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Comparison of Real-Time Quantitative PCR and Digital Droplet PCR for BCR-ABL1 Monitoring in Patients with Chronic Myeloid Leukemia
Journal Pre-proof Comparison of Real-Time quantitative PCR and digital droplet PCR for BCR-ABL1 Monitoring in Patients with Chronic Myeloid Leukemia Georg-Nikolaus Franke, Jacqueline Maier, Kathrin Wildenberger, Michael Cross, Francis J. Giles, Martin C. Müller, Andreas Hochhaus, Dietger Niederwieser, Thoralf Lange PII:
S1525-1578(19)30403-9
DOI:
https://doi.org/10.1016/j.jmoldx.2019.08.007
Reference:
JMDI 839
To appear in:
The Journal of Molecular Diagnostics
Received Date: 15 January 2019 Revised Date:
18 June 2019
Accepted Date: 28 August 2019
Please cite this article as: Franke G-N, Maier J, Wildenberger K, Cross M, Giles FJ, Müller MC, Hochhaus A, Niederwieser D, Lange T, Comparison of Real-Time quantitative PCR and digital droplet PCR for BCR-ABL1 Monitoring in Patients with Chronic Myeloid Leukemia, The Journal of Molecular Diagnostics (2019), doi: https://doi.org/10.1016/j.jmoldx.2019.08.007. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2019 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology and the Association for Molecular Pathology.
Comparison of Real-Time quantitative PCR and digital droplet PCR for BCRABL1 Monitoring in Patients with Chronic Myeloid Leukemia
Georg-Nikolaus Franke,* Jacqueline Maier,† Kathrin Wildenberger,* Michael Cross,* Francis J. Giles,‡ Martin C. Müller,§ Andreas Hochhaus,¶ Dietger Niederwieser,* and Thoralf Lange†ǁ From the Abt. Hämatologie und internistische Onkologie,* Universitätsklinikum Leipzig, Leipzig, Germany; the Universität Leipzig,† Leipzig, Germany; the Developmental Therapeutics Consortium,‡ Chicago, Illinois; the Medizinische Fakultät Mannheim,§ Mannheim, Germany; the Klinik Innere Medizin II,¶ Universitätsklinikum Jena, Jena, Germany; and the Asklepios Klinikum Weißenfels,ǁ Weißenfels, Germany
Address correspondence to: Georg-Nikolaus Franke, MD Medizinische Klinik und Poliklinik I, Universitätsklinikum Leipzig, Leipzig, Germany, Johannisallee 32a, 04103 Leipzig, Germany Email: [email protected]
Running head: BCR-ABL1 Monitoring in CML by dPCR.
Funding: Supported by Novartis Pharma GmbH project HTAS 157 and grant AN72255617 (T.L.).
Footnote: Portions of this manuscript have been presented at the meeting of the American Society of Hematology, December 5-8, 2015, Orlando, FL, and December 3-6, 2016, San Diego, CA.
Disclosures: G.N.F. received a research grant and honoraria from Novartis.
Abstract Real-time quantitative PCR (RT-qPCR) is routinely used to detect minimal residual disease in chronic myeloid leukemia patients. The absolute quantification with droplet digital PCR (dPCR) could reduce the inherent variability of RT-qPCR. We established a duplex dPCR assay using the EAC primer/probe system for BCR-ABL1 and ABL1 and compared the results to RT-qPCR. Two-hundred and thirty cDNA samples from patients with chronic myeloid leukemia were analyzed using both procedures. A second, commercially developed dPCR assay for BCR-ABL1 was also evaluated. ABL1 and BCR-ABL1 transcript levels were similar with all assays, but the proportion of deep molecular responses was lower with dPCR than with RT-qPCR. The EAC dPCR assay had a false-positive rate (FPR) of 4% using a cut-off of three BCR-ABL1 copies per duplicate, compared to 2% without cut-off for the commercial dPCR. The detection rate for molecular response 4.5 was 100 and a shift towards more minimal residual disease was seen in patient samples. In conclusion, using the EAC protocol for BCR-ABL1 quantification with dPCR is feasible and shows low intra- and interassay variation but requires a cut-off that reduces sensitivity. The commercial dPCR assay is highly sensitive and specific for BCR-ABL1. The use of either dPCR assay resulted in a shift to lower molecular response classes compared to RT-qPCR aligned to International Scale.
Introduction The introduction of tyrosine kinase inhibitors (TKI) has changed the management of chronic myeloid leukemia (CML) considerably. The aim of treatment has moved from prolonging survival or palliation towards cure or treatment-free remission [1]. The introduction of targeted therapy in CML was accompanied by the development of more sensitive detection of minimal residual disease based on Real-Time quantitative PCR (RT-qPCR) [2]. From the IRIS trial it soon became clear that BCR-ABL1 levels continue to decrease over time long after complete cytogenetic response is reached [3]. The value of RT-qPCR for monitoring BCR-ABL1 has been demonstrated in a number of studies using different TKIs [4–10]. Recently, several trials have explored the concept of TKI discontinuation in patients in whom deep molecular response is stably maintained under TKI treatment. The importance of a continuous deep molecular response (MR4 or deeper) as a prerequisite for stopping treatment has been demonstrated [11, 12] [ENESTop, EUROSKI]. The sensitive and precise monitoring of residual disease is therefore playing an increasingly important role in managing the treatment of CML, facilitating decisions regarding the discontinuation of treatment (6) and identifying patients at risk of progression (6,13-16). However, BCRABL1 transcript monitoring methodologies vary considerably in their performance and continue to do so despite intense efforts to standardize and harmonize both methodology and reporting. One reason for this is likely to be the variability inherent in the RT-qPCR technology employed [13]. Droplet digital PCR (dPCR) is based on the separation of a standard PCR reaction into many thousand nano-liter scale single droplets, the vast majority of which contain either one (or more) or no target copy. Endpoint amplification is performed within each droplet and the original target copy number is calculated from the proportion of
positive and negative drops using Poisson statistics [14]. This generates an absolute read out that is largely tolerant of variations in PCR efficiency, reducing the requirement for internal standardization by eliminating the need for a standard curve. This feature has been exploited to develop reference material [15]. dPCR is able to monitor even very low BCR-ABL1 levels (MR4.5 and below) in CML patients with a high degree of reliability and sensitivity [16–19]. Our aim was to first establish the Europe against Cancer protocol for e13a2 and e14a2 BCR-ABL1 quantification [20] as a dPCR assay, determine its linearity, intraand inter-assay variation, lower limit of blank, limit of detection, specificity and sensitivity. Subsequently, we aimed to compare the M-BCR-ABL1 monitoring by dPCR to standardized RT-qPCR in blinded samples from two independent laboratories with respect to the observed rates of molecular response (MR) in CML patients having undergone 18 months of nilotinib treatment in the ENEST1st trial. Finally, our aim was to compare the EAC dPCR assay to a commercially developed dPCR assay.
MATERIAL AND METHODS
Samples K562 cells (BCR-ABL1 positive samples; Leibnitz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Braunschweig, Germany, DSMZ no.: ACC 10), Ba/F3 (DSMZ no.: ACC 300) and HELA cells (wild-type samples, DSMZ no.: ACC 57) and samples from CML patients and healthy donors were used for determination of linearity, lower limit of blank (LoB), limit of detection (LoD), specificity, and sensitivity.
Two-hundred and thirty cDNA samples from CML patients carrying e13/a2 or e14/a2 BCR-ABL1 fusion genes who underwent treatment within the ENEST1st trial were analyzed with RT-qPCR in Leipzig (L, n=75) or Mannheim (M, n=155) between 2012 and 2013, then reanalyzed in Leipzig by dPCR.
Real-Time quantitative PCR BCR-ABL1, ABL1, and BCR-ABL1/ABL1 ratios were retrieved from the ENEST1st database. Quantitative Real-Time PCR was performed as described earlier [20, 21] using an in-house plasmid (Leipzig) or pME2 plasmid (Mannheim) to generate standard curves. Laboratory-specific conversion factors were used to convert the ratio %BCR-ABL1/ABL1 to international scale (IS, BCR-ABL1IS). Nuclease-free water was included as a non-template control (NTC) and K562 cell lysate as a positive control in each analysis.
Primer/Probes for the EAC assay RT-qPCR and dPCR were compared using primer/probe sets as previously published [20, 21]. Probes were FAM-TAMRA labelled for RT-qPCR (biomers.net GmbH, Ulm, Germany) and FAM/HEX-BHQ1 labelled for duplex dPCR (Eurofins Genomics Germany GmbH, Ebersberg, Germany).
Primer probes M-BCR-ABL1 Primer/Probe: ENF_501 ENR_561
5’-TCCGCTGACCATCAAYAAGGA-3’;
5’-CACTCAGACCCTGAGGCTCAA-3’; and ENP_541
CCCTTCAGCGGCCAGTAGCATCTGA-3’.
5’-
ABL1 Primer/Probe: ENF_1003 3’; ENR_1063
5’-TGGAGATAACACTCTAAGCATAACTAAAGGT-
5’-GATGTAGTTGCTTGGGACCCA-3’; andENP_1043
5’-
CCATTTTTGGTTTGGGCTTCACACCATT-3’.
