Effect of an epidermal growth factor receptor tyrosine kinase inhibitor on actin remodeling in an<i>in vitro</i>bladder cancer carcinogenesis model
Bibliographic record
Abstract
Alteration of actin remodeling is a marker of malignant-associated field defect and a potential surrogate biomarker for chemoprevention trials. We tested erlotinib, a specific tyrosine kinase inhibitor of epidermal growth factor receptor (EGFR), on actin remodeling in a bladder carcinogenic model consisting of untransformed HUC-PC cells and transformed MC-T11 cells, both derived from the same normal human urothelial clone immortalized by SV40. Erlotinib had a selective growth inhibitory and actin remodeling effect on MC-T11 cells over HUC-PC cells, as examined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and immunofluorescence labeling with laser scan cytometer analysis, respectively. The IC(50) of untransformed HUC-PC cells was significantly higher than that of transformed MC-T11 cells (P < 0.05, t test). The actin remodeling effect was more prominent at lower dosage levels (1/8-1/4 of IC(50)), which was accompanied by an increased cell adhesion and decreased motility. At higher dosage levels (1/2 of IC(50)), erlotinib induced a decreased adhesion and anoikis (detachment-associated apoptosis). The transformed MC-T11, but not HUC-PC, showed a weak constitutive EGFR phosphorylation activity, which was inhibited by erlotinib in a dose-response manner. However, on epidermal growth factor stimulation, both cell lines showed a similar dose-response inhibitory effect on phosphorylated EGFR and mitogen-activated protein kinase (MAPK; P44/P42) activities, and MAPK inhibitor PD98059 showed no specific effect on erlotinib-induced actin remodeling, suggesting that pathways other than MAPK (P44/P42) may be responsible for erlotinib-induced actin remodeling. The findings provide evidence to support erlotinib-based bladder cancer chemoprevention and using actin remodeling as a marker for erlotinib-based intervention trials.
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How this classification was reachedexpand
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) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".