Spatial invasion dynamics on random and unstructured meshes: implications for heterogeneous tumor populations

Area of Science:

• Evolutionary biology
• Cancer research
• Computational modeling

Background:

• Cancer is a heterogeneous disease driven by cell evolution.
• Gain-of-function mutations can alter cancer cell fitness and motility.
• Spatial modeling is crucial for understanding tumor dynamics.

Purpose of the Study:

• To develop a spatial evolutionary model for heterogeneous cancer cell populations.
• To investigate the fixation probability of mutants in structured and unstructured spatial models.
• To analyze the impact of migration potential on mutant dynamics and fixation.

Main Methods:

• Stochastic evolutionary system on structured grids (lattices) and unstructured meshes.
• Computational approach to calculate mutant fixation probability.
• Examination of neighborhood size distribution and migration potential effects.

Main Results:

• Fixation probability negatively correlates with neighborhood size distribution width.
• Migration potential increases mutant fixation probability, especially for neutral mutants on unstructured meshes.
• Fixation probability is lower on unstructured meshes and grid boundaries compared to regular grids.

Conclusions:

• Spatial structure and cell motility significantly influence cancer evolution.
• Migration potential is a critical factor for neutral mutant fixation, offering therapeutic insights.
• Computational models can inform the development of strategies to delay tumor progression.