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 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 | 0.001 | 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) | 0.000 | 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".