Eosinophils inhibit breast cancer pulmonary metastatic colonization and directly kill tumor cells via degranulation
Bibliographic record
Abstract
Abstract Metastatic breast cancer remains incredibly challenging to treat, highlighting the need for an improved understanding of host factors that prevent metastasis, as well as improved therapeutics to treat metastatic cancer. The lungs, which are one of the most common sites of breast cancer metastasis, are host to a variety of immune cell subsets, including eosinophils (Eo), which are innate immune cells that target pathogens via the secretion of cytotoxic granule proteins. Eo have been shown to be anti-tumorigenic when exposed to certain signals from their local microenvironment. To study the role of eosinophils in pulmonary breast cancer metastasis, we utilized transgenic mouse models of eosinophilia (IL5Tg mice) and eosinophil-deficiency (ddGATA mice). EO771 breast cancer cells were injected intravenously (IV) to seed the lungs. We found that IL5Tg mice, which have a systemic expansion of Eo, had significantly lower EO771 lung tumor burden compared to WT mice. We found that Eo-deficient ddGATA mice exhibited accelerated metastatic progression compared to both WT and IL5Tg mice injected IV with EO771 cells. We also found that WT mice had an increased number of lung EO771 tumor cells compared to IL5Tg mice merely 5 days post-IV injection, indicating that Eo may play a role in both initial tumor cell seeding and subsequent metastatic nodule progression. Importantly, we found that Eo co-cultured with EO771 and LLC tumor cells released eosinophil peroxidase, a cytotoxic granule protein, resulting in tumor cell killing. These results highlight a role for Eo in preventing tumor cell colonization to metastatic sites and suggest that developing drugs to trigger Eo degranulation may serve as a viable therapeutic option to treat metastatic disease.
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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) | 0.000 | 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".