Recent advances in vesicular stomatitis virus-based oncolytic virotherapy: a 5-year update
Bibliographic record
Abstract
Oncolytic virus (OV) therapy is an anti-cancer approach that uses viruses that preferentially infect, replicate in and kill cancer cells. Vesicular stomatitis virus (VSV, a rhabdovirus) is an OV that is currently being tested in the USA in several phase I clinical trials against different malignancies. Several factors make VSV a promising OV: lack of pre-existing human immunity against VSV, a small and easy to manipulate genome, cytoplasmic replication without risk of host cell transformation, independence of cell cycle and rapid growth to high titres in a broad range of cell lines facilitating large-scale virus production. While significant advances have been made in VSV-based OV therapy, room for improvement remains. Here we review recent studies (published in the last 5 years) that address 'old' and 'new' challenges of VSV-based OV therapy. These studies focused on improving VSV safety, oncoselectivity and oncotoxicity; breaking resistance of some cancers to VSV; preventing premature clearance of VSV; and stimulating tumour-specific immunity. Many of these approaches were based on combining VSV with other therapeutics. This review also discusses another rhabdovirus closely related to VSV, Maraba virus, which is currently being tested in Canada in phase I/II clinical trials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".