Pharmacological targeting of mammalian target of rapamycin inhibits ovarian granulosa cell tumor growth
Bibliographic record
Abstract
Few targeted therapies have been developed for ovarian granulosa cell tumor (GCT), even though it represents 5% of all malignant ovarian tumors in women. As misregulation of PI3K/AKT signaling has been implicated in GCT development, we hypothesized that the AKT signaling effector mammalian target of rapamycin (mTOR) may play a role in the pathogenesis of GCT and could represent a therapeutic target. Analyses of human GCT samples showed an increase in protein levels of mTOR and its downstream effectors RPS6KB1, RPS6, eIF4B and PPARG relative to normal granulosa cells, suggestive of an increase in mTOR pathway activity and increased translational activity and/or protein stability. We next sought to evaluate mTOR as a GCT therapeutic target using the Pten (tm1Hwu/tmiHwu);Ctnnb1 (tm1Mmt/+);Amhr2 (tm3(cre)Bhr/+) (PCA) mouse model, in which mTOR, RPS6KB1, eIF4B and PPARG are upregulated in tumor cells in a manner similar to human GCT. Treatment of PCA mice with the mTOR-specific inhibitor everolimus reduced tumor growth rate (1.5-fold; P < 0.05) and also reduced total tumor burden (4.7-fold; P < 0.05) and increased survival rate (78 versus 44% in the vehicle group) in a PCA surgical model of GCT peritoneal carcinomatosis. Everolimus decreased tumor cell proliferation and tumor cell volume relative to controls (P < 0.05), whereas apoptosis was unaffected. Phosphorylation of RPS6KB1 and RPS6 were decreased (P < 0.05) by everolimus, but RPS6KB1, RPS6, eIF4B and PPARG expressions were not affected. These results suggest that mTOR is a valid and clinically useful pharmacological target for the treatment of GCT, although its inhibition does not reverse all consequences of aberrant PI3K/AKT signaling in the PCA model.
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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) | 0.000 | 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".