Unsupervised Phenotype-Based Clustering of Clinicopathologic Features in Cutaneous Melanoma

Abstract

Pathogenic phenotypes in cutaneous melanoma have been vastly cataloged, although these classifications lack concordance and are confined to either morphological or molecular contexts. In this study, we perform unsupervised k-medoids clustering as a machine learning technique of 2,978 primary cutaneous melanomas at Mass General Brigham and apply this information to elucidate computer-defined subsets within the clinicopathologic domain. We identified five optimally separated clusters of melanoma that occupied two distinct clinicopathologic subspaces: a lower-grade partition associated with common or dysplastic nevi (i.e., nevus-associated melanomas) and a higher-grade partition lacking precursor lesions (i.e., de novo melanomas). Our model found de novo melanomas to be more mitogenic, more ulcerative, and thicker than nevus-associated melanomas, in addition to harboring previously unreported differences in radial and vertical growth phase status. The utilization of mixed clinicopathologic variables, reflective of actual clinical data contained in surgical pathology reports, has the potential to increase the biological relevance of existing melanoma classification schemes and facilitate the discovery of new genomic subtypes.