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.