Enhanced vesicular stomatitis virus (VSVΔ51) targeting of head and neck cancer in combination with radiation therapy or ZD6126 vascular disrupting agent
Bibliographic record
Abstract
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the 5th most common cancer worldwide. Locally advanced HNSCC are treated with either radiation or chemo-radiotherapy, but still associated with high mortality rate, underscoring the need to develop novel therapies. Oncolytic viruses have been garnering increasing interest as anti-cancer agents due to their preferential killing of transformed cells. In this study, we evaluated the therapeutic potential of mutant vesicular stomatitis virus (VSVΔ51) against the human hypopharyngeal FaDu tumour model in vitro and in vivo. RESULTS: Our data demonstrated high toxicity of the virus against FaDu cells in vitro, which was associated with induction of apoptosis. In vivo, systemic injection of 1 × 109 pfu had minimal effect on tumour growth; however, when combined with two doses of ionizing radiation (IR; 5 Gy each) or a single injection of the vascular disrupting agent (ZD6126), the virus exhibited profound suppression of tumour growth, which translated to a prolonged survival in the treated mice. Concordantly, VSVΔ51 combined with ZD6126 led to a significant increase in viral replication in these tumours. CONCLUSIONS: Our data suggest that the combinations of VSVΔ51 with either IR or ZD6126 are potentially novel therapeutic opportunities for HNSCC.
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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".