Epigenomic and somatic mutations of pituitary tumors with clinical and pathological correlations in 111 patients.

Objective: To profile clinically non-aggressive and aggressive pituitary adenomas (PAs)/pituitary neuroendocrine tumours (PitNETs) and pituitary carcinomas for somatic mutations and epigenetic alterations of genes involved in cell proliferation/differentiation, microRNAs (miRNA)/long noncoding RNA (LncRNA)-post-transcriptional regulators and therapy targets.

Design: Retrospective observational study.

Patients And Measurements: A total of 64 non-aggressive and 41 aggressive PAs/PitNETs and 6 pituitary carcinomas treated by endoscopic surgery with ≥1-year follow-up were included. Somatic mutations of 17 genes and DNA methylation of 22 genes were assessed. Ten normal pituitaries were used as control.

Results: We found at least one mutation in 17 tumours, including 6/64 non-aggressive, 10/41 aggressive PAs/PitNETs, and 1/6 pituitary carcinoma. AIP (N = 6) was the most frequently mutated gene, followed by NOTCH (4), and TP53 (3). Hypermethylation of PARP15, LINC00599, ZAP70 was more common in aggressive than non-aggressive PAs/PITNETs (p < .05). Lower levels of methylation of AIP, GNAS and PDCD1 were detected in aggressive PAs/PITNETs than non-aggressive ones (p < .05). For X-linked genes, males presented higher level of methylation of FLNA, UXT and MAGE family (MAGEA11, MAGEA1, MAGEC2) genes in aggressive vs. non-aggressive PAs/PITNETs (p < .05). In pituitary carcinomas, methylation of autosomal genes PARP15, LINC00599, MIR193 and ZAP70 was higher than in PAs/PITNETs, while X-linked genes methylation level was lower.

Conclusions: Somatic mutations and methylation levels of genes involved in cell proliferation/differentiation, miRNA/LncRNA-post-transcriptional regulators and targets of antineoplastic therapies are different in non-aggressive and in aggressive PAs/PitNETs. Methylation profile also varies according to gender. Combined genetic-epigenetic analysis, in association with clinico-radiological-pathological data, may be of help in predicting PA/PitNET behaviour.