Sequencing of Endocrine and Targeted Therapies in Hormone-Sensitive, Human Epidermal Growth Factor Receptor 2–Negative Advanced Breast Cancer
Bibliographic record
Abstract
The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology , to patients seen in their own clinical practice. Optimizing the selection and sequencing of endocrine and targeted therapies for hormone-sensitive, human epidermal growth factor receptor 2 (HER2)–negative advanced breast cancer is a rapidly evolving field owing in large part to an increasing pace of drug development coupled with a greater understanding of the genomic drivers of breast cancer. The recently published results from the MAINTAIN clinical trial begin to answer an important question in this patient population—can the well-established benefit with first-line cyclin-dependent kinase 4/6 (CDK 4/6) inhibitors be stretched further by continuing this drug class beyond progression and selecting an alternate endocrine therapy partner? We present a case of a patient with hormone-sensitive HER2 low metastatic breast cancer who underwent circulating tumor DNA next-generation sequencing to better inform her treatment options after progression on first-line therapy with a CDK 4/6 inhibitor and aromatase inhibitor. Our clinical approach in this patient population prioritizes the identification of actionable mutations with high-quality evidence for efficacy on the basis of clinical trials post-CDK 4/6 inhibitors, while balancing comorbidities and patient priorities for care. Several recent clinical trials discussed herein present clinically meaningful results linking emerging targeted therapies to actionable alterations in PIK3CA, ESR1, AKT1, and PTEN. Continued drug development in this space delays time to treatment with chemotherapy, and hopefully contributes to maintaining a high quality of life for these patients on primarily oral-based therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".