HER2-Positive Breast Cancer Immunotherapy: A Focus on Vaccine Development
Bibliographic record
Abstract
Clinical progress in the field of HER2-positive breast cancer therapy has been dramatically improved by understanding of the immune regulatory mechanisms of tumor microenvironment. Passive immunotherapy utilizing recombinant monoclonal antibodies (mAbs), particularly trastuzumab and pertuzumab has proved to be an effective strategy in HER2-positive breast cancer treatment. However, resistance to mAb therapy and relapse of disease are still considered important challenges in clinical practice. There are increasing reports on the induction of cellular and humoral immune responses in HER2-positive breast cancer patients. More recently, increasing efforts are focused on using HER2-derived peptide vaccines for active immunotherapy. Here, we discuss the development of various HER2-derived vaccines tested in animal models and human clinical trials. Different formulations and strategies to improve immunogenicity of the antigens in animal studies are also discussed. Furthermore, other immunotherapeutic approaches to HER2 breast cancer including, CTLA-4 inhibitors, immune checkpoint inhibitors, anti PD-1/PD-L1 antibodies are presented.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".