Role of mTOR Inhibitors in Growth Hormone-Producing Pituitary Adenomas Harboring Different FGFR4 Genotypes
Bibliographic record
Abstract
Pituitary adenomas (PAs) are common intracranial lesions. Available medical therapies are limited in PAs, and therefore, it is essential to identify treatments that control PA growth when surgery is not an option. Fibroblast growth factor 4 is implicated in PA pathogenesis; therefore, in this study, we used an isogenic mammosomatotroph cell line (GH4C1) harboring different fibroblast growth factor receptor (FGFR)-4 genotypes to establish and characterize intracranial xenograft mouse models that can be used for preclinical drug testing. We show that proliferating GH4C1 tumors have an average latency of 3 weeks to form. Histological analysis revealed that prototypic FGFR4 (G388) tumors express increased prolactin and less GH, whereas tumors possessing the polymorphic variant of FGFR4 (R388) express increased GH relative to prolactin. All tumors show abundant mammalian target of rapamycin (mTOR) signaling as confirmed using phosphorylated (p)-S6 and p-4E-binding protein 1 as downstream regulators of this pathway. We subsequently demonstrate that the mTOR inhibitor RAD001 decreases tumor growth rate and reduces p-S6 but not p-4E-binding protein 1 activation, regardless of FGFR4 status. More importantly, GH activity was significantly reduced after mTOR inhibition in the R388 polymorphic variant tumors. This reduction was also associated with a concomitant reduction in serum IGF-1 levels in the R388 group. In summary, we demonstrate that the GH4C1 FGFR polymorphic xenograft is a useful model for examining PAs. Furthermore, we show that RAD001 can efficiently reduce tumor growth rate by a reduction in mTOR signaling and more importantly results in control of GH expression and IGF-1 secretion, providing further support for using mTOR inhibitors in PA patients, in particular GH-producing adenomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".