<i>MET</i> gene amplification or <i>EGFR</i> mutation activate MET in lung cancers untreated with EGFR tyrosine kinase inhibitors
Bibliographic record
Abstract
We analyzed MET protein and copy number in NSCLC with or without EGFR mutations untreated with EGFR tyrosine kinase inhibitors (TKIs). MET copy number was examined in 28 NSCLC and 4 human bronchial epithelial cell lines (HBEC) and 100 primary tumors using quantitative real-time PCR. Positive results were confirmed by array comparative genomic hybridization and fluorescence in-situ hybridization. Total and phospho-MET protein expression was determined in 24 NSCLC and 2 HBEC cell lines using Western blot. EGFR mutations were examined for exon 19 deletions, T790M, and L858R. Knockdown of EGFR with siRNA was performed to examine the relation between EGFR and MET activation. High-level MET amplification was observed in 3 of 28 NSCLC cell lines and in 2 of 100 primary lung tumors that had not been treated with EGFR-TKIs. MET protein was highly expressed and phosphorylated in all the 3 cell lines with high MET amplification. In contrast, 6 NSCLC cell lines showed phospho-MET among 21 NSCLC cell lines without MET amplification (p = 0.042). Furthermore, those 6 cell lines harboring phospho-MET expression without MET amplification were all EGFR mutant (p = 0.0039). siRNA-mediated knockdown of EGFR abolished phospho-MET expression in examined 3 EGFR mutant cell lines of which MET gene copy number was not amplified. By contrast, phospho-MET expression in 2 cell lines with amplified MET gene was not down-regulated by knockdown of EGFR. Our results indicated that MET amplification was present in untreated NSCLC and EGFR mutation or MET amplification activated MET protein in NSCLC.
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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".