Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy

Area of Science:

• Computational oncology
• Mathematical modeling of tumor growth
• Pharmacokinetics and pharmacodynamics

Background:

• Understanding invasive solid tumor evolution under chemotherapy is vital for personalized oncotherapy.
• Tumor microenvironment heterogeneity influences drug diffusion, tumor growth, and treatment response.
• Current chemotherapy strategies require optimization for improved patient outcomes.

Purpose of the Study:

• To develop and utilize a hybrid 3D computational model for investigating invasive solid tumor dynamics under chemotherapy.
• To analyze the impact of heterogeneous microenvironments and varying chemotherapy dosing strategies on tumor evolution.
• To explore the potential of computational tools for in silico prognosis and treatment optimization.

Main Methods:

• Development of a hybrid 3D computational model integrating pharmacokinetic, diffusion-reaction, and cell automaton models.
• Simulation of avascular invasive solid tumor growth, invasion, and drug-tumor interactions within heterogeneous microenvironments.
• Investigation of different chemotherapy dosing strategies (constant vs. periodic) and microenvironmental factors.

Main Results:

• Constant chemotherapy dosing demonstrated superior suppression of primary tumor growth compared to periodic dosing due to sustained drug concentration.
• Highly heterogeneous microenvironments significantly enhanced tumor malignancy, leading to reduced chemotherapy effectiveness.
• The model also elucidated the effects of geometrically confined spaces and non-uniform drug delivery on tumor behavior.

Conclusions:

• A hybrid computational model provides a powerful tool for understanding complex tumor-chemotherapy interactions in 3D heterogeneous environments.
• Chemotherapy efficacy is highly sensitive to dosing schedules and the degree of microenvironmental heterogeneity.
• This in silico approach holds promise for developing predictive tools to optimize cancer treatment strategies.