Vanadyl sulfate-enhanced oncolytic virus immunotherapy mediates the antitumor immune response by upregulating the secretion of pro-inflammatory cytokines and chemokines
Bibliographic record
Abstract
Oncolytic viruses (OVs) are promising anticancer treatments that specifically replicate in and kill cancer cells and have profound immunostimulatory effects. We previously reported the potential of vanadium-based compounds such as vanadyl sulfate (VS) as immunostimulatory enhancers of OV immunotherapy. These compounds, in conjunction with RNA-based OVs such as oncolytic vesicular stomatitis virus (VSVΔ51), improve viral spread and oncolysis, leading to long-term antitumor immunity and prolonged survival in resistant tumor models. This effect is associated with a virus-induced antiviral type I IFN response shifting towards a type II IFN response in the presence of vanadium. Here, we investigated the systemic impact of VS+VSVΔ51 combination therapy to understand the immunological mechanism of action leading to improved antitumor responses. VS+VSVΔ51 combination therapy significantly increased the levels of IFN-γ and IL-6, and improved tumor antigen-specific T-cell responses. Supported by immunological profiling and as a proof of concept for the design of more effective therapeutic regimens, we found that local delivery of IL-12 using VSVΔ51 in combination with VS further improved therapeutic outcomes in a syngeneic CT26WT colon cancer model.
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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) | 0.001 | 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".