Home Comparative analysis of BCR::ABL1 p210 mRNA transcript quantification and ratio to ABL1 control gene converted to the International Scale by chip digital PCR and droplet digital PCR for monitoring patients with chronic myeloid leukemia
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Comparative analysis of BCR::ABL1 p210 mRNA transcript quantification and ratio to ABL1 control gene converted to the International Scale by chip digital PCR and droplet digital PCR for monitoring patients with chronic myeloid leukemia

  • Wannachai Saisaard and Weerapat Owattanapanich ORCID logo EMAIL logo
Published/Copyright: August 20, 2024
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Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 2

Abstract

Objectives

Chronic myeloid leukemia (CML) is characterized by the Philadelphia chromosome, leading to the BCR::ABL1 fusion gene and hyper-proliferation of granulocytes. Tyrosine kinase inhibitors (TKIs) are effective, and minimal residual disease (MRD) monitoring is crucial. Digital PCR platforms offer increased precision compared to quantitative PCR but lack comparative studies.

Methods

Eighty CML patient samples were analyzed in parallel using digital droplet PCR (ddPCR) (QXDx™ BCR-ABL %IS Kit) and chip digital PCR (cdPCR) (Dr. PCR™ BCR-ABL1 Major IS Detection Kit).

Results

Overall, qualitative and quantitative agreement was good. Sensitivity analysis showed positive percentage agreement and negative percentage agreement were both ≥90 %, and the quadratic weighted kappa index for molecular response (MR) level categorization was 0.94 (95 %CI 0.89, 0.98). MR levels subgroup analysis showed perfect categorical agreement on MR level at MR3 or above, while 35.4 % (17/48) of patient samples with MR4 or below showed discordant categorizations. Overall, Lin’s concordance correlation coefficient (CCC) for the ratio of %BCR::ABL1/ABL1 converted to the International Scale (BCR::ABL1IS) was almost perfect quantitative agreement (Lin’s CCC=0.99). By subgroups of MR levels, Lin’s CCC showed a quantitative agreement of BCR::ABL1IS decreased as MR deepened.

Conclusions

Both cdPCR and ddPCR demonstrated comparable performance in detecting BCR::ABL1 transcripts with high concordance in MR3 level or above. Choosing between platforms may depend on cost, workflow, and sensitivity requirements.

Keywords: BCR::ABL1 positive; chronic myelogenous leukemia; dPCR; International Scale; minimal residual disease; Philadelphia chromosome

Introduction

Chronic myeloid leukemia (CML) is a clonal myeloproliferative tumor of hematopoietic stem cells with an estimated rate of around 1 per 100,000 population annually worldwide [1]. CML occurs because of the translocation t (9;22) (q34;q11), or the Philadelphia chromosome, which leads to the formation of a fusion protein from the Breakpoint Cluster Region/Abelson (BCR::ABL1) fusion gene [2]. The BCR::ABL1 oncogenic fusion protein has been linked to growth factor dependence, apoptosis, proliferation, and cell adhesion, causing the hyper-proliferation of granulocytes and characteristics of chronic phase CML. In particular, disruption of tyrosine kinase activity is crucial for the development of CML, as proven by the effectiveness of tyrosine kinase inhibitor (TKI) therapy [3, 4]. Depending on the specific cleavage sites within the BCR gene, fusion proteins are categorized into three main types, including p210, p190, and p230 [5]. Around 90–95 % of CML patients have breakpoints in chromosome 22 in the BCR gene of either exon e13 (b2) or e14 (b3), giving rise to two differing chimeric transcripts, of which the most common related rearrangements of BCR::ABL1 are e13a2 (b2a2) and e14a2 (b3a2), coding for the p210 protein [6, 7].

TKIs can achieve cytogenetic and molecular response (MR) and treatment-free remission (TFR), which may be a functional cure [8]. Standard therapy involves performing a complete cytogenetic and deep MR (DMR) as quickly as possible [9]. To make decisions about management, current guidelines from the European LeukemiaNet (ELN) [10] and the National Comprehensive Cancer Network (NCCN) [11] recommend using a sensitive polymerase chain reaction (PCR) assay during treatment to detect and measure BCR::ABL1 fusion transcript copies per assay to monitor minimal residual disease (MRD) and risk of relapse. Regular testing at intervals of three months is recommended, with results of a ratio of %BCR::ABL1/ABL1 converted to the International Scale (IS; BCR::ABL1IS) for MR reporting [10]. Types of PCR available include quantitative PCR (qPCR) and digital PCR (dPCR). However, qPCR assays have limitations in their limit of detection (LOD) and quantification (LOQ) compared to dPCR [12].

