Modeling the Melanoma Tumor Microenvironment: From 3D Cultures to Predictive 4D In Vitro Platforms
TL;DR
This narrative review provides a critical overview of advanced melanoma modeling platforms, including scaffold-free and scaffold-based 3D systems, spheroids, patient-derived organoids, assembloids, bioprinted constructs, and microfluidic tumor-on-chip devices, together with 4D time-resolved approaches.
Abstract
: Melanoma is an aggressive skin cancer characterized by marked cellular heterogeneity and complex, dynamic interactions with the surrounding tumor microenvironment, which comprises fibroblasts, immune and endothelial cells, and the extracellular matrix. These interactions critically influence tumor progression, invasion, immune evasion, and the frequent emergence of resistance to targeted therapies and immunotherapies, underscoring the need for preclinical models that faithfully reproduce the complexity of human melanoma. Conventional two-dimensional (2D) culture systems, although still widely used, fail to recapitulate the structural architecture, cellular heterogeneity, and dynamic biochemical and mechanical cues of the tumor microenvironment, which limits their predictive value for therapeutic responses. Advanced three-dimensional (3D) and four-dimensional (4D) in vitro platforms have therefore emerged as more physiologically relevant alternatives capable of reproducing tissue-like organization and temporal tumor dynamics. The aim of this narrative review is to provide a critical overview of these advanced melanoma modeling platforms, including scaffold-free and scaffold-based 3D systems, spheroids, patient-derived organoids, assembloids, bioprinted constructs, and microfluidic tumor-on-chip devices, together with 4D time-resolved approaches. The review further examines the integration of high-resolution imaging, spatial omics, computational modeling, and artificial intelligence (AI)-based analysis as complementary tools for the quantitative, spatial, and temporal characterization of melanoma. By outlining the biological rationale and the technological landscape underlying these systems, this work aims to frame their potential contribution to more predictive, physiologically relevant, and translational melanoma research.