RAS transformation causes sustained activation of epidermal growth factor receptor and elevation of mitogen-activated protein kinase in human mammary epithelial cells
Bibliographic record
Abstract
Activation of the ras oncogene is an important step in carcinogenesis. Human MCF-10A mammary epithelial cells were transformed with a point-mutated form of the Ha-ras oncogene. Epidermal growth factor receptor (EGFR) phosphorylation levels were chronically elevated after EGF induction and the EGFR ligand-driven internalization rate was slower in Ha-ras transformed MCF-10A cells. Additionally, basal levels of p42/44 mitogen-activated protein kinase (MAPK) expression and enzyme activity were significantly higher in Ha-ras transformed cells, localized predominantly in the nucleus. The anti-EGFR monoclonal antibody (MAb) 225 and the EGFR tyrosine kinase inhibitor PD153035 blocked anchorage-independent growth of Ha-ras transformed cells in soft agar and were more effective when used in combination. The MEK inhibitor PD98059 and anti-erbB-2 MAb L26 also suppressed colony formation of Ha-ras transformed cells in soft agar. Therefore, Ha-ras transformation leads to an augmentation in signaling through the EGFR as a result of an increase in ligand-dependent phosphorylation, a decrease in its internalization and an up-regulation in basal p44/42 MAPK levels. These effects may contribute to uncontrolled growth of Ha-ras-transformed human mammary epithelial cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".