The association between EGFR variant III, HPV, p16, c-MET, EGFR gene copy number and response to EGFR inhibitors in patients with recurrent or metastatic squamous cell carcinoma of the head and neck
Bibliographic record
Abstract
BACKGROUND: We examine the potential prognostic and predictive roles of EGFR variant III mutation, EGFR gene copy number (GCN), human papillomavirus (HPV) infection, c-MET and p16INK4A protein expression in recurrent or metastatic squamous cell carcinoma of the head and neck (R/M SCCHN). METHODS: We analyzed the archival tumor specimens of 53 patients who were treated in 4 phase II trials for R/M SCCHN. Two trials involved the EGFR inhibitor erlotinib, and 2 trials involved non-EGFR targeted agents. EGFRvIII mutation was determined by quantitative RT-PCR, HPV DNA by Linear Array Genotyping, p16 and c-MET protein expression by immunohistochemistry, and EGFR GCN by FISH. RESULTS: EGFRvIII mutation, detected in 22 patients (42%), was associated with better disease control, but no difference was seen between erlotinib-treated versus non-erlotinib treated patients. EGFRvIII was not associated with TTP or OS. The presence of HPV DNA (38%), p16 immunostaining (32%), c-MET high expression (58%) and EGFR amplification (27%), were not associated with response, TTP or OS. CONCLUSION: EGFRvIII mutation, present in about 40% of SCCHN, appears to be an unexpected prognostic biomarker associated with better disease control in R/M SCCHN regardless of treatment with erlotinib. Larger prospective studies are required to validate its significance.
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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) | 0.001 | 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".