dPCR represents a third-generation PCR technology that increases precision and reproducibility in absolute quantitative analysis of target molecules without internal references or standard curves used by qPCR. In dPCR, the sample is partitioned into thousands of independent reaction vessels in PCR to improve the LOD. Based on the method of partitioning the reaction mixture, dPCR can be categorized into chip digital PCR (cdPCR) and droplet digital PCR (ddPCR). cdPCR relies on microfluidic technology, partitioning the reaction mixture into nanoliter-sized reaction chambers. After several cycles of reaction, fluorescence is detected using an imaging system, and the copy number of the target sequence is calculated using imaging software and Poisson statistics to approximate the binomial distribution when the probability of positive droplets is low compared with the large number of partitions [12, 13]. ddPCR partitions are monodispersed droplets made by microfluidic t-junctions combining an aqueous stream and an oil stream. These randomly partition nucleic acid into nano liter-sized water-in-oil droplets, mainly containing one or no target copies, undergoing PCR independently. Endpoint amplification is done in each droplet, and the original copy number is calculated using Poisson statistics [13]. This approach yields absolute copies per assay measurement and is highly tolerant of variations in PCR efficiency. ddPCR has been used to develop reference materials, facilitating monitoring very low BCR::ABL1 transcript levels in CML patients with satisfactory reliability and sensitivity. It can be easily scaled to more partitions than cdPCR. The main advantage of cdPCR is that it has fewer processing steps and obtains results significantly faster than ddPCR.

Numerous dPCR commercial kits are available for detecting BCR::ABL1 fusion transcripts of p210, including the QXDx™ BCR-ABL %IS kit (Bio-Rad, Hercules, CA), a ddPCR kit, and the Dr. PCR™ BCR-ABL1 Major IS Detection Kit (OPTOLANE, South Korea), a cdPCR kit. This study evaluates their correlation and agreement to assess their relative performance.

Materials and methods

Patients and samples

This study included 80 stored RNA samples from 73 CML patients. Sixty-six patients contributed one sample each, and seven patients contributed two samples each. All the patients were in the chronic phase and attended the Hematology Division of Siriraj Hospital, Bangkok, Thailand, from 30th July 2023 to 1st November 2023. All samples stored during the recruitment period were used. Forty-six males and 24 females were included, with missing data on the sex of three patients. The patient cohort’s mean age was 50.1 (SD 16.6) (range 14–84 years). The study protocol received approval from the Institutional Review Board and Ethics Committee. The study was conducted following the Declaration of Helsinki.

The samples were categorized into three subgroups based on MR as follows: subgroup one [no major molecular response (no MMR), MR1, and MR2], including 15 patient samples; subgroup two (MR3), including 17 patient samples, and subgroup three (MR4, MR4.5 and MR5) including 48 patient samples. All samples were then re-quantified for BCR::ABL1 fusion transcripts of p210 as BCR::ABL1IS using QXDx™ BCR-ABL %IS Kit and the Dr. PCR™ BCR-ABL1 Major IS Detection Kit. Samples were also categorized by transcript variants of e13a2, e14a2, or both.

Sample collection and storage

All samples were derived from ethylenediaminetetraacetic acid (EDTA)-anticoagulated peripheral blood samples. All samples underwent testing using the QXDx™ BCR-ABL %IS Kit on the QX200 system (Bio-rad), which employs ddPCR as part of routine workups for CML in the hematology laboratory unit at Division of Hematology, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University. Total RNA was extracted by trizol reagent following the manufacturer’s instructions and was quantified and check for purity using a NanoDrop™ Spectrophotometer (Thermo Fisher Scientific Inc, Waltham, MA). Absorbance ratio 260 and 280 nm (A260/A280) along with that of 230 and 260 nm (A230/A260) were used to determine RNA purity with acceptable purity defined as a ratio of A260/A280 of ∼ 2.0 and a A230/A260 ranging between 2.0 and 2.2 [14]. The RNA samples were stored at −80 °C after routine testing.

ddPCR and cdPCR assays

Stored samples were subsequently retrieved for re-quantification of BCR::ABL1 fusion transcripts of p210. First, they were re-checked for concentration and purity using a NanoDrop™ Spectrophotometer (Thermo Fisher Scientific Inc.). PCR-ready samples were prepared by reverse transcription to produce complementary DNA (cDNA) using the iScript Advanced cDNA Synthesis Kit (Bio-rad), which is part of the QXDx™ BCR-ABL %IS Kit (Bio-rad), and were then tested according to the QXDx™ BCR-ABL %IS Kit’s instructions. In brief, samples were tested in 80 samples. Each replicated was partitioned into around 20,000 droplets by a droplet generator and underwent PCR amplification. Next, plates were loaded into the QX200 droplet reader and analyzed using Quantasoft™ software (version 1.7.4, Bio-Rad). Samples were also classified by transcript variant by gel electrophoresis.

