Mutant p53 murine oviductal epithelial cells induce progression of high-grade serous carcinoma and are most sensitive to simvastatin therapy in vitro and in vivo
Bibliographic record
Abstract
Abstract High-grade serous carcinoma (HGSC) is the most common and aggressive subtype of epithelial ovarian cancer, characterized by gain-of-function TP53 mutations originating in the fallopian tube epithelium. Therapeutic intervention occurs at advanced metastatic disease, due to challenges in early-stage diagnosis, with common disease recurrence and therapy resistance despite initial therapy success. The mevalonate pathway is exploited by many cancers and is potently inhibited by statin drugs. Statins have shown anti-cancer activity in many, but not all cancers. Here, we investigated the role of p53 status in relation to mevalonate pathway signaling in murine oviductal epithelial (OVE) cells and identified OVE cell sensitivity to statin inhibition. We found that p53 R175H mutant and Trp53 knockout OVE cells have increased mevalonate pathway signaling compared to p53 wild-type OVE cells. Through orthotopic implantation to replicate the fallopian tube origin of HGSC, p53 R175H mutant cells upregulated the mevalonate pathway to drive progression to advanced-stage ovarian cancer, and simvastatin treatment abrogated this effect. Additionally, simvastatin was more efficacious at inhibiting cell metabolic activity in OVE cells than atorvastatin, rosuvastatin and pravastatin. In vitro, simvastatin demonstrated potent effects on cell proliferation, apoptosis, invasion and migration in OVE cells regardless of p53 status. In vivo, simvastatin induced ovarian cancer disease regression through decreased primary ovarian tumor weight and increased apoptosis. Simvastatin also significantly increased cytoplasmic localization of HMG-CoA reductase in ovarian tumors. Downstream of the mevalonate pathway, simvastatin had no effect on YAP or small GTPase activity. This study suggests that simvastatin can induce anti-tumor effects and could be an important inhibitor of ovarian cancer progression.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".