A Framework Linking Glycolytic Metabolic Capabilities and Tumor Dynamics

Area of Science:

• Oncology
• Systems Biology
• Computational Biology

Background:

• Metabolic reprogramming is a critical hallmark of cancer.
• Tumor growth is influenced by the heterogeneous microenvironment.
• Understanding metabolic adaptations is key to predicting tumor evolution.

Purpose of the Study:

• To integrate genome-scale metabolic models into a tumor growth framework.
• To investigate the impact of metabolic phenotypes on tumor evolution within a heterogeneous microenvironment.
• To explore how different metabolic strategies and microenvironmental factors affect tumor dynamics.

Main Methods:

• Developed a hybrid discrete-continuous tumor growth model incorporating sub-cellular metabolic descriptions.
• Simulated various tumor phenotypes by applying different metabolic constraints and optimization criteria.
• Analyzed tumor evolution under varying vasculature and extracellular matrix conditions.

Main Results:

• Metabolic capabilities varied counter-intuitively with resource availability.
• Tumor population, morphology, and spread were differentially affected by phenotypes and microenvironment.
• Polyclonal tumors with diverse metabolic phenotypes exhibited enhanced evolution by exploiting distinct capabilities.

Conclusions:

• Integrating metabolic phenotypes into tumor growth models is crucial for accurate predictions.
• The framework demonstrates the importance of microscopic metabolic characteristics in shaping tumor evolution.
• This approach enables personalized tumor evolution studies by linking genomic data to metabolic capabilities and clinical outcomes.