For Dr. PCR™ BCR-ABL1 Major IS Detection Kit, a simple three-step process involves preparing the master mix according to the manufacturer’s instructions and reagents, injecting it into the cartridge, and loading it into the PCR analyzer, followed by obtaining the results from the one-step reverse transcription (RT)-dPCR analyzer using a single set of primers for reverse transcription and amplification of BCR::ABL1 and ABL1 in a single reaction vessel. The PCR reaction mixture injected into the cartridge was divided into approximately 20,000 microwells.

Statistical analysis

The ratios of BCR::ABL1 to ABL1 copies were calculated using the BCR::ABL1/ABL1 ratio and converted to BCR::ABL1IS using the same method for cdPCR and ddPCR by using conversion factors provided by the manufacturers of each kit. For categorical comparison of sensitivity, McNemar’s test reported positive percentage agreement and negative percentage agreement with Clopper-Pearson exact confidence intervals. Categorical comparison between MR classes was performed using quadratic weighted Cohen’s kappa statistic and Bowker’s test of symmetry. For quantitative comparisons, descriptive statistics of PCR measurements are presented as both median (Q1, Q3) and range. Spearman’s correlation coefficient assessed the correlation between measurements by cdPCR and ddPCR. For correlation analysis, r of 0.90–1.00 is a very strong correlation; r of 0.70–0.89 is a strong correlation; r of 0.40–0.69 is a moderate correlation; r of 0.10–0.39 is a weak correlation; and r of 0.00–0.09 is a negligible correlation. Quantitative differences were compared using Wilcoxon’s sign rank test for dependent data and Mann Whitney U test for independent data. Quantitative agreement analysis was assessed by Lin’s concordance correlation coefficient (Lin’s CCC). Constant difference and proportional difference were evaluated all patient samples by Passing-Bablok regression, which only requires assumptions of continuously distributed data and linearity. Proportional bias was checked by ordinary least squares linear (OLR) regression of the logarithmic-transformed data by regressing the difference against the mean [15]. For interpretation of Lin’s CCC, >0.99 is almost perfect agreement; 0.95–0.99 is substantial agreement; 0.90–0.95 is moderate agreement; and <0.90 is poor agreement [16]. Passing-Bablok regression results were defined as no constant difference between the assays if the intercept’s 95 % confidence interval (CI) contained zero and no proportional difference between the assays if the 95 %CI of the slope coefficient contained one. Bland-Altman plots of BCR::ABL1IS with a horizontal constant difference and limits of agreement were obtained if assumptions of no proportional bias and homogeneity of variance were met. Otherwise, plots without them were presented.

Statistical analysis was performed by R 4.2.0 (R Core Team 2023. R Statistical Foundation for Computing, Vienna, Austria) using the mcr package and Stata 14.0 (Stata Corp LLC, College Station, TX). A p-value of <0.05 was considered significant.

Results

Qualitative comparisons

For all 80 RNA patient samples, the ddPCR method did not detect BCR::ABL1 transcripts of p210 in 11 samples, while the cdPCR method did not detect them in 14 samples. RT-qPCR did not detect BCR::ABL1 transcripts of p210 in 11 samples. Positive percentage agreement was 94.2 % (95 %CI 85.8, 98.4), while negative percentage agreement was 90.9 % (95 %CI 58.7, 99.8). There was no significant difference in detection rates by McNemar’s test (p=0.18) (Table 1).

Table 1:

BCR::ABL1 detection stratified by PCR type.

Detection of BCR::ABL1 by ddPCR (biorad) Total
Detected Not detected
Detection of BCR::ABL1 by cdPCR (Dr. PCR) Detected 65 1 66
Not detected 4 10 14
Total 69 11 80

  1. McNemar’s test: Chi2=1.80; p=0.18. cdPCR, chip digital PCR; ddPCR, droplet digital PCR.

For comparison of MR classes, Cohen’s weighted kappa was 0.94 (95 %CI 0.89, 0.98), and Bowker’s test for symmetry was not significant (p=0.43) (Table 2). Of all 80 patient samples, 63 (78.8 %) agreed on the MR category by both methods, while 17 (21.3 %) disagreed. The 32 samples with BCR::ABL1IS >0.01 % were all in agreement with both assays. Of 48 samples in subgroup three (BCR::ABL1IS ≤0.01 %), 17 samples (35.4 %) were in disagreement. Among the 17 samples in disagreement, cdPCR categorized 11 samples as having less residual disease and six as having more residual disease compared to ddPCR (Table 2). For transcript variants, only 57 patients each contributing one sample could be classified due to amounts of remaining patient sample. Twenty-one (36.8 %) were e13a2, and 36 (63.2 %) were e14a2. None were both e13a2 and e14a2.

Table 2:

Frequency table of the distribution of molecular response classes (n=80).

