Interferon-β Sensitivity of Tumor Cells Correlates With Poor Response to VA7 Virotherapy in Mouse Glioma Models
Bibliographic record
Abstract
In our recent study, replicative alphaviral vector VA7 was found to be effective against orthotopic human U87-glioma xenografts in an athymic mouse model eradicating the tumors with single intravenous (i.v.) injection. Here, we tested the efficacy of VA7 in immunocompetent orthotopic GL261 and CT-2A glioma models of C57BL/6 mouse in vivo. The cell lines were susceptible to VA7 infection in vitro, but GL261 infection was highly restricted in confluent cell cultures, and mouse interferon-β (IFNβ) pretreatment prevented the replication of VA7 in both cell lines. When mice bearing orthotopic GL261 or CT-2A tumors were administered neurotropic VA7, either i.v. or intracranially (i.c.), the vector was unable to infect the tumor and no survival benefit was achieved. Pretreatments with immunosuppressive cyclophosphamide (CPA) and rapamycin markedly lowered serum-neutralizing antibodies (NAbs) but had no effect on tumor infection or survival. Intracranial GL261 tumors were refractory also in athymic C57BL/6 mice, which have serious defects in their adaptive immunity. Implanted VA7-infected GL261 cells formed tumors with only slightly delayed kinetics and without improving survival thus excluding the participation of physical barriers and indicating robust host IFN action. Mouse and human IFNβ do not seem be species cross-reactive, which might limit the translational relevance of xenograft models in oncolytic virotherapy.
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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 | 0.001 | 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".