Minimal Residual Disease Detection by Droplet Digital PCR in Multiple Myeloma, Mantle Cell Lymphoma, and Follicular Lymphoma

Minimal Residual Disease Detection by Droplet Digital PCR in Multiple Myeloma, Mantle Cell Lymphoma, and Follicular Lymphoma

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The Journal of Molecular Diagnostics, Vol. 17, No. 6, November 2015

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Minimal Residual Disease Detection by Droplet Digital PCR in Multiple Myeloma, Mantle Cell Lymphoma, and Follicular Lymphoma A Comparison with Real-Time PCR Q13

Daniela Drandi,* Lenka Kubiczkova-Besse,y Simone Ferrero,* Nadia Dani,z Roberto Passera,x Barbara Mantoan,* Manuela Gambella,* Luigia Monitillo,* Elona Saraci,* Paola Ghione,* Elisa Genuardi,* Daniela Barbero,* Paola Omedè,{ Davide Barberio,z Roman Hajek,yk Umberto Vitolo,** Antonio Palumbo,* Sergio Cortelazzo,yy Mario Boccadoro,* Giorgio Inghirami,zzxx and Marco Ladetto*{{

Q1 Q2 From the Divisions of Hematology* and Pathology,xx Department of Molecular Biotechnologies and Health Sciences, University of Torino, Torino, Italy; the

Divisions of Nuclear Medicinex and Hematology{ and the Oncology Department,** A.O.U. Città della Salute e della Scienza, Torino, Italy; the Babak Myeloma Group,y Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic; the Bioclarma srl,z Torino, Italy; the Department of Hematooncology,k Faculty of Medicine University of Ostrava and University Hospital Ostrava, Ostrava, Czech Republic; the Hematology Department,yy S. Maurizio Regional Hospital, Bolzano, Italy; the Department of Pathology and Laboratory Medicine,zz New York Presbyterian Hospital-Cornell Medical Center, New York, New York; and the Hematology Division,{{ A.O. S. Antonio, Biagio and Cesare Arrigo, Alessandria, Italy Accepted for publication May 22, 2015. Address correspondence to Daniela Drandi, Ph.D., Department of Molecular Biotechnology and Health Sciences, Hematology Division, via Genova 3, 10126 Torino, Italy. E-mail: daniela.drandi@ unito.it.

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Real-time quantitative PCR (qPCR) is a well-established tool for minimal residual disease (MRD) detection in mature lymphoid malignancies. Despite remarkable sensitivity and specificity, qPCR has some limitations, particularly in the need for a reference standard curve, based on target serial dilutions. In this study, we established droplet digital PCR (ddPCR) for MRD monitoring in multiple myeloma, mantle cell lymphoma, and follicular lymphoma and compared it head-to-head with qPCR. We observed that ddPCR has sensitivity, accuracy, and reproducibility comparable with qPCR. We then compared the two approaches in 69 patients with a documented molecular marker at diagnosis (18 multiple myelomas, 21 mantle cell lymphomas assessed with the immunoglobulin gene rearrangement, and 30 follicular lymphomas with the use of the BCL2/ immunoglobulin gene major breakpoint region rearrangement). ddPCR was successful in 100% of cases, whereas qPCR failed to provide a reliable standard curve in three patients. Overall, 222 of 225 samples were evaluable by both methods. The comparison highlighted a good concordance (r Z 0.94, P < 0.0001) with 189 of 222 samples (85.1%; 95% CI, 80.4%e89.8%) being fully concordant. We found that ddPCR is a reliable tool for MRD detection with greater applicability and reduced labor intensiveness than qPCR. It will be necessary to authorize ddPCR as an outcome predictor tool in controlled clinical settings and multilaboratory standardization programs. (J Mol Diagn 2015, 17: 1e9; http://dx.doi.org/10.1016/j.jmoldx.2015.05.007)

