Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer

Mucosal melanoma (MM) is a rare and lethal subtype of melanoma, disproportionately affecting Asian populations and exhibiting distinct clinicopathological and genetic features compared to cutaneous melanoma (CM). Often diagnosed at advanced stages, MM shows poor responses to conventional therapies, and no standardized treatment regimen currently exists. Progress in preclinical modeling, including cell lines, patient-derived xenografts (PDXs), organoids (PDOs), and comparative animal models-has provided valuable tools for studying MM pathogenesis and therapeutic resistance. Yet these models remain limited in number, heterogeneity, and standardization, restricting their ability to capture MM's molecular diversity and immunosuppressive microenvironment. Beyond physical platforms, emerging virtual strategies-including computational simulations, artificial intelligence-driven multi-omics integration, and in silico clinical trials-offer scalable, cost-effective complements. By simulating tumor-immune-drug interactions using minimal biospecimens, these models offer a unique platform for hypothesis testing and patient stratification in this rare cancer type. This review summarizes MM's clinicopathological features and therapeutic challenges, evaluates current preclinical models, and highlights the synergistic integration of biological and virtual approaches. Future efforts should prioritize MM-specific repositories, multi-model integration, and incorporation of in silico pipelines to accelerate translational research and improve outcomes in this highly lethal malignancy.