MODL-15. ESTABLISHMENT OF ORGANOID MODELS OF PATIENT-DERIVED GH-PRODUCING PITUITARY ADENOMA

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Abstract BACKGROUND Growth hormone-producing pituitary adenomas (GHomas) reveal unfavorable clinical course due to systemic complications such as excessive sweating, impaired glucose tolerance, and hypertension, in addition to its characteristic facial appearance. For GHoma, pharmacological treatments including somatostatin analogs and dopamine agonists are widely performed, however, a subset of case reveals dismal prognosis. We need to elucidate molecular mechanisms contributing to GHoma growth, however, experimental models of GHoma such as cell line and animal model are quietly limited. OBJECTIVE In recent years, organoid models using 3D-culture technologies have gained attention as useful research models for various types of cancers. This study aims to establish a novel organoid model derived from GHoma patients. Using established GHoma organoid model, we analyzed their histological characteristics. METHODS Surgical specimens from five cases of GHoma were finely dissected to approximately 1mm and embedded in Matrigel basement membrane matrix. The specimens were then cultured under 37°C conditions in a 5% CO2 incubator using a medium supplemented with various growth factors. After 28 days of culture, formalin-fixed paraffin-embedded sections were prepared, and the histological similarity with patient tumors was examined. RESULTS Hematoxylin and eosin staining showed that organoid models consisted of acidophilic, granular cytoplasm and small round nuclei, maintaining a cell morphology similar with the original tumor. Immunohistochemical staining revealed positive expression of growth hormone, Pit-1, and CAM5.2, which are specific markers for GHoma. CONCLUSIONS We successfully established a patient-derived organoid model of GHoma that retains histological and endocrinological characteristics. We believe that this model could be a valuable research model for elucidating the molecular mechanisms of GHoma and developing novel treatment approaches targeting important molecular abnormalities. Additionally, in clinical settings, this model might be useful for predicting the responsiveness to drug treatment and selecting appropriate therapies for individual patients.