Relationship of<i>EGFR</i>Mutations, Expression, Amplification, and Polymorphisms to Epidermal Growth Factor Receptor Inhibitors in the NCI60 Cell Lines
Bibliographic record
Abstract
PURPOSE: The mechanism of sensitivity and resistance to epidermal growth factor receptor (EGFR) inhibitors is incompletely understood, particularly in cancers other than non-small-cell lung cancer (NSCLC). To understand the variable response to this class of drugs, we used the NCI60 cancer cell lines. We aimed to determine if there are interactions between EGFR expression, mutations, polymorphisms, and gene amplification, and whether these factors are associated with variability in response to EGFR inhibitors. EXPERIMENTAL DESIGN: The EGFRVIII and tyrosine kinase (TK) domain mutations were examined in the NCI60 cancer cell lines. Five polymorphisms, -216G/T, -191C/A, intron 1 (CA)n, R497K, and 2607A/G, were genotyped. EGFR amplification was also assessed with high-density single-nucleotide polymorphism chip and real-time PCR, respectively. The results were correlated with cytotoxicity data for erlotinib and other 11 EGFR inhibitors, as well as other publicly available data for these lines. RESULTS: All 12 inhibitors behaved similarly. No EGFRVIII but putative TK mutations in two cell lines were found. Both mutant cell lines were insensitive to all inhibitors. Meanwhile, response did not correlate with EGFR amplification but with EGFR gene expression, especially in the cell lines with relatively normal gene status. In addition, EGFR expression was associated with the -216G/T polymorphism but not with the intron 1 (CA)n polymorphism. A combination of -216G/T and R497K polymorphisms was weakly associated with drug response. CONCLUSIONS: These observations suggest that in addition to TK mutations, germ-line variability may also contribute to the pharmacodynamics of EGFR inhibitors, particularly when EGFR is genetically normal.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".