Vesicular Stomatitis Virus Oncolytic Treatment Interferes with Tumor-Associated Dendritic Cell Functions and Abrogates Tumor Antigen Presentation
Bibliographic record
Abstract
Oncolytic virotherapy is a promising biological approach to cancer treatment that contributes to tumor eradication via immune- and non-immune-mediated mechanisms. One of the remaining challenges for these experimental therapies is the necessity to develop a durable adaptive immune response against the tumor. Vesicular stomatitis virus (VSV) is a prototypical oncolytic virus (OV) that exemplifies the multiple mechanisms of oncolysis, including direct cell lysis, cellular hypoxia resulting from the shutdown of tumor vasculature, and inflammatory cytokine release. Despite these properties, the generation of sustained antitumor immunity is observed only when VSV is engineered to express a tumor antigen directly. In the present study, we sought to increase the number of tumor-associated dendritic cells (DC) in vivo and tumor antigen presentation by combining VSV treatment with recombinant Fms-like tyrosine kinase 3 ligand (rFlt3L), a growth factor promoting the differentiation and proliferation of DC. The combination of VSV oncolysis and rFLt3L improved animal survival in two different tumor models, i.e., VSV-resistant B16 melanoma and VSV-sensitive E.G7 T lymphoma; however, increased survival was independent of the adaptive CD8 T cell response. Tumor-associated DC were actively infected by VSV in vivo, which reduced their viability and prevented their migration to the draining lymph nodes to prime a tumor-specific CD8 T cell response. These results demonstrate that VSV interferes with tumor DC functions and blocks tumor antigen presentation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".