Gram-negative bacterial infection increases lung cancer metastasis via Toll-like receptor activation and increased cancer cell proliferation post-tumor adhesion
Bibliographic record
Abstract
Abstract Background Lung cancer is a leading cause of death partially due to high recurrence rates after surgical resection. Clinical data suggest that post-operative infections may increase the risk of recurrence. Our previous work indicated that increased adhesion of circulating tumors in the context of infection is partially responsible for this phenotype. However, cancer metastasis is a multi-step process, and it is likely that other events following tumor adhesion also play a role. Methods In vivo intrasplenic injection of murine lung cancer cells into wild type (WT) and Toll-like receptor 4 knockout (TLR4 -/- ) mice followed by cecal-ligation and puncture (CLP) as a model of post-operative infection or sham surgery were used. H&E staining and immunohistochemistry analysis of Ki67+ cells in the livers of those mice were performed. In vitro proliferation assays were performed on human lung cancer cells using combinations of TLR blockade. Results We found a 5-fold increase in hepatic metastases in WT CLP mice compared to WT sham mice. TLR4 -/- CLP mice had a significant decreased tumor burden compared to WT CLP mice. This indicated an important mechanistic role for the TLR4-initiated host response to gram negative infection post-tumor cell adhesion. By analyzing the livers of those mice, we observed an increase in proliferation of tumor micrometastases in vivo in WT CLP mice as compared to WT sham mice. Here again, CLP TLR4 -/- mice had significantly fewer replicating micrometastases than CLP WT mice. Indeed, we found that direct stimulation of lung cancer cells with heat-inactivated E . Coli resulted in increased proliferation of tumor growth in vitro . These effects were partially abrogated by tumor TLR4 blockade; combined TLR2, 4 and 5 blockades led to a more prominent decrease. Conditioned media from bronchoalveolar epithelial cells treated with lipopolysaccharide lead to increased lung cancer proliferation; these changes were reversed with TLR blockade, indicating that the host response to infection is TLR mediated. Conclusions Overall, these results imply a more complex mechanistic role of post-operative infection in metastasis. From a clinical standpoint, this evidence strengthens the case for the use of TLR blockade as a potential therapeutic target in the prevention of 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 | 0.001 |
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".