Supported by Progetto di Rilevante Interesse Nazionale (PRIN2009) from Ministero Italiano dell’Università e della Ricerca (MIUR; Roma, Italy) grant 7.07.02.60 AE01; Progetti di Ricerca Finalizzata 2008, head unit: IRCCS Centro di Riferimento Oncologico della Basilicata (CROB), Rionero in Vulture (Potenza), Italy, grant 7.07.08.60 P49 (S.C.); Progetto di Ricerca Sanitaria Finalizzata 2008 grant 7.07.08.60 P51, 2009 grant RF2009-1469205, and 2010 grant RF-2010-2307262 (S.C.); A.O. S. Maurizio, Bolzano/Bozen, Italy, Fondi di Ricerca Locale, Università degli Studi di Torino, Italy; progetti di ateneo 2012 Compagnia di San Paolo grant

to_call03_2012_0055; Fondazione Neoplasie Del Sangue (Fo.Ne.Sa; Torino, Italy) diagnostic investment award 2010 (Multiple Myeloma Research Foundation) and local grant GAP304/10/1395 (L.K.-B.). Q4 Preliminary results were presented at the American Society of Hematology (ASH) meeting held December 7e10, 2013, in New Orleans, LA; European Hematology Association (EHA) meeting held June12e15, 2014, in Milano, Italy; and Digital PCR conference: Technology and Tools for Precision Diagnostics held October 6e8, 2014, in La Jolla, CA. Disclosures: D.B. and N.D. are employed by Bioclarma srl.

Copyright ª 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jmoldx.2015.05.007

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Drandi et al Q5

Detection of minimal residual disease (MRD) allowed acquisition of valuable prognostic information in several mature lymphoid malignancies with a considerable impact on clinical research.1,2 Currently, it is often included as a secondary end point in clinical trials for multiple myeloma (MM), mantle cell lymphoma (MCL), and follicular lymphoma (FL).3e5 More recently, several cooperative groups have designed MRDbased risk-adapted studies in a number of therapeutic settings.6 Different methods can be used for MRD quantification, including flow cytometry (FC),7e9 real-time quantitative PCR (qPCR),10e13 and the more recent next-generation sequencing (NGS).14,15 So far, qPCR remains the most validated and standardized method in MCL and FL.4,5 In MM, for which FC also has a major role,16 the International Myeloma Working Group has included molecular complete response (tumor marker negativity by PCR at sensitivity 105), as a meaningful criterion for response evaluation.17 In MM and MCL, qPCR uses immunoglobulin gene (IGH) rearrangement as a clonal marker, whereas in FL the most reliable marker is the t(14;18) translocation, especially when the major breakpoint region (BCL2/IGH MBR) is involved.18 qPCR represents the most widely used method for MRD analysis. However, it has a major limitation from being a relative quantification approach. This results in the need of a reference standard curve usually built by dilutions of the tumor-specific target obtained from diagnostic DNA, plasmids, or cell lines that contain the rearrangement of interest. Moreover, qPCR is unable to provide reliable target quantification for a substantial proportion of samples that have a tumor burden between the sensitivity and the quantitative range of the method. Samples that fall in this window of inadequate quantification, which might range up to two logs and are sometimes difficult to categorize for clinical purposes, are usually defined as positive nonquantifiable (PNQ).19 Droplet digital PCR (ddPCR) is based on sample compartmentalization in single oil droplets that represent independent PCR reactions and on end point amplification and Poisson statistics.20e24 ddPCR has several theoretical advantages compared with qPCR,25e29 most notably allowing for absolute quantification of target DNA molecules and avoiding the need for a reference standard curve; thus, it is potentially valuable in the MRD setting. On the basis of these considerations, we sought to verify the utility of ddPCR as a MRD monitoring tool and to compare it head-to-head with qPCR in 69 patients, including 18 with MM, 21 with MCL, and 30 with FL for a total of 225 samples. Our aim was to verify whether ddPCR could overcome some limitations of qPCR without losing its critical advantages, especially in terms of sensitivity and reproducibility.

