Abstract A53: Lovastatin inhibits EGFR dimerization and AKT activation in squamous cell carcinoma cells: Potential regulation through targeting rho proteins
Bibliographic record
Abstract
Abstract We recently demonstrated the ability of lovastatin to inhibit the function of the epidermal growth factor receptor (EGFR) and its downstream signaling of the PI3K/AKT pathway. Combining lovastatin with gefitinib, a potent EGFR inhibitor, induced synergistic cytotoxicity in various tumor-derived cell lines. In this study, lovastatin treatment inhibits ligand-induced EGFR dimerization in squamous cell carcinoma (SCC) cells and its activation of AKT and its downstream targets 4EBP1 and S6K1. This inhibition was associated with global protein translational inhibition demonstrated by a decrease in RNA-associated polysome fractions. The effects of lovastatin on EGFR function were reversed by the addition of the geranylgeranyl pyrophosphate that acts as a protein membrane anchor. Lovastatin treatment induced actin cytoskeletal disorganization and the expression of the geranylgeranylated rho family proteins that regulate the actin cytoskeleton, including rhoA. Lovastatin-induced rhoA was inactive as EGF stimulation failed to activate rhoA and inhibition of the rho-associated kinase, a target and mediator of rhoA function, with Y-27632 also showed inhibitory effects on EGFR dimerization. The ability of lovastatin to inhibit EGFR dimerization is a novel exploitable mechanism regulating this therapeutically relevant target. To assess the potential of this approach, we evaluated the effect of statin use in patients enrolled in the BR21 erlotinib phase III study in non-small cell carcinoma (NSCLC) patients. In the erlotinib arm of this trial, although not statistically significant due to the limited number of patients on statins, in general, patients that were on statins performed better than patients without statins. Citation Information: Clin Cancer Res 2010;16(14 Suppl):A53.
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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 | 0.001 |
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".