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.