Fluorescence in situ hybridization gene amplification analysis of EGFR and HER2 in patients with malignant salivary gland tumors treated with lapatinib
Bibliographic record
Abstract
BACKGROUND: Gene amplification status of the epidermal growth factor receptor (EGFR) and the human epidermal growth factor receptor 2 (HER2) were analyzed and correlated with clinical outcome in patients with progressive malignant salivary glands tumors (MSGT) treated with the dual EGFR/Her2 tyrosine kinase inhibitor lapatinib. METHODS: Fluorescence in situ hybridization (FISH) analysis for both EGFR and HER2 gene amplification was performed successfully in the archival tumor specimens of 20 patients with adenoid cystic carcinomas (ACC) and 17 patients with non-ACC, all treated with lapatinib. RESULTS: For ACC, no EGFR or HER2 amplifications were detected. For non-ACC, no EGFR gene amplifications were detected but 3 patients (18%) were HER2 amplified and all had stained 3+ for both EGFR and HER2 by immunohistochemistry (IHC) in their archival specimens. Two of these patients had time-to-progression (TTP) durations of 8.3 months and 18.4 months, respectively. Interestingly, patients with low and high HER2/chromosome-specific centromeric enumeration probe (CEP) 17 ratio had a prolonged TTP than those with moderate ratios for both ACC and non-AAC subtypes. CONCLUSIONS: HER2 to CEP17 FISH ratio may predict which patients with MSGT have an increased likelihood to benefit from lapatinib. The finding of HER2:CEP17 ratio as a predictive marker of efficacy to lapatinib warrants further investigation.
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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 | 0.001 | 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".