for the IGH rearrangement and the DOHH-2 cell line for the BCL2/IGH MBR, as previously reported.10,30,31 For method comparison, genomic DNA (gDNA) derived from bone marrow (BM) and peripheral blood (PB) samples from 69 patients (18 with MM, 21 with MCL, and 30 with FL) was used. Samples were selected for having a molecular marker on the basis of the IGH (MM and MCL) or BCL2/IGH MBR (FL) rearrangements and were collected in the context of prospective clinical trials approved by the local institutional review board (MCL: EUdract2009-012807-25; MM: Eudract2004-000531-28 and Eudract2008-008599-15; FL: Eudract2009-012337-29). All patients provided written informed consent, which included PCR-based MRD determination, according to the Helsinki Declaration. Overall, 225 samples (180 BM and 45 PB) were analyzed: 95 MM, 70 MCL, and 60 FL. A total of 70 were diagnostic samples [for one patient two diagnostic samples (BM, PB) were available], and 155 were taken during patient follow-up on the basis of availability of DNA (Supplemental Table S1). MCL and FL sample mononuclear cells were separated by density gradient (Histopaque-1077; Sigma-Aldrich, St. Louis, MO), whereas MM samples were treated with erythrocyte lysis buffer. gDNA was extracted, depending on the amount of cells, by DNAzol (Life TechnologiesInvitrogen, Carlsbad, CA) or NucleoSpin Tissue (MachereyNagel, Bethlehem, PA), according to the manufacturer’s recommendations. gDNA quality and concentration were estimated by Nanodrop 2000C (Fisher Thermo Scientific, Waltham, MA) before experimental use. To avoid possible biases related to sampling, qPCR and ddPCR quantification were performed on the same diluted gDNA samples. Detailed information is included in Supplemental Table S2, as suggested by the guidelines for the Minimum Informa- Q6 tion for the Publication of Digital PCR Experiments (dMIQE).32

Materials and Methods

Preliminary evaluation of ddPCR performance was conducted with plasmid and purified neoplastic cell dilutions

IGH-based and BCL2/IGH MBR-based MRD detection by qPCR was performed with an AbiPrism7900HT (Life Technologies-Applied Biosystems, Carlsbad, CA), as previously described.18,19 For each patient, sample estimation was based on serial 10-fold dilution standard curves, prepared according to Euro-MRD guidelines, as previously

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Sample Characteristics and DNA Extraction

Tumor-Specific Molecular Marker Assessment In MM and MCL, patient-specific IGH rearrangements were amplified and direct sequenced from diagnostic gDNA.10,31 Sequences were analyzed with the IMGT/V-QUEST tool (http://imgt.org/IMGT_vquest/share/textes, last accessed March 26, 2015),33,34 and patient-specific allele-specific oligonucleotide primers and consensus probes were designed as previously described.10 FL patients were screened at diagnosis for the BCL2/IGH MBR translocation, as already described.18

qPCR

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MRD Detection: qPCR Compared with ddPCR

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described,19 starting from i) 105 plasmids that contain the patient-specific IGH rearrangement for MM; ii) 500 ng of diagnostic gDNA derived either from unpurified or CD19þ purified cells for MCL; and iii) 500 ng of DOHH-2 (BCL2/ IGH MBRþ cell line) gDNA, diluted in MCF-7 (BCL2/IGH MBR, human breast cancer cell line) gDNA for FL. In MCL, the proportion of tumor cells in diagnostic samples was assessed by standardized four-color FC for CD19, CD5, and k/l light chains (Miltenyi Biotec, Bergisch Gladbach, Germany)7; MRD analysis was interpreted according to the Euro-MRD guidelines.19

ddPCR

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ddPCR was performed with the QX100 Droplet Digital PCR system (Bio-Rad Laboratories, Hercules, CA). gDNA samples were loaded in triplicate with the use of either the manufacturer-recommended 100 ng gDNA dose, or an increased amount of 500 ng (aiming at greater sensitivity). The 20 mL ddPCR reaction included 10 mL of 2 ddPCR Master Mix (Bio-Rad Laboratories), 1 mL of 20 primers and probe (final concentration, 500 nmol/L and 200 nmol/ L), and 5 mL of gDNA. Of note, ddPCR experiments used the same primers and probes used in qPCR, with the identical nucleotide sequence, although MGB or BHQ-1 quenchers were used instead of TAMRA (Primmbiotech, West Roxbury, MA). Droplets were generated by a QX100 droplet generator device, and end point PCR was performed on a T100 Thermal Cycler (Bio-Rad Laboratories) following the manufacturer’s recommendations. PCR products were loaded into the QX100 droplet reader and analyzed by QuantaSoft version 1.2 (Bio-Rad Laboratories). Each experiment included a positive control sample (diagnostic sample gDNA) and a negative control (pool of PB mononuclear cells from 10 healthy donors, or MCF-7 gDNA). The final tumor load was calculated as a mean of all available technical replicates that were considered reliable when giving reproducible amplification after application of a Poisson correction. To be consistent with the rules established by Euro-MRD for qPCR, we defined MRDþ by ddPCR as those samples that had at least one replicate equal or superior to 1 log (equivalent to three Cq by qPCR) to the highest background signal. Finally, on the basis of the higher variability observed at low target concentrations and applying the EURO-MRD guidelines, cases with alternatively positive or negative replicates were scored as PNQ (PNQ determined by ddPCR, dPNQ).19

