Carrier Cell-based Delivery of an Oncolytic Virus Circumvents Antiviral Immunity
Bibliographic record
Abstract
Oncolytic viruses capable of tumor-selective replication and cytolysis have shown early promise as cancer therapeutics. However, the host immune system remains a significant obstacle to effective systemic administration of virus in a clinical setting. Here, we demonstrate the severe negative impact of the adaptive immune response on the systemic delivery of oncolytic vesicular stomatitis virus (VSV) in an immune-competent murine tumor model, an effect mediated primarily by the neutralization of injected virions by circulating antibodies. We show that this obstacle can be overcome by administering virus within carrier cells that conceal viral antigen during delivery. Infected cells were delivered to tumor beds and released virus to infect malignant cells while sparing normal tissues. Repeated administration of VSV in carrier cells to animals bearing metastatic tumors greatly improved therapeutic efficacy when compared with naked virion injection. Whole-body molecular imaging revealed that carrier cells derived from solid tumors accumulate primarily in the lungs following intravenous injection, whereas leukemic carriers disseminate extensively throughout the body. Furthermore, xenogeneic cells were equally effective at delivering virus as syngeneic cells. These findings emphasize the importance of establishing cell-based delivery platforms in order to maximize the efficacy of oncolytic therapeutics.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".