Pertuzumab in human epidermal growth-factor receptor 2-positive breast cancer: clinical and economic considerations
Bibliographic record
Abstract
Nathan WD Lamond, Tallal YounisDepartment of Medicine, Dalhousie University at the Queen Elizabeth II Health Sciences Centre, Halifax, NS, CanadaAbstract: In the absence of specific therapy, the 15%–20% of breast cancers demonstrating human epidermal growth-factor receptor 2 (HER2) protein overexpression and/or gene amplification are characterized by a more aggressive phenotype and poorer prognosis compared to their HER2-negative counterparts. Trastuzumab (Herceptin), the first anti-HER2-targeted therapy, has been associated with improved survival outcomes in HER2-positive breast cancer. However, many patients with early stage disease continue to relapse, and metastatic disease remains incurable. In order to further improve these outcomes, several novel HER2-targeted agents have recently been developed. Pertuzumab (Perjeta), a monoclonal antibody against the HER2 dimerization domain, has also been associated with improved patient outcomes in clinical trials, and has recently been approved in combination with chemotherapy and trastuzumab for neoadjuvant therapy of early stage, HER2-positive breast cancer and first-line treatment of metastatic disease. This review briefly summarizes pertuzumab's clinical development as well as the published evidence supporting its use, and highlights some of the currently unanswered questions that will influence pertuzumab’s incorporation into clinical practice.Keywords: HER2/neu, clinical trials, drug development, novel therapies, targeted anticancer therapy
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".