The Effect of Disease Stage on Melanoma Treatment Efficacy: Insights from Mathematical Modeling and In vivo Clinical Data.

Melanoma progression can be effectively modeled through mathematical frameworks, making it a pivotal focus for enhancing our understanding of cancer dynamics and informing personalized treatment strategies. The present research investigates the growth dynamics of melanoma by analyzing 16 individual melanoma cell lines, utilizing in vivo clinical data that spans a range of metastatic stages from primary melanoma to stage IV. The study estimates growth rates across these cell lines by implementing a power law model through nonlinear least squares, uncovering distinct mathematical patterns linked to melanoma stages. Furthermore, the research evaluates the efficacy of various treatment strategies tailored to each disease stage through a chemoimmunotherapy mathematical model. For primary and early-stage melanoma, where tumors are localized, surgical excision is identified as the most effective intervention, often enhanced by CD4⁺T cell immunotherapy. In cases of low to moderately metastatic melanoma, a combination of low-dose chemotherapy with CD8⁺T cell immunotherapy effectively targets metastatic lesions, reducing systemic toxicity while promoting a strong immune response. For highly metastatic melanoma, which presents significant treatment challenges, a combination therapy involving both CD8⁺T and CD4⁺T cell immunotherapy is recommended. This dual approach utilizes the direct tumor-killing effects of CD8⁺T cells alongside the supportive actions of CD4⁺T cells, resulting in improved treatment efficacy and survival outcomes. Overall, this research provides a comprehensive analysis of melanoma cell lines at various stages, integrating mathematical modeling with treatment efficacy to enhance personalized treatment strategies in melanoma management.