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.
ABSTRACT
OBJECTIVE: To explore pituitary tumors by methylome and transcriptome signatures in a heterogeneous ethnic population. METHODS: In this retrospective cross-sectional study, clinicopathological features, methylome, and transcriptome were evaluated in pituitary tumors from 77 patients (61% women, age 12-72 years) followed due to functioning (FPT: GH-secreting n = 18, ACTH-secreting n = 14) and nonfunctioning pituitary tumors (NFPT, n = 45) at Ribeirao Preto Medical School, University of São Paulo. RESULTS: Unsupervised hierarchical clustering analysis (UHCA) of methylome (n = 77) and transcriptome (n = 65 out of 77) revealed 3 clusters each: one enriched by FPT, one by NFPT, and a third by ACTH-secreting and NFPT. Comparison between each omics-derived clusters identified 3568 and 5994 differentially methylated and expressed genes, respectively, which were associated with each other, with tumor clinical presentation, and with 2017 and 2022 WHO classifications. UHCA considering 11 transcripts related to pituitary development/differentiation also supported 3 clusters: POU1F1-driven somatotroph, TBX19-driven corticotroph, and NR5A1-driven gonadotroph adenomas, with rare exceptions (NR5A1 expressed in few GH-secreting and corticotroph silent adenomas; POU1F1 in few ACTH-secreting adenomas; and TBX19 in few NFPTs). CONCLUSION: This large heterogenic ethnic Brazilian cohort confirms that integrated methylome and transcriptome signatures classify FPT and NFPT, which are associated with clinical presentation and tumor invasiveness. Moreover, the cluster NFPT/ACTH-secreting adenomas raises interest regarding tumor heterogeneity, supporting the challenge raised by the 2017 and 2022 WHO definition regarding the discrepancy, in rare cases, between clinical presentation and pituitary lineage markers. Finally, making our data publicly available enables further studies to validate genes/pathways involved in pituitary tumor pathogenesis and prognosis.