Sensitivity, Accuracy, and Reproducibility To assess sensitivity, accuracy, and reproducibility of ddPCR in comparison with qPCR, serial 10-fold dilutions were performed with plasmids, the DOHH-2 cell line, and purified tumor cells. We tested from 105 copies to 100 copy (including twofold intermediate dilution steps 5  104 and 5  105) of IGH target, diluted in gDNA obtained from a

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311 312 313 314 315 316 317 318 319 320 321 Analysis of Discordances 322 14 323 Discordances were classified as previously described. 324 Discordances in terms of positivity versus negativity were 325 defined as qualitative discordances and were classified as 326 major qualitative discordance, when a positive result was 327 >1  104, or minor qualitative discordance, when the 328 positive result was 1  104. Of note in this group, dis329 cordances might be related to statistical variability or min330 imal differences in sensitivity. Furthermore, concordantly 331 positive results, but with quantitative discrepancy >1 log, 332 333 were defined as quantitative discordances. 334 335 Statistical Analysis 336 337 For methods comparison, qPCR and ddPCR results were 338 5 expressed as the amount of target copies per 10 cells. qPCR 339 and ddPCR comparability was assessed with bivariate Pearson 340 correlation between methods calculated as an index of inter341 method reliability of quantitative data (R version 3.1.0, 342 package irr; R Foundation for Statistical Computing, Vienna, 343 Austria). The variance of ratings did not differ between 344 345 methods. The strength of agreement between the two methods 346 was also calculated with the Bland-Altman difference analysis 35 347 with a 95% limit of agreement (Supplemental Figure S1). 348 349 Results 350 351 Sensitivity, Accuracy, and Reproducibility of ddPCR 352 over Different Targets and Tumor Levels 353 354 To assess the sensitivity of ddPCR in tumor target detection, 355 we performed a series of 10-fold dilutions, loading both 100 356 357 ng of gDNA, as recommended by general ddPCR protocols, 358 and 500 ng, which is the amount of gDNA usually used in 359 the MRD setting.19 Representative results for IGH (with the 360 use of the patient-specific IGH rearrangement incorporated 361 into a plasmid from a MM patient and tumor cells taken at 362 diagnosis from a heavily infiltrated MCL patient) and BCL2/ 363 IGH MBR (cell line) target quantification are shown in 364 Figure 1, AeC, and compared with qPCR (Figure 1, DeF). ½F1 365 Of note, scaling up the ddPCR reaction to 500 ng did not 366 have a negative impact on reaction performance, as already 367 described in other studies.36 ddPCR ensured quantitative 368 369 discrimination over a broad range of target amounts (105 to 370 100), which were comparable with qPCR also at low levels 371 of target concentration. Moreover, the use of 500 ng of 372

pool of PB mononuclear cells from 10 healthy donors and from 104 copies to 100 copy of BCL2/IGH MBR target diluted in MCF-7 gDNA. These dilutions were escalated to eight replicates to verify accuracy and reproducibility at different dilution steps. Standard curves quantification was tested by both ddPCR and qPCR and repeated over time to assess whether the consistency (therefore, the data accuracy and reproducibility for the samples) was maintained.

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Figure 1 Comparison of serial DNA dilutions assessed by both qPCR and ddPCR. Linear 10-fold dilution standard curve with the Cq value for qPCR (AeC) and copy numbers for ddPCR (DeF) were plotted against the corresponding starting quantity of gDNA. A and D: Plasmid standard curves from an IGH rearrangement derived from a MM patient; 500 ng (R2 Z 0.9934) versus 100 ng (R2 Z 0.9951) by qPCR (A) and 500 ng (R2 Z 0.9978) versus 100 ng (R2 Z 0.9986) by ddPCR (D). B and E: gDNA standard curves from a MCL patient-specific IGH rearrangement; 500 ng (R2 Z 0.9948) versus 100 ng (R2 Z 0.9796) by qPCR (B) and 500 ng (R2 Z 0.9979) versus 100 ng (R2 Z 0.9640) by ddPCR (E). C and F: gDNA from Bcl-2/IGH MBRþ cell line (DOHH-2); 500 ng (R2 Z 0.9966) versus 100 ng (R2 Z 0.9016) by qPCR (C) and 500 ng (R2 Z 0.9980) versus 100 ng (R2 Z 0.9937) by ddPCR (F). R2 (coefficient of determination), represents how well the experimental data fit the regression line. Each dilution point represents the mean value of quantifiable replicates for different template amount (500 ng and 100 ng). ddPCR, droplet digital PCR; gDNA, genomic DNA; MBR, major breakpoint region; MCL, mantle cell lymphoma; MM, multiple myeloma; qPCR, quantitative real-time PCR.

