On-target off-tumor toxicity from HER2-targeting chimeric antigen receptor (CAR) engineered T cell therapy: current solutions
Bibliographic record
Abstract
One of the biggest threats to women’s lives and health is breast cancer, with HER2+ breast cancer accounting for a significant proportion of cases. This subtype is characterized by aggressive behavior, a high recurrence rate, and generally poor prognosis. While traditional HER2-CAR-T cell therapy has proven to show great success in treating HER2+ breast cancer, it carries the risk of on-target off-tumor toxicity, which could be life-threatening for patients. This review outlines the challenges associated with traditional HER2-CAR-T cell therapy and explores current strategies aimed at mitigating on-target off-tumor toxicity. The review categorizes these strategies into three main approaches, providing a comprehensive overview to help the medical and research community better understand the current state and future directions of HER2-CAR-T cell therapy. By discussing these approaches and the underlying mechanisms that make them effective, this review aims to inspire further innovation in improving existing HER2-CAR-T cell therapies. A thorough understanding of the current challenges and promising avenues for enhancement in HER2-CAR-T cell therapy is essential for advancing future research and clinical applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".