Related Experiment Video
Updated: Sep 21, 2025
A Syngeneic Mouse B-Cell Lymphoma Model for Pre-Clinical Evaluation of CD19 CAR T Cells
Published on: October 16, 2018
T cell therapy against cancer: A predictive diffuse-interface mathematical model informed by pre-clinical studies
G Pozzi1, B Grammatica1, L Chaabane2
1MOX Laboratory, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Abstract:
T cell therapy has become a new therapeutic opportunity against solid cancers. Predicting T cell behaviour and efficacy would help therapy optimization and clinical implementation. In this work, we model responsiveness of mouse prostate adenocarcinoma to T cell-based therapies. The mathematical model is based on a Cahn-Hilliard diffuse interface description of the tumour, coupled with Keller-Segel type equations describing immune components dynamics. The model is fed by pre-clinical magnetic resonance imaging data describing anatomical features of prostate adenocarcinoma developed in the context of the Transgenic Adenocarcinoma of the Mouse Prostate model. We perform computational simulations based on the finite element method to describe tumor growth dynamics in relation to local T cells concentrations. We report that when we include in the model the possibility to activate tumor-associated vessels and by that increase the number of T cells within the tumor mass, the model predicts higher therapeutic effects (tumor regression) shortly after therapy administration. The simulated results are found in agreement with reported experimental data. Thus, this diffuse-interface mathematical model well predicts T cell behavior in vivo and represents a proof-of-concept for the role such predictive strategies may play in optimization of immunotherapy against cancer.
Insights
This study models cancer treatment response using a mathematical approach. Activating tumor vessels to increase T cells significantly enhances predicted therapeutic effects and tumor regression.
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.
