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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".