Toll-like receptor 4 inhibition sensitizes non-small cell lung cancer to radiotherapy
Bibliographic record
Abstract
Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Although radiotherapy (RT) is used to treat over half of NSCLC patients, about 30% have inherent or acquired radioresistance leading to treatment failure. There's a clinically unmet need to investigate mechanisms of radioresistance that can be targeted in combination with RT. Among these, HMGB1 has been shown to play a key role in tumor progression. Our research investigates TLR4, a receptor for HMGB1, highly expressed in NSCLC tissues, as a mediator of radioresistance.Methods The TLR4 inhibitor, TAK242, was tested in NSCLC cell lines (murine: LLCI, KLN205; human: H1975, SW900). Cells were irradiated at 2 and 10 Gy. In vivo, KLN205 cells were implanted in DBA/2 mice and tumors were irradiated at 10Gy. Gene and protein expression of TLR4 and MyD88 were assessed in vitro and in vivo. HMGB1 secretion was quantified after RT. Clonogenic assays were performed to evaluate the effect of TAK242 on radiosensitivity in vitro. The combination of TAK242 and RT was investigated in vivo in mice bearing KLN205 tumors.Results TAK242 significantly decreased NSCLC cell proliferation and migration. Radiation at 2 and 10 Gy increased TLR4 gene expression in vitro and in vivo in a dose-dependent manner. In vitro, TLR4 and HMGB1 protein expression was upregulated following radiation. TAK242 in combination with radiation enhanced radiosensitivity in vitro. TAK242 decreased the percentage of cells in the G1 phase, coupled with an increase in late S and G2/M, suggesting radiosensitization via cell cycle modulation. In vivo, the combination of RT and TAK242 significantly reduced growth of KLN205 tumors.Conclusion These findings show that TLR4 inhibition enhances RT sensitivity in NSCLC.
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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) | 0.001 | 0.001 |
| 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".