Activating Peripheral Innate Immunity Enables Safe and Effective Oncolytic Virotherapy in the Brain
Bibliographic record
Abstract
The oncolytic mutant vesicular stomatitis virus VSVΔ51 achieves robust efficacy in multiple extracranial tumor models. Yet for malignancies of the brain, direct intratumoral infusion of VSVΔ51 causes lethal virus-induced neuropathology. Here, we have developed a novel therapeutic regime that uses peripheral immunization with a single sub-lethal dose of VSVΔ51 to establish an acute anti-viral state that enables the safe intracranial (IC) infusion of an otherwise lethal dose of VSVΔ51 within just 6 hr. Although type I interferons alone appeared insufficient to explain this protective phenotype, serum isolated at early time points from primed animals conferred protection against an IC dose of virus. Adaptive immune populations had minimal contributions. Finally, the therapeutic utility of this novel strategy was demonstrated by peripherally priming and intracranially treating mice bearing aggressive CT2A syngeneic astrocytomas with VSVΔ51. Approximately 25% of animals achieved complete regression of established tumors, with no signs of virus-induced neurological impairment. This approach may harness an early warning system in the brain that has evolved to protect the host against otherwise lethal neurotropic viral infections. We have exploited this protective mechanism to safely and efficaciously treat brain tumors with an otherwise neurotoxic virus, potentially widening the available treatment options for oncolytic virotherapy in the brain.
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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".