Variable Class No MMR MR1 MR2 MR3 MR4 MR4.5 MR5 ND ddPCR (Biorad)
No MMR 5 0 0 0 0 0 0 0 5
MR1 0 5 0 0 0 0 0 0 5
MR2 0 0 5 0 0 0 0 0 5
MR3 0 0 0 17 0 0 0 0 17
MR4 0 0 0 0 13 4a 0 0 17
MR4.5 0 0 0 0 7b 8 1a 1a 17
MR5 0 0 0 0 0 0 0 0 0
ND 0 0 0 0 1b 3b 0 10 14
cdPCR (Dr. PCR) 5 5 5 17 21 15 1 11 80

  1. aShift to more residual disease by cdPCR compared with ddPCR. bShift to less residual disease by cdPCR compared with ddPCR. Bowker’s test: Chi2=3.82; p=0.43. Weighted quadratic kappa: 0.94 (95 %CI 0.89, 0.98). cdPCR, chip digital PCR; ddPCR, droplet digital PCR; MMR, major molecular response; MR, molecular response; ND, not detected.

Quantitative comparison

For BCR::ABL1 copy numbers in all 80 patient samples, the descriptive statistics, Spearman’s correlation, and agreement analysis are shown in Table 3. Wilcoxon’s sign rank test was significant (p<0.001), and Figures 1A and 2A showed that BCR::ABL1 copies per assay tended to be higher in cdPCR compared with ddPCR at higher copy numbers. Spearman’s correlation was very strong, but Lin’s CCC showed poor agreement. For BCR::ABL1 copy numbers by subgroups one to three, all Wilcoxon’s sign rank tests were significant for differences between cdPCR and ddPCR (all p<0.05), and Figure 3 shows BCR::ABL1 copies per assay tended to be higher in cdPCR compared with ddPCR in subgroups one and two. In subgroup three, Figure 4A shows that cdPCR tended to give high copy numbers with a wider interquartile range than ddPCR. Spearman’s correlation was strong in subgroup one, while only moderate in subgroups two and three. Lin’s CCC showed poor agreement in all subgroups, which became poorer as MR deepened, especially in subgroup three.

Table 3:

Distributions of measurements along with agreement and correlation statistics.

Variable Median (Q1, Q3) [min, max] Wilcoxon’s sign rank test Spearman’s correlation Lin’s CCC
All patients samples (n=80)
ddPCR
BCR::ABL1 (copies/assay) 3 (1, 26.5) [0, 197,200] <0.001c 0.91 0.88
ABL1 (copies/assay) 54,920 (43,925, 69,845) [10,590, 228,100] <0.001c 0.42 0.22
BCR::ABL1/ABL1 ratio 0.006 (0.002, 0.051) [0, 86.5] 0.23 0.94 0.95
BCR::ABL1 IS 0.007 (0.002, 0.057) [0, 96.0] 0.49 0.94 0.99
cdPCR
BCR::ABL1 (copies/assay) 6.13 (1.76, 52.3) [0, 292,924]
ABL1 (copies/assay) 119,618 (103,307, 145,007) [59,069, 234,618]
BCR::ABL1/ABL1 ratio 0.0056 (0.002, 0.044) [0, 96.1]
BCR::ABL1 IS 0.006 (0.002, 0.042) [0, 100]
Subgroup 1 (n=15)
ddPCR
BCR::ABL1 (copies/assay) 2,284 (291, 7,212) [59, 197,200] <0.001c 0.96 0.86
ABL1 (copies/assay) 63,410 (43,390, 73,680) [10,590, 228,100] <0.001c 0.26 0.43
BCR::ABL1/ABL1 ratio 3.249 (0.506, 29.4) 0.002b 0.99 0.98
BCR::ABL1 IS 3.61 (0.562, 32.6) [0.11, 0.865] 0.04a 0.99 0.99
cdPCR
BCR::ABL1 (copies/assay) 4,546 (649.6, 52,931) [117.9, 292,924]
ABL1 (copies/assay) 115,264 (86,758, 150,811) [62,870, 234,618]
BCR::ABL1/ABL1 ratio 3.81 (0.564, 38.5) [0.18, 96.1]
BCR::ABL1 IS 4.00 (0.593, 40.5) [0.19, 100]
Subgroup 2 (n=17)
ddPCR
BCR::ABL1 (copies/assay) 23 (14, 27) [6, 41] <0.001c 0.53 0.13
ABL1 (copies/assay) 52,790 (47,190, 59,600) [18,660, 138,100] <0.001c 0.35 0.14
BCR::ABL1/ABL1 ratio 0.0353 (0.026, 0.053) [0.01, 0.07] 0.85 0.81 0.77
BCR::ABL1 IS 0.0392 (0.029, 0.059) [0.01, 0.08] 0.15 0.81 0.78
cdPCR
BCR::ABL1 (copies/assay) 38.0 (24.4, 57.0) [16.0, 153.5]
ABL1 (copies/assay) 128,140 (104,944, 151,121) [94,021, 215,154]
BCR::ABL1/ABL1 ratio 0.034 (0.021, 0.045) [0.01, 0.10]
BCR::ABL1 IS 0.033 (0.020, 0.043) [0.01, 0.099]
Subgroup 3 (n=48)
ddPCR
BCR::ABL1 (copies/assay) 1 (1, 3) [0, 9] 0.02a 0.59 0.32
ABL1 (copies/assay) 54,535 (41,335, 70,160) [27,410, 155,100] <0.001c 0.55 0.17
BCR::ABL1/ABL1 ratio 0.003 (0.001, 0.006) [0, 0.0085] 0.34 0.71 0.61
BCR::ABL1 IS 0.003 (0.001, 0.006) [0, 0.0094] 0.07 0.71 0.58
cdPCR
BCR::ABL1 (copies/assay) 1.81 (0, 5.3) [0, 11]
ABL1 (copies/assay) 117,737 (101,900, 131,119) [59,069, 218,764]
BCR::ABL1/ABL1 ratio 0.002 (0, 0.004) [0, 0.0092]
BCR::ABL1 IS 0.002 (0, 0.005) [0, 0.0097]

