Clinicopathologic and molecular characterization of low-grade, early-stage, and HER2-positive invasive breast carcinoma
Bibliographic record
Abstract
OBJECTIVES: Breast carcinomas overexpressing human epidermal growth factor receptor 2 (HER2) are typically associated with higher tumor grade and faster progression. HER2 positivity is rare in low-grade breast carcinomas with unclear biological implications. We aimed to characterize their clinicopathologic and molecular profiles in this study. METHODS: There were 2 cohorts of Nottingham grade 1, HER2-positive invasive breast carcinomas examined: (1) an institutional series (n = 14) and (2) tumors from patients (n = 59) enrolled in the FLEX multicenter clinical registry with MammaPrint and BluePrint profiling. RESULTS: Most (79%) in the case series were both estrogen receptor (ER) and progesterone receptor (PR)-positive. Over half were pathologic or clinical T1N0 tumors. In the 9 cases with adequate material for next-generation sequencing, the majority (66%) demonstrated ERBB2 copy number variations. Most (66%) received HER2-targeted therapy. No recurrences were observed, with a median follow-up time of 43 months. In the FLEX cohort, most tumors were ER-positive (86%) and PR-positive (68%), and over half were clinical T1. Most (70%) were of the luminal phenotype, and over half (54%) were low-risk on MammaPrint. CONCLUSIONS: Low-grade HER2-positive breast carcinomas constitute mostly low-stage, luminal-type, and apparently low-risk tumors, warranting investigation into whether therapy de-escalation could achieve favorable outcomes with less toxicity in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".