Intravesical VSVd51-GM-CSF virotherapy is superior to BCG in treating bladder cancer in preclinical and translational models
Bibliographic record
Abstract
Non-muscle-invasive bladder cancer (NMIBC) can progress to muscle-invasive disease, with transurethral resection followed by Bacillus Calmette-Guérin (BCG) immunotherapy reducing this risk. Effective immunotherapies for BCG-resistant NMIBC are lacking. This study directly compares the efficacy of the oncolytic vesicular stomatitis virus (VSVd51) encoding the granulocyte macrophage colony-stimulating factor (GM-CSF) transgene (VSVd51-GM-CSF) to BCG in preclinical and translational models of aggressive bladder cancer. VSVd51-GM-CSF and BCG were tested in mouse and human bladder cancer spheroids and in bladder cancer patient-derived organoids, to evaluate immunogenic cell death biomarkers, cytokine release, and immune cell activation. VSVd51-GM-CSF and BCG treatments were then administered to C57Bl/6 mice with MB49 or N-butyl-N-(4-hydroxybutyl)-nitrosamine(BBN)-induced bladder tumors via intravesical instillation. VSVd51-GM-CSF treatment induced a heightened release of immunogenic factors and cytokines, which then activated M1-like tumor-targeting monocytes. Mice treated with VSVd51-GM-CSF exhibited stronger tumor-infiltrating immune responses, longer survival, and reduced tumor volume compared to BCG-treated mice. Importantly, VSVd51-GM-CSF treatment extends survival in BCG-failed mice. This anti-tumor immunity was also observed in patient-derived organoids, suggesting clinical relevance. These translational findings suggest that VSVd51-GM-CSF has significant potential for early-phase clinical trials in NMIBC patients. As a promising viro-immunotherapy, it could provide an alternative for patients with BCG-resistant disease, marking an important step forward in bladder cancer immunotherapy.
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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) | 0.001 | 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".