Gram negative bacteria increase non‐small cell lung cancer metastasis <i>via</i> toll‐like receptor 4 activation and mitogen‐activated protein kinase phosphorylation
Bibliographic record
Abstract
Surgery is required for the curative treatment of lung cancer but is associated with high rates of postoperative pneumonias predominantly caused by gram negative bacteria. Recent evidence suggests that these severe infectious complications may decrease long term survival after hospital discharge via cancer recurrence, but the mechanism is unclear. Lung cancer cells have recently been demonstrated to express Toll-like receptors (TLR) that mediate pathogen recognition. We hypothesized that incubation of non-small cell lung cancer (NSCLC) cells with heat-inactivated Escherichia coli can augment cancer cell adhesion, migration and metastasis via TLR4 signaling. Incubation of murine and human NSCLC cells with E. coli increased in vitro cell adhesion to collagen I, collagen IV and fibronectin, and enhanced in vitro migration. Using hepatic intravital microscopy, we demonstrated that NSCLC cells have increased in vivo adhesion to hepatic sinusoids after coincubation with gram negative bacteria. These enhanced cell adhesion and migration phenotypes following incubation with E. coli were attenuated at three levels: inhibition of TLR4 (Eritoran), p38 MAPK (BIRB0796) and ERK1/2 phosphorylation (PD184352). Incubation of murine NSCLC cells in vitro with E. coli prior to intrasplenic injection significantly augmented formation of in vivo hepatic metastases 2 weeks later. This increase was abrogated by NSCLC TLR4 blockade using Eritoran. TLR4 represents a potential therapeutic target to help prevent severe postoperative infection driven cancer metastasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".