Methylome Analysis in Nonfunctioning and GH-Secreting Pituitary Adenomas.

Pituitary adenomas (PAs), usually benign lesions, can sometimes present with "aggressive" features (rapid growth, local invasiveness, scarce response to conventional treatments). Despite the fact that a few genetic alterations have been associated to this clinical behavior, the role of epigenetic modifications, mainly methylation and miRNAs activity, is now opening new frontiers in this field. We evaluated the methylation profile of 21 PA (11 GH-omas, 10 nonfunctioning tumors-NFPAs) samples from TNS surgery and 5 normal pituitaries, collected at our neurosurgery between 2015 and 2017. DNA was extracted and sequenced, selecting 184,841 target regions. Moreover, methylation profiles were correlated with demographic, radiological, and clinicopathological features. NFPAs showed higher methylation levels vs. GH-omas, with 178 differentially methylated regions (DMRs) mainly consisting of noncoding and intronic sequences, and mostly localized in the open sea regions. We also found three hypermethylated genes (C7orf50, GNG7, and BAHCC1) involved in tumorigenesis processes and potentially influencing pituitary tumor pathophysiology. Among the clinicopathological features, only the maximum diameter resulted significantly higher in NFPAs. Our data provide further evidence of the complex epigenetic background of pituitary tumors. In line with the current literature, we confirmed a significant prevalence of hypermethylation in NFPAs vs. GH-omas, whose pathophysiological consequence is yet to be defined.

Pharmacogenetic and pharmacogenomic discovery strategies.

Genetic/genomic profiling at a single-patient level is expected to provide critical information for determining inter-individual drug toxicity and potential efficacy in cancer therapy. A better definition of cancer subtypes at a molecular level, may correspondingly complement such pharmacogenetic and pharmacogenomic approaches, for more effective personalized treatments. Current pharmacogenetic/pharmacogenomic strategies are largely based on the identification of known polymorphisms, thus limiting the discovery of novel or rarer genetic variants. Recent improvements in cost and throughput of next generation sequencing (NGS) are now making whole-genome profiling a plausible alternative for clinical procedures. Beyond classical pharmacogenetic/pharmacogenomic traits for drug metabolism, NGS screening programs of cancer genomes may lead to the identification of novel cancer-driving mutations. These may not only constitute novel therapeutic targets, but also effector determinants for metabolic pathways linked to drug metabolism. An additional advantage is that cancer NGS profiling is now leading to discovering targetable mutations, e.g., in glioblastomas and pancreatic cancers, which were originally discovered in other tumor types, thus allowing for effective repurposing of active drugs already on the market.

Prolactin in obese children: a bridge between inflammation and metabolic-endocrine dysfunction.

OBJECTIVE: Adipocytes, regulated by insulin, represent the major peripheral source of prolactin (PRL), which play a pivotal role in energy balance, acting on adipogenesis and lipolysis. The aim of this study was to investigate whether PRL was associated with obesity-related inflammatory status and metabolic parameters. The diagnostic and prognostic role of PRL for metabolic syndrome (MS) was assessed. The effects of short-term lifestyle therapy on PRL levels were evaluated. SUBJECTS: Prolactin was assessed in 94 obese patients and compared with 40 healthy children (HS).Patients were followed up for 1 year. Receiver operating characteristics (ROC) analysis was employed to find the best cut-off values capable of identifying MS in obese children for PRL, IL-6 and TNF-α. Kaplan-Meier curves were also generated. Adjusted risk estimates for MS were calculated using Cox proportional hazard regression analysis. An obesity intervention programme was administered for 12 months. RESULTS: Prolactin levels were lower in obese patients than controls (P < 0·0001). PRL was found to be inversely correlated with BMI, IL-6 and HOMA-IR, whereas a direct correlation was found with HDL values. At ROC analysis, PRL showed higher sensitivity and specificity than IL-6 and TNF-α in identifying MS in obese children. Cox proportional hazard regression analysis showed that PRL predicted MS independently of other potential confounders. The lifestyle intervention improved PRL and metabolic parameters. CONCLUSIONS: Prolactin represents a prognostic marker for obese children and a predictive factor for progression to MS. PRL measurement may be useful as part of the endocrine work-up of obese children.