Targeting Prostate Cancer for Gene Therapy Utilizing Lentivirus and Oncolytic VSV Virus
Bibliographic record
Abstract
Abstract : Prostate cancer is the most commonly diagnosed non-skin carcinoma, and one of the leading causes of cancerrelated deaths in North American men. Presently there are no curative therapies available for advanced metastatic prostate cancer. Oncolytic viral therapy provides an opportunity to efficiently kill primary and metastatic cancer cells while sparing normal cells. Vesicular Stomatitis Virus (VSV) is an oncolytic virus which is able to replicate in cells with a defective interferon (INF) response. Here, we examined the effect of a mutated VSV (AV3), which expresses luciferase and has an enhanced INF-sensitivity, on the viability of prostate tumours that develop in prostate-specific PTEN null transgenic mice. Prostates of PTEN knockout and control mice were injected with 5x108 pfu/ml of VSV(AV3) and monitored for luminescence over a 96h time period using the IVIS-Xenogen machine to track the virus distribution. Plaque analyses for live virus n tissues extracted at various time points revealed that VSV(AV3)predominantly replicated in the prostates of transgenic PTEN knockout mice. Additionally, using TUNNEL staining of paraffin embedded tissues, we demonstrated that VSV(AV3) is capable of selectively infecting and killing malignant prostate cells while sparing normal cells. This cancer-specific cell death was not due to infiltration of neutrophils into the prostate tumours of PTEN null mice's has been reported for other tumour mode. However, there was an increase in macrophage and Blymphocyte infiltration into the prostates of PTEN null mice compared to control mice. In conclusion, VSV(AV3) is able to replicate and selectively kill the prostate cancer cells that develop in the PTEN null mouse and hence prove clinically useful for treating locally advanced prostate cancer while sparing normal prostate tissue.
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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".