Splicing Machinery is Dysregulated in Pituitary Neuroendocrine Tumors and is Associated with Aggressiveness Features

Pituitary neuroendocrine tumors (PitNETs) constitute approximately 15% of all brain tumors, and most have a sporadic origin. Recent studies suggest that altered alternative splicing and, consequently, appearance of aberrant splicing variants, is a common feature of most tumor pathologies. Moreover, spliceosome is considered an attractive therapeutic target in tumor pathologies, and the inhibition of SF3B1 (e.g., using pladienolide-B) has been shown to exert antitumor effects. Therefore, we aimed to analyze the expression levels of selected splicing-machinery components in 261 PitNETs (somatotropinomas/non-functioning PitNETS/corticotropinomas/prolactinomas) and evaluated the direct effects of pladienolide-B in cell proliferation/viability/hormone secretion in human PitNETs cell cultures and pituitary cell lines (AtT-20/GH3). Results revealed a severe dysregulation of splicing-machinery components in all the PitNET subtypes compared to normal pituitaries and a unique fingerprint of splicing-machinery components that accurately discriminate between normal and tumor tissue in each PitNET subtype. Moreover, expression of specific components was associated with key clinical parameters. Interestingly, certain components were commonly dysregulated throughout all PitNET subtypes. Finally, pladienolide-B reduced cell proliferation/viability/hormone secretion in PitNET cell cultures and cell lines. Altogether, our data demonstrate a drastic dysregulation of the splicing-machinery in PitNETs that might be associated to their tumorigenesis, paving the way to explore the use of specific splicing-machinery components as novel diagnostic/prognostic and therapeutic targets in PitNETs

Epigenetic control of adamantinomatous craniopharyngiomas

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.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)