Therapeutic potential of CAR-T cell-derived exosomes: a cell-free modality for targeted cancer therapy
Bibliographic record
Abstract
// Xiang-Jun Tang 1,* , Xu-Yong Sun 2,* , Kuan-Ming Huang 1,* , Li Zhang 1 , Zhuo-Shun Yang 1 , Dan-Dan Zou 1 , Bin Wang 1,3 , Garth L. Warnock 4 , Long-Jun Dai 1,4 and Jie Luo 1 1 Department of Neurosurgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China 2 Guangxi Key Laboratory for Transplantation Medicine, Institute of Transplant Medicine, 303 Hospital of People’s Liberation Army, Nanning, China 3 The Biomedical Research Center, University of British Columbia, Vancouver, Canada 4 Department of Surgery, University of British Columbia, Vancouver, Canada * These authors have contributed equally to this work Correspondence to: Long-Jun Dai, email: // Jie Luo, email: // Keywords : immunotherapy, chimeric antigen receptor (CAR), exosomes, cancer therapy, extracellular vesicles Received : August 12, 2015 Accepted : October 06, 2015 Published : October 19, 2015 Abstract Chimeric antigen receptor (CAR)-based T-cell adoptive immunotherapy is a distinctively promising therapy for cancer. The engineering of CARs into T cells provides T cells with tumor-targeting capabilities and intensifies their cytotoxic activity through stimulated cell expansion and enhanced cytokine production. As a novel and potent therapeutic modality, there exists some uncontrollable processes which are the potential sources of adverse events. As an extension of this impactful modality, CAR-T cell-derived exosomes may substitute CAR-T cells to act as ultimate attackers, thereby overcoming some limitations. Exosomes retain most characteristics of parent cells and play an essential role in intercellular communications via transmitting their cargo to recipient cells. The application of CAR-T cell-derived exosomes will make this cell-based therapy more clinically controllable as it also provides a cell-free platform to diversify anticancer mediators, which responds effectively to the complexity and volatility of cancer. It is believed that the appropriate application of both cellular and exosomal platforms will make this effective treatment more practicable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".