ArticlePituitary2026
Ex Vivo drug sensitivity in patient-derived 3D cultures in acromegaly and its association with clinical predictors.
Article in Pituitary, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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19 authors.
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Abstract
purposePersonalized therapy in acromegaly is limited by interindividual variability in drug responses and the lack of robust markers predicting tumor shrinkage, rather than biochemical control alone. To test whether ex vivo drug-induced viability changes in patient-derived 3D cultures (Pd3D) of GH-secreting pituitary adenomas reflect tumor cell-intrinsic pharmacological sensitivity and align with established clinical predictors.
methodsSpheroid-based Pd3D cultures were established from 27 patients with acromegaly. Cultures were exposed to octreotide, cabergoline, pasireotide, or vehicle control. We assessed cell viability changes; sample-level responder status (viability reduction vs vehicle, p < 0.05); and associations between responder status and known predictive markers, including clinical characteristics, MRI findings, dynamic drug tests, and pathological features. In 6 cases, AI-based digital image analysis quantified pre- and post-treatment SSTR2 expression in liquid-based cytology (LBC).
resultsAll agents modestly reduced median cell viability (84-86%, p < 0.05), with responder rates of 33-40%. Concordance with established predictors was observed: octreotide responders correlated with T2 hypointensity (88% vs 44%, p = 0.04); cabergoline with positive bromocriptine tests (100% vs 45%, p = 0.03); and pasireotide with sparsely granulated patterns (64% vs 19%, p = 0.04). AI-based dynamic analysis demonstrated that ex vivo responders showed relatively stable SSTR2 expression after treatment, whereas nonresponders exhibited marked depletion.
conclusionSpheroid-based Pd3D ex vivo viability assays revealed modest but significant cohort-level effects and sample-level concordance with clinical predictors, suggesting its potential utility as an exploratory model. Additionally, AI-based quantification of SSTR2 dynamics captured functional receptor shifts.
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