Validation of a High-Sensitivity Assay for Detection of Chimeric Antigen Receptor T-Cell Vectors Using Low-Partition Digital PCR Technology

The Journal of molecular diagnostics : JMD(2023)

引用 0|浏览9
暂无评分
摘要
Although in vivo engraftment, expansion, and persistence of chimeric antigen receptor (CAR) T cells are pivotal components of treatment efficacy, quantitative monitoring has not been implemented in routine clinical practice. We describe the development and analytical validation of a digital PCR assay for ultrasensitive detection of CAR constructs after treatment, circumventing known technical limitations of low-partitioning platforms. Primers and probes, designed for detection of axicabtagene, brexucabtagene, and Memorial Sloan Kettering CAR constructs, were employed to validate testing on the Bio-Rad digital PCR low-partitioning platform; results were compared with Raindrop, a high-partitioning system, as reference method. Bio-Rad protocols were modified to enable testing of DNA inputs as high as 500 ng. Using dual-input reactions (20 and 500 ng) and a combined analysis approach, the assay demonstrated consistent target detection around 1 x 10(-5) (0.001%) with excellent specificity and reproducibility and 100% accuracy compared with the reference method. Dedicated analysis of 53 clinical samples received during validation/implementation phases showed the assay effectively enabled monitoring across multiple time points of early expansion (day 6 to 28) and long-term persistence (up to 479 days). CAR vectors were detected at levels ranging from 0.005% to 74% (vector versus reference gene copies). The highest levels observed in our cohort correlated strongly with the temporal diagnosis of grade 2 and 3 cytokine release syndrome diagnosis (P < 0.005). Only three patients with undetectable constructs had disease progression at the time of sampling. (J Mol Diagn 2023, 25: 634-645; https://doi.org/10.1016/ j.jmoldx.2023.06.002)
更多
查看译文
关键词
pcr,antigen,high-sensitivity high-sensitivity,t-cell,low-partition
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要