Exploring the Onset and Progression of Prostate Cancer through a Multicellular Agent-based Model.

Area of Science:

• Computational biology
• Oncology
• Biophysics

Background:

• Prostate cancer affects over 10% of men.
• The tumor microenvironment significantly influences prostate cancer onset and progression.
• Understanding early-stage drivers is crucial for prevention and treatment.

Purpose of the Study:

• To develop an agent-based model of the prostatic acinus and its microenvironment.
• To investigate factors driving prostate cancer development *in silico*.
• To validate model predictions against human prostate cancer specimens and patient data.

Main Methods:

• Developed an agent-based computational model of the prostatic acinus.
• Incorporated tumor cells, cancer-associated fibroblasts (CAFs), and macrophages.
• Validated model growth patterns with histopathological analysis of human prostate cancer.
• Compared *in silico* tumor load with clinical outcomes of 494 cancer patients.

Main Results:

• Early-stage tumor development is strongly affected by stochastic interactions between macrophages and tumor cells.
• Systematic deviations in tumor growth arise from mutation acquisition probability and CAF/macrophage promotion.
• The model demonstrated a strong association between predicted tumor load and patient clinical outcomes.

Conclusions:

• Prostate tumor formation likelihood depends on both random events and systematic factors.
• While stochasticity is uncontrollable, systematic factors offer targets for personalized prevention strategies.
• Computational modeling provides valuable insights into prostate cancer development and patient outcomes.