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Droplet digital PCR vs RT-PCR for MRD monitoring in Ph+ ALL

Minimal residual disease (MRD) is a widely established prognostic marker that is used to assign risk and drive therapeutic decisions for patients with Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL). Conventional methods for MRD assessment include multiparametric flow cytometry and real-time polymerase chain reaction (RT-PCR).1 Droplet digital PCR (ddPCR) has been reported as a more precise and sensitive method than RT-PCR when samples have low levels of nucleic acids or variable levels of other contaminants.2 Recently, the research group of our Steering Committee member, Sabina Chiaretti, published in Hematological Oncology a comparison of ddPCR with RT-PCR of the BCR-ABL1 gene fusion for MRD assessment in samples from Ph+ ALL patients.3

Study design

In their analysis, Ansuinelli et al.3 included 88 follow-up samples from 36 patients with Ph+ ALL and 10 diagnostic samples from 40 Ph+ ALL cases (N = 98 samples) who were enrolled in the GIMEMA LAL2116 D-ALBA trial (NCT02744768). Prior sample analysis with RT-PCR from the GIMEMA LAL2116 trial had identified 10 positive, 54 positive but non-quantifiable (PNQ), and 24 negative samples. An additional four cases that were not from the GIMEMA LAL2116 trial were also assessed, including one PNQ and three negative samples.

The group first determined the optimal complementary DNA (cDNA) input for ddPCR, testing different cDNA amounts (1, 2.5, and 5 µL). To simulate MRD, cDNA of diagnostic material was serially diluted with cDNA from mononuclear cells from healthy donors (10−1, 10−2, 10−3, 10−4, and 10−5). Each analysis was performed at least in triplicate.

The limit of detection (LOD) for the ddPCR assay was determined from a plasmid standard curve based on five 10-fold dilutions. Specificity of the assay was determined from healthy donor pools and no template control (NTC) samples. Reproducibility was assessed from three diagnostic Ph+ ALL samples at 10−1, 10−2, and 10−3 dilutions, each one was assayed in duplicate, and in two independent experiments, and compared.

The ddPCR experiment itself was performed using the same primers and probes as those used for the RT-PCR. Negative controls included mononuclear cells (MNCs), NTCs, and Ph− diagnostic samples. Positive controls were the first and last dilution from the plasmid LOD curve, and these were randomly distributed across the analysis plate. The ABL1 gene was also amplified as a control to test the quality of the genetic material, and to enable a ratio of the BCR-ABL1 gene fusion and the ABL1 control. Briefly, the cDNA, primers, probes (for both BCR-ABL1 p190 and p210), and ddPCR supermix were combined with droplet oil. Droplets were generated and transferred to a PCR plate and placed in a thermal cycler. Once the droplets had been through the PCR process, the droplets were read using a fluorescent reader and analyzed.


The initial experiment to determine the cDNA input found that 5 µL of undiluted cDNA was sufficient for signal detection, even in samples with low levels of the BCR-ABL1 transcript, and so this was used in all follow-up samples. To avoid saturation, 1 µL of cDNA was used for diagnostic samples.

In terms of LOD, all replicates were positive at 1 × 10−4 and 5 × 10−5 dilutions, and 75% of the replicates (6/8) were positive at 1 × 10−5 dilution. At the lower concentrations of 5 × 10−6, and 1 × 10−6, only 17% and 14% of replicates scored positive, respectively. Therefore, the group defined the 1 × 10−5 dilution as the maximum sensitivity.

All NTC and healthy donor pools tested negative for BCR-ABL1 transcripts, and there was high reproducibility between replicates of diagnostic samples and dilutions, and between independent experimental repeats, demonstrating the robustness of this method.

Comparison of RT-PCR and ddPCR in diagnostic samples

To determine the strength of the assay, 10 diagnostic samples were assayed in triplicate and showed a high degree of concordance with the RT-PCR assay results (Pearson correlation coefficient = 0.87; Table 1). Any apparent higher sensitivity of the RT-PCR method was attributed to the fact that in the ddPCR assay, samples were diluted 5-fold, but samples were not diluted in the RT-PCR method.

Table 1. Comparison of the values obtained using the RT-PCR and ddPCR methods in diagnostic samples*

(n = 10)



1 (p210)



2 (p210)



3 (p190)



4 (p190/p210)



5 (p190)



6 (p210)



7 (p190)



8 (p210)



9 (p190)



10 (p190)



ddPCR, droplet digital polymerase chain reaction; RT-PCT, real-time polymerase chain reaction.
*Adapted from Ansuinelli et al.3
Number of copies (BCR-ABL1/ABL1) × 100/µL.

Comparison of RT-PCR and ddPCR in follow-up samples

There was 100% concordance between samples found positive by RT-PCR and their ddPCR assay result. Of the samples classed as PNQ by RT-PCR, 54% were positive by ddPCR analysis. Finally, of the samples that were negative by RT-PCR, 29% were positive and 17% were PNQ by ddPCR (Table 2). The rate of concordance overall between RT-PCR and ddPCR was 41%, and ddPCR improved the number of quantifiable samples by 46% (p < 0.0001).

Table 2. Comparison of RT-PCR and ddPCR values in follow-up samples (n = 88)*

ddPCR outcome

RT-PCR outcome, %

(n = 10)

(n = 54)

(n = 24)











ddPCR, droplet digital polymerase chain reaction; PNQ, positive but non-quantifiable; RT-PCT, real-time polymerase chain reaction.
*Adapted from Ansuinelli et al.3


Ansuinelli et al. found that the use of ddPCR reduced the number of samples designated PNQ, compared to RT-PCR. PNQ represents a grey area in clinical practice. Of the seven samples—corresponding to five patients—that were found to be MRD-positive by ddPCR but negative by RT-PCR during follow-up, four of the patients relapsed. Whether the increased sensitivity of ddPCR to detect MRD will have a clinical impact has yet to be determined as a longer follow-up is required. The study group feel that the use of ddPCR for assessment of MRD may well improve the overall clinical management of Ph+ ALL patients.

  1. Terwilliger T and Abdul-Hay M. Acute lymphoblastic leukemia: a comprehensive review and 2017 update. Blood Cancer J. 2017;7(6):e577. DOI: 1038/bcj.2017.53
  2. Taylor SC, Laperriere G and Germain H. Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: from variable nonsense to publication quality data. Sci Rep 2017;7:2409. DOI: 1038/s41598-017-02217-x
  3. Ansuinelli M, Della Starza I, Lauretti A, et al. Applicability of droplet digital polymerase chain reaction for minimal residual disease monitoring in Philadelphia-positive acute lymphoblastic leukaemia. Hematol Oncol. DOI: 10.1002/hon.2913