Primer and probes for the Bio-Rad dPCR assay for BCR-ABL1 and ABL1 are undisclosed intellectual property of Bio-Rad, Pleasanton, CA.
dPCR EAC-Assay A final volume of 20µL duplex dPCR reaction mix consisted of: ddPCR Supermix for Probes (fc 1x); primers ENF_501, ENR_561, ENF_1003, and ENR_1063 (fc 900nM); probes ENP_541, ENP_1043 (fc 250nM), and 5µL template cDNA (product of 500ng reverse transcribed RNA). After sealing and centrifugation (1min/800rpm) the 96well plate was transferred into the QX200 Droplet Generator (Bio-Rad) to generate approximately 20,000 droplets. The PCR was performed in a Thermocycler T100 (Bio-Rad) in a sample volume of 40µL using the following protocol: enzyme activation 95
°C/10min
followed
by
40
cycles
of
denaturation
94
°C/30sec,
and
annealing/extension 60 °C/60sec (ramp rate settings 2.5 °C; heated lid set to 105 °C with a final 10 minute incubation at 98 °C before c ooling to 12 °C. Bio-Rad Assay The assay was performed according to the manufacturer’s instructions. For both assays, positive and negative droplets were detected using the QX200 Droplet Reader and data analyzed using the Software QuantaSoft 1.7.4.0917 (BioRad). Samples for which no negative droplet fraction was evident were subject to
another round of PCR analysis using a higher dilution of input material. Samples generating fewer than 10,000 analyzed droplets were excluded from further analysis.
The specificity and sensitivity of the dPCR assays was determined using nontemplate control (NTC), healthy donor wild-type cDNA, and patient cDNA using the formulas,
specificity = true negative / (true negative + false positive)
and sensitivity = true positives / (true positives + false negatives).
The false positive rate (FPR) was calculated using the formula
FPR = number of false positive samples/(false positive + true negative samples)
using NTC and healthy donor wild-type cDNA. To evaluate the linearity of the dPCR assays, log10 measured concentrations of BCR-ABL1 and ABL1 were plotted against log10 sample input of BCR-ABL1 and ABL1 from K562 cells for each assay and linear regression analysis was performed. To determine Intra-Assay-Variation 4 log10 dilutions of K562 cells between 20 and 20,000 BCR-ABL1 and ABL1 copies were measured 6 times in triplicates in the same run. The coefficient of variation (CV) was calculated for each dilution by dividing the standard deviation (SD) by the mean of the six results per dilution in percent (%CV = Mean of SD x 100 / Mean). Finally, the average of the individual CVs is denoted as intra-assay CV. Unlike Intra-AssayVariation the Inter-Assay-Variation was determined from runs of 16 replicates per dilution performed on three different days. The limit of blank was defined as the
highest measurement that is likely to be observed over 95% of the time. The LoB was calculated using a non-parametric approach as described in the EP17-A2 guidelines [22] using NTC and wild-type cDNA from healthy donors. The limit of detection (LoD) of an assay is the sample input level that can be successfully detected by the assay over 95% of the time. In this study, the LoD for dPCR was calculated as the number of copies per reaction well using the formula
LoD = LoB + 1.645 x SDlow concentration
using a serial dilution of K²EDTA blood from a CML patient in K²EDTA blood from a healthy volunteer to cover the range MR4.5 to MR5.5 (mean BCR-ABL1 copies 2.87, 1.58, and 0.51/reaction) as described in the EP17-A2 guidelines [22]. Each concentration was measured 16 fold on three different days using the same reagent lots.
RESULTS Specificity and False-Positive-Rate of the dPCR EAC assay The specificity from three different runs on three different days (3x 94 wells wild-type and NTC) of BCR-ABL1 dPCR ranged from 71% to 93%, corresponding to a falsepositive-rate of 7% to 29% (7% to 22% for NTC and 11% to 29% for wild-type control BA/F3 murine pro B cells). In comparison, ABL1 dPCR yielded a constant specificity of 98% and a FPR of 2% for NTC. However, it is possible to increase the specificity of dPCR EAC assay for BCR-ABL1 to >95% by using a cut-off of 3 positive droplets for positivity, which reduces the FPR to level lower than 5% (described below).
Sensitivity of the dPCR EAC assay
The sensitivity of BCR-ABL1 was determined in six dilutions of BCR-ABL1 positive cDNA from K562 cells (equivalent to 0, 0.25, 2.5, 5, 10, and 25 BCR-ABL1 copies per well) in high wild-type background (mean 71,000 ABL1 copies from BA/F3 cDNA). Each dilution was tested in 96well format and assessed in duplicate. BCR-ABL1 dilutions of 25 to 5 copies/assay were detected in 100% by both methods. The 2.5 copies dilution was detected in 100% of the samples by dPCR and in 96% by RTqPCR. At the theoretical dilution of 0.25 copies, 71 % of the samples were positive by dPCR and 27% by RT-qPCR. Accordingly, dPCR showed a higher FPR with BCRABL1 negative cDNA (58%, 28 of 48 samples) than with NTC (7% to 29%, see above) (Figure 1A). Using a cut-off of 2 (requiring at least two positive droplets in duplicates for a positive result) the sensitivity of the two methods was comparable from 25 to 0.25 copies, but the BCR-ABL1 negative wild-type control still showed a FPR of 27% in dPCR (Figure 1B). Applying a cut-off of 3 reduced the FPR to 4%. However, this also resulted in a loss of sensitivity for dPCR compared to RT-qPCR (nearly 100% detection of 2.5 copies/assay by RT-qPCR vs 80% by dPCR, Figure 1C). For future experiments, the 3 droplet cut-off was adopted for duplicates. This means a sample with only 1 or 2 BCR-ABL1 positive droplets will be declared negative whereas a sample with at least three positive drops per duplicate for BCR-ABL1 will be counted as positive.
Linearity, intra-, and inter-assay variation of the EAC dPCR assay Linearity was high for both ABL1 and BCR-ABL1 (R² >0.99). The intra-assay coefficient of variation was 7% for ABL1 and BCR-ABL1 (range 3% to 19%). The median inter-assay coefficient of variation was 7% for ABL1 (range 4% to 12%) and 8% for BCR-ABL1 (range 4% to 22%) (Table 1).
Comparison of Real-Time quantitative PCR and droplet dPCR For the whole group of 230 cDNA samples from CML patients of the ENEST1st trial, the median ABL1 copy number (CN) was 59,350 (range 7,690 to 176,000) by dPCR and 53,537 (range 4,013 to 250,800) by RT-qPCR, whereas the median BCR-ABL1 CN was 12.25 (range 0 to 2,050) and 10.45 (0 to 1,529), respectively. More than 77% of all samples had a detection limit %BCR-ABL1/ABL1 (IS) of 0.0032 and lower (ABL CN for MR4.5 > 32,000) by both dPCR and RT-qPCR. Minimal residual disease was detected in 186 samples by dPCR, 189 samples by RT-qPCR, and in 170 samples by both methods. The median BCR-ABL1/ABL1 ratio measured by dPCR was 0.022% (range 0 to 2.783%) and by RT-qPCR 0.019% (range 0 to 6.881%), corrected to 0.014% (range 0 to 2.380%) after conversion to international scale (IS) (figure 2). Fifty-three samples (23%) had BCR-ABL1/ABL1 ratios >0.1% by dPCR, compared to 57 (25%) before and 39 (17%) samples after conversion to IS by RTqPCR. The correlation coefficient between dPCR and RT-qPCR in the whole cohort was 0.85 for BCR-ABL1, 0.81 for ABL1, 0.61 for % BCR-ABL1/ABL1 and 0.83 for % BCRABL1/ABL1 (IS) (Figure 3A-C). Detailed results for the separate cohorts from Leipzig and Mannheim are shown in Table 2.
Classification according to method and depth of molecular remission In the next step, all samples were classified according to the depth of molecular remission according to the EUTOS guidelines used in the ENEST1st trial. % BCRABL1/ABL1 (IS) was used for the results obtained by RT-qPCR and the unadjusted % BCR-ABL1/ABL1 for dPCR. By RT-qPCR, 39 pts (17%) were > MR3, 91 pts (40%) were scored MR3, 33 pts (14%) MR4, 44 pts (19%) MR4.5, and 23 pts (10%) MR5.
In contrast, by dPCR 53 pts (23%) did not achieve MR3, 100 pts (43%) were in MR3, 35 pts (15%) in MR4, 20 pts (9%) in MR4.5, and 22 (10%) in MR5 (Table 3, Figure 4A). This distribution was significantly different between the two methods (P = 0.02). Significantly fewer pts (n=77) achieved MR4 or better by dPCR, compared to RTqPCR (n=100, P = 0.035, Chi-Square-Test). Although the significance is reduced by decreasing the sample size, the same effect was observed as a trend in the samples from each participating laboratory, with 28% (n=21) of pts from Leipzig in MR4 or better by dPCR EAC assay vs 43% (n=32) by RT-qPCR and 36% (n=56) of pts from Mannheim in MR4 or better by dPCR compared to 44% (n=68) by RT-qPCR (Figure 4B). At the level of the individual samples, 134 pts (58%) were classified in the same MR category by both methods. Twenty-five pts (11%) were scored into deeper molecular response categories and 71 pts (31%) into a less deep molecular response group by dPCR (Table 3). Measurement by dPCR EAC assay therefore resulted in an overall shift towards more residual disease. A significant difference in ABL1 CN was not observed between the two methods in the 71 samples that switched into a worse molecular response group (P = 0.95, Students t-test); however, corresponding BCRABL1 copies were higher by dPCR in 79% (56/71 samples, P = 0.02). Focusing on the BCR-ABL1 positive samples, the median BCR-ABL1 CN was 2.4-fold higher by dPCR than by RT-qPCR, the largest difference arising from samples with less than 15 copies. The reclassification of 25 samples into a deeper molecular response category by dPCR resulted predominantly from the detection of slightly higher ABL1 copies in samples that were BCR-ABL1 negative (1.2-fold, n=19/25). However, 10 samples were BCR-ABL1 positive by RT-qPCR and BCR-ABL1 negative by dPCR. Of these,
all 10 samples had become negative by dPCR only after applying the cut-off of 3 positive droplets.
