Sensitivity of cervical carcinoma cells to vesicular stomatitis virus‐induced oncolysis: Potential role of human papilloma virus infection
Bibliographic record
Abstract
High-risk carcinogenic subtypes of human papilloma virus (HPV) are associated with the development of squamous cell carcinomas of the cervix (CC) and a subset of head and neck (HNSCC). Recurrent metastatic diseases of these sites display a dismal prognosis. Therefore, there is an urgent need to uncover innovative therapeutic strategies in this clinical setting. Oncolytic viruses, including vesicular stomatitis virus (VSV), were identified due to their ability to specifically target tumor cells that generally display defects in interferon (IFN) signaling. HPV expressed proteins can inhibit IFN signaling; therefore, HPV-infected cells may be particularly sensitive to VSV oncolysis. In this study, we evaluated the sensitivity of four CC (HPV+) and four HNSCC (HPV-) derived cell lines to VSV oncolysis. Interestingly, the CC cell lines were consistently more sensitive to VSV cytotoxicity than the HNSCC cell lines tested. Exogenous IFN addition or infection with two attenuated VSV variants that are more susceptible to IFN inhibition failed to attenuate VSV oncolysis in hypersensitive CC cell lines. Furthermore, the expression of HPV-E6, that inhibits IFN receptor signaling, in the VSV-resistant HNSCC cell line SCC25 attenuated VSV-induced IFN response and significantly enhanced VSV cytotoxicity. Finally, differential VSV infection and replication was confirmed in xenograft murine tumor models and explant tumor tissues from two patients with CC. Taken together, these results demonstrate that HPV-infected cells are susceptible to oncolytic virus therapy and that this approach may represent a novel therapeutic approach in HPV positive CC and HNSCC 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 | 0.001 | 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".