Targeting the Mevalonate Pathway Inhibits the Function of the Epidermal Growth Factor Receptor
Bibliographic record
Abstract
PURPOSE: The epidermal growth factor receptor (EGFR) is a key regulator of growth, differentiation, and survival of epithelial cancers. In a small subset of tumors, the presence of activating mutations within the ATP binding site confers increased susceptibility to gefitinib, a potent tyrosine kinase inhibitor of EGFR. Agents that can inhibit EGFR function through different mechanisms may enhance gefitinib activity in patients lacking these mutations. Mevalonate metabolites play significant roles in the function of the EGFR; therefore, mevalonate pathway inhibitors may potentiate EGFR-targeted therapies. EXPERIMENTAL DESIGN: In this study, we evaluated the effect of lovastatin on EGFR function and on gefitinib activity. Effects on EGFR function were analyzed by Western blot analysis using phosphospecific antibodies to EGFR, AKT, and extracellular signal-regulated kinase. Cytotoxic effects of lovastatin and/or gefitinib were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and flow cytometry. RESULTS: Lovastatin treatment inhibited EGF-induced EGFR autophosphorylation by 24 hours that was reversed by the coadministration of mevalonate. Combining lovastatin and gefitinib treatments showed enhanced inhibition of AKT activation by EGF in SCC9 cells. The combination of 10 mumol/L lovastatin and 10 mumol/L gefitinib treatments showed cooperative cytotoxicity in all 8 squamous cell carcinomas, 4 of 4 non-small cell lung carcinoma and 4 of 4 colon carcinoma cell lines tested. Isobologram and flow cytometric analyses of three representative cell lines with wild-type EGFR ATP binding sites confirmed that this combination was synergistic inducing a potent apoptotic response. CONCLUSIONS: Taken together, these results show that targeting the mevalonate pathway can inhibit EGFR function. They also suggest the potential utility of combining these clinically relevant therapeutic approaches.
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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".