Development of highly sensitive detection for BCR-ABL transcripts in chronic myeloid leukemia by droplet digital PCR
DOI:
https://doi.org/10.14456/tjg.2015.16Keywords:
BCR-ABL transcripts, complete molecular response (CMR), conventional RQ-PCR, droplet digital PCR (ddPCR), minimal residual disease (MRD)Abstract
BCR-ABL tyrosine kinase inhibitors (TKIs) have been the standard treatment for chronic myeloid leukemia (CML) patients. It is known that complete molecular response (CMR) is necessarily equated with eradication of the CML clone, until 50% of patients with stable CMR relapse when imatinib (the first-line TKI treatment) is withdrawn. More sensitive assays for minimal residual disease (MRD) are required to identify patients who are more likely to remain in remission without therapy. In order to improve the performance of conventional real-time polymerase chain reaction (RQ-PCR), it is necessary to consider the factors that limit of detection and specificity of the method. However, droplet digital PCR (ddPCR) approaches this problem in a different way. Using nanofluidic technology, the nucleic acid sample is divided into hundreds of individual replicate PCR reactions. We demonstrated a high sensitive method of ddPCR with lower limit of detection using BCR-ABL p210 plasmid and BCR-ABL transcripts from patient sample dilutions, and compared the results with conventional RQ-PCR method. The results from ddPCR showed successful detection of BCR-ABL transcripts in undetectable samples by RQ-PCR, and sensitivity limit by detecting down to 10 copies per microliter. The correlation of measurements between RQ-PCR and ddPCR from plasmid and clinical samples indicated remarkable correlation between both techniques. Moreover, we used ddPCR to investigate MRD in 3 specimens from one CML patient who sustained CMR by conventional RQ-PCR before relapsed. This approach results in more efficient amplification of BCR–ABL transcripts that may provide an accurate measurement of MRD in CML patients with CMR. Application of this approach in prospective samples enrolled in clinical trials will shed light on its role in predicting relapse. We anticipate that the method will be employed in multiple applications in the clinic, including diagnosis, cancer recurrence monitoring, and treatment management.
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