Retrospective study to estimate the prevalence and describe the clinicopathological characteristics, treatments received, and outcomes of HER2-low breast cancer
Bibliographic record
Abstract
BACKGROUND: Approximately 80% of all breast cancers (BCs) are currently categorized as human epidermal growth factor receptor 2 (HER2)-negative [immunohistochemistry (IHC) 0, 1+, or 2+/in situ hybridization (ISH) negative]; approximately 60% of BCs traditionally categorized as HER2-negative express low levels of HER2. HER2-low (IHC 1+ or IHC 2+/ISH-) status became clinically actionable with approval of trastuzumab deruxtecan to treat unresectable/metastatic HER2-low BC. Greater understanding of patients with HER2-low disease is urgently needed. PATIENTS AND METHODS: This global, multicenter, retrospective study (NCT04807595) included tissue samples from patients with confirmed HER2-negative unresectable/metastatic BC [any hormone receptor (HR) status] diagnosed from 2014 to 2017. Pathologists rescored HER2 IHC-stained slides as HER2-low (IHC 1+ or IHC 2+/ISH-) or HER2 IHC 0 after training on low-end expression scoring using Ventana 4B5 and other assays at local laboratories (13 sites; 10 countries) blinded to historical scores. HER2-low prevalence and concordance between historical scores and rescores were assessed. Demographics, clinicopathological characteristics, treatments, and outcomes were examined. RESULTS: In rescored samples from 789 patients with HER2-negative unresectable/metastatic BC, the overall HER2-low prevalence was 67.2% (HR positive, 71.1%; HR negative, 52.8%). Concordance was moderate between historical and rescored HER2 statuses (81.3%; κ = 0.583); positive agreement was numerically higher for HER2-low (87.5%) than HER2 IHC 0 (69.9%). More than 30% of historical IHC 0 cases were rescored as HER2-low overall (all assays) and using Ventana 4B5. There were no notable differences between HER2-low and HER2 IHC 0 in patient characteristics, treatments received, or clinical outcomes. CONCLUSIONS: Approximately two-thirds of patients with historically HER2-negative unresectable/metastatic BC may benefit from HER2-low-directed treatments. Our data suggest that HER2 reassessment in patients with historical IHC 0 scores may be considered to help optimize selection of patients for treatment. Further, accurate identification of patients with HER2-low BC may be achieved with standardized pathologist training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".