Epidermal Growth Factor Receptor Inhibition Promotes Desmosome Assembly and Strengthens Intercellular Adhesion in Squamous Cell Carcinoma Cells
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) has been proposed as a key modulator of cadherin-containing intercellular junctions, particularly in tumors that overexpress this tyrosine kinase. Here the EGFR tyrosine kinase inhibitor PKI166 and EGFR blocking antibody C225, both of which are used clinically to treat head and neck cancers, were used to determine the effects of EGFR inhibition on intercellular junction assembly and adhesion in oral squamous cell carcinoma cells. EGFR inhibition resulted in a transition from a fibroblastic morphology to a more epithelial phenotype in cells grown in low calcium; under these conditions cadherin-mediated cell-cell adhesion is normally reduced, and desmosomes are absent. The accumulated levels of desmoglein 2 (Dsg2) and desmocollin 2 increased 1.7-2.0-fold, and both desmosomal cadherin and plaque components were recruited to cell-cell borders. This redistribution was paralleled by an increase in Dsg2 and desmoplakin in the Triton-insoluble cell fraction, suggesting that EGFR blockade promotes desmosome assembly. Importantly, E-cadherin expression and solubility were unchanged. Furthermore, PKI166 blocked tyrosine phosphorylation of Dsg2 and plakoglobin following epidermal growth factor stimulation, whereas no change in phosphorylation was detected for E-cadherin and beta-catenin. The increase in Dsg2 protein was in part due to the inhibition of matrix metalloproteinase-dependent proteolysis of this desmosomal cadherin. These morphological and biochemical changes were accompanied by an increase in intercellular adhesion based on functional assays at all calcium concentrations tested. Our results suggest that EGFR inhibition promotes desmosome assembly in oral squamous cell carcinoma cells, resulting in increased cell-cell adhesion.
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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 | 0.000 |
| 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".