Reovirus Activates Human Dendritic Cells to Promote Innate Antitumor Immunity
Bibliographic record
Abstract
Oncolytic viruses can exert their antitumor activity via direct oncolysis or activation of antitumor immunity. Although reovirus is currently under clinical investigation for the treatment of localized or disseminated cancer, any potential immune contribution to its efficacy has not been addressed. This is the first study to investigate the ability of reovirus to activate human dendritic cells (DC), key regulators of both innate and adaptive immune responses. Reovirus induced DC maturation and stimulated the production of the proinflammatory cytokines IFN-alpha, TNF-alpha, IL-12p70, and IL-6. Activation of DC by reovirus was not dependent on viral replication, while cytokine production (but not phenotypic maturation) was inhibited by blockade of PKR and NF-kappaB signaling. Upon coculture with autologous NK cells, reovirus-activated DC up-regulated IFN-gamma production and increased NK cytolytic activity. Moreover, short-term coculture of reovirus-activated DC with autologous T cells also enhanced T cell cytokine secretion (IL-2 and IFN-gamma) and induced non-Ag restricted tumor cell killing. These data demonstrate for the first time that reovirus directly activates human DC and that reovirus-activated DC stimulate innate killing by not only NK cells, but also T cells, suggesting a novel potential role for T cells in oncolytic virus-induced local tumor cell death. Hence reovirus recognition by DC may trigger innate effector mechanisms to complement the virus's direct cytotoxicity, potentially enhancing the efficacy of reovirus as a therapeutic agent.
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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) | 0.002 | 0.001 |
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".