A Mechanistic Model of Oncolytic Virus–CAR T Therapy Identifies Memory-Dependent Control of Solid Tumors
Year: 2025
Type: preprint
Abstract: Chimeric antigen receptor (CAR) T-cell therapy can induce durable remissions in hematologic malignancies, yet its efficacy in solid tumors remains limited by antigen heterogeneity, an immunosuppressive tumor microenvironment, and poor T-cell persistence. Oncolytic viruses (OVs) provide a complementary therapeutic strategy by directly lysing tumor cells and reshaping local immune responses. Motivated by emerging evidence that OVs can also induce dual-specific, memory-like CAR T cells, we develop a unified mathematical framework that integrates these interacting modalities. The model distinguishes antigen-positive and antigen-negative tumor populations, tracks key viral and immune compartments, and incorporates OV-mediated microenvironmental activation alongside CAR T-cell stimulation. This baseline formulation provides a platform for assessing whether additional mechanisms—such as OV-induced CAR T-cell memory—are necessary for durable tumor control. Sensitivity analysis of the memory-free system using partial rank correlation coefficients (PRCCs) identifies four dominant drivers of therapeutic outcome: antigen-negative tumor dynamics, viral kinetics, CAR T-cell cytotoxic effectiveness, and PD-1/PD-L1–mediated T-cell exhaustion. While these mechanisms can generate strong early responses, they are insufficient to prevent late relapse driven by antigen-negative tumor regrowth. Introducing a reduced memory variable to represent OV-driven CAR T recall fundamentally alters this behavior: enhanced memory induction and CAR T responsiveness sustain effector T-cell levels and enable long-term control of antigen-negative tumor populations. Together, these results highlight OV-induced CAR T-cell memory as a critical determinant of durable therapeutic efficacy in solid tumors.
Authors: Neslihan N. Pelen, Trachette L. Jackson
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7117304232