Expert recommendations on treatment sequencing and challenging clinical scenarios in human epidermal growth factor receptor 2-positive (HER2-positive) metastatic breast cancer
Bibliographic record
Abstract
Human epidermal growth factor receptor 2 (HER2) overexpression and/or ERBB2 gene amplification occurs in approximately 15-20% of breast cancers and is associated with poor prognosis. While the introduction of HER2-targeted therapies has significantly improved survival in patients with HER2-positive metastatic breast cancer, the incidence of brain metastases has increased due to patients living longer. Current recommendations sequence treatments by line of therapy, as well as by the status of brain metastases in patients with HER2-positive breast cancer. However, in the third-line treatment setting and beyond, there is a lack of clarity of the preferred choice of therapy. In clinical practice, clinicians may also encounter challenging scenarios where the optimal therapeutic approach has not been defined by clinical studies, so there is a need for clarity in such situations. Two consensus meetings of expert oncologists (12 from Europe and one from Canada) were convened to discuss these scenarios. We subsequently developed this article to present an overview of current treatment recommendations for HER2-positive metastatic breast cancer and give practical guidance on addressing challenging scenarios in a real-world setting. Based on our clinical experience, we provide a unanimous consensus concerning the treatment of elderly patients as well as those with brain-only metastases, leptomeningeal disease, oligometastatic disease, central nervous system oligo-progressive disease or ERBB2-mutant disease. We also discuss how to combine HER2-targeted therapy with endocrine therapy in patients with HER2-positive/hormone-receptor-positive disease, considerations for potential discontinuation of HER2-targeted therapy in patients with long-term remission and how to treat patients whose metastatic biopsy no longer confirms their HER2-positive status.
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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 | 0.041 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".