Human Epidermal Growth Factor Receptor 2 Testing in Primary Breast Cancer in the Era of Standardized Testing: A Canadian Prospective Study
Bibliographic record
Abstract
PURPOSE: Therapies that target overexpression of human epidermal growth factor receptor 2 (HER2) rely on accurate and timely assessment of all patients with new diagnoses. This study examines HER2 testing of primary breast cancer tissue when performed with immunohistochemistry (IHC) and additional in situ hybridization (ISH) for negative cases (IHC 0/1+). The analysis focuses on the rate of false-negative HER2 tests, defined as IHC 0/1+ with an ISH ratio ≥ 2.0, in eight pathology centers across Canada. PATIENTS AND METHODS: Whole sections of surgical resections or tissue microarrays (TMAs) from invasive breast carcinoma tissue were tested by both IHC and ISH using standardized local methods. Samples were scored by the local breast pathologist, and consecutive HER2-negative IHC results (IHC 0/1+) were compared with the corresponding fluorescence or silver ISH result. RESULTS: Overall, 711 surgical excisions of primary breast cancer were analyzed by IHC and ISH; HER2 and chromosome 17 centromere (CEP17) counts were available in all cases. The overall rate of false-negative samples was 0.84% (six of 711 samples). Interpretable IHC and ISH scores were available in 1,212 cases from TMAs, and the overall rate of false-negative cases was 1.6% (16 of 978 cases). CONCLUSION: Our observation confirms that IHC is an adequate test to predict negative HER2 status in primary breast cancer in surgical excision specimens, even when different antibodies and IHC platforms are used. The study supports the American Society of Clinical Oncology/College of American Pathologists and Canadian testing algorithms of using IHC followed by ISH for equivocal cases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".