Toward Personalized Response Monitoring in Melanoma Patients Treated with Immunotherapy and Target Therapy

Background/Objectives: Immunotherapy and targeted therapy have revolutionized the treatment of advanced cutaneous melanoma. However, predicting individual response and managing resistance remain major challenges. This narrative review aims to evaluate the prognostic and predictive value of treatment-related adverse events (TRAEs) and circulating biomarkers—including lactate dehydrogenase (LDH), circulating tumor DNA (ctDNA), and microRNAs (miRNAs)—in anticipating therapeutic outcomes and personalizing treatment strategies. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science for studies published between January 2010 and September 2025. Eligible studies included clinical trials, observational cohorts, and translational research evaluating biomarkers or toxicity profiles in melanoma patients receiving immune checkpoint inhibitors or BRAF/MEK inhibitors. Emphasis was placed on dynamic indicators of treatment efficacy and integrative modeling approaches. Results: Evidence indicates that the emergence of low-to-moderate grade TRAEs—especially immune-related events like vitiligo, thyroiditis, and rash—is positively associated with response to immunotherapy. Similarly, pyrexia and dermatologic toxicities may correlate with outcomes under BRAF/MEK inhibition. ctDNA clearance within 6–12 weeks of therapy strongly predicts durable response and precedes radiologic changes. Specific miRNAs (e.g., miR-21-5p, miR-146a-5p) demonstrate dynamic modulation during treatment and may signal response or resistance. Interferon-driven gene expression profiles further stratify tumors into “hot” or “cold” immune phenotypes, refining predictive accuracy. Conclusions: Integrative models combining TRAEs, ctDNA, miRNA signatures, and interferon-related gene expression offer a multi-dimensional framework for early, individualized response monitoring. Prospective validation, harmonization of assays, and incorporation into adaptive clinical workflows are key to translating these insights into personalized melanoma care.