Primary tumors induce neutrophil extracellular traps with targetable metastasis-promoting effects
Bibliographic record
Abstract
Targeting the dynamic tumor immune microenvironment (TIME) can provide effective therapeutic strategies for cancer. Neutrophils are the predominant leukocyte population in mice and humans, and mounting evidence implicates these cells during tumor growth and metastasis. Neutrophil extracellular traps (NETs) are networks of extracellular neutrophil DNA fibers that are capable of binding tumor cells to support metastatic progression. Here we demonstrate for the first time that circulating NET levels are elevated in advanced esophageal, gastric and lung cancer patients compared to healthy controls. Using pre-clinical murine models of lung and colon cancer in combination with intravital video microscopy, we show that NETs functionally regulate disease progression and that blocking NETosis through multiple strategies significantly inhibits spontaneous metastasis to the lung and liver. Further, we visualize how inhibiting tumor-induced NETs decreases cancer cell adhesion to liver sinusoids following intrasplenic injection - a mechanism previously thought to be driven primarily by exogenous stimuli. Thus, in addition to neutrophil abundance, the functional contribution of NETosis within the TIME has critical translational relevance and represents a promising target to impede metastatic dissemination.
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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".