Clinicopathologic Features and Genomic Profile of Human Epidermal Growth Factor Receptor 2–Low and Human Epidermal Growth Factor Receptor 2–Ultralow Invasive Breast Carcinomas
Bibliographic record
Abstract
Context.—: Recent clinical trials have identified significant benefits of human epidermal growth factor receptor 2 (HER2)-targeting antibody conjugates in invasive breast carcinomas with HER2-low and HER2-ultralow expression, challenging the conventional binary HER2 status. Objective.—: To examine the clinicopathologic features and genomic profile of HER2-low and HER2-ultralow invasive breast carcinomas. Design.—: Two hundred thirteen cases were identified with HER2 immunohistochemistry (IHC) reported as 0, 1+, and 2+/in situ hybridization-negative with Oncotype DX results from 2017-2022. One hundred seventy-eight cases with hematoxylin-eosin and HER2 slides available were independently scored by 5 pathologists blinded to the reported HER2 results as HER2 0, 0-1, 1+, and 2+, using light microscopy. For each HER2 IHC score, patient age, tumor characteristics, and HER2 mRNA expression scores were compared. Additionally, each hormone receptor IHC score was compared to its respective mRNA expression scores. Results.—: The overall interobserver agreement of HER2 IHC scoring was substantial, with a κ value of 0.689 (0.658-0.710; P < .001). There was no statistically significant difference in age and tumor characteristics by HER2 IHC scores. HER2 IHC scores were significantly associated with median HER2 mRNA expression scores (P < .001). However, for all 3 biomarkers, significant overlaps in mRNA expression scores existed between the different IHC scores. Conclusions.—: In our study, there were no significant differences in clinicopathologic features among HER2 IHC scores. In addition, there was considerable overlap in HER2 and hormone receptor mRNA scores across different IHC categories, limiting their utility as predictors of HER2 and hormone receptor IHC scores.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".