Toll-like receptor 3 in Epstein-Barr virus-associated nasopharyngeal carcinomas: consistent expression and cytotoxic effects of its synthetic ligand poly(A:U) combined to a Smac-mimetic
Bibliographic record
Abstract
BACKGROUND: Nasopharyngeal carcinomas (NPC) are consistently associated with the Epstein-Barr virus (EBV). Though NPCs are more radiosensitive and chemosensitive than other tumors of the upper aero-digestive tract, many therapeutic challenges remain. In a previous report, we have presented data supporting a possible therapeutic strategy based on artificial TLR3 stimulation combined to the inhibition of the IAP protein family (Inhibitor of Apoptosis Proteins). The present study was designed to progress towards practical applications of this strategy pursuing 2 main objectives: 1) to formally demonstrate expression of the TLR3 protein by malignant NPC cells; 2) to investigate the effect of poly(A:U) as a novel TLR3-agonist more specific than poly(I:C) which was used in our previous study. METHODS: TLR3 expression was investigated in a series of NPC cell lines and clinical specimens by Western blot analysis and immunohistochemistry, respectively. The effects on NPC cells growth of the TLR3 ligand poly(A:U) used either alone or in combination with RMT5265, an IAP inhibitor based on Smac-mimicry, were assessed using MTT assays and clonogenic assays. RESULTS: TLR3 was detected at a high level in all NPC cell lines and clinical specimens. Low concentrations of poly(A:U) were applied to several types of NPC cells including cells from the C17 xenograft which for the first time have been adapted to permanent propagation in vitro. As a single agent, poly(A:U) had no significant effects on cell growth and cell survival. In contrast, dramatic effects were obtained when it was combined with the IAP inhibitor RMT5265. These effects were obtained using concentrations as low as 0.5 μg/ml (poly(A:U)) and 50 nM (RMT5265). CONCLUSION: These data confirm that TLR3 expression is a factor of vulnerability for NPC cells. They suggest that in some specific pathological and pharmacological contexts, it might be worth to use Smac-mimetics at very low doses, allowing a better management of secondary effects. In light of our observations, combined use of both types of compounds should be considered for treatment of nasopharyngeal carcinomas.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".