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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".