Epidermal growth factor receptor mutation‐specific immunohistochemical antibodies in lung adenocarcinoma
Bibliographic record
Abstract
AIMS: We investigated the sensitivity and specificity of two novel Epidermal growth factor receptor (EGFR) mutation-specific antibodies in the detection of the most common EGFR mutations in lung adenocarcinoma. METHODS AND RESULTS: A total of 241 resected lung adenocarcinoma specimens and six resected post-neoadjuvant gefitinib adenocarcinomas were analysed for EGFR mutation using mass spectrometry, fragment analysis and direct PCR sequencing platforms. Tissue arrays and/or full sections of these cases were evaluated using immunohistochemistry with two novel antibodies (clones SP125 and SP111) and two previously reported antibodies (clones 43B2 and 6B6), specific for L858R or 15-nucleotide exon-19 deletion EGFR mutations. SP125 antibody detected EGFR L858R mutation with a sensitivity of 76% and positive predictive value of 73%. SP111 antibody stained the 15-nucleotide EGFR exon-19 deletions with a sensitivity of 83% and a positive predictive value of 94%. Pretreatment with gefitinib did not affect antibody performance. Full-section immunohistochemical staining detected heterogeneous mutant EGFR proteins expression in tumours, and revealed L858R mutation in the non-neoplastic bronchial epithelium adjacent to EGFR L858R-carrying carcinomas in three of 16 (19%) cases. CONCLUSIONS: Immunohistochemistry using EGFR mutant-specific antibodies may be useful in shortening the diagnostic time of lung adenocarcinoma with most common EGFR mutations, especially in samples with low tumour cellularity.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".