Reovirus as an Oncolytic Agent Against Experimental Human Malignant Gliomas
Bibliographic record
Abstract
BACKGROUND: Reovirus is a naturally occurring oncolytic virus that usurps activated Ras-signaling pathways of tumor cells for its replication. Ras pathways are activated in most malignant gliomas via upstream signaling by receptor tyrosine kinases. The purpose of this study was to determine the effectiveness of reovirus as an experimental treatment for malignant gliomas. METHODS: We investigated whether reovirus would infect and lyse human glioma cell lines in vitro. We also tested the effect of injecting live reovirus in vivo on human gliomas grown subcutaneously or orthotopically (i.e., intracerebrally) in mice. Finally, reovirus was tested ex vivo against low-passage cell lines derived from human glioma specimens. All P values were two-sided. RESULTS: Reovirus killed 20 (83%) of 24 established malignant glioma cell lines tested. It caused a dramatic and often complete tumor regression in vivo in two subcutaneous (P =.0002 for both U251N and U87) and in two intracerebral (P =.0004 for U251N and P =.0009 for U87) human malignant glioma mouse models. As expected, serious toxic effects were found in these severely immunocompromised hosts. In a less immunocompromised mouse model, a single intratumoral inoculation of live reovirus led to a dramatic prolongation of survival (compared with control mice treated with dead virus; log-rank test, P<.0001 for both U251N and U87 cell lines). The animals treated with live virus also appeared to be healthier and gained body weight (P =.0001). We then tested the ability of reovirus to infect and kill primary cultures of brain tumors removed from patients and found that it killed nine (100%) of nine glioma specimens but none of the cultured meningiomas. CONCLUSIONS: Reovirus has potent activity against human malignant gliomas in vitro, in vivo, and ex vivo. Oncolysis with reovirus may be a potentially useful treatment for a broad range of human cancers.
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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) | 0.001 | 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".