Bio-Rad Assay The use of the EAC protocol for BCR-ABL1 quantification by dPCR generated a background signal that limits sensitivity and thus the ability to detect very low levels of disease. For this reason, a commercial assay developed by Bio-Rad, Pleasanton, CA, which was designed specifically to avoid the problem of false positive droplets, was tested. The rate of false positive samples for BCR-ABL1 and ABL1 was 0% (n=0/176) and 1% (n=2/176), respectively, in no-template controls. The FPR for BCR-ABL1 in HELA cells and in healthy donors was 0% (n=0/44) and 2% (n=5/234) respectively, resulting in a limit of blank of zero. This virtually eliminates the background associated with the EAC protocol, reported here and by others [23]. Linearity was stable over 5 log10 dilutions of K562 cells with BCR-ABL1 copy numbers from 80,000 to 1 and ABL1 copy numbers from 100,000 to 1 with R² = 0.9997 for both BCR-ABL1 and ABL1. The resulting average ratio was 77% BCRABL1/ABL1 (Standard deviation 5.0, Figure 5). When restricting this analysis to concentrations outside possible significant deviations resulting from Poisson statistics, the average ratio was 75% (standard deviation 2.28, range 72% to 78%). Inter-assay variation was determined to be 10%CV for MR3, 37%CV for MR4, and 88%CV for M4.5. Intra-assay variation was 9% for MR3, 31%CV for MR4 and 76%CV for MR4.5 samples. The limit of detection was calculated to be 1.96 copies in a background of wild-type cDNA with 100,000 copies of ABL, meaning that a sample containing more than two copies will yield a positive result in more than 95% of the tests. Some uncertainty remains for samples that are tested negative, as they could still contain up to 1.96
copies. For 2-well (20,000 droplets/well – in sum 40,000 droplets) analysis, the detection rates for MR4.5, MR5, and MR5.5 were 100%, 88%, and 42%, respectively. Extending this to a 4 well analysis (in sum 80,000 generated droplets) increased these detection rates to 100%, 100%, and 67%, respectively (Table 4). The assay was then tested on low level disease using a MR4.7 sample certified by the College of American Pathologists (CAP) and used for calibration (this test was performed in the Bio-Rad dPCR laboratory). This MR4.7 sample was subjected to repeated analysis. Fifty-nine of 60 replicates of the MR4.7 samples produced results (one sample had less than 10,000 droplets analyzed and had to be excluded). A total of
89
BCR-ABL1
copies
(1.51/replicate)
and
4,329,846
ABL1
copies
(73,387/replicate) were detected, resulting in a ratio of 0.0021 (MR4.69). When combining any two replicates, the false negative rate was below 3%, indicating a LOD of at least MR4.7 in two replicates for this assay. This is consistent with the distribution predicted by the Poisson distribution (Figure 6). Next, we tested 40 samples in duplicates from patients with CML with MR levels ranging from MR3 to MR5. Twenty (50%) samples were MRD negative by RT-qPCR compared to 17 (42%) by dPCR. Median copy numbers for ABL1 were 59,500 (mean 66,855) by dPCR and 54,945 (mean 60,469) by RT-qPCR whereas those for BCRABL1 were 3.1 by dPCR and 6.2 by RT-qPCR. The variation coefficient for low copy number BCR-ABL1 was similar in the two groups (%CV = 50 vs 52 for dPCR vs RTPCR), but at high copy number ABL1 the variation was lower in dPCR (%CV = 0.8 for dPCR versus 6.3 for RT-PCR). The correlations between dPCR and RT-qPCR for both ABL1 and BCR-ABL1 were high at 0.83 and 0.90, respectively. Median (mean) ratio BCR-ABL1/ABL1 was 0.014% (0.031%) by dPCR and 0.005% (0.046%) and 0.002% (0.0134%) by RT-qPCR before and after conversion to the International scale, respectively. The correlation for BCR-ABL1/ABL1 by dPCR to BCR-
ABL1/ABL1 by RT-qPCR before and after conversion to International Scale was 0.98. Nonetheless, when looking at groups of molecular response, 30% of patients (n=12) were grouped in lower molecular response categories by dPCR than by RT-qPCR, confirming the observation made with the EAC protocol in the ENEST1st samples, although this has to be verified in a larger patient cohort comparing both dPCR assays and RT-qPCR.
DISCUSSION Here, we demonstrate that the use of dPCR for quantitative BCR-ABL1 monitoring is feasible and safe. The partition of an analyte into thousands of single droplets enables an endpoint measurement and absolute quantification of a target sequence. This makes the assay less prone to PCR inhibition and independent of a standard curve, adding to the simplicity of the technique. dPCR was shown to have an LoD as low as 1.96 copies in wild-type background, with 0.5 copies being detected in 50% of the samples. Linearity is stable over 5 orders of magnitude from 2.5x100 to 1x105, making duplex assays with the reference gene and BCR-ABL1 feasible. The ratio BCR-ABL1/ABL1 was stable over the range from 2x101 to 1x105, which is important for determination of a stable conversion factor to the international scale [15]. However, the EAC assay effectively loses sensitivity as a consequence of the rate of false positive samples in NTC and wild-type (duplicates are positive up to 58% for one or two droplets per replicate), making it necessary to impose a cut-off of 3 positive droplets in duplicate assays. In the patient samples from the ENEST1st studies from two separate EUTOS laboratories in Germany, it was demonstrated that quantification by dPCR reads out similar copy numbers for BCR-ABL1 and ABL1 and similar ratios of BCRABL1/ABL1. Also, the percentage of samples with ABL1 CN high enough to detect
MR4.5 or better was similar with both techniques using the EAC protocol. However, after conversion of the RT-qPCR results to International Scale, dPCR tends to read out higher levels of BCR-ABL1/ABL1 than does standard RT-qPCR, resulting in a significant shift of samples towards worse MR classes. Although this effect was observed in both of the available cohorts and was also seen when using the Bio-Rad assay in a third cohort of patient samples, this has to be confirmed in a multicenter ring trial. When assaying the same samples with both methods, most of the discrepancy to IS seems to result from steps before the quantification (RNA extraction, cDNA synthesis). The e13a2/e14a2BCR-ABL1 primer/ probe set in widespread use in the EAC dPCR protocol is reliable and reproducible but still suboptimal, since the high sensitivity is currently accompanied by a high false positive rate, requiring the use of a 3 droplet cut-off to ensure specificity. Using the cut-off 3 for dPCR EAC assay the high false-positive-rate was <5%, resulting in turn in a loss of sensitivity compared to RT-PCR EAC protocol. To access the full potential of dPCR in terms of sensitivity, it will be necessary to develop assays with a LoB of zero and to implement a dPCR specific conversion factor. The first requirement is met by the newly developed Bio-Rad assay, which has a very low false positive rate and a limit of blank of zero, enabling the reliable assessment of very deep molecular responses. It is especially noteworthy that we determined a limit of detection (1.96 copies) below the theoretical limit given by the Poisson statistic of 3 copies/sample for a single replicate. This enables us to push the detection limit of the assay towards a reliable detection of MR4 and beyond by performing multiple replicates of sample. This is likely to be of clinical relevance, as there is a clear correlation between minimal residual disease and the likelihood of successful treatment discontinuation. Patients with MRD levels of MMR or worse are rarely able to successfully stop the treatment. The failure to detect a difference in TFR rate
between MR4 and MR4.5 in the EURO SKI trial might be due to the inherent variability of the RT-qPCR assays used. The inter-assay variation of the dPCR assay reported here appears to be much lower than the variations usually observed with RT-qPCR [15, 24]. Thus, this dPCR assay could help to better identify patients for successful TKI discontinuation. It is important to note that the conversion factor for the dPCR assay is yet to be determined, as different results were found comparing RT-qPCR and dPCR in patient samples. However, the CAP MR4.7 sample yielded a result very close to the nominal value. Here, the recent development of a set of secondary reference materials (which are calibrated using dPCR) will help to determine the conversion factor for each laboratory more easily. In conclusion, monitoring minimal residual disease by dPCR seems to be feasible and safe. It reliably detects deep molecular responses relevant for guiding treatment. Its potential to eliminate the need for a conversion factor due to the absolute measurement and to reliably detect very deep molecular response greater than MR4.5 should be explored further. This could help to better identify patients prone to relapse after stopping TKI treatment.
Acknowledgements We thank Bio-Rad Laboratories for supplying the assay, Svilen Tzonev and Dawne Shelton from the dPCR Laboratory for their help with technical issues regarding the dPCR assays and providing the assays, Novartis for supporting this trial,Ines Kovacs and Scarlett Schwabe in preparing and storing the samples, and all patients for providing their blood for the analysis.
