Anti-VEGFA Therapy Reduces Tumor Growth and Extends Survival in a Murine Model of Ovarian Granulosa Cell Tumor
Bibliographic record
Abstract
Although angiogenesis has been proposed as a therapeutic target for the treatment of ovarian granulosa cell tumor (GCT), its potential has not been evaluated in controlled studies. To do so, we used the Pten (tm1Hwu/tm1Hwu); Ctnnb1 (tm1Mmt/+);Amhr2 (tm3(cre)Bhr/+) (PCA) mouse model, which develops GCTs that mimic the advanced disease in women. A monoclonal anti-vascular endothelial growth factor A (VEGFA) antibody was administered weekly to PCA mice beginning at 3 weeks of age. By 6 weeks of age, anti-VEGFA therapy significantly decreased tumor weights relative to controls (P < .05) and increased survival, with all treated animals but none of the controls surviving to 8 weeks of age. Analyses of PCA tumors showed that anti-VEGFA treatment resulted in significant decreases in tumor cell proliferation and microvessel density relative to controls (P < .05). However, treatment did not have a significant effect on apoptosis or tumor necrosis. The VEGFA receptor 2 (VEGFR2) signaling effector p44/p42 mitogen-activated protein kinase (MAPK), whose activity is associated with cell proliferation, was significantly less phosphorylated (i.e., activated) in tumors from the treated group (P < .05). Conversely, no significant difference was found in the activation of protein kinase B, a VEGFR2 signaling effector associated with cell survival. Together, these results suggest that anti-VEGFA therapy is effective at inhibiting GCT growth in the PCA model and acts by reducing microvascular density and cell proliferation through inhibition of the VEGFR2-MAPK pathway. Findings from this preclinical model therefore support the investigation of targeting VEGFA for the adjuvant treatment of GCT in women.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".