Optimizing oncolytic and immune-based cancer therapies: A mathematical approach to tumor dynamics

Understanding the complex interplay between tumor cells, the immune system, and oncolytic viral therapy is very important for optimizing cancer treatment strategies. In this study, we develop and analyze a mathematical model that captures key tumor–immune–virus dynamics, incorporating both viral therapy and immunotherapy interventions. Bifurcation analysis reveals critical equilibrium transitions driven by system parameters, highlighting thresholds where tumor control strategies may either succeed or fail. Sensitivity analysis using the partial rank correlation coefficient method further underscores the essential role of important system parameters in shaping tumor and immune cell populations. We extend our analysis by incorporating optimal control strategies to evaluate the effectiveness of combined oncolytic viral therapy and immunotherapy. Numerical simulations demonstrate that the simultaneous application of both therapies under optimal conditions leads to the most significant tumor reduction, with immunotherapy proving more effective than viral therapy when applied individually. Cost-functional analysis confirms that optimal control minimizes both tumor burden and therapeutic cost. Spatial simulations further illustrate the emergence of oscillatory behaviors in tumor-virus-immune interactions, emphasizing the importance of parameter selection and spatial heterogeneity in treatment outcomes. These findings underscore the potential of integrating mathematical modeling with experimental and clinical approaches to refine cancer treatment strategies. By carefully tuning key parameters, it may be possible to achieve more stable tumor control or complete eradication, providing valuable insights for the development of effective combination therapies.