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Record W2039162017 · doi:10.1158/1538-7445.am2012-2972

Abstract 2972: Neutrophil extracellular traps sequester circulating tumor cells <i>in vitro</i> and in a murine model of metastasis

2012· article· en· W2039162017 on OpenAlexaff
Jonathan Cools‐Lartigue, Jonathan Spicer, Braedon McDonald, Simon C. Chow, Paul Kubes, Lorenzo Ferri

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsUniversity of CalgaryMcGill University
Fundersnot available
KeywordsNeutrophil extracellular trapsExtracellularCancer cellMolecular biologyIntravital microscopyChemistryCell biologyIn vivoBiologyCancer researchCancerInflammationImmunology

Abstract

fetched live from OpenAlex

Abstract Introduction: Emerging evidence suggests that neutrophil mediated factors may be implicated in cancer progression, however the mechanisms for this are unclear. Neutrophil extracellular traps (NET's) constitute a mechanism by which pathogens are trapped and killed in extracellular neutrophil derived DNA webs containing antimicrobial proteins. The role of NETs in cancer progression is unknown. We hypothesized that circulating tumor cells could become trapped within NET's, potentially favoring the development of metastatic disease. Materials and Methods: Static adhesion assays were performed using H59 lung cancer cells added to neutrophil monolayers. NETs were induced with phorbol myristate acetate (PMA). NET formation was inhibited with DNAse. Adhesion under dynamic conditions was quantified under flow at 1 dyne/cm2. Lung cancer cells (A549 or H59) were perfused over neutrophil monolayers stimulated with PMA, PMA + DNAse, or media alone, and tumor cell adhesion was quantified. NET formation after stimulation was verified by staining extracellular DNA with Sytox green and assessed for the presence of tumor cell-NET association. To study in vivo interactions between NETs and cancer cells, control and bacterial lipopolysaccharide (LPS) stimulated mice (4 hours prior) were prepared for spinning disc confocal intravital microscopy and H59 cells were injected intra-arterially. Neutrophils were identified by injection of anti-GR1 Alexa 647 conjugated mAb and NETs were stained by injection of anti-histone H2A.X Alexa 555 conjugated mAb.Results: Static H59 cells adhesion to neutrophils stimulated with PMA increased 8-fold over media alone, an effect that was completely attenuated by DNAse. Under flow conditions, PMA increased A549 and H59 cell adherence to neutrophil monolayers 10-fold over media alone, DNAse reduced cancer cell adhesion in PMA stimulated neutrophils by a factor of 3. Staining with Sytox green demonstrated a high degree of co-localization of NETs with clusters of malignant cells in neutrophils stimulated with PMA alone, but not in the presence of DNAse or media alone. Mice stimulated with LPS demonstrated heavy histone H2A.X staining suggestive of NET formation and co-localization with H59 cells. LPS induced NET formation correlated with a previously published 50% increase in H59 cell adhesion to hepatic sinusois. Conclusions: Neutrophil extracellular trap formation is associated with increased adherence and capture of cancer cells in both static and dynamic conditions. This is the first time this in vitro finding has been described and correlates with the first demonstration of in vivo NET/cancer cell interactions. These results suggest a novel mechanism by which neutrophils may promote cancer progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2972. doi:1538-7445.AM2012-2972

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.088
GPT teacher head0.337
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2012
Admission routes1
Has abstractyes

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