The Role of her2-Targeted Therapies in Women with her2-Overexpressing Metastatic Breast Cancer
Bibliographic record
Abstract
The role of targeted therapies in the treatment of women with breast cancer has been rapidly evolving. Trastuzumab, a monoclonal antibody against the human epidermal growth factor receptor 2 (HER2), was the first HER2-targeted therapy that clearly demonstrated a significant clinical benefit for women with HER2-overexpressing metastatic breast cancer (mbc). However, in recent years it has become increasingly apparent that, when trastuzumab is used in the first-line setting in combination with chemotherapy, most women eventually develop progressive disease. Determining the treatment options available to women who have progressed while on trastuzumab therapy has been hampered by a paucity of high-quality published data. In addition, with the standard use of trastuzumab in the adjuvant setting (for eligible HER2-positive patients), the role of anti-HER2 agents for patients who experience a breast cancer relapse has become a clinically relevant question. This manuscript reviews current available data and outlines suggestions from a panel of Canadian oncologists about the use of trastuzumab and other HER2-targeted agents in two key mbc indications:Treatment for women with HER2-positive mbc progressing on trastuzumab (that is, treatment beyond progression)Treatment for women with HER2-positive mbc recurring following adjuvant trastuzumab (that is, re-treatment)The suggestions set out here will continue to evolve as data and future trials with trastuzumab and other HER2-targeted agents emerge.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".