HER2 expression in breast cancer primary tumours and corresponding metastases. Original data and literature review
Bibliographic record
Abstract
The aim of this study was to evaluate whether the HER2 expression in breast cancer is retained in metastases. The HER2 expression in primary tumours and the corresponding lymph node metastases were evaluated in parallel samples from 47 patients. The HercepTest was used for immunohistochemical analyses of HER2 overexpression in all cases. CISH/FISH was used for analysis of gene amplification in some cases. HER2 overexpression (HER2-scores 2+ or 3+) was found in 55% of both the primary tumours and of the lymph node metastases. There were only small changes in the HER2-scores; six from 1+ to 0 and one from 3+ to 2+ when the metastases were compared to the corresponding primary tumours. However, there were no cases with drastic changes in HER2 expression between the primary tumours and the corresponding lymph node metastases. The literature was reviewed for similar investigations, and it is concluded that breast cancer lymph node metastases generally overexpress HER2 to the same extent as the corresponding primary tumours. This also seems to be the case when distant metastases are considered. It has been noted that not all patients with HER2 overexpression respond to HER2-targeted Trastuzumab treatment. The stability in HER2 expression is encouraging for efforts to develop complementary forms of therapy, for example, therapy with radionuclide-labelled Trastuzumab.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".