Long-term hazard of recurrence in HER2+ breast cancer patients untreated with anti-HER2 therapy
Bibliographic record
Abstract
INTRODUCTION: Worldwide, many patients with HER2+ (human epidermal growth factor receptor 2-positive) early breast cancer (BC) do not receive adjuvant trastuzumab. Hazards of recurrence of these patients with respect to hormone receptor status of the primary tumor have not been described. METHODS: Using data from 1,260 patients randomized to placebo in the adjuvant TEACH trial, we report 10-year annual hazards of recurrence in HER2+ patients not treated with anti-HER2 therapy. RESULTS: Disease-free survival (DFS) was 75% after 5 and 61% after 10 years, respectively. Patients with HER2+ hormone receptor-positive (HR+ (hormone receptor-positive); ER+ (estrogen receptor-positive) or PR+ (progesterone receptor-positive)) disease had a significantly better DFS than patients with HER2+ HR- (ER-/PR-) disease (hazard ratio 0.72, P=0.02). This difference was explainable by a significantly higher hazard of recurrence in years 1 to 5 in HER2+ HR- compared to HER2+ HR+ patients, with a mean risk of recurrence of 9%/year for HR- versus 5%/year in HR+ patients (hazard ratio 0.59, P=0.002 for years 1 to 5). The high early risk of recurrence of HER2+ HR- patients declined sharply over time, so that it was similar to that seen in HER2+ HR+ patients in years 6 to 10 (hazard ratio 0.97, P=0.92 for years 6 to 10). CONCLUSIONS: Our results show that outcomes in HER2+ patients with early BC not receiving anti-HER2 therapy strongly depend on HR expression. The very high early risk of relapse seen in HER2+ HR- patients is particularly relevant in health care settings with limited access to adjuvant anti-HER2 treatment. The event rates shown for subpopulations of HER2+ BC patients suggest that in resource-constrained environments patients with HER2+ HR- early BC should be prioritized for consideration of adjuvant anti-HER2 therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".