References 1
Hughes TP, Ross DM: Moving treatment-free remission into mainstream clinical practice in CML. Blood 2016;128:17–23. 2 Hughes T, Deininger M, Hochhaus A, Branford S, Radich J, Kaeda J, Baccarani M, Cortes J, Cross NCP, Druker BJ, Gabert J, Grimwade D, Hehlmann R, Kamel-Reid S, Lipton JH, Longtine J, Martinelli G, Saglio G, Soverini S, Stock W, Goldman JM: Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood 2006;108:28– 37. 3 Hughes TP, Kaeda J, Branford S, Rudzki Z, Hochhaus A, Hensley ML, Gathmann I, Bolton AE, van Hoomissen IC, Goldman JM, Radich JP: Frequency of major molecular responses to imatinib or interferon alfa plus cytarabine in newly diagnosed chronic myeloid leukemia. N Engl J Med 2003;349:1423–1432. 4 O'Brien Stephen G., Guilhot François, Larson Richard A., Gathmann Insa, Baccarani Michele, Cervantes Francisco, Cornelissen Jan J., Fischer Thomas, Hochhaus Andreas, Hughes Timothy, Lechner Klaus, Nielsen Johan L., Rousselot Philippe, Reiffers Josy, Saglio Giuseppe, Shepherd John, Simonsson Bengt, Gratwohl Alois, Goldman John M., Kantarjian Hagop, Taylor Kerry, Verhoef Gregor, Bolton Ann E., Capdeville Renaud, Druker Brian J.: Imatinib Compared with Interferon and Low-Dose Cytarabine for Newly Diagnosed Chronic-Phase Chronic Myeloid Leukemia. 5 Druker BJ, Guilhot F, O'Brien SG, Gathmann I, Kantarjian H, Gattermann N, Deininger MWN, Silver RT, Goldman JM, Stone RM, Cervantes F, Hochhaus A, Powell BL, Gabrilove JL, Rousselot P, Reiffers J, Cornelissen JJ, Hughes T, Agis H, Fischer T, Verhoef G, Shepherd J, Saglio G, Gratwohl A, Nielsen JL, Radich JP, Simonsson B, Taylor K, Baccarani M, So C, Letvak L, Larson RA: Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med 2006;355:2408–2417. 6 Hochhaus A, Rosti G, Cross NCP, Steegmann JL, Le Coutre P, Ossenkoppele G, Petrov L, Masszi T, Hellmann A, Griskevicius L, Wiktor-Jedrzejczak W, Rea D, Coriu D, Brummendorf TH, Porkka K, Saglio G, Gastl G, Muller MC, Schuld P, Di Matteo P, Pellegrino A, Dezzani L, Mahon F-X, Baccarani M, Giles FJ: Frontline nilotinib in patients with chronic myeloid leukemia in chronic phase: results from the European ENEST1st study. Leukemia 2016;30:57–64. 7 Hochhaus A, Saglio G, Hughes TP, Larson RA, Kim D-W, Issaragrisil S, Le Coutre PD, Etienne G, Dorlhiac-Llacer PE, Clark RE, Flinn IW, Nakamae H, Donohue B, Deng W, Dalal D, Menssen HD, Kantarjian HM: Long-term benefits and risks of frontline nilotinib vs imatinib for chronic myeloid leukemia in chronic phase: 5-year update of the randomized ENESTnd trial. Leukemia DOI: 10.1038/leu.2016.5. 8 Cortes JE, Saglio G, Kantarjian HM, Baccarani M, Mayer J, Boque C, Shah NP, Chuah C, Casanova L, Bradley-Garelik B, Manos G, Hochhaus A: Final 5-Year Study Results of DASISION: The Dasatinib Versus Imatinib Study in Treatment-Naive Chronic Myeloid Leukemia Patients Trial. J Clin Oncol 2016;34:2333–2340. 9 Branford, Hughes, Rudzki: Monitoring chronic myeloid leukaemia therapy by real-time quantitative PCR in blood is a reliable alternative to bone marrow cytogenetics. Br J Haematol 1999;107:587–599. 10 Baccarani M, Deininger MW, Rosti G, Hochhaus A, Soverini S, Apperley JF, Cervantes F, Clark RE, Cortes JE, Guilhot F, Hjorth-Hansen H, Hughes TP, Kantarjian HM, Kim DW, Larson RA, Lipton JH, Mahon FX, Martinelli G, Mayer J, Muller MC, Niederwieser D, Pane F, Radich JP, Rousselot P, Saglio G, Saussele S, Schiffer C, Silver R, Simonsson
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B, Steegmann JL, Goldman JM, Hehlmann R: European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood 2013;122:872–884. Mahon F-X, Réa D, Guilhot J, Guilhot F, Huguet F, Nicolini F, Legros L, Charbonnier A, Guerci A, Varet B, Etienne G, Reiffers J, Rousselot P: Discontinuation of imatinib in patients with chronic myeloid leukaemia who have maintained complete molecular remission for at least 2 years: The prospective, multicentre Stop Imatinib (STIM) trial. The Lancet Oncology 2010;11:1029–1035. Hochhaus A, Masszi T, Giles FJ, Radich JP, Ross DM, Gómez Casares MT, Hellmann A, Stentoft J, Conneally E, García-Gutiérrez V, Gattermann N, Wiktor-Jedrzejczak W, Le Coutre PD, Martino B, Saussele S, Menssen HD, Deng W, Krunic N, Bedoucha V, Saglio G: Treatment-free remission following frontline nilotinib in patients with chronic myeloid leukemia in chronic phase: results from the ENESTfreedom study. Leukemia 2017;31:1525–1531. Branford S, Fletcher L, Cross NCP, Muller MC, Hochhaus A, Kim D-W, Radich JP, Saglio G, Pane F, Kamel-Reid S, Wang YL, Press RD, Lynch K, Rudzki Z, Goldman JM, Hughes T: Desirable performance characteristics for BCR-ABL measurement on an international reporting scale to allow consistent interpretation of individual patient response and comparison of response rates between clinical trials. Blood 2008;112:3330– 3338. Sykes PJ, Neoh SH, Brisco MJ, Hughes E, Condon J, Morley AA: Quantitation of targets for PCR by use of limiting dilution. Biotechniques 1992;13:444–449. Cross NC, White HE, Ernst T, Welden L, Dietz C, Saglio G, Mahon FX, Wong CC, Zheng D, Wong S, Wang SS, Akiki S, Albano F, Andrikovics H, Anwar J, Balatzenko G, Bendit I, Beveridge J, Boeckx N, Cerveira N, Cheng SM, Colomer D, Czurda S, Daraio F, Dulucq S, Eggen L, El HH, Gerrard G, Gniot M, Izzo B, Jacquin D, Janssen JJ, Jeromin S, Jurcek T, Kim DW, Machova-Polakova K, Martinez-Lopez J, McBean M, Mesanovic S, Mitterbauer-Hohendanner G, Mobtaker H, Mozziconacci MJ, Pajic T, Pallisgaard N, Panagiotidis P, Press RD, Qin YZ, Radich J, Sacha T, Touloumenidou T, Waits P, Wilkinson E, Zadro R, Muller MC, Hochhaus A, Branford S: Development and evaluation of a secondary reference panel for BCR-ABL1 quantitation on the International Scale. Leukemia DOI: 10.1038/leu.2016.90. Jennings LJ, George D, Czech J, Yu M, Joseph L: Detection and quantification of BCRABL1 fusion transcripts by droplet digital PCR. J Mol Diagn 2014;16:174–179. Maier J, Franke G-N, Schubert K, Wildenberger K, Cross M, Niederwieser D, Lange T: A Comparison of Droplet Digital PCR and Quantitative RT-PCR for Low Level BCRABL in CML Patients with Molecular Responses. Blood 2014;124:1792. Andersen RF, Pallisgaard N: Exceeding MR5.0 Sensitivity in Routine BCR-ABL1 Analysis Using Multiplex Digital PCR. Blood 2014;124:4540. Maier J, Lange T, Cross M, Wildenberger K, Niederwieser D, Franke G-N: Optimized Digital Droplet PCR for BCR-ABL. J Mol Diagn 2019;21:27–37. Gabert J, Beillard E, van der Velden VHJ, Bi W, Grimwade D, Pallisgaard N, Barbany G, Cazzaniga G, Cayuela JM, Cave H, Pane F, Aerts JLE, Micheli D de, Thirion X, Pradel V, Gonzalez M, Viehmann S, Malec M, Saglio G, van Dongen JJM: Standardization and quality control studies of 'real-time' quantitative reverse transcriptase polymerase chain reaction of fusion gene transcripts for residual disease detection in leukemia - a Europe Against Cancer program. Leukemia 2003;17:2318–2357. Lange T, Deininger M, Brand R, Hegenbart U, Al-Ali H, Krahl R, Poenisch W, Uharek L, Leiblein S, Gentilini C, Petersdorf E, Storb RF, Niederwieser D: BCR-ABL transcripts are early predictors for hematological relapse in chronic myeloid leukemia after hematopoietic cell transplantation with reduced intensity conditioning. Leukemia 2004;18:1468–1475.
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Figure Legends
Figure 1. A: Sensitivity of BCR-ABL1 PCR according to the method, assessed without cut-off. Application of the cut-off 2 (B) and cut-off 3 (C) for BCR-ABL1 dPCR. RT-qPCR: Real-Time quantitative PCR; dPCR: droplet digital PCR.
Figure 2: Comparison of RT-qPCR and dPCR. ABL1, BCR-ABL1, and BCRABL1/ABL1 ratio for whole cohort using the EAC protocol for RT-qPCR and dPCR. Box plots depict the observation range, lower quartil – 25th percentile, upper quartile 75th percentile. ***P < 0.001.
Figure 3A: Correlation of RT-qPCR and dPCR for ABL1 (A) and BCR-ABL1 (B) using the EAC protocol. C: Correlation of RT-qPCR and dPCR for %BCR-ABL1/ABL1 IS using the EAC protocol. IS: International Scale.
Figure 4. A: Distribution of molecular response classes by methods: whole cohort. Whole cohort, n = 230. n= number of samples; MR: molecular response; MMR: major molecular response; DMR: deep molecular response. B: Distribution of molecular response classes by methods: by laboratory. RT-qPCR-M: Real-Time quantitative PCR for samples from Mannheim; dPCR-M droplet dPCR for samples from Mannheim (n=155); RT-qPCR-L: Real-time quantitative PCR for samples from Leipzig; dPCR-L droplet dPCR for samples from Leipzig (n=75);
Figure 5: Linearity Bio-Rad assay. Measured BCR-ABL1 and ABL1 copy numbers of log10 diluted K562 cells (BCR-ABL1 80,000 to 1 copies, ABL1 100,000 to 1 copies, y-axis) and %BCR-ABL1/ABL1 against sample input (x-axis)
Figure 6: Observed versus predicted BCR-ABL1 copy numbers in a MR4.7 sample using the Bio-Rad assay.
Table 1: Linearity, intra- and inter assay variation for ABL and BCR-ABL using the EAC protocol for dPCR. Target
Linearity (R²)
Intra assay variation
Inter assay variation
(%CV)
(%CV)
ABL1
0.997
7 (range 3-19)
7 (4-22)
BCR-ABL1
0.999
7 (range 3-19)
8 (4-22)
%CV: coefficient of variation.