  1. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; Lin’s CCC, Lin’s concordance correlation coefficient; BCR::ABL1IS, ratio %BCR::ABL1/ABL1 converted to the International Scale.

Figure 1: Boxplots comparing ddPCR and cdPCR in all the patient samples (n=80). (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS. Y-axes are on log10 scales. cdPCR, digital PCR; ddPCR, digital droplet PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.
Figure 1:

Boxplots comparing ddPCR and cdPCR in all the patient samples (n=80). (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS. Y-axes are on log10 scales. cdPCR, digital PCR; ddPCR, digital droplet PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.

Figure 2: Log-log plots of the comparison of ddPCR and cdPCR all 80 patient samples. (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.
Figure 2:

Log-log plots of the comparison of ddPCR and cdPCR all 80 patient samples. (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.

Figure 3: Log-log plots of the comparison of ddPCR and cdPCR by molecular response subgroups. (A) BCR::ABL1 copies/assay in group 1, (B) ABL1 copies/assay in group 1, (C) BCR::ABL1/ABL1 ratio in group 1, (D) BCR::ABL1IS in group 1, (E) BCR::ABL1 copies/assay in group 2, (F) ABL1 copies/assay in group 2, (G) BCR::ABL1/ABL1 ratio in group 2, (H) BCR::ABL1IS in group 2, (I) BCR::ABL1 copies/assay in group 3, (J) ABL1 copies/assay in group 3, (K) BCR::ABL1/ABL1 ratio in group 3, and (L) BCR::ABL1IS in group 3. Group 1 n=15, group 2 n=17, and group 3 n=48. cdPCR, chip digital PCR; CN, copy numbers per assay; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.
Figure 3:

Log-log plots of the comparison of ddPCR and cdPCR by molecular response subgroups. (A) BCR::ABL1 copies/assay in group 1, (B) ABL1 copies/assay in group 1, (C) BCR::ABL1/ABL1 ratio in group 1, (D) BCR::ABL1IS in group 1, (E) BCR::ABL1 copies/assay in group 2, (F) ABL1 copies/assay in group 2, (G) BCR::ABL1/ABL1 ratio in group 2, (H) BCR::ABL1IS in group 2, (I) BCR::ABL1 copies/assay in group 3, (J) ABL1 copies/assay in group 3, (K) BCR::ABL1/ABL1 ratio in group 3, and (L) BCR::ABL1IS in group 3. Group 1 n=15, group 2 n=17, and group 3 n=48. cdPCR, chip digital PCR; CN, copy numbers per assay; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.

Figure 4: Boxplots comparing ddPCR and cdPCR in subgroup 3. (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS (n=48). Y-axes are on log10 scales. cdPCR, digital PCR; ddPCR, digital droplet PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.
Figure 4:

Boxplots comparing ddPCR and cdPCR in subgroup 3. (A) BCR::ABL1 copies/assay, (B) ABL1 copies/assay, (C) BCR::ABL1/ABL1 ratio, and (D) BCR::ABL1IS (n=48). Y-axes are on log10 scales. cdPCR, digital PCR; ddPCR, digital droplet PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS; %ratio, BCR::ABL1/ABL1 ratio.

For ABL1 copy numbers in all 80 patient samples, the descriptive statistics and significant Wilcoxon’s sign rank test (p<0.001) in Table 3, along with Figures 1B, 2B, and 3, showed that ABL1 copy numbers were systematically higher by the cdPCR assay compared to ddPCR. Lin’s CCC showed poor agreement, and Spearman’s correlation was only moderate. ABL1 copy numbers by subgroups one to three showed significant differences between assays by Wilcoxon’s sign rank tests (all p<0.001). In subgroup three, Figure 4B shows interquartile ranges did not overlap between assays, with cdPCR giving markedly higher readings. Spearman’s correlations by subgroups were moderate or weak, and all subgroups showed poor agreement by Lin’s CCC, with agreement worsening as with deeper MR (Table 3).

