HER2 Expression Discordance between Ductal Carcinoma In situ and Invasive Breast Carcinoma. How to Analyze Oncotype DX
Bibliographic record
Abstract
Although histologically invasive breast carcinomas can be of no special subtype (ductal) or of special subtypes (as lobular carcinoma), based on their immunohistochemical and molecular features, they are subclassified in four different groups: luminal A, luminal B (with and without HER2 overexpression), HER2 subtype and triple negative. They vary in their gene expression signature, biological potential and clinical course. Luminal A subtype is considered to have a better prognosis and most are treated with hormone therapy alone after surgery because they express hormone receptors and show a low proliferation index. Oncotype DX (ODX) is a molecular score assay that estimates recurrence risk for early-stage hormone receptor-positive, human epidermal growth factor receptor 2 (HER2) negative breast cancer. It can predict which tumours may benefit from adjuvant chemotherapy. It has been reported that occasionally a breast carcinoma can have immunohistochemical and molecular differences between the in situ and the invasive components. We report one case that may lead us to misinterpret ODX results, where the in situ ductal component amplified HER2 gene while invasive component did not. Therefore, carefulness must be taken when evaluating ODX results, and be sure that we are evaluating the invasive component.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".