Table 2: Correlation between RT-qPCR and dPCR for ABL1, BCR-ABL1, % BCRABL1/ABL1, and % BCR-ABL1/ABL1 IS.
Whole cohort (n=230) dPCR
RT-qPCR
Leipzig (n=75) dPCR
RT-qPCR
median ABL1 CN (range)
Cor
59,350 (7,690176,000) 53,537 (4,013250,800)
0.85 12.25 (02,050)
0.81 0.022 (02.783)
10.45 (01,529)
0.019 (06.881)
29,670 (7,69099,600) 30,734 (4,01391,540)
0.84 9.10 (0538)
Mannheim (n=155) dPCR 80,000 (10,870176,000) RT-qPCR 66,570 (10,080250,800)
median BCR-ABL1 CN (range)
Cor
Median %BCRABL1/ABL1 (range)
0.95 0.023 (01.530)
10.17 (01,444)
0.035 (06.882)
0.74 15.50 (02,050)
0.95 0.022 (02.783)
10.47 (01,529)
0.017 (01.609)
Cor
Median %BCRABL1/ABL1 IS (range)
Cor
0.61
-
0.83
0.014 (02.380)
0.91
-
0.93
0.013 (02.380)
0.89
-
0.89
0.015 (01.416)
CN: Copy number; Cor: Correlation coefficient; n: number of samples; %IS: %BCRABL/ABL on the International scale.
Table 3: Distribution of molecular response classes. RT-qPCR
dPCR EAC
>MR3
MR3
MR4
MR4.5
MR5
n
>MR3
37
1
1
0
0
39
MR3
16
68
6
0
1
91
MR4
0
15
10
5
3
33
MR4.5
0
15
12
9
8
44
MR5
0
1
6
6
10
23
N
53
100
35
20
22
230
assay
MR: molecular response; RT-qPCR: Real-time quantitative PCR; dPCR: droplet digital PCR; Shift to more residual disease –bold numbers; less residual disease – italic; n: number of samples.
Table 4: Detection rate of MR3 to MR5.5 by the Bio-Rad assay. 2-well analysis BCR-ABL1 CN
4 well analysis ABL1 CN
Detection
BCR-ABL1 CN
ABL1 CN
rate (%)
Detection
%BCR-ABL1/ABL1
rate
MR3
285
200,750
100
571
401,500
100%
0.142
MR4
26
200,375
100
52
400,750
100%
0.013
MR4.5
6
198,992
100
11
397,983
100%
0.0028
MR5
3
200,558
88
6
401,117
100%
0.0015
MR5.5
1
199,350
42
2
398,700
67%
0.00051
CN: copy numbers; MR: molecular response.
A
B
C
m e d ia n A B L ( n = 2 3 0 )
m e d ia n B C R - A B L ( n = 2 3 0 )
1000000
10000
53,537 c o p ie s /a s s a y
c o p ie s /a s s a y
59,350 100000
10000
1000
12
10
dPCR
qPCR
1000
100
10
1 dPCR
qPCR
m e d ia n B C R - A B L /A B L ( n = 2 3 0 )
0.022
0.019
0.014
*** 10
1
%
0 .1
0 .0 1
0 .0 0 1
0 .0 0 0 1 dPCR
qPCR
q P C R ( IS )
A dPCR ABL1 copy numbers total
R²=0.81 100000
10000
1000 1000
10000
100000
RT-qPCR ABL1 copy numbers total
1000000
C 10.000 10,00000
10000
R²=0.83
R²=0.85
1000
1.000
1,00000
dPCR %BCR-ABL1/ABL1
dPCR BCR-ABL1 copy numbers total
B 1000000
100
0.100 0,10000
10
0.010 0,01000
1 0,1 0.1 1
10
100
1000
RT-qPCR BCR-ABL1 copy numbers total
10000
0.001 0,00100 0.001 0,001
0.010 0,01
0.100 0,1
RT-qPCR %BCR-ABL1 IS
1.000 1
10 10.000
B
Molecular Response
Molecular Response
qPCR-M
19
10
dPCR-M
21
qPCR-L
23
dPCR
43
15
9
16 27 0%
100%
75%
50%
25%
41
MR3 (MMR)
MR4
43 45
> MMR
> MMR
36
10 dPCR-L
0%
43
MR3 (MMR)
28
MR4-5 (DMR)
100%
14
44
75%
40
39
50%
17
qPCR
17
25%
A
Linearity 1000000
R²=0.9997 for BCRABL and ABL
Copies/assay
100000 10000 1000 100 10 1 0 undiluted
0.1
0.01
0.001
Serial log10 dilutions of K562 cells BCR-ABL
ABL
BCR-ABL/ABL
0.0001
0.00001
0.30 0,30 0.25 0,25
probability
0.20 0,20 0.15 0,15 0.10 0,10 0.05 0,05 0.00 0,00 0
1
2 3 4 BCR-ABL1 copies/duplicate observed
calculated
5
>=6
S1525-1578(19)30403-9
DOI:
https://doi.org/10.1016/j.jmoldx.2019.08.007
Reference:
JMDI 839
To appear in:
The Journal of Molecular Diagnostics
Received Date: 15 January 2019 Revised Date:
18 June 2019
Accepted Date: 28 August 2019
Please cite this article as: Franke G-N, Maier J, Wildenberger K, Cross M, Giles FJ, Müller MC, Hochhaus A, Niederwieser D, Lange T, Comparison of Real-Time quantitative PCR and digital droplet PCR for BCR-ABL1 Monitoring in Patients with Chronic Myeloid Leukemia, The Journal of Molecular Diagnostics (2019), doi: https://doi.org/10.1016/j.jmoldx.2019.08.007. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Copyright © 2019 Published by Elsevier Inc. on behalf of the American Society for Investigative Pathology and the Association for Molecular Pathology.
Comparison of Real-Time quantitative PCR and digital droplet PCR for BCRABL1 Monitoring in Patients with Chronic Myeloid Leukemia
Georg-Nikolaus Franke,* Jacqueline Maier,† Kathrin Wildenberger,* Michael Cross,* Francis J. Giles,‡ Martin C. Müller,§ Andreas Hochhaus,¶ Dietger Niederwieser,* and Thoralf Lange†ǁ From the Abt. Hämatologie und internistische Onkologie,* Universitätsklinikum Leipzig, Leipzig, Germany; the Universität Leipzig,† Leipzig, Germany; the Developmental Therapeutics Consortium,‡ Chicago, Illinois; the Medizinische Fakultät Mannheim,§ Mannheim, Germany; the Klinik Innere Medizin II,¶ Universitätsklinikum Jena, Jena, Germany; and the Asklepios Klinikum Weißenfels,ǁ Weißenfels, Germany
Address correspondence to: Georg-Nikolaus Franke, MD Medizinische Klinik und Poliklinik I, Universitätsklinikum Leipzig, Leipzig, Germany, Johannisallee 32a, 04103 Leipzig, Germany Email: [email protected]
Running head: BCR-ABL1 Monitoring in CML by dPCR.
Funding: Supported by Novartis Pharma GmbH project HTAS 157 and grant AN72255617 (T.L.).
Footnote: Portions of this manuscript have been presented at the meeting of the American Society of Hematology, December 5-8, 2015, Orlando, FL, and December 3-6, 2016, San Diego, CA.
Disclosures: G.N.F. received a research grant and honoraria from Novartis.
Abstract Real-time quantitative PCR (RT-qPCR) is routinely used to detect minimal residual disease in chronic myeloid leukemia patients. The absolute quantification with droplet digital PCR (dPCR) could reduce the inherent variability of RT-qPCR. We established a duplex dPCR assay using the EAC primer/probe system for BCR-ABL1 and ABL1 and compared the results to RT-qPCR. Two-hundred and thirty cDNA samples from patients with chronic myeloid leukemia were analyzed using both procedures. A second, commercially developed dPCR assay for BCR-ABL1 was also evaluated. ABL1 and BCR-ABL1 transcript levels were similar with all assays, but the proportion of deep molecular responses was lower with dPCR than with RT-qPCR. The EAC dPCR assay had a false-positive rate (FPR) of 4% using a cut-off of three BCR-ABL1 copies per duplicate, compared to 2% without cut-off for the commercial dPCR. The detection rate for molecular response 4.5 was 100 and a shift towards more minimal residual disease was seen in patient samples. In conclusion, using the EAC protocol for BCR-ABL1 quantification with dPCR is feasible and shows low intra- and interassay variation but requires a cut-off that reduces sensitivity. The commercial dPCR assay is highly sensitive and specific for BCR-ABL1. The use of either dPCR assay resulted in a shift to lower molecular response classes compared to RT-qPCR aligned to International Scale.
Introduction The introduction of tyrosine kinase inhibitors (TKI) has changed the management of chronic myeloid leukemia (CML) considerably. The aim of treatment has moved from prolonging survival or palliation towards cure or treatment-free remission [1]. The introduction of targeted therapy in CML was accompanied by the development of more sensitive detection of minimal residual disease based on Real-Time quantitative PCR (RT-qPCR) [2]. From the IRIS trial it soon became clear that BCR-ABL1 levels continue to decrease over time long after complete cytogenetic response is reached [3]. The value of RT-qPCR for monitoring BCR-ABL1 has been demonstrated in a number of studies using different TKIs [4–10]. Recently, several trials have explored the concept of TKI discontinuation in patients in whom deep molecular response is stably maintained under TKI treatment. The importance of a continuous deep molecular response (MR4 or deeper) as a prerequisite for stopping treatment has been demonstrated [11, 12] [ENESTop, EUROSKI]. The sensitive and precise monitoring of residual disease is therefore playing an increasingly important role in managing the treatment of CML, facilitating decisions regarding the discontinuation of treatment (6) and identifying patients at risk of progression (6,13-16). However, BCRABL1 transcript monitoring methodologies vary considerably in their performance and continue to do so despite intense efforts to standardize and harmonize both methodology and reporting. One reason for this is likely to be the variability inherent in the RT-qPCR technology employed [13]. Droplet digital PCR (dPCR) is based on the separation of a standard PCR reaction into many thousand nano-liter scale single droplets, the vast majority of which contain either one (or more) or no target copy. Endpoint amplification is performed within each droplet and the original target copy number is calculated from the proportion of
positive and negative drops using Poisson statistics [14]. This generates an absolute read out that is largely tolerant of variations in PCR efficiency, reducing the requirement for internal standardization by eliminating the need for a standard curve. This feature has been exploited to develop reference material [15]. dPCR is able to monitor even very low BCR-ABL1 levels (MR4.5 and below) in CML patients with a high degree of reliability and sensitivity [16–19]. Our aim was to first establish the Europe against Cancer protocol for e13a2 and e14a2 BCR-ABL1 quantification [20] as a dPCR assay, determine its linearity, intraand inter-assay variation, lower limit of blank, limit of detection, specificity and sensitivity. Subsequently, we aimed to compare the M-BCR-ABL1 monitoring by dPCR to standardized RT-qPCR in blinded samples from two independent laboratories with respect to the observed rates of molecular response (MR) in CML patients having undergone 18 months of nilotinib treatment in the ENEST1st trial. Finally, our aim was to compare the EAC dPCR assay to a commercially developed dPCR assay.
