Intravesical immunotherapy with a GM-CSF armed oncolytic vesicular stomatitis virus improves outcome in bladder cancer
Bibliographic record
Abstract
A significant proportion of non-muscle invasive bladder cancer cases will progress to muscle invasive disease. Transurethral resection followed by Bacillus Calmette Guerin immunotherapy can reduce this risk, while cystectomy prior to muscle invasion provides the best option for survival. Currently, there are no effective treatments for Bacillus Calmette Guerin refractory disease. A novel oncolytic vesicular stomatitis virus containing the human GM-CSF transgene (VSVd51-hGM-CSF) was rescued and tested as a potential bladder-sparing therapy for aggressive bladder cancer. The existing variant expressing mouse GM-CSF was also used. Measurement of gene expression and protein level alterations of canonical immunogenic cell death associated events on mouse and human bladder cancer cell lines and spheroids showed enhanced release of danger signals and immunogenic factors following infection with VSVd51-m/hGM-CSF. Intravesical instillation of VSVd51-mGM-CSF into MB49 bladder cancer bearing C57Bl/6 mice demonstrated enhanced activation of peripheral and bladder infiltrating effector immune cells, along with improved survival and reduced tumor volume. Importantly, virus-mediated anti-tumor immunity was recapitulated in bladder cancer patient-derived organoids. These results suggest that VSVd51-hGM-CSF is a promising viro/immunotherapy that could benefit bladder cancer patients.
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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 | 0.000 |
| 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".