From radiation dose to cellular dynamics: A discrete model for simulating cancer therapy

Area of Science:

• Computational biology
• Biophysics
• Cancer research

Background:

• Radiation therapy is a common cancer treatment, but optimizing dose and targeting is critical due to effects on both cancerous and healthy cells.
• Existing models often treat tissue as a continuum, neglecting crucial cellular-scale effects on tumor growth and metastasis.
• The linear-quadratic (LQ) model is a standard approach for predicting cell survival probability after radiation exposure.

Purpose of the Study:

• To develop a novel computational method for modeling radiation therapy effects at the cellular level.
• To integrate the linear-quadratic (LQ) model with a mechanobiological simulation for cell growth and proliferation (CellSim3D).
• To investigate the impact of radiation on distinct cell types, including cancer and healthy cells.

Main Methods:

• Incorporation of a Monte Carlo procedure and LQ model into the CellSim3D simulation to determine cell survival probability.
• Simulation of systems with two cell types: stiff, slow-proliferating healthy cells and soft, fast-proliferating cancer cells.
• Implementation of phagocytosis for effective removal of dead cells and analysis of cell contact forces and jamming transitions.

Main Results:

• The CellSim3D model demonstrated good agreement with experimental data for prostate cancer (PC-3 cell line) across various radiation doses.
• Simulations accurately predicted the probability density of contact forces in proliferating cell systems.
• The model's predictions regarding the jamming transition showed strong correlation with experimental findings.

Conclusions:

• The proposed CellSim3D model offers a more detailed, cell-scale approach to simulating radiation therapy effects compared to continuum models.
• This method enhances the understanding of radiation's impact on tumor and tissue dynamics, potentially improving treatment planning.
• The model's validation against experimental data highlights its potential for predicting cancer cell behavior and treatment outcomes.