Oncolytic vesicular stomatitis virus alone or in combination with JAK inhibitors is effective against ovarian cancer
Bibliographic record
Abstract
Therapy-resistant ovarian cancers have a poor prognosis and novel effective treatment options are urgently needed. In this study, we evaluated the therapeutic efficacy of the oncolytic vesicular stomatitis virus (VSV) against a panel of patient-derived ovarian cancer cell lines of all epithelial subtypes. Notably, we found that most of the cell lines were sensitive to VSV virotherapy. With the objective of improving treatment efficacy for the oncolytic virus-resistant cell lines, we tested various combinations with ovarian cancer standard of care drugs: olaparib, carboplatin, paclitaxel, doxorubicin, cyclophosphamide, and gemcitabine. While none of these combinations revealed to be beneficial, further experiments demonstrated that the antiviral interferon pathway was functional in VSV-resistant cell lines. Given that interferons signal through Janus kinase (JAK)-STAT to mediate their antiviral function, we tested combinations of oncolytic VSV with clinically relevant JAK inhibitors. Our results show that combining VSV with various JAK inhibitors, including ruxolitinib, enhances VSV virotherapy and treatment efficacy. Altogether, we show that VSV, either as a stand-alone treatment or in combination with JAK inhibitors provides an effective therapeutic option for ovarian cancer patients.
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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 | 0.000 |
| 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".