Baseline Characteristics, Treatment Patterns, and Outcomes in Patients with HER2-Positive Metastatic Breast Cancer by Hormone Receptor Status from SystHERs
Bibliographic record
Abstract
Abstract Purpose: We report treatments and outcomes in a contemporary patient population with HER2-positive metastatic breast cancer (MBC) by hormone receptor (HR) status from the Systemic Therapies for HER2-positive Metastatic Breast Cancer Study (SystHERs). Experimental Design: SystHERs (NCT01615068) was an observational, prospective registry study of U.S.-based patients with newly diagnosed HER2-positive MBC. Endpoints included treatment patterns and clinical outcomes. Results: Of 977 eligible patients (enrolled from 2012 to 2016), 70.1% (n = 685) had HR-positive and 29.9% (n = 292) had HR-negative disease. Overall, 59.1% (405/685) of patients with HR-positive disease received any first-line endocrine therapy (with or without HER2-targeted therapy or chemotherapy); 34.9% (239/685) received HER2-targeted therapy + chemotherapy + sequential endocrine therapy. Patients with HR-positive versus HR-negative disease had longer median overall survival (OS; 53.0 vs 43.4 months; hazard ratio, 0.70; 95% confidence interval, 0.56–0.87). Compared with patients with high HR-positive staining (10%–100%, n = 550), those with low HR-positive staining (1%–9%, n = 60) received endocrine therapy less commonly (64.2% vs 33.3%) and had shorter median OS (53.8 vs 40.1 months). Similar median OS (43.4 vs 40.1 months) was observed in patients with HR-negative versus low HR-positive tumors (1%–9%). Conclusions: Despite evidence that first-line HER2-targeted therapy, chemotherapy, and sequential endocrine therapy improves survival in patients with HR-positive, HER2-positive disease, only 34.9% of patients in this real-world setting received such treatment. Patients with low tumor HR positivity (1%–9%) had lower endocrine therapy use and worse survival than those with high tumor HR positivity (10%–100%).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".