T cell therapy against cancer: A predictive diffuse-interface mathematical model informed by pre-clinical studies.

Area of Science:

• Computational biology
• Mathematical oncology
• Immunotherapy modeling

Background:

• T cell therapy shows promise for solid cancers, but predicting T cell behavior is crucial for optimization.
• Current methods lack robust predictive capabilities for T cell-mediated cancer therapy efficacy.

Purpose of the Study:

• To develop and validate a mathematical model predicting the responsiveness of mouse prostate adenocarcinoma to T cell-based therapies.
• To investigate the impact of tumor-associated vessel activation on T cell infiltration and therapeutic outcomes.

Main Methods:

• A diffuse interface mathematical model (Cahn-Hilliard equation) coupled with Keller-Segel equations for immune dynamics was employed.
• The model was parameterized using pre-clinical MRI data from the Transgenic Adenocarcinoma of the Mouse Prostate (TRAMP) model.
• Finite element method simulations were used to analyze tumor growth dynamics and T cell concentrations.

Main Results:

• The model successfully simulated tumor growth and T cell interactions within the prostate adenocarcinoma microenvironment.
• Including tumor-associated vessel activation in the model predicted significantly higher therapeutic effects and tumor regression.
• Simulated outcomes aligned with existing experimental data, validating the model's predictive power.

Conclusions:

• The developed diffuse-interface mathematical model accurately predicts in vivo T cell behavior during cancer immunotherapy.
• This work serves as a proof-of-concept for using predictive mathematical strategies to optimize cancer immunotherapy.
• Mathematical modeling offers a powerful tool for enhancing the clinical implementation of T cell-based cancer therapies.