Beyond Empiricism: AI-Driven Optimization of Dendritic Cell Immunotherapy for Melanoma

Dendritic cell (DC) immunotherapy is a promising approach for treating cancers such as melanoma and prostate cancer. Although DC-based vaccines can elicit potent anti-tumor immune responses, dosing schedules in both preclinical and clinical settings are often chosen empirically rather than through quantitative optimization. In this work, we develop an enhanced mathematical model of tumor-immune dynamics that incorporates a more realistic tumor growth law and an estimated immune-response delay, enabling the systematic design of DC vaccination protocols. Tumor-growth and immunotherapy parameters were calibrated using experimental melanoma data and two metaheuristic optimization methods: Genetic Algorithm and Particle Swarm Optimization. Using the calibrated model, we derived vaccination schedules consisting of three injections totaling 2.4 × 106 DCs. Despite using the same total dose as the baseline four-injection protocol, the optimized schedules reduced tumor burden by approximately 52% over a 5000-h window, as measured by the area under the tumor-time curve, while also lowering the number of administrations. These results demonstrate that effective tumor control can be achieved without increasing treatment intensity and with substantially fewer vaccinations than previously assumed. Prior optimization studies often required cumulative doses exceeding 1 × 107 cells to obtain comparable therapeutic effects. In contrast, our findings show that metaheuristic algorithms can produce dose-efficient and biologically grounded schedules that significantly enhance treatment performance. This work highlights the value of computational optimization as a decision-support tool for designing efficient and clinically meaningful DC immunotherapy protocols.