Eosinophils Decrease Pulmonary Metastatic Mammary Tumor Growth
Bibliographic record
Abstract
Metastatic breast cancer is challenging to effectively treat, highlighting the need for an improved understanding of host factors that influence metastatic tumor cell colonization and growth in distant tissues. The lungs are a common site of breast cancer metastasis and are host to a population of tissue-resident eosinophils. Eosinophils are granulocytic innate immune cells known for their prominent roles in allergy and Th2 immunity. Though their presence in solid tumors and metastases have been reported for decades, the influence of eosinophils on metastatic tumor growth in the lungs is unclear. We used transgenic mouse models characterized by elevated pulmonary eosinophils (IL5Tg mice) and eosinophil-deficiency (ΔdblGATA mice), as well as antibody-mediated depletion of eosinophils, to study the role of eosinophils in EO771 mammary tumor growth in the lungs. We found that IL5Tg mice exhibit reduced pulmonary metastatic colonization and decreased metastatic tumor burden compared to wild-type (WT) mice or eosinophil-deficient mice. Eosinophils co-cultured with tumor cellsex vivoproduced peroxidase activity and induced tumor cell death, indicating that eosinophils are capable of releasing eosinophil peroxidase (EPX) and killing EO771 tumor cells. We found that lung eosinophils expressed phenotypic markers of activation during EO771 tumor growth in the lungs, and that metastatic growth was accelerated in eosinophil-deficient mice and in WT mice after immunological depletion of eosinophils. Our results highlight an important role for eosinophils in restricting mammary tumor cell growth in the lungs and support further work to determine whether strategies to trigger local eosinophil degranulation may decrease pulmonary metastatic growth.
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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) | 0.002 | 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".