IL-15 has innate anti-tumor activity independent of NK and CD8 T cells
Bibliographic record
Abstract
The innate immune system is crucial for host defense and immunosurveillance against pathogens and tumor cells. IL-15 is a pleiotropic cytokine with important effects on cells of the innate and adaptive immune systems. The NK cell- and CD8(+) T cell-mediated functions of IL-15 against tumor cells have been well documented. However, it has not been established whether IL-15 has innate anti-tumor functions independent of these cells. Here, we explored the innate anti-tumor potential of IL-15 using a B16F10 melanoma tumor model. IL-15tg mice exhibited significantly more resistance to tumor growth and metastasis compared to B6 mice, and to IL-15(-/-) mice, which exhibited increased susceptibility to B16F10 challenge. In vivo depletion of NK cells and CD8(+) T cells abrogated the innate resistance to B16F10 cells in B6 but not in IL-15tg mice. In addition, lung macrophages from IL-15tg mice produced significantly higher levels of NO and IL-12 compared with macrophages from B6 or IL-15(-/-) mice. To examine whether IL-15 has innate anti-tumor activity independent of NK cells and CD8(+) T cells, we developed Ad-Op-hIL-15; this resulted in significantly higher levels of biologically active hIL-15. Delivery of Ad-Op-hIL-15 into RAG-2(-/-)/gamma(c)(-/-) mice significantly suppressed tumor burden in the lungs compared with the control adenovirus vector. Our results show that IL-15 can have innate anti-tumor activity independent of NK cells and CD8(+) T cells and the common gamma(c)R.
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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".