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Threshold dynamics of a stochastic tumor-immune model combined oncolytic virus and chimeric antigen receptor T cell therapies

Tong Zhou, Jin Yang, Yuanshun Tan and Zijian Liu

Chaos, Solitons & Fractals, 2025, vol. 197, issue C

Abstract: The combination of chimeric antigen receptor T (CAR-T) cell immunotherapy and oncolytic viruses (OVs) has been identified in preclinical studies as a promising treatment approach for eradicating solid tumors, demonstrating synergistic effects that significantly promote tumor reduction. This paper proposes a stochastic differential equation to describe the combination of OVs and CAR-T cell immunotherapy under the influence of white noise. The existence of a unique global positive solution, stochastic eventually boundedness and permanence of the system are determined. Sufficient conditions for the global attractiveness of the solution are given. Threshold conditions for tumor extinction and persistence are derived and the stationary distribution and ergodicity of the system are examined. Numerical simulations are conducted to validate the model, revealing that an increase in white noise can suppress tumor growth to a certain extent. It is shown that the joint effect between white noise and model parameters determines whether tumor eradication or persistence occurs. It is further demonstrated that enhancing CAR-T cell-induced viral release promotes tumor elimination, although excessive viral release may lead to recurrent instability. The therapy was highly effective in eliminating tumors when both virus-induced lysis intensity and CAR-T cell-induced viral release were high. In contrast, oncolytic virotherapy alone are observed to be less effective than the combined therapy.

Keywords: Chimeric antigen receptor (CAR) T cells; Oncolytic virotherapy; Persistence; Extinction; Stationary distribution; Tumor immune system (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:197:y:2025:i:c:s096007792500431x

DOI: 10.1016/j.chaos.2025.116418

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