Neutrophil extracellular trap gene expression signatures identify prognostic and targetable signaling axes for inhibiting pancreatic tumour metastasis
Bibliographic record
Abstract
Tumour associated neutrophils (TANs) promote metastasis through interactions of Neutrophil Extracellular Traps (NETs) with tumour cells. However, molecular details surrounding the interactions between NETs and Pancreatic Ductal Adenocarcinoma (PDAC) cells are poorly understood. Here, we examine the contribution of NETs in the progression of PDAC, which is characterized by high metastatic propensity. We carry out consensus clustering and pathway enrichment analysis of NET-related genes in an integrated cohort of 369 resectable and metastatic PDAC patient tumour samples, and compile two gene expression signatures comprising of either, integrin-actin cytoskeleton and Epithelial to Mesenchymal Transition (EMT) signaling, or cell death signaling, which identifies patients with very poor to better overall survival, respectively. Tumour Infiltrating neutrophils and NETs associate with ITGB1, CCDC25 and ILK, within clinical and experimental PDAC tumours. Functionally, exposure of PDAC cells to NETs identifies a cytoskeletal dynamic-associated CCDC25-ITGB1-ILK signaling complex which stimulates EMT and migration/invasion. NETosis-driven experimental metastasis to the lungs of PDAC cells delivered through the tail vein of female non-obese diabetic (NOD) scid gamma (NSG) mice is significantly inhibited by ILK knock down. Our data identify novel NET-related gene expression signatures for PDAC patient stratification, and reveal targetable signaling axes to prevent and treat disease progression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".