Gefitinib Inhibits the Growth and Invasion of Urothelial Carcinoma Cell Lines in which Akt and MAPK Activation Is Dependent on Constitutive Epidermal Growth Factor Receptor Activation
Bibliographic record
Abstract
PURPOSE: Abnormally high levels of epidermal growth factor receptor (EGFR) protein are associated with advanced tumor stage/grade. The objective of this study was to evaluate the effects of the specific EGFR tyrosine kinase inhibitor gefitinib on activation of the Akt and mitogen-activated protein kinase (MAPK) pathways in human urothelial cell carcinoma (UCC) cell lines and to identify potential markers of gefitinib responsiveness in biopsy samples of UCC. EXPERIMENTAL DESIGN: Changes in markers of UCC growth and invasion after exposure to gefitinib were studied in six human UCC cell lines expressing various levels of EGFR. The findings were related to activation of Akt and MAPK. We studied the influence of gefitinib on intraepithelial expansion of the responsive 1207 cell line. EGFR, Akt, and MAPK activation was studied by Western blot analysis of a panel of 57 human UCC. RESULTS: Gefitinib had a growth-inhibitory and anti-invasive effect in two of six UCC cell lines (i.e., 647V and 1207). Gefitinib was also able to block the expansion of 1207 at the expense of normal urothelial cells. These effects did not depend on the level of expression of EGFR but they were associated with the down-regulation of MAPK and Akt activity; in 1207 cells, gefitinib activity was associated with p27 up-regulation and p21 and matrix metalloproteinase-9 down-regulation. Similarly, the Akt and MAPK pathways were found to be strongly phosphorylated in association with EGFR activation in a subset of human UCC specimens. CONCLUSIONS: Activation of EGFR, Akt, and MAPK defines a subset of UCC which might provide information for the identification of gefitinib responders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".