In this study, we develop a biologically informed mathematical model of breast cancer that integrates key tumor, cytokine, and immune-cell interactions. To balance mechanistic detail with computational efficiency, a stepwise modeling strategy is adopted. In the first phase, the dynamics of two major cytokines, Interleukin-2, (IL-2) and Interferon-???? (IFN-????), are formulated together with their regulatory effects on tumor cells. Simulations show that cytokine activity alone is insufficient to control tumor progression. Motivated by this result, the model is expanded to incorporate essential immune cell populations, including natural killer (NK) cells, CD4+ helper T cells, and CD8+ cytotoxic T cells, with their interactions described through nonlinear feedback mechanisms such as Michaelis–Menten kinetics. Unlike classical compartmental models that treat cytokines or chemotherapy as external factors, the proposed framework introduces a novel two-phase cytokine–immune structure that mechanistically bridges molecular and cellular scales within a unified system. Despite this enhanced coupling, the immune response alone fails to eliminate tumor cells for realistic initial tumor burdens. To overcome this limitation, a constant-dose chemotherapy component is incorporated and solved using the non-standard finite difference (NSFD) method. The combined immune–chemotherapy model demonstrates complete tumor clearance even for an initial tumor size of 109 cells. Finally, sensitivity analysis identifies ???????? (chemotherapy-induced tumor cell death rate) and ???????? (tumor growth rate) as the most influential parameters, offering valuable guidance for optimizing treatment strategies. It should be noted that model validation is performed using rescaled, population-averaged tumor growth data, and therefore the reported fit supports reproduction of population-level trends rather than individualized patient-specific prediction.

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