Response of Some Head and Neck Cancers to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors May Be Linked to Mutation of<i>ERBB2</i>rather than<i>EGFR</i>
Bibliographic record
Abstract
PURPOSE: Small-molecule tyrosine kinase inhibitors (TKI) of the epidermal growth factor receptor (EGFR) have shown modest yet reproducible response rates in patients with squamous cell carcinoma of the head and neck (SCCHN). Somatic mutations in EGFR have recently been shown to be predictive of a clinical response in patients with non-small cell lung cancer (NSCLC) treated with these inhibitors. The objective of this study was to determine if such mutations, or recently reported mutations in ERBB2, also underlie EGFR-TKI responsiveness in SCCHN patients. EXPERIMENTAL DESIGN: We sequenced the kinase domain of EGFR and exon 20 of ERBB2 in tumor specimens from eight responsive patients. In addition, mutational analysis was done on tumor specimens from nine gefitinib nonresponders and 65 unselected cases of SCCHN. RESULTS: None of eight TKI-responsive specimens had mutations within the kinase domain of EGFR. EGFR amplification was also not associated with drug responsiveness. However, a single responsive case had a somatic missense mutation within exon 20 of ERBB2. CONCLUSION: Our data indicate that unlike NSCLC, EGFR kinase mutations are rare in unselected cases of SCCHN within the United States and are not linked to gefitinib or erlotinib responses in SCCHN. Alternative mechanisms, including ERBB2 mutations, may underlie responsiveness in this tumor type.
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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 | 0.001 |
| 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) | 0.002 | 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".