gDNA (ie, 105 copies at the first dilution step) allowed reaching a sensitivity of 105 in all settings. As theoretically expected, the use of 100 ng prevented amplification of the lowest dilution in some cases. We then examined the concordance between ddPCR and qPCR on serial dilutions. Our results found the excellent concordance of the two methods at different levels of target concentration (Supplemental Figure S2, AeC). Then we compared the reproducibility of the two methods by performing ddPCR and qPCR on eight replicates for each of the three types of dilutions (in at least two separate experiments for every target). Again, the two methods were

highly comparable in terms of reproducibility with minimal divergence between replicates by both tools. As expected, with both methods, the 105 dilutions scored some negative replicates, reflecting Poisson statistics (Figure 2). ½F2

Feasibility of MRD Detection by the Two Approaches Overall, MRD analysis was successful in 95.6% of patients (66 of 69) by qPCR and 100% by ddPCR. In total, 100% of MM and FL samples were evaluated by both methods. In contrast, in three MCL patients, qPCR failed to generate reliable results because of the production of an inadequate

Figure 2

Sensitivity and accuracy of qPCR and ddPCR on serial DNA dilution. AeC: qPCR data. DeF: ddPCR data of 500 ng DNA standard curves. Each dot represents a mean value of eight replicates, with SD shown as whiskers. Table below each graph shows the amount of negative replicates at each dilution point. ddPCR, droplet digital PCR; MBR, major breakpoint region; MCL, mantle cell lymphoma; MM, multiple myeloma; qPCR, quantitative real-time PCR.

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Figure 3

Correlation between qPCR and ddPCR in patient samples. Pearson correlation between target quantification in MM, MCL, and FL by both methods shows a significant concordance (r Z 0.94, P < 0.0001). Forty-eight samples were undetectable by both methods (/). One hundred forty-two samples were scored positive by both methods, 141 of which were fully concordantly positive and 1 quantitative discordance (green). Major and minor qualitative discordances are labeled in red and yellow, respectively. PNQ samples are included between lines. Thirteen cases scored PNQ by both methods. ddPCR, droplet digital PCR; dPNQ, PNQ determined by ddPCR; FL, follicular lymphoma; MCL, mantle cell lymphoma; MM, multiple myeloma; PNQ, positive nonquantifiable; qPNQ, PNQ determined by qPCR; qPCR, quantitative real-time PCR.

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MRD Detection: qPCR Compared with ddPCR

standard curve, according to Euro-MRD guidelines, whereas ddPCR performed successfully.19 Of note, two of these three diagnostic samples were analyzed by FC and indicated high tumor infiltration (60%, 79% of CD5þ/ CD19þ cells), indicating that quantification failure was not caused by minimal infiltration, and one indicated a low amount of tumor cells by FC. Because qPCR was unsuccessful in three cases, method comparison was used in 67 diagnostic samples from 66 patients (for one patient both BM and PB at diagnosis were analyzed) and 155 follow-up samples (Supplemental Table S1).

Concordance Analysis A total of 222 of 225 samples (98.7%) were evaluated by both methods and included in the concordance analysis. As Table 1

expected, when the BCL2/IGH was used, we never observed positivity in the no-template samples, whereas in the IGH rearrangement setting we observed a background amplification signal in 4 of 39 patients (10.3%) by ddPCR (consisting of one or two events in only one of the replicates) and in 3 of 36 (8.3%) by qPCR. Of note, in two cases the nonspecific background was detected by both tools overlapped. In total, we evaluated 67 diagnostic and 155 followup samples (Supplemental Table S1) that were quantifiable on the basis of a standard curve built with plasmids (for MM), purified cells (for MCL), or a BCL2/IGH MBRþ cell line (for FL). Of these, 95 samples were MM (18 diagnostic BM and 77 follow-up), 67 were MCL (18 BM and 4 PB diagnostic and 45 follow-up samples), and 60 were FL (27 diagnostic and 33 follow-up samples). A highly significant level of concordance was observed between qPCR and

