Navigating Treatment Sequencing in Advanced HR+/HER2− Breast Cancer After CDK4/6 Inhibitors: Biomarker-Driven Strategies and Emerging Therapies
Bibliographic record
Abstract
Breast cancer remains a major global health challenge. In 2022, there were an estimated 2.3 million new cases and 670,000 deaths among women worldwide. Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer accounts for approximately 70% of breast cancer diagnoses. The treatment landscape for advanced HR+)/HER2- breast cancer has been transformed by the introduction of CDK4/6 inhibitors in the first-line setting. However, therapeutic strategies following progression on CDK4/6 inhibitors remain heterogeneous and uncertainty exists in their optimal integration in clinical practice. This review aims to systematically examine available second-line and subsequent treatment options for HR+/HER2- metastatic breast cancer after progression on CDK4/6 inhibitors, with a focus on biomarker-driven strategies and emerging therapies. The therapeutic landscape beyond CDK4/6 inhibitors includes targeted agents guided by actionable biomarkers as well as novel selective estrogen receptor degraders (SERDs). In biomarker-unselected populations, options include CDK4/6 continuation strategies, endocrine monotherapy in selected cases, and cytotoxic therapy. The integration of molecular testing via next-generation sequencing has become standard of care in guiding these decisions. However, overlapping molecular alterations and a lack of consensus on treatment sequencing pose significant challenges. Prognostic factors such as circulating tumor DNA dynamics may further refine treatment personalization. Post-CDK4/6 therapy in HR+/HER2- metastatic breast cancer is an evolving and increasingly complex area of practice. Optimal treatment selection should be tailored to both tumor biology and patient-specific factors, supported by molecular testing and high-quality evidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".