A mechanistic modeling framework reveals the key principles underlying tumor metabolism

Area of Science:

• Oncology
• Metabolic Engineering
• Computational Biology

Background:

• Aerobic glycolysis, or the Warburg effect, is a known hallmark of tumor metabolism.
• Tumor cells exhibit significant metabolic heterogeneity and plasticity, adapting to environmental cues and therapies.
• A comprehensive framework for analyzing tumor metabolic heterogeneity and plasticity is currently lacking.

Purpose of the Study:

• To develop a mechanistic model to analyze and explain metabolic heterogeneity and plasticity in tumor cells.
• To investigate the drivers of the Warburg effect and metabolic phenotype switching.
• To predict metabolic and gene expression changes in cancer cells during drug treatment.

Main Methods:

• Developed a mechanistic model incorporating key metabolic pathways in tumor cells.
• Investigated the role of phosphofructokinase inhibition by ATP in driving aerobic glycolysis.
• Coupled tumor cell metabolic phenotype with migratory phenotype.
• Analyzed the dependence of proliferating cells on anaplerotic pathways based on glucose and glutamine availability.
• Validated the model using melanoma cells treated with a BRAF inhibitor.

Main Results:

• Excess cytoplasmic ATP can inhibit phosphofructokinase, driving aerobic glycolysis in fast-proliferating tumor cells.
• Tumor cell ATP utilization rates contribute to heterogeneity in the Warburg effect.
• Model predictions align with experimental data regarding metabolic and migratory phenotypes.
• Nutrient availability (glucose and glutamine) influences reliance on anaplerotic pathways, further driving heterogeneity.
• The model successfully predicted metabolic and gene expression changes in melanoma cells upon BRAF inhibitor treatment.

Conclusions:

• The developed framework provides a generalizable and interpretable approach to understanding tumor cell metabolism.
• Identified key principles governing tumor cell metabolic heterogeneity and plasticity.
• The findings highlight potential strategies for targeting metabolic vulnerabilities in cancer therapy.