Discordances Observed between qPCR and ddPCR MM (n Z 95)

Type of discordance

1 (1.1)

Major qualitative discordances, n (%) (MRDþ >1  1004) ddPCR positive qPCR positive Minor qualitative discordances, n (%) (MRDþ 1 1004) ddPCR positive qPCR positive Quantitative discordances, n (%) (discrepancy 1 log) ddPCR higher qPCR higher Total, n (%) ddPCR positive or higher qPCR positive or higher

1 (1.1)

MCL (n Z 67)

FL (n Z 60)

1 (1.5)

17 (17.9) [2 qPNQ-2 dPNQ] 15 (15.8) 2 (2.1)

1 (1.5) 7 (10.4) [2 qPNQ] 2 (3.0) 5 (7.5)

18 (19) 16 (16.8) 2 (2.1)

8 (11.9) 2 (3.0) 6 (8.9)

Total (N Z 222 samples) 2 (0.9)

6 (10.0) [2 qPNQ-3 dPNQ] 4 (6.7) 2 (3.3) 1 (1.7)

1 (0.45) 1 (0.45) 30 (13.5) [6 qPNQ-5 dPNQ] 21 (9.5) 9 (4.1) 1 (0.45)

1 7 4 3

1 33 22 11

(1.7) (11.7) (6.7) (5.0)

(0.45) (14.9) (9.9) (4.9)

Numbers in the table refer to the total number of comparable samples, sorted by disease. Amount and type of discordances recorded for each disease are summarized. Definitions of discordances are reported in the manuscript in Materials and Methods. ddPCR, droplet digital PCR; dPNQ, PNQ determined by ddPCR; FL, follicular lymphoma; MCL, mantle cell lymphoma; MM, multiple myeloma; MRD, minimal residual disease; PNQ, positive nonquantifiable; qPNQ, PNQ determined by qPCR; qPCR, quantitative real-time PCR.

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Drandi et al 621 A B 622 623 624 625 626 627 628 629 630 631 632 C D 633 634 635 636 637 638 639 640 641 642 643 644 E F 645 646 647 648 649 650 651 652 653 654 655 656 657 ddPCR (r Z 0.94, P < 0.0001; 95% CI, 0.9495e0.9712) 658 ½F3 (Figure 3 and Supplemental Figure S2). Of 22 samples, 189 659 (85.1%; 95% CI, 80.4%e89.8%) were fully concordant. 660 MRD detection was fully concordantly positive in 141 of 661 222 samples (63.5%; 95% CI, 57.2%e69.8%; 75 MM, 41 662 MCL, and 25 FL) and concordantly negative in 48 of 222 663 (21.6%; 95% CI, 16.2%e27%; 8 MM, 11 MCL, and 29 664 FL), whereas 33 of 222 samples (14.9%; 95% CI, 10.2%e 665 19.6%) were identified as discordant. 666 667 Discordances between qPCR and ddPCR quantification 668 ½T1 are reported in Table 1. On the basis of the previously re669 Q10 ported criteria for discordance, only 2 of 222 samples 670 (0.9%; 1 MM, 1 MCL) exhibited a major qualitative 671 discordance (Analysis of Discordances). Of interest, in the 672 MM case, qPCR found a nonoptimal sensitivity of 104. 673 Most cases, 30 of 222 (13.5%; 17 MM, 7 MCL, and 6 FL), 674 were indeed classified as minor qualitative discordances, 675 which might reflect Poisson statistic discrepancies related to 676 the low number (<104) of tumor cells in the sample. In 1 677 of 222 cases (0.45%), a quantitative discordance was 678 679 observed to occur in a FL sample (Figure 3). 680 All of the discordances occurred in follow-up samples 681 and did not appear to cluster in specific patients, with the 682

