Down‐regulation of type I interferon receptor sensitizes bladder cancer cells to vesicular stomatitis virus‐induced cell death
Bibliographic record
Abstract
The intrinsic oncolytic specificity of vesicular stomatitis virus (VSV) is currently being exploited to develop alternative therapeutic strategies for bladder cancer and other cancers. Previously we reported that oncolytic VSV is a potent agent for intravesical treatment of high risk bladder cancer. We observed that VSV preferentially targeted bladder cancer cells resistant to type I interferon (IFN) treatment. The goal of the current study was to further elucidate the nature of the molecular defect of IFN signaling by which bladder cancer cells become susceptible to VSV infection. Using a tissue microarray composed of human bladder cancer cores, we observed that expression of type I IFN receptor (IFNAR) was decreased relative to normal bladder tissue. Advanced bladder cancers had even lower expression of IFNAR. We found that bladder cancer cells susceptible to VSV-induced lysis had low expression of IFNAR as well. We hypothesized that down-regulation of IFNAR in bladder cancer cells may be a molecular mechanism responsible for resistance to type I IFN treatment and sensitivity to VSV oncolysis. SiRNA knockdown of IFNAR indeed facilitated replication of VSV in cells previously resistant to VSV treatment. Blocking IFNAR with a neutralizing antibody showed a similar effect. Hence down-regulation of IFNAR in bladder cancer may be one of the primary molecular mechanisms for clinical IFN resistance. However, this also facilitates VSV replication and oncolysis in high risk bladder cancers and provides a basis for selecting bladder cancer patients for IFN or oncolytic VSV therapy in future clinical trials.
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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".