Real-World Predictors of Survival in CDK4/6 Inhibitor-Treated Metastatic Breast Cancer: The Significance of ER Expression Level and Treatment Naivety
Bibliographic record
Abstract
Objective: CDK4/6 inhibitors constitute standard first-line therapy for hormone receptor (HR)-positive, HER2-negative metastatic breast cancer (MBC). We investigated real-world predictors of overall survival (OS), with particular focus on high ER expression (≥90%). Methods: In this multicenter, retrospective study, we analyzed 603 HR-positive/HER2-negative MBC patients treated with CDK4/6 inhibitors (ribociclib or palbociclib) between May 2020 and June 2024. We evaluated demographic, clinical, and pathological factors for their impact on OS using univariate and multivariate Cox regression analyses. Results: In univariate analysis, significantly longer OS was observed in endocrine therapy-naive patients (median OS: 51.0 vs. 33.3 months; p < 0.001), those without liver metastases (50.0 vs. 34.0 months; p = 0.019), bone-only metastases (57.7 vs. 40.5 months; p = 0.022), and PR-positive patients (50.0 vs. 36.0 months; p = 0.037). Patients with ER expression ≥90% showed a strong trend toward longer OS (49.0 vs. 41.0 months; p = 0.072). In multivariate analysis, endocrine therapy naivety (p = 0.045) and high ER expression (≥90%) (p = 0.031) emerged as independent predictors of superior OS. Conclusions: Our study identifies treatment naivety and exceptionally high ER expression (≥90%) as key independent predictors of prolonged OS in CDK4/6 inhibitor-treated MBC patients. These findings underscore the importance of early CDK4/6 inhibitor implementation and suggest that quantitative ER assessment may refine patient selection beyond conventional positivity thresholds.
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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 | 0.000 | 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".