EGFR, HER2 and HER3 expression in esophageal primary tumours and corresponding metastases
Bibliographic record
Abstract
The expression of EGFR, HER2 and HER3 receptors were analyzed in immunohistochemical preparations from primary esophageal tumours and corresponding lymph node metastases. The goal was to evaluate whether any of these receptors are suitable as targets for radionuclide based imaging and therapy. The receptor expressions were evaluated in parallel samples, primary tumour and metastasis, from each patient (n=51). The majority of the cases were esophageal squamous cell carcinomas, ESCC (n=40). The HercepTest scoring was used for the analysis of both HER2 and EGFR expression (0, 1+, 2+ or 3+). HER3 was only evaluated as negative, weak or strong staining. EGFR overexpression (2+/3+) was found in 67.5% (27/40) of both the ESCC primary tumours and the corresponding lymph node metastases. There were only a few changes in these EGFR-scores: two cases from 2+/3+ to 0/1+ when the primary tumours were compared to the corresponding metastases and 2 changes the other way around. HER2 overexpression (2+/3+) was found in only 3 of the primary ESCC tumours and 2 of the lymph node metastases. EGFR and HER2 stainings were found mainly in the cell membranes. The HER3 staining (weak or strong) was mainly cytoplasmic and granular and was observed in about half (20/39) of the cases, for both the ESCC primary tumours and the corresponding lymph node metastases. It was concluded that ESCC lymph node metastases generally have a strong expression of EGFR in their cell membranes and to the same extent as in the primary tumours. The stability in EGFR expression is encouraging for efforts to develop radionuclide based EGFR imaging agents. It is also possible that EGFR targeting agents (e.g. Iressa, Tarceva, Erbitux or radiolabelled antibodies) can be applied for therapy of ESCC.
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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 | 0.000 |
| 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) | 0.002 | 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".