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Record W2320753189 · doi:10.1158/1538-7445.am2012-1394

Abstract 1394: Acute bacterial infection increases lung cancer metastasis via toll-like receptors 2 and 4

2012· article· en· W2320753189 on OpenAlexaff
Simon C. Chow, Carlos H.F. Chan, Mathieu Rousseau, Jonathan Cools‐Lartigue, Lucie Roussel, Betty Giannias, Heewon Yoon, Crystal Chen, Lorenzo Ferri

Bibliographic record

VenueCancer Research · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsMetastasisImmunologyIn vivoLipoteichoic acidBiologyReceptorPneumoniaA549 cellCancer researchAntigenMicrobiologyBacterial pneumoniaCancerMedicineLung cancerPathologyStaphylococcus aureusBacteriaInternal medicine

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION: Surgery is required for curative treatment of lung cancer but is associated with a high rate of bacterial infections such as pneumonia. There is emerging clinical evidence to suggest that infectious complications may decrease survival after cancer surgery, but the mechanism is unclear. Interactions between bacteria and host cells are mediated by specialized pathogen recognition receptors, of which the Toll-like receptors (TLRs) are best characterized. We and others have shown that cancer cells express TLRs, but their function is largely unknown. The aim of this study is to investigate the mechanisms of bacterial infection-facilitated lung cancer metastasis with a particular focus on the role of TLRs. METHODS: Two lung cancer cell lines (murine H-59 and human A549) were incubated a) directly with heat-inactivated bacteria (S.pneumonia or E.coli) or purified bacterial antigens (lipopolysaccharide or lipoteichoic acid), or b) indirectly with an in vitro pneumonia model using conditioned media from pulmonary bronchial epithelial cells incubated with heat-inactivated bacteria or bacterial antigens. Migratory and metastatic ability of thus treated cells were determined in vitro (adhesion, migration) and in vivo (hepatic intravital microscopy and intra-splenic liver metastasis). A clinically relevant in vivo model of severe infection, cecal ligation and puncture (CLP) or sham control, was employed in some animals. To assess the role of TLRs, we used transgenic TLR4 knockout mice, and small molecule inhibiting molecules or antibodies of TLRs. RESULTS: Direct and indirect (in vitro pneumonia) infection of H-59 and A549 increased adhesion to extracellular matrix components approximately 2-8 fold and 2-4 fold over negative control, respectively. In vitro pneumonia increased in vitro migration of H-59 approximately 2-5 fold. Direct and indirect incubation of H-59 with S.pneumonia and E.coli increased the in vivo adhesion to liver sinusoids 2-4 fold. CLP (in vivo infection model) enhanced the in vivo adhesion of H-59 3-fold and increased number of liver metastases at 2 weeks post-injection approximately 12 fold. These effects were attenuated in TLR4 -/- mice and with TLR2 and TLR4 inhibition. CONCLUSION: Activation of TLR2 or TLR4 on lung cancer cells, either directly by S.pneumonia and E.coli, indirectly though activated bronchial epithelial cells, or via an in vivo infection, increases cancer cell adhesion, migration and metastasis. TLRs thus represent a potential therapeutic target in bacterial infection facilitated cancer metastasis. 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 1394. doi:1538-7445.AM2012-1394

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.035
GPT teacher head0.404
Teacher spread0.369 · 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 designObservational
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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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