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Figure 4 Example of MRD detection discordances in patient’s follow-up samples. Comparison of MRD level at different tps, taken on the basis of availability of DNA, evaluated by qPCR (solid lines) and ddPCR (dashed lines), for three MM (A, D, and F) and three MCL (B, C, and E) patients. AeC: Three patients with fully concordant follow-up samples. D: A patient with three minor qualitative discordances (tp 3, 5, and 8). E: One case with a major qualitative discordance (tp 2) and a minor qualitative discordance (tp 4). F: A follow-up sample PNQ by qPCR but quantifiable by ddPCR at tp 5. The amount of target copies was log10 transformed. ddPCR, droplet digital PCR; MCL, mantle cell lymphoma; MM, multiple myeloma; MRD, minimal residual disease; PNQ, positive nonquantifiable; qPCR, quantitative real-time PCR; tp, time point.

exception of two MM patients: one showing three minor discordances (Figure 4D) and one displaying four qualita- ½F4 tive discordances (one major, three minors) (data not shown). Of note, 22 of 222 samples (9.9%) were scored positive or higher by ddPCR (median, 7 copies; range, 2 to 74 copies), whereas 11 of 222 samples (4.9%) were scored positive or higher by qPCR (median, 13 copies; range, 2 to 44 copies). Interestingly, among minor discordances, an excess of positive cases was observed by ddPCR compared with qPCR, mostly in MM cases (Table 1). Figure 4 shows representative examples of MRD levels assessed by both methods, including three fully concordant cases (two MCL and one MM) (Figure 4, AeC), two cases with minor qualitative discordances (one MM and one MCL) (Figure 4, D and E, respectively), and one case with a major qualitative discordance (Figure 4E). Of note, one MM patient showed a follow-up sample PNQ by qPCR (qPNQ) but quantifiable by ddPCR (Figure 4F).

PNQ Samples On the basis of Euro-MRD guidelines,19 26 of 222 samples (11.7%) were qPNQ. According to our definition, 19 of 222

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MRD Detection: qPCR Compared with ddPCR samples (8.6%) scored dPNQ. Of 222 samples, 13 (5.9%) were PNQ by both methods. For qPNQ cases, in 7 of 26 samples (26.9%; five MM, one MCL, and one FL) ddPCR was able to provide a reliable quantitative result (median, 8 copies; range; 6 to 63 copies), whereas in 6 of 26 cases (23.1%; two MM, two MCL, and two FL) no amplification signal was observed, and samples were scored negative by ddPCR. In the dPNQ group, 5 of 19 samples (26.3%) scored negative by qPCR, whereas only 1 of 19 (5.3%) was quantifiable by qPCR.

Discussion In this study, we used for the first time ddPCR in the context of MRD evaluation in MM, MCL, and FL patients and compared its performance with the well-established qPCRbased method. Our results indicate that i) ddPCR has sensitivity, accuracy, and reproducibility at least comparable with qPCR; ii) ddPCR allows bypassing the development of a dilution-based standard curve with substantial benefit in terms of reduced costs and labor intensiveness; and iii) ddPCR and qPCR exhibited excellent correlation in all of the assessed disease entities and over a broad range of tumor infiltration rates. On the basis of these findings, ddPCR may be considered an attractive alternative to qPCR for MRD evaluation in mature B-cell lymphoid malignancies. Our data indicate that ddPCR is at least superimposable on qPCR. Nevertheless, ddPCR has several practical advantages, mostly related to its absolute quantification nature with no need for a standard curve and easier data interpretation. This ensures quantification of samples in which a standard curve could not be built and results in reduced cost, labor intensiveness, and spares precious diagnostic tissues, as summarized in Supplemental Table S3. Of note, the starting point for building the qPCR standard curve consists either of unpurified diagnostic tissue (needing FC quantification of tumor cell invasion not always superimposable on molecular results) or purified cells or plasmids, which are both expensive and laborious to generate. In addition, ddPCR ensures direct homogeneous absolute quantification of diagnostic and follow-up samples. Moreover, ddPCR is less susceptible to potential PCR performance inhibitors, such as anticoagulants, DNA extraction residual reagents (alcohol), or blood components (heme) and should allow easier multiplexing set-up.37e42 One relevant issue was to verify that ddPCR might reach a sensitivity that is comparable with qPCR. Recommendations from the producer indicate that the optimal performance of ddPCR is expected to occur when 100 ng of gDNA is used in each reaction. This would represent a major limitation for MRD purposes because achieving optimal sensitivity requires larger DNA amounts. However, our results clearly show that ddPCR also has excellent MRD performance when, as recommended by Euro-MRD guidelines, 500 ng of gDNA is used.19 This result, which mirrors

