Monensin Inhibits Epidermal Growth Factor Receptor Trafficking and Activation: Synergistic Cytotoxicity in Combination with EGFR Inhibitors
Why this work is in the frame
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Bibliographic record
Abstract
Targeting the EGFR, with inhibitors such as erlotinib, represents a promising therapeutic option in advanced head and neck squamous cell carcinomas (HNSCC). However, they lack significant efficacy as single agents. Recently, we identified the ability of statins to induce synergistic cytotoxicity in HNSCC cells through targeting the activation and trafficking of the EGFR. However, in a phase I trial of rosuvastatin and erlotinib, statin-induced muscle pathology limited the usefulness of this approach. To overcome these toxicity limitations, we sought to uncover other potential combinations using a 1,200 compound screen of FDA-approved drugs. We identified monensin, a coccidial antibiotic, as synergistically enhancing the cytotoxicity of erlotinib in two cell line models of HNSCC, SCC9 and SCC25. Monensin treatment mimicked the inhibitory effects of statins on EGFR activation and downstream signaling. RNA-seq analysis of monensin-treated SCC25 cells demonstrated a wide array of cholesterol and lipid synthesis genes upregulated by this treatment similar to statin treatment. However, this pattern was not recapitulated in SCC9 cells as monensin specifically induced the expression of activation of transcription factor (ATF) 3, a key regulator of statin-induced apoptosis. This differential response was also demonstrated in monensin-treated ex vivo surgical tissues in which HMG-CoA reductase expression and ATF3 were either not induced, induced singly, or both induced together in a cohort of 10 patient samples, including four HNSCC. These results suggest the potential clinical utility of combining monensin with erlotinib in patients with HNSCC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it