Natural and molecular history of prolactinoma: insights from a Prlr -/- mouse model

Lactotroph adenoma, also called prolactinoma, is the most common pituitary tumor but little is known about its pathogenesis. Mouse models of prolactinoma can be useful to better understand molecular mechanisms involved in abnormal lactotroph cell proliferation and secretion. We have previously devel...

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Bibliographic Details
Published in:Oncotarget 2018-01, Vol.9 (5), p.6144-6155
Main Authors: Bernard, Valérie, Villa, Chiara, Auguste, Aurélie, Lamothe, Sophie, Guillou, Anne, Martin, Agnès, Caburet, Sandrine, Young, Jacques, Veitia, Reiner A, Binart, Nadine
Format: Article
Language:English
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Life Sciences
Research Paper
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Summary:Lactotroph adenoma, also called prolactinoma, is the most common pituitary tumor but little is known about its pathogenesis. Mouse models of prolactinoma can be useful to better understand molecular mechanisms involved in abnormal lactotroph cell proliferation and secretion. We have previously developed a prolactin receptor deficient ( ) mouse, which develops prolactinoma. The present study aims to explore the natural history of prolactinoma formation in mice, using hormonal, radiological, histological and molecular analyses to uncover mechanisms involved in lactotroph adenoma development. females develop large secreting prolactinomas from 12 months of age, with a penetrance of 100%, mimicking human aggressive densely granulated macroprolactinoma, which is a highly secreting subtype. Mean blood PRL measurements reach 14 902 ng/mL at 24 months in females while PRL levels were below 15 ng/mL in control mice ( < 0.01). By comparing pituitary microarray data of mice and an estrogen-induced prolactinoma model in ACI rats, we pinpointed 218 concordantly differentially expressed (DE) genes involved in cell cycle, mitosis, cell adhesion molecules, dopaminergic synapse and estrogen signaling. Pathway/gene-set enrichment analyses suggest that the transcriptomic dysregulation in both models of prolactinoma might be mediated by a limited set of transcription factors (i.e., STAT5, STAT3, AhR, ESR1, BRD4, CEBPD, YAP, FOXO1) and kinases (i.e., JAK2, AKT1, BRAF, BMPR1A, CDK8, HUNK, ALK, FGFR1, ILK). Our experimental results and their bioinformatic analysis provide insights into early genomic changes in murine models of the most frequent human pituitary tumor.
ISSN:1949-2553
1949-2553
DOI:10.18632/oncotarget.23713