Oncolytic virus treatment differentially affects the CD56<sup>dim</sup> and CD56<sup>bright</sup> NK cell subsets in vivo and regulates a spectrum of human NK cell activity
Bibliographic record
Abstract
Abstract Natural killer (NK) cells protect against intracellular infection and cancer. These properties are exploited in oncolytic virus (OV) therapy, where antiviral responses enhance anti‐tumour immunity. We have analysed the mechanism by which reovirus, an oncolytic dsRNA virus, modulates human NK cell activity. Reovirus activates NK cells in a type I interferon (IFN‐I) dependent manner, inducing STAT1 and STAT4 signalling in both CD56 dim and CD56 bright NK cell subsets. Gene expression profiling revealed the dominance of IFN‐I responses and identified induction of genes associated with NK cell cytotoxicity and cell cycle progression, with distinct responses in the CD56 dim and CD56 bright subsets. However, reovirus treatment inhibited IL‐15 induced NK cell proliferation in an IFN‐I dependent manner and was associated with reduced AKT signalling. In vivo , human CD56 dim and CD56 bright NK cells responded with similar kinetics to reovirus treatment, but CD56 bright NK cells were transiently lost from the peripheral circulation at the peak of the IFN‐I response, suggestive of their redistribution to secondary lymphoid tissue. Coupled with the direct, OV‐mediated killing of tumour cells, the activation of both CD56 dim and CD56 bright NK cells by antiviral pathways induces a spectrum of activity that includes the NK cell‐mediated killing of tumour cells and modulation of adaptive responses via the trafficking of IFN‐γ expressing CD56 bright NK cells to lymph nodes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".