Mechanisms Underlying the Action and Synergism of Trastuzumab and Pertuzumab in Targeting HER2-Positive Breast Cancer
Bibliographic record
Abstract
Human epidermal growth factor receptor (HER) 2 (HER2) is overexpressed in 20⁻30% of breast cancers. HER2 is a preferred target for treating HER2-positive breast cancer. Trastuzumab and pertuzumab are two HER2-targeted monoclonal antibodies approved by the Food and Drug Administration (FDA) to use as adjuvant therapy in combination with docetaxel to treat metastatic HER2-positive breast cancer. Adding the monoclonal antibodies to treatment regimen has changed the paradigm for treatment of HER2-positive breast cancer. Despite improving outcomes, the percentage of the patients who benefit from the treatment is still low. Continued research and development of novel agents and strategies of drug combinations is needed. A thorough understanding of the molecular mechanisms underlying the action and synergism of trastuzumab and pertuzumab is essential for moving forward to achieve high efficacy in treating HER2-positive breast cancer. This review examined and analyzed findings and hypotheses regarding the action and synergism of trastuzumab and pertuzumab and proposed a model of synergism based on available information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".