RESUMEN
INTRODUCTION: Studies addressing the methylation pattern in adamantinomatous craniopharyngioma (ACP) are lacking. OBJECTIVE: To identify methylation signatures in ACPs regarding clinical presentation and outcome. METHODS: Clinical and pathology data were collected from 35 patients with ACP (54% male; 18.1 years [2-68]). CTNNB1 mutations and methylation profile (MethylationEPIC/Array-Illumina) were analyzed in tumoral DNA. Unsupervised machine learning analysis of this comprehensive methylome sample was achieved using hierarchical clustering and multidimensional scaling. Statistical associations between clusters and clinical features were achieved using the Fisher test and global biological process interpretations were aided by Gene Ontology enrichment analyses. RESULTS: Two clusters were revealed consistently by all unsupervised methods (ACP-1: n = 18; ACP-2: n = 17) with strong bootstrap statistical support. ACP-2 was enriched by CTNNB1 mutations (100% vs 56%, P = .0006), hypomethylated in CpG island, non-CpG Island sites, and globally (P < .001), and associated with greater tumor size (24.1 vs 9.5 cm3, P = .04). Enrichment analysis highlighted pathways on signaling transduction, transmembrane receptor, development of anatomical structures, cell adhesion, cytoskeleton organization, and cytokine binding, and cell type-specific biological processes as regulation of oligodendrocytes, keratinocyte, and epithelial cells differentiation. CONCLUSION: Two clusters of patients with ACP were consistently revealed by unsupervised machine learning methods, with one of them significantly hypomethylated, enriched by CTNNB1 mutated ACPs, and associated with increased tumor size. Enrichment analysis reinforced pathways involved in tumor proliferation and in cell-specific tumoral microenvironment.