Challenges in the Development of NK-92 Cells As an Effective Universal Off-the-Shelf Cellular Therapeutic.
JOURNAL OF IMMUNOLOGY（2024）
Abstract
The human-derived NK-92 cell-based CAR-NK therapy exhibits inconsistency with overall suboptimal efficacy and rapid in vivo clearance of CAR-NK92 cells in cancer patients. Analysis indicates that although pre-existing IgM in healthy individuals (n 5 10) strongly recognizes both NK-92 and CAR-NK92 cells, IgG and IgE do not. However, only a subset of cancer patients (3/8) exhibit strong IgM recognition of these cells, with some (2/8) showing pre-existing IgG recognition. These results suggest a natural immunoreactivity between NK-92 and CAR-NK92 cells and pre-existing human Abs. Furthermore, the therapy's immunogenicity cytotoxicity toward these cells is reduced by complement inhibitors, suggesting that Abs may facilitate the rapid clearance of CAR-NK92 cells through complement-dependent cytotoxicity. Given that NK-92 cells lack known receptors for IgG and IgM, identifying and modifying the recognition targets for these Abs on NK-92 and CAR-NK92 cells may improve clinical outcomes. Moreover, we discovered that the 72nd amino acid of the NKG2D receptor on NK-92 cells is alanine. Previous studies have demonstrated polymorphism at the 72nd amino acid of the NKG2D on human NK cells, with NKG2D72Thr exhibiting a superior activation effect on NK cells compared with NKG2D72Ala. We confirmed this conclusion also applies to NK-92 cells by in vitro cytotoxicity experiments. Therefore, reducing the immunoreactivity and immunogenicity of CAR-NK92 and directly switching shelf cellular therapeutics. The Journal of Immunology, 2024, 213: 1318-1328.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined