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Published September 20, 2023 | Version v1
Journal article Open

Epigenetic control of adamantinomatous craniopharyngiomas

Creators

  • 1. Department of Internal Medicine, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, SP, Brazil.
  • 2. Department of Pediatrics, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, SP, Brazil.*
  • 3. Faculty of Medicine of Universidade Federal do Mato Grosso do Sul, Campo Grande, Brazil.
  • 4. Department of Medical Imaging, Hematology and Oncology, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, SP, Brazil.
  • 5. Department of Computation and Mathematics Biology, Faculty of Philosophy, Sciences and Letters at Ribeirao Preto, University of São Paulo, Ribeirao Preto, SP, Brazil.
  • 6. Neuroendocrinology Research Center/Endocrinology Section, Medical School and Hospital Universitário Clementino Fraga Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil.
  • 7. Department of Surgery and Anatomy, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, SP, Brazil.
  • 8. Department of Pediatrics, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, SP, Brazil.

Description

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.
 

Notes

Data Availability The data generated or analyzed during this study are included in this published article and have been deposited in NCBI's Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number GSE239695 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE239695).

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