Regarding the BCR::ABL1/ABL1 ratio for all 80 patient samples, descriptive distribution was quite similar, while Spearman’s correlation was very strong, but Lin’s CCC was moderate (Table 3). Proportional bias was observed in Figures 2C and 3 with BCR::ABL1/ABL1 ratio data points above the identity line as the values become larger. Spearman’s correlations were strong for subgroups one to three, while Lin’s CCC for subgroups one and two was substantial but poor for subgroup three (Table 3).

Regarding BCR::ABL1IS, the IS conversion showed substantial agreement in all 80 patient samples with a Lin’s CCC of 0.99 compared to 0.95 for BCR::ABL1/ABL1 ratio, and Spearman’s correlation for BCR::ABL1IS was very strong (Table 3). No significant difference in BCR::ABL1IS was found in all 80 patient samples. Passing-Bablok regression showed no constant difference between BCR::ABL1IS of cdPCR and ddPCR (intercept 95 %CI=−7.33, 0.0004). Passing-Bablok regression did not demonstrate a proportional difference (slope 95 %CI=0.85, 1.12). OLR regression did not show a significant proportional difference between BCR::ABL1IS of cdPCR and ddPCR, but showed a trend to significance (p=0.071). A graphical proportional difference was demonstrated by Figures 2D, 3, and 5, which showed a proportional difference with the tendency for cdPCR to give higher BCR::ABL1IS readings at high BCR::ABL1IS values and lower readings at lower BCR::ABL1IS compared to ddPCR.

Figure 5: Difference plot of BCR::ABL1IS as a ratio of cdPCR to ddPCR to their geometric mean. The difference plot shows 65 patients with non-zero BCR::ABL1IS values. The dashed black line is in perfect agreement and has no proportional bias. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS.
Figure 5:

Difference plot of BCR::ABL1IS as a ratio of cdPCR to ddPCR to their geometric mean. The difference plot shows 65 patients with non-zero BCR::ABL1IS values. The dashed black line is in perfect agreement and has no proportional bias. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS.

For BCR::ABL1IS by subgroups one to three, Spearman’s correlation in subgroup one was very strong while strong in subgroups two and three, and only subgroup one showed substantial agreement by Lin’s CCC, while groups two and three showed poor agreement (Table 3). Figure 6 shows the increasing variance of differences between cdPCR and ddPCR for BCR::ABL1IS as the mean BCR::ABL1IS becomes more prominent with a tendency of cdPCR to be smaller values than ddPCR. Near the cutoff of BCR::ABL1IS of 0.001 for MR5, differences in BCR::ABL1IS ranged from around +0.002 to −0.002.

Figure 6: Difference plot of BCR::ABL1IS comparing cdPCR and ddPCR for group three (n=48). The dashed black line is in perfect agreement and has no proportional bias. The dashed grey line vertical lines are the cutoffs for MR5 (BCR::ABL1IS ≤0.001). cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS.
Figure 6:

Difference plot of BCR::ABL1IS comparing cdPCR and ddPCR for group three (n=48). The dashed black line is in perfect agreement and has no proportional bias. The dashed grey line vertical lines are the cutoffs for MR5 (BCR::ABL1IS ≤0.001). cdPCR, chip digital PCR; ddPCR, droplet digital PCR; PCR, polymerase chain reaction; %IS, BCR::ABL1IS.

For comparison of BCR::ABL1IS by transcript variant, the median BCR::ABL1IS of cdPCR and ddPCR together showed a non-significant difference between e13a2 and e14a2 (0.018 vs. 0.006, respectively; p=0.06) with similar non-significant differences cdPCR and ddPCR separately (Table 4).

Table 4:

Distribution of BCR::ABL1IS by transcript variant.

BCR::ABL1 IS
Median Range p-Value
ddPCR/cdPCR
 e13a2 0.018 0–101.2 0.06
 e14a2 0.006 0–70.7
ddPCR
 e13a2 0.017 0–96.0 0.17
 e14a2 0.006 0–70.7
cdPCR
 e13a2 0.019 0–101.2 0.18
 e14a2 0.006 0–68.1

  1. e13a2 n=21 and e14a2 n=36. cdPCR, chip digital PCR; ddPCR, droplet digital PCR; BCR::ABL1IS, ratio %BCR::ABL1/ABL1 converted to the International Scale.

