The Absence or Overexpression of IL-15 Drastically Alters Breast Cancer Metastasis via Effects on NK Cells, CD4 T Cells, and Macrophages
Bibliographic record
Abstract
IL-15 is a cytokine that can affect many immune cells, including NK cells and CD8 T cells. In several tumor models, IL-15 delays primary tumor formation and can prevent or reduce metastasis. In this study, we have employed a model of breast cancer metastasis to examine the mechanism by which IL-15 affects metastasis. When breast tumor cells were injected i.v. into IL-15(-/-), C57BL/6, IL-15 transgenic (TG) and IL-15/IL-15Rα-treated C57BL/6 mice, there were high levels of metastasis in IL-15(-/-) mice and virtually no metastasis in IL-15 TG or IL-15-treated mice. In fact, IL-15(-/-) mice were 10 times more susceptible to metastasis, whereas IL-15 TG mice were at least 10 times more resistant to metastasis when compared with control C57BL/6 mice. Depletion of NK cells from IL-15 TG mice revealed that these cells were important for protection from metastasis. When NK cells were depleted from control C57BL/6 mice, these mice did not form as many metastatic foci as IL-15(-/-) mice, suggesting that other cell types may be contributing to metastasis in the absence of IL-15. We then examined the role of CD4 T cells and macrophages. In IL-15(-/-) mice, in vivo depletion of CD4 T cells decreased metastasis. The lack of IL-15 in IL-15(-/-) mice, and possibly the Th2-polarized CD4 T cells, was found to promote the formation of M2 macrophages that are thought to contribute to metastasis formation. This study reveals that whereas IL-15 effects on NK cells are important, it also has effects on other immune cells that contribute to metastasis.
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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".