Juan Luis Valdivieso-Shephard, Elisabet Matas-Pérez, Silvia García-Bujalance, Isabel Mirones-Aguilar, Berta González-Martínez, Antonio Pérez-Martínez, Eduardo López-Granados, Ana Martínez-Feito, Elena Sánchez-Zapardiel
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引用次数: 0
Abstract
Chimeric antigen receptor (CAR) T-cell therapy is a breakthrough in hematologic malignancies, such as acute B lymphoblastic leukemia (B-ALL). Monitoring this treatment is recommended, although standardized protocols have not been developed yet. This work compares two flow cytometry monitoring strategies and correlates this technique with qPCR method. CAR-T cells were detected by two different flow-cytometry protocols (A and B) in nine blood samples from one healthy donor and five B-ALL patients treated with Tisagenlecleucel (Kymriah®, USA). HIV-1 viral load allowed CAR detection by qPCR, using samples from seven healthy donors and nine B-ALL patients. CAR detection by protocol A and B did not yield statistically significant differences (1.9% vs. 11.8% CD3 + CAR+, p = 0.07). However, protocol B showed a better discrimination of the CD3 + CAR+ population. A strong correlation was observed between protocol B and qPCR (r = 0.7, p < 0.0001). CD3 + CAR+ cells were detected by flow cytometry only when HIV-1 viral load was above 104 copies/mL. In conclusion, protocol B was the most specific flow-cytometry procedure for the identification of CAR-T cells and showed a high correlation with qPCR. Further efforts are needed to achieve a standardized monitoring approach.
期刊介绍:
Cytometry Part A, the journal of quantitative single-cell analysis, features original research reports and reviews of innovative scientific studies employing quantitative single-cell measurement, separation, manipulation, and modeling techniques, as well as original articles on mechanisms of molecular and cellular functions obtained by cytometry techniques.
The journal welcomes submissions from multiple research fields that fully embrace the study of the cytome:
Biomedical Instrumentation Engineering
Biophotonics
Bioinformatics
Cell Biology
Computational Biology
Data Science
Immunology
Parasitology
Microbiology
Neuroscience
Cancer
Stem Cells
Tissue Regeneration.