Discussion

BCR::ABL1 transcripts follow-up is vital in monitoring treatment response, determining resistance development, and reorganizing treatment protocol in CML patients [17]. The sensitivity of the measurement is necessary for detecting MRD and taking early measures. The RT-qPCR determination of BCR::ABL1 transcript levels was the gold-standard method for monitoring MRD in CML and for the best management of CML patients [18]. However, it often shows insufficient sensitivity and inconsistent detection of minimal BCR::ABL1 levels [19]. Despite the standardization of RT-qPCR results relative to the IS, reproducibility issues across laboratories persist [19, 20], and alternative methods are tested. dPCR has been suggested as a robust and reproducible option. This method is not affected by the inhibitors present, primer efficiency, or calibrators, and it does not require standard reference curves [21]. dPCR has transformed the molecular surveillance of MRD in hematological malignancies in recent years and has recently emerged as a more precise and accurate technique for detecting MRD among CML patients by absolutely quantifying BCR::ABL1 transcript levels. This technique is based on partitioning a reaction mix into thousands of micro-PCR reactions. Independent of the dPCR platform (chip- or droplet-based), amplification reveals the target’s presence through fluorescence, allowing for absolute quantification by counting the positive micro-reactions. Moreover, it seems to surpass the sensitivity and accuracy of RT-qPCR by 10–100 fold, thus increasing the interest in its use in clinical practice. At present, different studies are investigating whether dPCR may also help in the better identification of patients who will not relapse after discontinuation of TKI therapy by taking advantage of TFR [22].

In the present study, we compared the agreement between ddPCR and cdPCR on measurement values in 80 CML patient samples. The cdPCR assay investigated (Dr. PCR, OPTOLANE) systematically yielded more copies of the control ABL1 gene and BCR::ABL1 per sample compared to the ddPCR examined (QXDx™ BCR-ABL %IS Kit, Biorad). However, the amount of RNA input into the reaction was less than one. The systematic difference in copy numbers observed may have been due to a difference in between the pre-set droplet (partition) volume in the ddPCR kit’s software and the mean droplet volumes in our laboratory procedure. Studies independently measuring mean droplet volume in ddPCR by optical methods reported that mean droplet volume was lower than the pre-set droplet volume, leading to lower estimated concentrations and thereby copy numbers per assay using the pre-set droplet volume [23, 24]. Furthermore, droplet size may have been affected by manual transfer of droplets to well plates in our laboratory as one previous study reported a difference in mean droplet volume between manual and automated droplet transfer [23]. The cdPCR kit tested may have had more stable partition volumes due to physical partitioning compared and its one-step reverse transcription and amplification procedure.

The performance of the dPCR assay makes it a promising method for routine MRD monitoring in CML patients. Many studies allowed the QXDx BCR-ABL %IS ddPCR assay to be a reliable tool for MRD monitoring in CML patients [21, 25–27] because ddPCR is a technology that became commercially available in 2011 [28, 29], while OPTOLANE’s Dr. PCR was launched only a few years ago. With the increasing importance of molecular monitoring in CML management, this study addresses a clinically relevant question regarding the comparative performance of the two dPCR techniques. The statistical analyses performed are comprehensive, including correlation, agreement, and regression analyses to provide robust data evaluation. The MR level categorical agreement between cdPCR and ddPCR was perfect agreement at the level MR3 and above (BCR::ABL1IS >0.01 %) regardless of any differences in BCR::ABL1IS readings between assays. Using ddPCR, turnaround time is a consideration, with ddPCR typically requiring longer processing times due to steps such as RNA-to-cDNA conversion and droplet generation. Moreover, it requires many samples to perform each run. In contrast, cdPCR offers a more straightforward workflow with quicker turnaround times, and can perform each sample separately. Therefore, the present study’s findings demonstrate that cdPCR is equally suitable as ddPCR for CML diagnosis workup and early disease response monitoring within a one-year follow-up. There may be a proportional difference of BCR::ABL1IS with cdPCR tending to higher values in the upper part of the range and lower values in the lower part. Still, the conclusion based on regression results is limited by small subgroup sizes in the upper part of the range and the overall sample size.

For samples in DMR (BCR::ABL1IS ≤0.01; MR4.0 and below), the results revealed a substantial percentage of discordant categorizations of MR levels, while sensitivity analysis showed good negative percentage agreement and positive percentage agreement, both at ≥90 %. Both positive and negative quantitative disagreement above and below BCR::ABL1IS cutoffs were large enough to cause discordance in the qualitative categorization of the deepest levels of MR observed. We did not perform regression of constant difference in the DMR subgroup because the subgroup sample size was too small to detect a significant difference by Passing-Bablok regression and poor correlation for OLR regression [30–32]. Homogeneity of variance could not be obtained for Bland-Altman horizontal limits of agreement in the DMR group, which would have been over- and underestimated at opposite ends of the mean range [15]. Thus, further studies collecting more samples, especially in DMR, should be performed to compare ddPCR and cdPCR to clarify the agreement between the two methods. A previous study computed an ‘alignment factor’ between a commercial cdPCR kit and a commercial ddPCR kit for low transcript levels, using the Bland-Altman method to compute the mean bias by all pairs of combinations of replicates between dPCR kits instead of averaging across replicates [33–35]. Future studies could also compute an alignment factor for the commercial kits tested in the present study for MR classes of MR4.0 or lower, especially for the decision to discontinue TKI [36].

