Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors
Year: 2025
Type: article
Abstract: BACKGROUND: The tumor microenvironment (TME) represents a promising avenue to understand gonadotroph tumors and develop therapeutic tools. Here, we aimed to gain insight into the tumorigenesis mechanisms driven by the gonadotroph TME. METHODS: Single-cell and spatial-omics were combined with histological analysis. Mice engrafted with tumor cells were used for functional validation. RESULTS: Using single-cell and spatial transcriptomic data from gonadotroph tumors and normal tissues, we identified mast cells in the microenvironment of gonadotroph tumors and confirmed their physical and functional interaction with endothelial cells. Quantification of mast cells in 40 patients suggested their pro-tumoral role as tumors relapsing after surgery harbored more mast cells. More interestingly, the distribution of mast cells was associated with the presence of a higher number of blood vessels, with an increased microvessel density (MVD), and with blood vessels with thicker walls. Ligand-receptor network analysis highlighted VEGFA as a modulator of mast/endothelial cell communication, a result confirmed by the identification of intratumoral mast cells expressing VEGFA in mouse and human gonadotroph tumors. Finally, using mice engrafted with gonadotroph tumor cells, we demonstrated that the depletion of mast cells reduces tumor volume through increased apoptosis. These observations were associated with increased hemorrhagic areas and a significant reduction of the number of blood vessels and MVD as evidenced in human gonadotroph tumors. CONCLUSION: We demonstrate that mast cells represent a new actor of the gonadotroph TME, and highlight their pro-angiogenic and pro-tumorigenic roles as potential targets for the therapeutic treatment of gonadotroph tumors.
Source: Neuro-Oncology
Institutions: Lyon 1 Université, Centre National de la Recherche Scientifique, Inserm, Institutul Național de Endocrinologie C.I. Parhon, Centre de Recherche en Cancérologie de Lyon, École Normale Supérieure de Lyon, Laboratoire de Biologie et Modélisation de la Cellule, Hospices Civils de Lyon, Universitatea de Medicină, Farmacie, Științe și Tehnologie „George Emil Palade” din Târgu Mureș, Centre Inria de l'Université Grenoble Alpes
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $5,132
Awards: Machine Learning methods to identify Spatial Gene Networks and understand tumor-microenvironment interactions of pituitary adenomas, Statistics and Machine Learning for Single Cell Genomics, G4656594441, G640260551, G6803667113, Institut François Rabelais pour la recherche multidisciplinaire sur le cancer, MultiScale AI for SingleCell-Based Precision Medicine, G8944445660
OpenAlex ID: W4415249108
