From Intractable to Treatable: Milestones and Horizons in the Management of HER2+ Breast Cancer
Bibliographic record
Abstract
The human epidermal growth factor receptor 2 (HER2) is a member of the epidermal growth factor receptor (EGFR) family that initiates various signalling pathways that control cell proliferation and tumourigenesis. Historically, approximately 15% of breast cancers have been characterized by overexpression or amplification of HER2, known as “HER2+” breast cancers. This subtype has been associated with an adverse prognosis, along with a high risk of recurrence and worse survival outcomes. However, with the discovery and subsequent development of HER2‑targeted therapies, the clinical course of HER2+ breast cancers has fundamentally changed. Optimizing therapeutic strategies using existing and emerging HER2-targeted therapies to build upon these advances remains a major priority for clinical development and treatment delivery. In 1998, the American Food and Drug Administration (FDA) and Health Canada approved trastuzumab, the first HER2-targeted therapy. Trastuzumab, a monoclonal antibody that binds to the HER2 receptor, has demonstrated clinical activity and improved outcomes in patients with metastatic HER2+ breast cancer when combined with chemotherapy. Following soon after, the first trial of adjuvant trastuzumab (HERA) demonstrated improvements in outcomes when combined with chemotherapy for early HER2+ breast cancer. More than 25 years after its first approval, trastuzumab retains a central role in the treatment of both early and advanced HER2+ breast cancer and has provided a backbone for both new therapeutic combinations (eg. with small molecule inhibitors of HER2) and new classes of therapeutic agents (antibody drug conjugates [ADC]). These successors of trastuzumab are currently redefining the HER2+ treatment landscape in both advanced and early breast cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".