Ki-MEKi condition affecting ornithine and polyamine biosynthesis. These metabolic shifts provide insight into drug synergy mechanisms and highlight potential therapeutic vulnerabilities. To support reproducibility, we developed an open-source Python package, MTEApy, implementing both TIDE frameworks.