Overexpression of IL-15 promotes tumor destruction via NK1.1+ cells in a spontaneous breast cancer model
Bibliographic record
Abstract
BACKGROUND: Natural Killer (NK) cells play an important role in tumor prevention, but once tumors form, the numbers as well as the cytotoxic functions of NK cells are reduced. IL-15 is a cytokine that increases and activates NK cells. Here we will examine the anti-tumor role of IL-15 in a spontaneous breast cancer model. METHODS: To achieve this, Polyoma Middle T (MT) mice that form spontaneous breast cancer were crossed with mice that either overexpress IL-15 (IL-15 transgenic (TG)) or mice that lack IL-15 (IL-15 knockout (KO)). We compared survival curves and tumor formation in IL-15 KO/MT, MT and IL-15 TG/MT groups. In addition, the phenotype, activation and contribution of NK cells and CD8 T cells to tumor formation were examined in each of these mouse strains via flow cytometry, ELISA, adoptive transfer and antibody depletion experiments. RESULTS: IL-15KO/MT tumors formed and progressed to endpoint more quickly than MT tumors. These tumors displayed little apoptosis and poor CD8 T cell infiltration. In contrast, IL-15 TG/MT mice had increased survival and the tumors displayed extensive cell death, high proportions of activated NK cells and a higher infiltration of CD8 T cells than MT tumors. CD8 T cells in IL-15 TG/MT tumors were capable of secreting IFNγ, possessed markers of memory, did not display an exhausted phenotype and were frequently NK1.1+. Long-term antibody depletion studies in IL-15 TG/MT mice revealed that NK1.1+, but not CD8 T cells, were critical for tumor destruction. Lastly, human NK cells, when exposed to a similar cytokine environment as that found in IL-15TG/MT tumors, were capable of killing human breast cancer cells. CONCLUSIONS: This study reveals that high levels of IL-15 can promote tumor destruction and reduce metastasis in breast cancer via effects on NK1.1+ cells. Our results suggest that strategies aimed at increasing NK cell activation may be effective against solid epithelial cancers.
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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) | 0.000 | 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 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".