Effect of neutrophil extracellular traps on tumor lymph nodes.
Bibliographic record
Abstract
4033 Background: In most human cancers, regional lymph nodes (LNs) are the first sites of metastasis. In addition to being an important part of the tumor staging system, with the advent of novel therapies, lymph node metastasis has become a crucial clinical intervention point before distant metastasis, the leading cause of cancer-associated deaths. To initiate metastasis, the conditions of LNs need to be optimized for tumor cell deposition and growth. This process is believed to be mediated by the activation of immune cells including polymorphonuclear neutrophils (PMNs). However, the cellular mechanism is not well defined. Our early observations suggest that PMNs and neutrophil extracellular traps (NETs), DNA comprising structures that are extruded in response to inflammatory cues, are associated with adverse oncologic outcomes. Thus, one potential mechanism of increased LN metastasis is that tEVs recruit PMNs and propend NETs formation. Methods: Human tissue micro-arrays (TMAs) of gastroesophageal (GEA) cancer patients were stained with PMN and NETs markers and quantified by HALO software. C57BL/6 or pad4-/- mice were injected with B16F10 or H59 cells alone or treated with neutrophil elastase inhibitor (NEi) or PMN depletion antibody. LN sections were stained with NETs markers and quantified by ImageJ (NIH). Results: In the study of 175 GEA cancer patients, lymphatic NET deposition was observed in both tumor infiltrated lymph nodes and tumor negative lymph nodes. We also demonstrated high LN NETs deposition was associated with reduced survival, even in the absence of overt metastasis ( p=0.03). Next, we sought to investigate the dynamic and the consequence of LN NETs deposition using animal models. We found that LN Neutrophil Recruitment and NET deposition happens in a pre-metastatic manner. Moreover, LN metastasis was abrogated through different kinds of NETs inhibition (neutrophil depletion, pad4 knockout and NEi treatment, n=10, p<0.001), demonstrating the consequences of LN NETs deposition and its potential as a treatment target. Finally, we showed that the LN PMN recruitment and NETs formation was mediated by increased production of IL-8 by Lymphatic Endothelial Cells (LEC). Conclusions: Together, we demonstrated that NETs can contribute to LN metastasis, and can serve as a potential therapeutic targets. By further investigating the detailed mechanism, this project will lead to major advances in the management of cancer patients.
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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) | 0.004 | 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".