Neutrophils Promote Liver Metastasis via Mac-1–Mediated Interactions with Circulating Tumor Cells
Bibliographic record
Abstract
Although circulating neutrophils are associated with distant metastasis and poor outcome in a number of epithelial malignancies, it remains unclear whether neutrophils play an active causal role in the metastatic cascade. Using in vivo models of metastasis, we found that neutrophils promote cancer cell adhesion within liver sinusoids and, thereby, influence metastasis. Neutrophil depletion before cancer cell inoculation resulted in a decreased number of gross metastases in an intrasplenic model of liver metastasis. This effect was reversed when inflamed neutrophils were co-inoculated with cancer cells. In addition, early adhesion within liver sinusoids was inhibited in the absence of neutrophils and partially restored with a short perfusion of isolated activated neutrophils. Intravital microscopy showed that cancer cells adhered directly on top of arrested neutrophils, indicating that neutrophils may act as a bridge to facilitate interactions between cancer cells and the liver parenchyma. The adhesion of lipopolysaccharide-activated neutrophils to cancer cells was mediated by neutrophil Mac-1/ICAM-1. Our findings, therefore, show a novel role for neutrophils in the early adhesive steps of liver metastasis.
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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".