Neutrophil Extracellular Traps in Cancer Therapy Resistance
Bibliographic record
Abstract
Neutrophils and their products are increasingly recognized to have a key influence on cancer progression and response to therapy. Their involvement has been shown in nearly every aspect of cancer pathophysiology with growing evidence now supporting their role in resistance to a variety of cancer therapies. Recently, the role of neutrophils in cancer progression and therapy resistance has been further complicated with the discovery of neutrophil extracellular traps (NETs). NETs are web-like structures of chromatin decorated with a variety of microbicidal proteins. They are released by neutrophils in a process called NETosis. NET-dependent mechanisms of cancer pathology are beginning to be appreciated, particularly with respect to tumor response to chemo-, immuno-, and radiation therapy. Several studies support the functional role of NETs in cancer therapy resistance, involving T-cell exhaustion, drug detoxification, angiogenesis, the epithelial-to-mesenchymal transition, and extracellular matrix remodeling mechanisms, among others. Given this, new and promising data suggests NETs provide a microenvironment conducive to limited therapeutic response across a variety of neoplasms. As such, this paper aims to give a comprehensive overview of evidence on NETs in cancer therapy resistance with a focus on clinical applicability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".