Evolving Role of Risk Tailored Therapy in Early Stage HER2-Positive Breast Cancer: A Canadian Perspective
Bibliographic record
Abstract
The advent of HER2-targeted therapies has led to an important shift in the management of HER2-positive early breast cancer. However, initial treatment approaches apply uniform treatment regimens to all patients, with significant treatment-related and financial toxicities for both the patient and the health care system. Recent data demonstrates that for many patients, the chemotherapy backbone, duration and nature (mono- versus dual-targeted therapy) of the HER2 blockade can be better targeted to an individual patient's risk of recurrence. We will provide a review of current data supporting risk tailored therapy in early stage HER2-positive breast cancer along with key completed and ongoing Canadian and international risk tailored trials. Neoadjuvant systemic therapy should now be considered for patients with clinical stage 2 disease, with greater use of non-anthracycline based chemotherapy regimens. Patients with residual disease following neoadjuvant therapy should be considered for escalated treatment with adjuvant T-DM1. Patients with stage I disease can often be managed with upfront surgery and evidence-based de-escalated adjuvant chemotherapy regimens. The modest benefit of 12- versus 6 months of adjuvant HER2 therapy and/or dual adjuvant HER2 therapy should be carefully weighed against the toxicities. All patients with HER2-positive breast cancer should be enrolled in ongoing risk tailored treatment trials whenever possible. Increasing data supports risk tailored therapy in early stage HER2-positive breast cancer in place of the routine application of aggressive and toxic systemic therapy regimens to all patients. While much progress has been made towards treatment de-escalation in appropriate patients, more is needed, as we highlight in this review. Indeed, Canadian-led clinical trials are helping to lead these efforts.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".