The Small GTPase Ran Increases Sensitivity of Ovarian Cancer Cells to Oncolytic Vesicular Stomatitis Virus
Bibliographic record
Abstract
Background/Objectives: Ovarian cancer is the deadliest gynecologic cancer, and with the majority of patients dying within the first five years of diagnosis, new therapeutic options are required. The small guanosine triphosphatase (GTPase) Ras-related nuclear protein (Ran) has been reported to be highly expressed in high-grade serous ovarian cancers (HGSOCs) and associated with poor outcomes. Blocking Ran function or preventing its expression were shown to be promising treatment strategies, however, there are currently no small molecule inhibitors available to specifically inhibit Ran function. Interestingly, a previous study suggested that the Vesicular stomatitis virus (VSV) could inhibit Ran activity. Given that VSV is an oncolytic virus (OV) and, therefore, has anti-cancer activity, we reasoned that oncolytic VSV (oVSV) might be particularly effective against ovarian cancer via Ran inhibition. Methods: We evaluated the sensitivity of patient-derived ovarian cancer cell lines to oVSV, as well as the impact of oVSV on Ran and vice versa, using overexpression systems, small interfering RNAs (siRNAs), and drug inhibition. Results: In this study, we evaluated the interplay between oVSV and Ran and found that, although oVSV does not consistently block Ran, increased Ran activation allows for better oVSV replication and tumor cell killing. Conclusions: Our study reveals a positive impact of Ran on oVSV sensitivity. Given the high expression of Ran in HGSOCs, which are particularly aggressive ovarian cancers, our data suggest that oVSV could be effective against the deadliest form of the disease.
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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".