Spatially resolved diversity in molecular states underlies congenital melanocytic nevi and associated tumors
The congenital melanocytic nevus (CMN) is a developmental skin disorder characterized by prenatal melanocyte overgrowth, exhibiting heterogeneity in surface size, depth, and clinical behavior. Large/giant CMN carry an elevated, anticipatory melanoma risk relative to more common, small CMN. However, deeper insights into melanocytic states, genomic and epigenomic alterations, and microenvironmental cues governing disease progression are needed to predict lesions disposed to transformation. Although large/giant CMN melanocyte heterogeneity was recently established, spatial organization of these states and their niche interactions are unknown. Using advanced spatial and single-cell transcriptomics and bulk methylomics, we characterized ten CMN, seven CMN-associated proliferative nodules and three clinically diagnosed melanomas arising in CMN from children, integrating data from healthy skin references to contextualize melanocyte states in situ. CMN-specific melanocytic states, distinct in differentiation and proliferation, were spatially stratified, with immature melanocytes in deep dermis and more differentiated melanocytes approaching the epidermis. We then constructed a robust atlas of CMN cellular states by integrating single-cell transcriptomic data from five new large/giant CMN with published datasets, using it to deconvolute the spatial information. Cell-cell communication inference uncovered enhanced signaling (e.g. pleiotrophin, IGF1, periostin, semaphorin pathways) between CMN melanocytes, fibroblasts, and hair follicle-associated cells. Analysis of CMN-derived tumors, including longitudinal cases with [≥]2 samples, revealed divergent spatial melanocytic transcription distinguishing immune-enriched lesions from tumors with oncogenic/pro-invasive signatures. Collectively, these findings establish a spatially resolved framework linking melanocyte heterogeneity, signaling, and genomic instability in CMN, providing mechanistic insights to refine risk stratification and prognosis for CMN-associated tumors.