Navigating melanoma heterogeneity: Advanced microfluidic CTC capture strategies and AI-assisted immunotherapy response prediction

Malignant melanoma's high somatic mutation burden and phenotypic plasticity limit the efficacy of immune checkpoint inhibitors, creating a clinical “response ceiling” despite initial response rates of 80.0%. Traditional tissue biopsies fail to capture the real-time clonal evolution and transient phenotypic shifts that drive resistance. While circulating tumor cells offer a critical monitoring window, TCF4-driven mesenchymal-like (MES) transitions cause a 40%–60% false-negative risk in conventional assays due to lineage marker downregulation. This review deconstructs how advanced microfluidics reshapes the liquid biopsy paradigm through label-free, antigen-independent capture—utilizing deterministic lateral displacement and inertial focusing—to recover aggressive MES clones and heterogeneous clusters. We evaluate on-chip multi-dimensional strategies, including single-cell omics (e.g., KEAP1 resistance tracking) and metabolic reprogramming (e.g., SREBP2-mediated ferroptosis inhibition), while prospectively exploring AI-driven “Digital Twin” models for closed-loop immunotherapy navigation. Integrating label-free physics with functional single-cell profiling is indispensable for overcoming the clinical barriers in melanoma precision medicine.