Abstract 2332: Repurposing simvastatin to inhibit the mevalonate pathway as a therapeutic strategy to treat high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Introduction: Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer, and its five-year survival rate has not changed appreciably in decades. As such, there is an imperative need for new and innovative therapies. Our lab has previously discovered that metastatic ascites-derived tumor cells have acquired a gain-of-function p53 mutation following interaction with the ovarian microenvironment in our orthotopic murine model of EOC. This p53 mutation is associated with an upregulation of the mevalonate pathway, which is known for its vital role in producing cholesterol. Tumor cells upregulate metabolic pathways as a survival advantage to fuel their rapid growth and metastasis. Simvastatin, a statin therapy, specifically targets HMG-CoA reductase, the rate-limiting enzyme of this pathway, and inhibits its activity. As such, simvastatin may serve as a potential therapeutic opportunity for EOC. Gain-of-function p53 mutations in the epithelial cells of the distal fallopian tube is now widely accepted as the origin of high-grade serous ovarian cancer. Thus, the purpose of our study was to investigate the relationship between p53 status and the mevalonate pathway in oviductal epithelial (OVE) cells as well as evaluate the effect of simvastatin treatment on OVE cell viability. Methods/Results: OVE cells were isolated from the distal oviducts of FVB/N mice with a p53 wildtype status, and we either deleted the Trp53 gene or introduced a gain-of-function R175H p53 mutation using CRISPR/Cas9. Cell viability was measured following treatment with varying doses of simvastatin and p53 mutant cells showed the greatest sensitivity (IC50: 5.48 uM) compared to p53 wildtype (IC50: 14.80 uM) and p53 knockout (IC50:9.78 uM) cells. For subsequent experiments, OVE cells were treated with 10 uM simvastatin or DMSO and subjected to Resazurin, Transwell migration, CyQUANT proliferative and Caspase-Glo 3/7 assays. Simvastatin significantly decreased resazurin reduction to resorufin in a time-dependent manner over 48 hours, regardless of p53 status, demonstrating reduced metabolic activity. Additionally, OVE cell invasion was significantly reduced in simvastatin treated p53 mutant cells, compared to p53 wildtype or p53 knockout cells. Simvastatin treatment diminished cell proliferation and enhanced apoptotic activity in all simvastatin treated OVE cells. Conclusion: Overall, simvastatin appears to inhibit tumorigenic processes in vitro. The next steps of this project are to test simvastatin’s effect on disease regression in a novel murine oviductal cell injection model of EOC recently developed by our lab. In light of these results, repurposing simvastatin as a therapy for ovarian cancer may significantly improve the way we treat this vicious disease. Citation Format: Madison Pereira, Kathy Matuszewska, Jacob Haagsma, Alice Glogova, Trevor G. Shepherd, Jim Petrik. Repurposing simvastatin to inhibit the mevalonate pathway as a therapeutic strategy to treat high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2332.
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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) | 0.002 | 0.001 |
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".