Quantifying immunosuppressive barriers using a mathematical framework of tumor-associated myeloid cell mediated resistance in oncolytic virotherapy

Oncolytic virotherapy (OVT) represents a promising frontier in oncology, utilizing engineered viruses to selectively lyse malignant tumor cells while simultaneously triggering a systemic anti-tumor immune response. However, the therapeutic efficacy of OVT is frequently hampered by the emergence of resistance, often driven by the immunosuppressive landscape of the tumor microenvironment (TME). This study addresses a pivotal challenge: why do some tumors resist OVT, and how can this resistance be overcome? We examine local interactions between tumor cells and tumor-associated myeloid cells (TAMCs) to understand the local mechanisms facilitating OVT resistance. In particular, we focus on how tumor cell populations actively cooperate with TAMCs to establish an immunosuppressive niche that facilitates tumor growth and OVT resistance, thereby blunting the adaptive response necessary for durable tumor clearance. To explore this dynamic phenomenon, we propose a novel mechanistic mathematical model based on a system of ordinary differential equations (ODEs). Our analyses reveal a complex duality: while transient TAMC-mediated suppression of effector CD \\(8^{+}\\) T cells may initially increase the likelihood of oncolytic viruses (OVs) spread, it ultimately undermines long-term efficacy of OVT by impairing T-cell-mediated destruction. Notably, our stability analysis indicates that treatment resistance occurs when OV infection recruits more immunosuppressive TAMCs. Our key therapeutic metrics identify treatment conditions that facilitate tumor eradication and those that enable OVT resistance, thereby highlighting a delicate equilibrium between viral infection kinetics and immune activation rates. Our simulations indicate that (i) effectiveness of OVT is determined by the virus’s lytic speed, with high lysis rates enabling TAMCs to transiently suppress CD \\(8^{+}\\) T cells and improve OVT outcomes; and (ii) low infection and oncolytic lysis rates accelerate the onset of resistance compared to more rapid viral dynamics. This model provides a quantitative foundation for developing combinatorial strategies that target immunosuppressive networks to optimize OVT outcomes.