Novel Therapies for the Treatment of HER2-Positive Advanced Breast Cancer: A Canadian Perspective
Bibliographic record
Abstract
The advent of anti-HER2 targeted therapies has dramatically improved the outcome of HER2-positive breast cancer; however, resistance to treatment in the metastatic setting remains a challenge, highlighting the need for novel therapies. The arrival of new treatment options and clinical trials examining the efficacy of novel agents may improve outcomes in the metastatic setting, including in patients with brain metastases. In the first-line setting, we can potentially cure a selected number of patients treated with pertuzumab + trastuzumab + taxane. In the second-line setting, clinical trials show that trastuzumab deruxtecan (T-DXd) is a highly effective option, resulting in a shift from trastuzumab emtansine (T-DM1) as the previous standard of care. Moreover, we now have data for patients with brain metastases to show that tucatinib + trastuzumab + capecitabine can improve survival in this higher-risk group and be an effective regimen for all patients in the third-line setting. Finally, we have a number of effective anti-HER2 therapies that can be used in subsequent lines of therapy to improve patient outcomes. This review paper discusses the current treatment options and presents a practical treatment sequencing algorithm in the context of the Canadian landscape.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".