Efficacy of EZH2 inhibitory drugs in human papillomavirus-positive and human papillomavirus-negative oropharyngeal squamous cell carcinomas
Bibliographic record
Abstract
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the sixth most prevalent cancer worldwide with rates of HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) dramatically increasing. The overexpression of enhancer of zeste homolog 2 (EZH2), a histone methyltransferase responsible for the trimethylation at lysine 27 of histone 3 (H3K27me3), is associated with a poor clinical prognosis and aggressive HPV-positive phenotypes. METHODS: We utilized three EZH2 pathway inhibitors, GSK-343, DZNeP, and EPZ-5687, and tested their efficacy in two HPV-positive and two HPV-negative OPSCC cell lines. RESULTS: Treatment with GSK-343 decreased H3K27me3 in all cell lines and treatment with DZNeP decreased H3K27me3 in only HPV-negative cell lines as determined by Western blot. Cells treated with EPZ-5687 displayed no appreciable change in H3K27me3. Epigenetic effect on gene expression was measured via ddPCR utilizing 11 target probes. Cells treated with DZNeP showed the most dramatic expressional changes, with decreased EGFR in HPV-positive cell lines and an overall increase in proliferation markers in HPV-negative cell lines. GSK-343-treated cells displayed moderate expressional changes, with CCND1 increased in HPV-positive cell lines and decreased TP53 in HPV-negative SCC-1. EPZ-5687-treated cell lines displayed few expressional changes overall. Only DZNeP-treated cells displayed anti-proliferative characteristics shown in wound-healing assays. CONCLUSIONS: Our findings suggest that EZH2 inhibitors are a viable therapeutic option for the role of epigenetic effect, potentially sensitizing tumors to current chemotherapies or limiting cell differentiation.
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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 | 0.000 |
| 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) | 0.002 | 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".