Data from Blocking Vascular Endothelial Growth Factor-A Inhibits the Growth of Pituitary Adenomas and Lowers Serum Prolactin Level in a Mouse Model of Multiple Endocrine Neoplasia Type 1
Year: 2023
Type: other
Abstract: Purpose: Multiple endocrine neoplasia type 1 (MEN1) is defined clinically by the combined occurrence of multiple tumors, typically of the parathyroid glands, pancreatic islet cells, and anterior pituitary gland. A mouse model with a heterozygous deletion of the Men1 gene recapitulates the tumorigenesis of MEN1. We wished to determine the role of vascular endothelial growth factor (VEGF)-A in the vascularization and growth of MEN1-associated tumors, with an emphasis on pituitary adenomas. Experimental Design: To investigate whether tumor growth in Men1+/− mice is mediated by VEGF-A dependent angiogenesis, we carried out a monotherapy with the anti–VEGF-A monoclonal antibody (mAb) G6-31. We evaluated tumor growth by magnetic resonance imaging and assessed vascular density in tissue sections. We also measured hormone levels in the serum. Results: During the treatment with mAb G6-31, a significant inhibition of the pituitary adenoma growth was observed, leading to an increased mean tumor doubling-free survival compared with mice treated with a control antibody. Similarly, the growth of s.c. pituitary adenoma transplants was effectively inhibited by administration of anti–VEGF-A mAb. Serum prolactin was lowered by mAb G6-31 treatment but not by control antibody, potentially providing a new therapeutic approach for treating the hormonal excess in MEN1 patients. Additionally, the vascular density in pancreatic islet tumors was significantly reduced by the treatment. Conclusions: These results suggest that VEGF-A blockade may represent a nonsurgical treatment for benign tumors of the endocrine system.
Authors: Nina Korsisaari, Jed Ross, Xiumin Wu, Marcin Kowanetz, Navneet Pal, Linda Hall, Jeffrey Eastham-Anderson, William F. Forrest, Nicholas Van Bruggen, Franklin V. Peale, Napoleone Ferrara
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4361949609