MATERIAL AND METHODS
Samples K562 cells (BCR-ABL1 positive samples; Leibnitz-Institut Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, Braunschweig, Germany, DSMZ no.: ACC 10), Ba/F3 (DSMZ no.: ACC 300) and HELA cells (wild-type samples, DSMZ no.: ACC 57) and samples from CML patients and healthy donors were used for determination of linearity, lower limit of blank (LoB), limit of detection (LoD), specificity, and sensitivity.
Two-hundred and thirty cDNA samples from CML patients carrying e13/a2 or e14/a2 BCR-ABL1 fusion genes who underwent treatment within the ENEST1st trial were analyzed with RT-qPCR in Leipzig (L, n=75) or Mannheim (M, n=155) between 2012 and 2013, then reanalyzed in Leipzig by dPCR.
Real-Time quantitative PCR BCR-ABL1, ABL1, and BCR-ABL1/ABL1 ratios were retrieved from the ENEST1st database. Quantitative Real-Time PCR was performed as described earlier [20, 21] using an in-house plasmid (Leipzig) or pME2 plasmid (Mannheim) to generate standard curves. Laboratory-specific conversion factors were used to convert the ratio %BCR-ABL1/ABL1 to international scale (IS, BCR-ABL1IS). Nuclease-free water was included as a non-template control (NTC) and K562 cell lysate as a positive control in each analysis.
Primer/Probes for the EAC assay RT-qPCR and dPCR were compared using primer/probe sets as previously published [20, 21]. Probes were FAM-TAMRA labelled for RT-qPCR (biomers.net GmbH, Ulm, Germany) and FAM/HEX-BHQ1 labelled for duplex dPCR (Eurofins Genomics Germany GmbH, Ebersberg, Germany).
Primer probes M-BCR-ABL1 Primer/Probe: ENF_501 ENR_561
5’-TCCGCTGACCATCAAYAAGGA-3’;
5’-CACTCAGACCCTGAGGCTCAA-3’; and ENP_541
CCCTTCAGCGGCCAGTAGCATCTGA-3’.
5’-
ABL1 Primer/Probe: ENF_1003 3’; ENR_1063
5’-TGGAGATAACACTCTAAGCATAACTAAAGGT-
5’-GATGTAGTTGCTTGGGACCCA-3’; andENP_1043
5’-
CCATTTTTGGTTTGGGCTTCACACCATT-3’.
Primer and probes for the Bio-Rad dPCR assay for BCR-ABL1 and ABL1 are undisclosed intellectual property of Bio-Rad, Pleasanton, CA.
dPCR EAC-Assay A final volume of 20µL duplex dPCR reaction mix consisted of: ddPCR Supermix for Probes (fc 1x); primers ENF_501, ENR_561, ENF_1003, and ENR_1063 (fc 900nM); probes ENP_541, ENP_1043 (fc 250nM), and 5µL template cDNA (product of 500ng reverse transcribed RNA). After sealing and centrifugation (1min/800rpm) the 96well plate was transferred into the QX200 Droplet Generator (Bio-Rad) to generate approximately 20,000 droplets. The PCR was performed in a Thermocycler T100 (Bio-Rad) in a sample volume of 40µL using the following protocol: enzyme activation 95
°C/10min
followed
by
40
cycles
of
denaturation
94
°C/30sec,
and
annealing/extension 60 °C/60sec (ramp rate settings 2.5 °C; heated lid set to 105 °C with a final 10 minute incubation at 98 °C before c ooling to 12 °C. Bio-Rad Assay The assay was performed according to the manufacturer’s instructions. For both assays, positive and negative droplets were detected using the QX200 Droplet Reader and data analyzed using the Software QuantaSoft 1.7.4.0917 (BioRad). Samples for which no negative droplet fraction was evident were subject to
another round of PCR analysis using a higher dilution of input material. Samples generating fewer than 10,000 analyzed droplets were excluded from further analysis.
The specificity and sensitivity of the dPCR assays was determined using nontemplate control (NTC), healthy donor wild-type cDNA, and patient cDNA using the formulas,
specificity = true negative / (true negative + false positive)
and sensitivity = true positives / (true positives + false negatives).
The false positive rate (FPR) was calculated using the formula
FPR = number of false positive samples/(false positive + true negative samples)
using NTC and healthy donor wild-type cDNA. To evaluate the linearity of the dPCR assays, log10 measured concentrations of BCR-ABL1 and ABL1 were plotted against log10 sample input of BCR-ABL1 and ABL1 from K562 cells for each assay and linear regression analysis was performed. To determine Intra-Assay-Variation 4 log10 dilutions of K562 cells between 20 and 20,000 BCR-ABL1 and ABL1 copies were measured 6 times in triplicates in the same run. The coefficient of variation (CV) was calculated for each dilution by dividing the standard deviation (SD) by the mean of the six results per dilution in percent (%CV = Mean of SD x 100 / Mean). Finally, the average of the individual CVs is denoted as intra-assay CV. Unlike Intra-AssayVariation the Inter-Assay-Variation was determined from runs of 16 replicates per dilution performed on three different days. The limit of blank was defined as the
highest measurement that is likely to be observed over 95% of the time. The LoB was calculated using a non-parametric approach as described in the EP17-A2 guidelines [22] using NTC and wild-type cDNA from healthy donors. The limit of detection (LoD) of an assay is the sample input level that can be successfully detected by the assay over 95% of the time. In this study, the LoD for dPCR was calculated as the number of copies per reaction well using the formula
LoD = LoB + 1.645 x SDlow concentration
using a serial dilution of K²EDTA blood from a CML patient in K²EDTA blood from a healthy volunteer to cover the range MR4.5 to MR5.5 (mean BCR-ABL1 copies 2.87, 1.58, and 0.51/reaction) as described in the EP17-A2 guidelines [22]. Each concentration was measured 16 fold on three different days using the same reagent lots.
RESULTS Specificity and False-Positive-Rate of the dPCR EAC assay The specificity from three different runs on three different days (3x 94 wells wild-type and NTC) of BCR-ABL1 dPCR ranged from 71% to 93%, corresponding to a falsepositive-rate of 7% to 29% (7% to 22% for NTC and 11% to 29% for wild-type control BA/F3 murine pro B cells). In comparison, ABL1 dPCR yielded a constant specificity of 98% and a FPR of 2% for NTC. However, it is possible to increase the specificity of dPCR EAC assay for BCR-ABL1 to >95% by using a cut-off of 3 positive droplets for positivity, which reduces the FPR to level lower than 5% (described below).
Sensitivity of the dPCR EAC assay
The sensitivity of BCR-ABL1 was determined in six dilutions of BCR-ABL1 positive cDNA from K562 cells (equivalent to 0, 0.25, 2.5, 5, 10, and 25 BCR-ABL1 copies per well) in high wild-type background (mean 71,000 ABL1 copies from BA/F3 cDNA). Each dilution was tested in 96well format and assessed in duplicate. BCR-ABL1 dilutions of 25 to 5 copies/assay were detected in 100% by both methods. The 2.5 copies dilution was detected in 100% of the samples by dPCR and in 96% by RTqPCR. At the theoretical dilution of 0.25 copies, 71 % of the samples were positive by dPCR and 27% by RT-qPCR. Accordingly, dPCR showed a higher FPR with BCRABL1 negative cDNA (58%, 28 of 48 samples) than with NTC (7% to 29%, see above) (Figure 1A). Using a cut-off of 2 (requiring at least two positive droplets in duplicates for a positive result) the sensitivity of the two methods was comparable from 25 to 0.25 copies, but the BCR-ABL1 negative wild-type control still showed a FPR of 27% in dPCR (Figure 1B). Applying a cut-off of 3 reduced the FPR to 4%. However, this also resulted in a loss of sensitivity for dPCR compared to RT-qPCR (nearly 100% detection of 2.5 copies/assay by RT-qPCR vs 80% by dPCR, Figure 1C). For future experiments, the 3 droplet cut-off was adopted for duplicates. This means a sample with only 1 or 2 BCR-ABL1 positive droplets will be declared negative whereas a sample with at least three positive drops per duplicate for BCR-ABL1 will be counted as positive.
Linearity, intra-, and inter-assay variation of the EAC dPCR assay Linearity was high for both ABL1 and BCR-ABL1 (R² >0.99). The intra-assay coefficient of variation was 7% for ABL1 and BCR-ABL1 (range 3% to 19%). The median inter-assay coefficient of variation was 7% for ABL1 (range 4% to 12%) and 8% for BCR-ABL1 (range 4% to 22%) (Table 1).
