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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it