Recent treatment advances in HER2-positive metastatic breast cancer: a clinical approach
Bibliographic record
Abstract
The use of targeted therapy directed against HER2 is currently the standard of care in patients with metastatic HER2-positive breast cancer. The combination of trastuzumab with a taxane as first-line treatment in HER2-positive metastatic breast cancer patients is the most common therapeutic approach in this population. The combination of trastuzumab with other chemotherapeutic agents, including vinorelbine and capecitabine; and hormonal therapy agents, such as aromatase inhibitors, have also demonstrated significant activity, and may be considered as an option for selected patients. Recently, the addition of pertuzumab to trastuzumab and docetaxel in first-line therapy has demonstrated an increased progression-free survival in HER2-positive metastatic breast cancer patients. Novel strategies against HER2 in first-line treatment or after progression include HER tyrosine kinase inhibitors such as lapatinib in combination with either chemotherapy, aromatase inhibitors or trastuzumab. An increasing list of new compounds are currently under investigation, such as trastuzumab–emtansine, afatinib, everolimus and antiangiogenic agents, among others. This review discusses potential therapeutic approaches in the first-line setting and after progression beyond trastuzumab in metastatic breast cancer HER2-positive tumors based on the latest 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".