Comparison of Real-Time quantitative PCR and droplet dPCR For the whole group of 230 cDNA samples from CML patients of the ENEST1st trial, the median ABL1 copy number (CN) was 59,350 (range 7,690 to 176,000) by dPCR and 53,537 (range 4,013 to 250,800) by RT-qPCR, whereas the median BCR-ABL1 CN was 12.25 (range 0 to 2,050) and 10.45 (0 to 1,529), respectively. More than 77% of all samples had a detection limit %BCR-ABL1/ABL1 (IS) of 0.0032 and lower (ABL CN for MR4.5 > 32,000) by both dPCR and RT-qPCR. Minimal residual disease was detected in 186 samples by dPCR, 189 samples by RT-qPCR, and in 170 samples by both methods. The median BCR-ABL1/ABL1 ratio measured by dPCR was 0.022% (range 0 to 2.783%) and by RT-qPCR 0.019% (range 0 to 6.881%), corrected to 0.014% (range 0 to 2.380%) after conversion to international scale (IS) (figure 2). Fifty-three samples (23%) had BCR-ABL1/ABL1 ratios >0.1% by dPCR, compared to 57 (25%) before and 39 (17%) samples after conversion to IS by RTqPCR. The correlation coefficient between dPCR and RT-qPCR in the whole cohort was 0.85 for BCR-ABL1, 0.81 for ABL1, 0.61 for % BCR-ABL1/ABL1 and 0.83 for % BCRABL1/ABL1 (IS) (Figure 3A-C). Detailed results for the separate cohorts from Leipzig and Mannheim are shown in Table 2.
Classification according to method and depth of molecular remission In the next step, all samples were classified according to the depth of molecular remission according to the EUTOS guidelines used in the ENEST1st trial. % BCRABL1/ABL1 (IS) was used for the results obtained by RT-qPCR and the unadjusted % BCR-ABL1/ABL1 for dPCR. By RT-qPCR, 39 pts (17%) were > MR3, 91 pts (40%) were scored MR3, 33 pts (14%) MR4, 44 pts (19%) MR4.5, and 23 pts (10%) MR5.
In contrast, by dPCR 53 pts (23%) did not achieve MR3, 100 pts (43%) were in MR3, 35 pts (15%) in MR4, 20 pts (9%) in MR4.5, and 22 (10%) in MR5 (Table 3, Figure 4A). This distribution was significantly different between the two methods (P = 0.02). Significantly fewer pts (n=77) achieved MR4 or better by dPCR, compared to RTqPCR (n=100, P = 0.035, Chi-Square-Test). Although the significance is reduced by decreasing the sample size, the same effect was observed as a trend in the samples from each participating laboratory, with 28% (n=21) of pts from Leipzig in MR4 or better by dPCR EAC assay vs 43% (n=32) by RT-qPCR and 36% (n=56) of pts from Mannheim in MR4 or better by dPCR compared to 44% (n=68) by RT-qPCR (Figure 4B). At the level of the individual samples, 134 pts (58%) were classified in the same MR category by both methods. Twenty-five pts (11%) were scored into deeper molecular response categories and 71 pts (31%) into a less deep molecular response group by dPCR (Table 3). Measurement by dPCR EAC assay therefore resulted in an overall shift towards more residual disease. A significant difference in ABL1 CN was not observed between the two methods in the 71 samples that switched into a worse molecular response group (P = 0.95, Students t-test); however, corresponding BCRABL1 copies were higher by dPCR in 79% (56/71 samples, P = 0.02). Focusing on the BCR-ABL1 positive samples, the median BCR-ABL1 CN was 2.4-fold higher by dPCR than by RT-qPCR, the largest difference arising from samples with less than 15 copies. The reclassification of 25 samples into a deeper molecular response category by dPCR resulted predominantly from the detection of slightly higher ABL1 copies in samples that were BCR-ABL1 negative (1.2-fold, n=19/25). However, 10 samples were BCR-ABL1 positive by RT-qPCR and BCR-ABL1 negative by dPCR. Of these,
all 10 samples had become negative by dPCR only after applying the cut-off of 3 positive droplets.
Bio-Rad Assay The use of the EAC protocol for BCR-ABL1 quantification by dPCR generated a background signal that limits sensitivity and thus the ability to detect very low levels of disease. For this reason, a commercial assay developed by Bio-Rad, Pleasanton, CA, which was designed specifically to avoid the problem of false positive droplets, was tested. The rate of false positive samples for BCR-ABL1 and ABL1 was 0% (n=0/176) and 1% (n=2/176), respectively, in no-template controls. The FPR for BCR-ABL1 in HELA cells and in healthy donors was 0% (n=0/44) and 2% (n=5/234) respectively, resulting in a limit of blank of zero. This virtually eliminates the background associated with the EAC protocol, reported here and by others [23]. Linearity was stable over 5 log10 dilutions of K562 cells with BCR-ABL1 copy numbers from 80,000 to 1 and ABL1 copy numbers from 100,000 to 1 with R² = 0.9997 for both BCR-ABL1 and ABL1. The resulting average ratio was 77% BCRABL1/ABL1 (Standard deviation 5.0, Figure 5). When restricting this analysis to concentrations outside possible significant deviations resulting from Poisson statistics, the average ratio was 75% (standard deviation 2.28, range 72% to 78%). Inter-assay variation was determined to be 10%CV for MR3, 37%CV for MR4, and 88%CV for M4.5. Intra-assay variation was 9% for MR3, 31%CV for MR4 and 76%CV for MR4.5 samples. The limit of detection was calculated to be 1.96 copies in a background of wild-type cDNA with 100,000 copies of ABL, meaning that a sample containing more than two copies will yield a positive result in more than 95% of the tests. Some uncertainty remains for samples that are tested negative, as they could still contain up to 1.96
copies. For 2-well (20,000 droplets/well – in sum 40,000 droplets) analysis, the detection rates for MR4.5, MR5, and MR5.5 were 100%, 88%, and 42%, respectively. Extending this to a 4 well analysis (in sum 80,000 generated droplets) increased these detection rates to 100%, 100%, and 67%, respectively (Table 4). The assay was then tested on low level disease using a MR4.7 sample certified by the College of American Pathologists (CAP) and used for calibration (this test was performed in the Bio-Rad dPCR laboratory). This MR4.7 sample was subjected to repeated analysis. Fifty-nine of 60 replicates of the MR4.7 samples produced results (one sample had less than 10,000 droplets analyzed and had to be excluded). A total of
89
BCR-ABL1
copies
(1.51/replicate)
and
4,329,846
ABL1
copies
(73,387/replicate) were detected, resulting in a ratio of 0.0021 (MR4.69). When combining any two replicates, the false negative rate was below 3%, indicating a LOD of at least MR4.7 in two replicates for this assay. This is consistent with the distribution predicted by the Poisson distribution (Figure 6). Next, we tested 40 samples in duplicates from patients with CML with MR levels ranging from MR3 to MR5. Twenty (50%) samples were MRD negative by RT-qPCR compared to 17 (42%) by dPCR. Median copy numbers for ABL1 were 59,500 (mean 66,855) by dPCR and 54,945 (mean 60,469) by RT-qPCR whereas those for BCRABL1 were 3.1 by dPCR and 6.2 by RT-qPCR. The variation coefficient for low copy number BCR-ABL1 was similar in the two groups (%CV = 50 vs 52 for dPCR vs RTPCR), but at high copy number ABL1 the variation was lower in dPCR (%CV = 0.8 for dPCR versus 6.3 for RT-PCR). The correlations between dPCR and RT-qPCR for both ABL1 and BCR-ABL1 were high at 0.83 and 0.90, respectively. Median (mean) ratio BCR-ABL1/ABL1 was 0.014% (0.031%) by dPCR and 0.005% (0.046%) and 0.002% (0.0134%) by RT-qPCR before and after conversion to the International scale, respectively. The correlation for BCR-ABL1/ABL1 by dPCR to BCR-
ABL1/ABL1 by RT-qPCR before and after conversion to International Scale was 0.98. Nonetheless, when looking at groups of molecular response, 30% of patients (n=12) were grouped in lower molecular response categories by dPCR than by RT-qPCR, confirming the observation made with the EAC protocol in the ENEST1st samples, although this has to be verified in a larger patient cohort comparing both dPCR assays and RT-qPCR.
