T cell therapy against cancer: A predictive diffuse-interface mathematical model informed by pre-clinical studies. (21st August 2022)

• Record Type: Journal Article
• Authors: Pozzi, G.; Grammatica, B.; Chaabane, L.; Catucci, M.; Mondino, A.; Zunino, P.; Ciarletta, P.
• Is Part Of: Journal of theoretical biology. Volume 547(2022)
• Publication Date: 2022-08-21
• Subjects: T cell therapy -- Vessel activation -- Cahn-Hilliard -- Keller-Segel -- Prostate cancer -- TRAMP model -- MRI; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2022.111172
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: T cell therapy can be an effective immunotherapeutic strategy for treating cancer. Combining T cell therapy and vessel activation promotes T cells delivery in the tumor. The tumor evolution is studied with a theoretical and numerical model. The tumor-T cells interaction is modeled by a Michaelis Menten law. The model has a threshold behavior for the number of T-cells in the target organ. 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.