In the present study, comparisons of BCR::ABL1IS by patients expressing transcript variants of e13a2 only or e14a2 only showed a non-significant difference in median BCR::ABL1IS with e13a2 values higher than e14a2 values. Previous studies comparing RT-qPCR to dPCR have found RT-qPCR measurements were higher for shorter amplicon length, while dPCR measurements were not different by amplicon length [37–39]. dPCR is an quantitative endpoint measurement method, so measurements may not be affected by amplification efficiency due to amplicon length as much as those by RT-qPCR. It has been suggested that deeper molecular treatment response to TKI in patients expressing e14a2 compared to e13a2 measured by RT-qPCR may be a measurement artefact caused by more efficient amplification of a shorter e13a2 transcript variant, or there may be a true biological difference between transcript variants in treatment response [40]. In the present study, our comparison was not independent of treatment because we were unable to perform an analysis of measurements at diagnosis due to only having one sample each of three patients expressing e13a2 only and one patient expressing e14a2 only at diagnosis. dPCR may provide more reliable measurements for transcript variants. However, interpreting MR trends and milestones, while monitoring response to TKI regardless of transcript variant is more important in clinical practice.

There are some limitations of this study. First, the study is limited by its focus on specific BCR::ABL1 fusion transcripts (e13a2 and e14a2), potentially excluding patients with other rare transcripts and limiting the generalizability of the findings. Additionally, while the total sample size was reasonable for most analyses, a larger sample size and subgroup sample sizes of each MR level are required for regression-based quantitative analysis to confirm our results. The MR4 or below subgroup is of particular interest due to discordant categorizations for clinical decision-making. Thus, assuming an infinite range ratio, OPTOLANE’s published percentage coefficient of variation (%CV) for MR4 of 33.7, and most anticipated slopes, an MR4 or below subgroup sample size of ≥90 patient samples would be preferrable for Passing-Bablok regression analysis [30, 31]. More patient samples in the MR3 level or higher part of the range are required to confirm the graphical proportional difference between assays observed in the present study. Furthermore, comparisons between assays of BCR::ABL1IS on linearity, precision, including %CV, along with sensitivity and specificity, including LOQ, LOD, and false-positive rate, would be valuable in appropriate sample sizes for each analysis. Lastly, the study acknowledges limitations in the detection limits of both PCR methods, which could affect the accuracy of MRD assessment, especially at lower levels.

Conclusions

This study demonstrates comparable performance between cdPCR and ddPCR in detecting BCR::ABL1 transcripts in CML patients. Both platforms exhibited high concordance and correlation in MR3 or above, suggesting their utility for diagnostic workup and early BCR::ABL1 monitoring in clinical practice. However, the choice between cdPCR and ddPCR may depend on cost, workflow, and sensitivity requirements.

Corresponding author: Weerapat Owattanapanich, MD, Associate Professor, Division of Hematology, Department of Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkok Noi, Bangkok 10700, Thailand; and Center of Excellence of Siriraj Adult Acute Myeloid/Lymphoblastic Leukemia (SiAML), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand, E-mail:

Funding source: Biogenic Company Limited, Thailand

Award Identifier / Grant number: N/A

Acknowledgments

The authors express their gratitude to Dr. Anthony Tan and Ms. Pataraporn Tunsing for their collaboration in the data collection and statistical analyses. Dr. Anthony Tan edited the English language.

  1. Research ethics: This study was approved by the Ethics Committee for Research in Human Subjects of the Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand (COA no. Si 507/2023). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

  2. Informed consent: Informed consent was waived due to the utilization of leftover samples.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. W.S. and W.O. contributed to conceptualization, data curation, investigation, and methodology. W.S. conducted laboratory testing and drafted the manuscript, while W.O. was responsible for project administration, data analysis, supervision, and reviewing and editing the manuscript.

  4. Competing interests: All authors state no conflict of interest.

  5. Research funding: The cdPCR reagent and chip were funded by Biogenic Company Limited, Thailand. However, the company did not participate in the research process, influence the results, or contribute to manuscript writing.

  6. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0456).

Received: 2024-04-13
Accepted: 2024-08-02
Published Online: 2024-08-20
Published in Print: 2025-01-29

© 2024 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/cclm-2024-0456
Keywords for this article
BCR::ABL1 positive; chronic myelogenous leukemia; dPCR; International Scale; minimal residual disease; Philadelphia chromosome
Creative Commons
BY 4.0

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  40. Sigma metric is more correlated with analytical imprecision than bias
  41. Utility and limitations of monitoring kidney transplants using capillary sampling
  42. Simple flow cytometry method using a myeloma panel that easily reveals clonal proliferation of mature B-cells
  43. Is sweat conductivity still a relevant screening test for cystic fibrosis? Participation over 10 years
  44. Hb D-Iran interference on HbA1c measurement
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