J Clin Endocrinol Metab. 2024 Jan 5:dgae006. doi: 10.1210/clinem/dgae006. Online ahead of print.
ABSTRACT
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 ACP patients (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 multi-dimensional scaling. Statistical associations between clusters and clinical features were achieved using Fisher’s 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 = 0.0006), hypomethylated in CpG Island (CGI), non-CGI sites, and globally (P < 0.001), and associated with greater tumor size (24.1 vs 9.5cm3, P = 0.04). Enrichment analysis highlighted pathways on signaling transduction, transmembrane receptor, development of anatomical structures, cell-adhesion, cytoskeleton organization, and cytokine binding, and also cell-type specific biological processes as regulation of oligodendrocytes, keratinocyte, and epithelial cells differentiation.
CONCLUSION: Two clusters of ACP patients were consistently revealed by unsupervised machine learning methods, being 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.
PMID:38181427 | DOI:10.1210/clinem/dgae006