DISCUSSION Here, we demonstrate that the use of dPCR for quantitative BCR-ABL1 monitoring is feasible and safe. The partition of an analyte into thousands of single droplets enables an endpoint measurement and absolute quantification of a target sequence. This makes the assay less prone to PCR inhibition and independent of a standard curve, adding to the simplicity of the technique. dPCR was shown to have an LoD as low as 1.96 copies in wild-type background, with 0.5 copies being detected in 50% of the samples. Linearity is stable over 5 orders of magnitude from 2.5x100 to 1x105, making duplex assays with the reference gene and BCR-ABL1 feasible. The ratio BCR-ABL1/ABL1 was stable over the range from 2x101 to 1x105, which is important for determination of a stable conversion factor to the international scale [15]. However, the EAC assay effectively loses sensitivity as a consequence of the rate of false positive samples in NTC and wild-type (duplicates are positive up to 58% for one or two droplets per replicate), making it necessary to impose a cut-off of 3 positive droplets in duplicate assays. In the patient samples from the ENEST1st studies from two separate EUTOS laboratories in Germany, it was demonstrated that quantification by dPCR reads out similar copy numbers for BCR-ABL1 and ABL1 and similar ratios of BCRABL1/ABL1. Also, the percentage of samples with ABL1 CN high enough to detect
MR4.5 or better was similar with both techniques using the EAC protocol. However, after conversion of the RT-qPCR results to International Scale, dPCR tends to read out higher levels of BCR-ABL1/ABL1 than does standard RT-qPCR, resulting in a significant shift of samples towards worse MR classes. Although this effect was observed in both of the available cohorts and was also seen when using the Bio-Rad assay in a third cohort of patient samples, this has to be confirmed in a multicenter ring trial. When assaying the same samples with both methods, most of the discrepancy to IS seems to result from steps before the quantification (RNA extraction, cDNA synthesis). The e13a2/e14a2BCR-ABL1 primer/ probe set in widespread use in the EAC dPCR protocol is reliable and reproducible but still suboptimal, since the high sensitivity is currently accompanied by a high false positive rate, requiring the use of a 3 droplet cut-off to ensure specificity. Using the cut-off 3 for dPCR EAC assay the high false-positive-rate was <5%, resulting in turn in a loss of sensitivity compared to RT-PCR EAC protocol. To access the full potential of dPCR in terms of sensitivity, it will be necessary to develop assays with a LoB of zero and to implement a dPCR specific conversion factor. The first requirement is met by the newly developed Bio-Rad assay, which has a very low false positive rate and a limit of blank of zero, enabling the reliable assessment of very deep molecular responses. It is especially noteworthy that we determined a limit of detection (1.96 copies) below the theoretical limit given by the Poisson statistic of 3 copies/sample for a single replicate. This enables us to push the detection limit of the assay towards a reliable detection of MR4 and beyond by performing multiple replicates of sample. This is likely to be of clinical relevance, as there is a clear correlation between minimal residual disease and the likelihood of successful treatment discontinuation. Patients with MRD levels of MMR or worse are rarely able to successfully stop the treatment. The failure to detect a difference in TFR rate
between MR4 and MR4.5 in the EURO SKI trial might be due to the inherent variability of the RT-qPCR assays used. The inter-assay variation of the dPCR assay reported here appears to be much lower than the variations usually observed with RT-qPCR [15, 24]. Thus, this dPCR assay could help to better identify patients for successful TKI discontinuation. It is important to note that the conversion factor for the dPCR assay is yet to be determined, as different results were found comparing RT-qPCR and dPCR in patient samples. However, the CAP MR4.7 sample yielded a result very close to the nominal value. Here, the recent development of a set of secondary reference materials (which are calibrated using dPCR) will help to determine the conversion factor for each laboratory more easily. In conclusion, monitoring minimal residual disease by dPCR seems to be feasible and safe. It reliably detects deep molecular responses relevant for guiding treatment. Its potential to eliminate the need for a conversion factor due to the absolute measurement and to reliably detect very deep molecular response greater than MR4.5 should be explored further. This could help to better identify patients prone to relapse after stopping TKI treatment.
Acknowledgements We thank Bio-Rad Laboratories for supplying the assay, Svilen Tzonev and Dawne Shelton from the dPCR Laboratory for their help with technical issues regarding the dPCR assays and providing the assays, Novartis for supporting this trial,Ines Kovacs and Scarlett Schwabe in preparing and storing the samples, and all patients for providing their blood for the analysis.
References 1
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Figure Legends
Figure 1. A: Sensitivity of BCR-ABL1 PCR according to the method, assessed without cut-off. Application of the cut-off 2 (B) and cut-off 3 (C) for BCR-ABL1 dPCR. RT-qPCR: Real-Time quantitative PCR; dPCR: droplet digital PCR.
Figure 2: Comparison of RT-qPCR and dPCR. ABL1, BCR-ABL1, and BCRABL1/ABL1 ratio for whole cohort using the EAC protocol for RT-qPCR and dPCR. Box plots depict the observation range, lower quartil – 25th percentile, upper quartile 75th percentile. ***P < 0.001.
Figure 3A: Correlation of RT-qPCR and dPCR for ABL1 (A) and BCR-ABL1 (B) using the EAC protocol. C: Correlation of RT-qPCR and dPCR for %BCR-ABL1/ABL1 IS using the EAC protocol. IS: International Scale.
Figure 4. A: Distribution of molecular response classes by methods: whole cohort. Whole cohort, n = 230. n= number of samples; MR: molecular response; MMR: major molecular response; DMR: deep molecular response. B: Distribution of molecular response classes by methods: by laboratory. RT-qPCR-M: Real-Time quantitative PCR for samples from Mannheim; dPCR-M droplet dPCR for samples from Mannheim (n=155); RT-qPCR-L: Real-time quantitative PCR for samples from Leipzig; dPCR-L droplet dPCR for samples from Leipzig (n=75);
Figure 5: Linearity Bio-Rad assay. Measured BCR-ABL1 and ABL1 copy numbers of log10 diluted K562 cells (BCR-ABL1 80,000 to 1 copies, ABL1 100,000 to 1 copies, y-axis) and %BCR-ABL1/ABL1 against sample input (x-axis)
Figure 6: Observed versus predicted BCR-ABL1 copy numbers in a MR4.7 sample using the Bio-Rad assay.
Table 1: Linearity, intra- and inter assay variation for ABL and BCR-ABL using the EAC protocol for dPCR. Target
Linearity (R²)
Intra assay variation
Inter assay variation
(%CV)
(%CV)
ABL1
0.997
7 (range 3-19)
7 (4-22)
BCR-ABL1
0.999
7 (range 3-19)
8 (4-22)
%CV: coefficient of variation.
Table 2: Correlation between RT-qPCR and dPCR for ABL1, BCR-ABL1, % BCRABL1/ABL1, and % BCR-ABL1/ABL1 IS.
Whole cohort (n=230) dPCR
RT-qPCR
Leipzig (n=75) dPCR
RT-qPCR
median ABL1 CN (range)
Cor
59,350 (7,690176,000) 53,537 (4,013250,800)
0.85 12.25 (02,050)
0.81 0.022 (02.783)
10.45 (01,529)
0.019 (06.881)
29,670 (7,69099,600) 30,734 (4,01391,540)
0.84 9.10 (0538)
Mannheim (n=155) dPCR 80,000 (10,870176,000) RT-qPCR 66,570 (10,080250,800)
median BCR-ABL1 CN (range)
Cor
Median %BCRABL1/ABL1 (range)
0.95 0.023 (01.530)
10.17 (01,444)
0.035 (06.882)
0.74 15.50 (02,050)
0.95 0.022 (02.783)
10.47 (01,529)
0.017 (01.609)
Cor
Median %BCRABL1/ABL1 IS (range)
Cor
0.61
-
0.83
0.014 (02.380)
0.91
-
0.93
0.013 (02.380)
0.89
-
0.89
0.015 (01.416)
CN: Copy number; Cor: Correlation coefficient; n: number of samples; %IS: %BCRABL/ABL on the International scale.
Table 3: Distribution of molecular response classes. RT-qPCR
dPCR EAC
>MR3
MR3
MR4
MR4.5
MR5
n
>MR3
37
1
1
0
0
39
MR3
16
68
6
0
1
91
MR4
0
15
10
5
3
33
MR4.5
0
15
12
9
8
44
MR5
0
1
6
6
10
23
N
53
100
35
20
22
230
assay
MR: molecular response; RT-qPCR: Real-time quantitative PCR; dPCR: droplet digital PCR; Shift to more residual disease –bold numbers; less residual disease – italic; n: number of samples.
Table 4: Detection rate of MR3 to MR5.5 by the Bio-Rad assay. 2-well analysis BCR-ABL1 CN
4 well analysis ABL1 CN
Detection
BCR-ABL1 CN
ABL1 CN
rate (%)
Detection
%BCR-ABL1/ABL1
rate
MR3
285
200,750
100
571
401,500
100%
0.142
MR4
26
200,375
100
52
400,750
100%
0.013
MR4.5
6
198,992
100
11
397,983
100%
0.0028
MR5
3
200,558
88
6
401,117
100%
0.0015
MR5.5
1
199,350
42
2
398,700
67%
0.00051
CN: copy numbers; MR: molecular response.
A
B
C
m e d ia n A B L ( n = 2 3 0 )
m e d ia n B C R - A B L ( n = 2 3 0 )
1000000
10000
53,537 c o p ie s /a s s a y
c o p ie s /a s s a y
59,350 100000
10000
1000
12
10
dPCR
qPCR
1000
100
10
1 dPCR
qPCR
m e d ia n B C R - A B L /A B L ( n = 2 3 0 )
0.022
0.019
0.014
*** 10
1
%
0 .1
0 .0 1
0 .0 0 1
0 .0 0 0 1 dPCR
qPCR
q P C R ( IS )
A dPCR ABL1 copy numbers total
R²=0.81 100000
10000
1000 1000
10000
100000
RT-qPCR ABL1 copy numbers total
1000000
C 10.000 10,00000
10000
R²=0.83
R²=0.85
1000
1.000
1,00000
dPCR %BCR-ABL1/ABL1
dPCR BCR-ABL1 copy numbers total
B 1000000
100
0.100 0,10000
10
0.010 0,01000
1 0,1 0.1 1
10
100
1000
RT-qPCR BCR-ABL1 copy numbers total
10000
0.001 0,00100 0.001 0,001
0.010 0,01
0.100 0,1
RT-qPCR %BCR-ABL1 IS
1.000 1
10 10.000
B
Molecular Response
Molecular Response
qPCR-M
19
10
dPCR-M
21
qPCR-L
23
dPCR
43
15
9
16 27 0%
100%
75%
50%
25%
41
MR3 (MMR)
MR4
43 45
> MMR
> MMR
36
10 dPCR-L
0%
43
MR3 (MMR)
28
MR4-5 (DMR)
100%
14
44
75%
40
39
50%
17
qPCR
17
25%
A
Linearity 1000000
R²=0.9997 for BCRABL and ABL
Copies/assay
100000 10000 1000 100 10 1 0 undiluted
0.1
0.01
0.001
Serial log10 dilutions of K562 cells BCR-ABL
ABL
BCR-ABL/ABL
0.0001
0.00001
0.30 0,30 0.25 0,25
probability
0.20 0,20 0.15 0,15 0.10 0,10 0.05 0,05 0.00 0,00 0
1
2 3 4 BCR-ABL1 copies/duplicate observed
calculated
5
>=6