Early Neutrophil Sensing Shapes the Innate Response to Preneoplastic Mammary Cells
Bibliographic record
Abstract
ABSTRACT Breast cancer (BC) is the leading cause of cancer-related death in women. However, early detection of BC remains a major clinical challenge and represents a significant obstacle to effective prevention. To improve early clinical management, a deeper understanding of the preneoplastic immune microenvironment of BC is crucial. Among innate immune populations, neutrophils have emerged as important modulators of tumor development, but their role during the initiation of BC remains poorly understood. By integrating depletion experiments with transcriptomic profiling of sorted preneoplatic epithelial cells and neutrophils in spontaneous breast cancer mouse models, we observed that neutrophils contribute to tumor surveillance of preneoplastic stage with the activation of the unfolded protein response (UPR) in the preneoplastic epithelial compartment. To decipher the early anti-tumoral role of neutrophil, we developed an in vitro co-culture model of human mammary epithelial cells undergoing oncogenic stress with activation of the UPR (eHMEC), with human primary neutrophils. eHMEC display an immunoactive secretome as well as immunogenic membrane ligands, and neutrophils are the only immune cell population detecting eHMEC immunogenic signals leading to their recruitment, activation, production of reactive oxygen species and degranulation. Altogether, our work identifies for the first-time neutrophils as the earliest immune cell involved in immunosurveillance of preneoplastic BC epithelial cells, paving the way for potential therapeutic approaches targeting neutrophils to intercept early steps of BC tumorigenesis.
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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 | 0.000 |
| 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".