Natural and molecular history of prolactinoma: insights from a mouse model.

Journal: Oncotarget
Published Date:

Abstract

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.

Authors

  • Valérie Bernard
    Unité INSERM 1185, Faculté de Médecine Paris Sud, Université Paris-Saclay, le Kremlin-Bicêtre, France.
  • Chiara Villa
    Service d'Anatomie et Cytologie Pathologiques, Hôpital Foch, Suresnes, France.
  • Aurélie Auguste
    Unité INSERM 981, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France.
  • Sophie Lamothe
    Unité INSERM 1185, Faculté de Médecine Paris Sud, Université Paris-Saclay, le Kremlin-Bicêtre, France.
  • Anne Guillou
    Unité INSERM 1191, CNRS, Institut de Génomique Fonctionnelle, Montpellier, France.
  • Agnès Martin
    Unité INSERM 1191, CNRS, Institut de Génomique Fonctionnelle, Montpellier, France.
  • Sandrine Caburet
    Institut Jacques Monod, Université Paris Diderot, Paris, France.
  • Jacques Young
    Unité INSERM 1185, Faculté de Médecine Paris Sud, Université Paris-Saclay, le Kremlin-Bicêtre, France.
  • Reiner A Veitia
    Institut Jacques Monod, Université Paris Diderot, Paris, France.
  • Nadine Binart
    Unité INSERM 1185, Faculté de Médecine Paris Sud, Université Paris-Saclay, le Kremlin-Bicêtre, France.

Keywords

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