Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Area of Science:

• Computational biology
• Mathematical oncology
• Cancer systems biology

Background:

• Cancer cell proliferation is driven by metabolic reprogramming and complex pathways.
• Understanding these processes is crucial for developing effective cancer therapies.

Purpose of the Study:

• To develop a multiscale, multicellular mathematical model for simulating avascular tumor growth, specifically applied to pancreatic cancer.
• To investigate the intracellular metabolic dynamics and their influence on tumor evolution and response to perturbations.
• To identify potential therapeutic strategies targeting tumor cell metabolism.

Main Methods:

• A multiscale model integrating extracellular, cellular, and subcellular levels.
• Reaction-diffusion equations for nutrient concentrations (seconds).
• Lattice-based stochastic approach for cellular phenomena (hours).
• Detailed kinetic model of intracellular metabolite dynamics (minutes).

Main Results:

• The model quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids.
• Simulations reveal how intracellular metabolite concentrations influence cancer cell growth, proliferation, and death.
• The model predicts tumor response to metabolic perturbations and identifies potential therapeutic targets.

Conclusions:

• The developed mathematical model provides a novel framework for studying tumor growth and metabolic reprogramming.
• It offers quantitative insights into intracellular dynamics, aiding in the prediction of therapeutic strategy effectiveness.
• This approach can identify novel metabolic targets and predict the effects of targeted therapies on tumor growth.