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similar observations in unrelated disorders,27,36 allowed us to reach sensitivity levels comparable with qPCR and perfectly suitable for MRD purposes. The rate of concordance between qPCR and ddPCR is indeed high and superior to what we observed in a recent comparison between qPCR and NGS-based MRD analysis. Notably, most discordances were observed in the presence of a low number of target copies (minor qualitative discordances), which might reflect variability associated to Poisson’s statistics and subtle differences in sensitivity usually in favor of ddPCR. One critical limitation of qPCR is the frequent occurrence of cases that are defined as PNQ, which reflects the often-observed gap between sensitivity and the quantitative range, resulting in cases for which a truly quantitative value cannot be defined. Because ddPCR does not require a standard curve, on the basis of reproducibility data and good comparison with qPCR, whenever ddPCR is positive in all replicates, the results can be considered as positive and quantifiable. However, we decided to maintain the PNQ definition also for ddPCR for cases in which only a fraction of replicates was PCRþ, because a much larger number of replicates would be necessary for a reliable quantification of these samples. One important achievement in the qPCR field was the multilaboratory standardization established by large cooperative efforts such as the Euro-MRD group. Indeed, ddPCR still has to undergo such a critical validation step. However, considering the reduced labor intensiveness of the experimental set-up and the current robust structure of multilaboratory validation groups, we believe that ddPCR standardization would be definitely easier and faster than qPCR. Another important issue could be the application of ddPCR to the broad range of different targets that are used in acute lymphoblastic leukemia.43 Recently, our group and others have shown the value of NGS in the MRD setting.14,15,44 We think that NGS might substitute for PCR-based MRD tools in a number of settings. However, NGS has a substantial intrinsic complexity and involves major costs. Furthermore, robust and broadly applicable NGS-based MRD protocols are still not available in academic laboratories; moreover, its superiority in comparison with qPCR has not been proved in the context of all diseases. Although NGS ensures a higher rate of marker identification in MM, results are broadly superimposable in MCL, whereas reliability and cost-effectiveness are probably inferior in FL.14,15,44 Therefore, we believe that PCRbased approaches will remain extensively used at least for the next 5 years. As a practical example, the new large phase 3 trials of the European MCL Network (Munich, Bavaria, Germany) will still rely on PCR for MRD monitoring.

Conclusion ddPCR appears to be a feasible and attractive alternative method for MRD assessment and may complement or even

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Drandi et al substitute for qPCR in routine clinical laboratories. However, the potential advantages and predictive value need to be further studied in the context of prospective clinical trials. On the basis of the results reported here, we are currently assessing MRD by both ddPCR and qPCR on four large prospective clinical trials (>800 patients) in the context of Fondazione Italiana Linfomi studies.

8.

Acknowledgments 9.

We thank Franca Trotto Gatta, Carla Garbero, and Antonella Fiorillo for excellent administrative support.

Supplemental Data Supplemental material for this article can be found at http://dx.doi.org/10.1016/j.jmoldx.2015.05.007. 10.

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Supplemental Figure S1 Bland-Altman plot for quantifiable patient samples. Mean of samples quantification (log10 transformed) performed by qPCR and ddPCR (x axis) is plotted against the difference between the same two results (y axis). The overall mean of the differences (0) and the 95% limits of agreement (1) are shown as dotted lines. The only one quantitative discordance is labeled in green, major and minor qualitative discordances are labeled in red and yellow, respectively. PNQ samples are not included in the graph. ddPCR, droplet digital PCR; PNQ, positive nonquantifiable; qPCR, quantitative realtime PCR. Supplemental Figure S2

Correlation between qPCR and ddPCR quantification. Log10-transformed copy numbers determined by ddPCR were plotted against the corresponding Cq value and fitted with a linear regression model, R2 Z 0.997 (A), R2 Z 0.996 (B), and R2 Z 0.997 (C). Every dilution point represents a mean value of quantifiable replicates. Pearson correlation coefficient shows high correlation degree between ddPCR and qPCR, r Z 0.9992 (A), r Z 0.9978 (B), and r Z 0.9995 (C). P < 0.0001. ddPCR, droplet digital PCR; MBR, major breakpoint region; MCL, mantle cell lymphoma; MM, multiple myeloma; qPCR, quantitative real-time PCR.

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