Maraba MG1 Virus Enhances Natural Killer Cell Function via Conventional Dendritic Cells to Reduce Postoperative Metastatic Disease
Bibliographic record
Abstract
This study characterizes the ability of novel oncolytic rhabdoviruses (Maraba MG1) to boost natural killer (NK) cell activity. Our results demonstrate that MG1 activates NK cells via direct infection and maturation of conventional dendritic cells. Using NK depletion and conventional dendritic cells ablation studies in vivo, we established that both are required for MG1 efficacy. We further explored the efficacy of attenuated MG1 (nonreplicating MG1-UV(2min) and single-cycle replicating MG1-Gless) and demonstrated that these viruses activate conventional dendritic cells, although to a lesser extent than live MG1. This translates to equivalent abilities to remove tumor metastases only at the highest viral doses of attenuated MG1. In tandem, we characterized the antitumor ability of NK cells following preoperative administration of live and attenuated MG1. Our results demonstrates that a similar level of NK activation and reduction in postoperative tumor metastases was achieved with equivalent high viral doses concluding that viral replication is important, but not necessary for NK activation. Biochemical characterization of a panel of UV-inactivated MG1 (2-120 minutes) revealed that intact viral particle and target cell recognition are essential for NK cell-mediated antitumor responses. These findings provide mechanistic insight and preclinical rationale for safe perioperative virotherapy to effectively reduce metastatic disease following cancer surgery.
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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 | 0.000 |
| 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".