A new clinical tool to predict outcome in early-stage melanoma patients

Abstract

Around 25% of early-stage melanoma patients eventually develop metastasis. Thus, we set out to define serological biomarkers that could be used along with clinical and histopathological features of the disease to predict these events. We previously demonstrated that in stage II melanoma patients, serum levels of dermcidin (DCD) were associated with metastatic progression. Based on the relevance of the immune response on the cancer progression and the recent association of DCD with local and systemic immune response against cancer cells, serum DCD was analyzed in a new cohort of patients along with IL-4, IL-6, IL-10, IL-17A, IFN{gamma} TGF{beta} and GM-CSF. We included 448 melanoma patients, 323 of whom were diagnosed as stages I-II according to AJCC. Levels of selected cytokines were determined by ELISA and Luminex and obtained data were analyzed employing Machine Learning and Kaplan-Meier techniques to define an algorithm capable of accurately classifying early-stage melanoma patients with a high and low risk of developing metastasis. The results show that in early-stage melanoma patients, serum levels of the cytokines IL-4, GM-CSF and DCD together with the Breslow thickness are those that best predict melanoma metastasis. Moreover, resulting algorithm represents a new tool to discriminate subjects with good prognosis from those with high risk for a future metastasis. Novelty and ImpactWe have developed a prognostic equation that considers the serum IL-4, GM-CSF and DCD levels, along with the Breslow thickness to accurately classify melanoma outcome in patients. In this sense, a rigorous follow-up is recommended for early-stage melanoma patients with a high Breslow thickness, high serum IL-4 levels and low GM-CSF and DCD levels at the time of diagnosis, given the elevated risk for these patients to develop metastasis during follow-up.