Anti-VEGFA Therapy Reduces Tumor Growth and Extends Survival in a Murine Model of Ovarian Granulosa Cell Tumor
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it