Neutrophil extracellular traps sequester circulating tumor cells via β1-integrin mediated interactions
Bibliographic record
Abstract
Despite advances in cancer treatment, metastasis remains today the main cause of cancer death. Local control through complete surgical resection of the primary tumor continues to be a key principle in cancer treatment. However, surgical interventions themselves lead to adverse oncologic outcomes and are associated with significantly increased rates of metastasis. Neutrophils through release of neutrophil extracellular traps (NETs) in response to infections were shown to be able to capture circulating cancer cells, and in doing so, support the development of metastatic disease. To be able to intervene on this process, understanding the exact molecular nature of these mechanisms is crucial. We therefore hypothesize and demonstrate that β1-integrin is an important factor mediating the interactions between circulating tumor cells and NETs. We show that β1-integrin expression on both cancer cells and NETs is important for the adhesion of circulating tumor cells to NETs both in vitro and in vivo. Using a murine model of intra-abdominal sepsis to mimic the postoperative inflammatory environment, we show that β1-integrin expression is upregulated in the context of inflammation in vivo. Ultimately, we show that this increased early cancer cell adhesion to NETs in vivo and this effect is abrogated when mice are administered DNAse 1. Our data therefore sheds light on the first molecular mechanism by which NETs can trap circulating tumor cells (CTCs), broadening our understanding of this process.
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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) | 0.001 | 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 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".