Vesicular Stomatitis Virus-Based Oncolytic Virotherapy: Recent Progress and Emerging Trends
Bibliographic record
Abstract
Oncolytic virotherapy has emerged as a promising and innovative approach to cancer treatment, leveraging viruses that selectively replicate in tumor cells and cause their destruction (oncolysis), while simultaneously stimulating anti-tumor immune responses. Vesicular stomatitis virus (VSV), a prototypic rhabdovirus, is among the most versatile oncolytic virus platforms due to its favorable biological characteristics, including rapid replication and cell lysis, lack of pre-existing immunity in humans, and amenability to genetic engineering. Over the past decade, significant progress has been made in VSV-based oncolytic virotherapy. This review presents a comprehensive update on developments since our last review, emphasizing improvements in VSV safety, oncoselectivity, tumor-specific replication, direct oncolysis, and induction of antitumor immunity. By integrating recent applied discoveries with foundational knowledge, this review aims to guide ongoing efforts to advance VSV-based oncolytic virotherapy toward broader clinical translation and improved cancer patient outcomes. Additionally, we provide an overview of three closely related rhabdoviruses (Maraba, Morreton, and Jurona viruses) as emerging oncolytic platforms currently under preclinical and clinical investigation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".