Context.—Quantification and detection of the t(9;22) (BCR-ABL1) translocation in chronic myelogenous leukemia and B-lymphoblastic leukemia are important for directing treatment protocols and monitoring disease relapse. However, quantification using traditional reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) is dependent on a calibration curve and is prone to laboratory-to-laboratory variation. Droplet digital polymerase chain reaction (ddPCR) is a novel method that allows for highly sensitive absolute quantification of transcript copy number. As such, ddPCR is a good candidate for disease monitoring, an assay requiring reproducible measurements with high specificity and sensitivity. Objective.—To compare results of ddPCR and RT-qPCR BCR-ABL1 fusion transcript measurements of patient samples and determine if either method is superior. Design.—We optimized and standardized a 1-step multiplexed ddPCR assay to detect BCR-ABL1 p190 and ABL1 e10 transcripts. The ddPCR optimization included varying cycle number and primer concentration with standardization of droplet generation and droplet number and analyses to improve data sensitivity. Following optimization, ddPCR measurements were performed on clinical samples and compared with traditional RT-qPCR results. Results.—Droplet digital polymerase chain reaction was able to detect the BCR-ABL1 p190 transcript to 0.001% (1:10-5) with a calculated limit of detection and limit of quantitation of 4.1 and 5.3 transcripts, respectively. When tested on patient samples, ddPCR was able to identify 20% more positives than a laboratory-developed 2-step RT-qPCR assay. Conclusions.—Droplet digital polymerase chain reaction demonstrated increased detection of BCR-ABL1 compared with RT-qPCR. Improved detection of BCRABL1 p190 and the potential for improved standardization across multiple laboratories makes ddPCR a suitable method for disease monitoring in patients with acute B-lymphoblastic leukemia.
|Original language||English (US)|
|Number of pages||9|
|Journal||Archives of Pathology and Laboratory Medicine|
|State||Published - Jan 2022|
Bibliographical noteFunding Information:
We thank the University of Minnesota Molecular Diagnostic Lab and University of Minnesota Genomics Center for support and training for this project. This work was supported by the Department of Laboratory Medicine and Pathology Innovation Initiative Program.
Funding: Innovation Initiative grant, Department of Laboratory Medicine and Pathology with grant number 1028-11751-20078-X-XX-8005525. The authors have no relevant financial interest in the products or companies described in this article.
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