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The Journal of Molecular Diagnostics : JMD logoLink to The Journal of Molecular Diagnostics : JMD
. 2024 Mar;26(3):168–178. doi: 10.1016/j.jmoldx.2023.11.009

Quantification of Measurable Residual Disease Detection by Next-Generation Sequencing–Based Clonality Testing in B-Cell and Plasma Cell Neoplasms

Ying Liu ∗,†,∗, Caleb Ho ‡, Wayne Yu ∗, Ying Huang §, Jeffrey Miller §, Qi Gao †, Mustafa Syed ∗, Yuanyuan Ma ∗, Meiyi Wang ∗, Lidia Maciag ∗, Kseniya Petrova-Drus ∗,†, Menglei Zhu ∗,†, JinJuan Yao ∗, Chad Vanderbilt ∗, Benjamin Durham ∗, Jamal Benhamida ∗, Mark D Ewalt ∗,†, Ahmet Dogan †, Mikhail Roshal †, Khedoudja Nafa ∗, Maria E Arcila ∗,†,∗
PMCID: PMC10918645  PMID: 38103591

Abstract

Next-generation sequencing (NGS)–based measurable residual disease (MRD) monitoring in post-treatment settings can be crucial for relapse risk stratification in patients with B-cell and plasma cell neoplasms. Prior studies have focused on validation of various technical aspects of the MRD assays, but more studies are warranted to establish the performance characteristics and enable standardization and broad utilization in routine clinical practice. Here, the authors describe an NGS-based IGH MRD quantification assay, incorporating a spike-in calibrator for monitoring B-cell and plasma cell neoplasms based on their unique IGH rearrangement status. Comparison of MRD status (positive or undetectable) by NGS and flow cytometry (FC) assays showed high concordance (91%, 471/519 cases) and overall good linear correlation in MRD quantitation, particularly for chronic lymphocytic leukemia and B-lymphoblastic leukemia/lymphoma (R = 0.85). Quantitative correlation was lower for plasma cell neoplasms, where underestimation by FC is a known limitation. No significant effects on sequencing efficiency by the spike-in calibrator were observed, with excellent inter- and intra-assay reproducibility within the authors’ laboratory, and in comparison to an external laboratory, using the same assay and protocols. Assays performed both at internal and external laboratories showed highly concordant MRD detection (100%) and quantitation (R = 0.97). Overall, this NGS-based MRD assay showed highly reproducible results with quantitation that correlated well with FC MRD assessment, particularly for B-cell neoplasms.

The concept of measurable residual disease (MRD) is built upon the principle that the likelihood of tumor recurrence is directly related to the amount of residual tumor cells that remain during and after cancer treatment and which may not be detectable by conventional morphologic evaluation.1 Studies have shown the strong prognostic power of MRD status in predicting clinical outcome for patients with B-cell lineage malignancies, such as chronic lymphocytic leukemia (CLL), B-lymphoblastic leukemia/lymphoma (B-ALL), and plasma cell neoplasms (PCN).2, 3, 4, 5, 6 Immunoglobulin heavy chain (IGH) clonality testing by next-generation sequencing (NGS) can detect unique clonal IGH rearrangements in >95% of all B-cell neoplasms, which allow for the tracking of patient and disease-specific clonotypes with high sensitivity (approximately 10−6), making this methodology particularly suitable for disease monitoring.7, 8, 9, 10 Furthermore, this MRD approach is not affected by shifts in tumor mutational and immunophenotypic profiles over time.7,11,12 Unlike immunophenotypic-based assays, such as flow cytometry (FC), NGS-based MRD assessment can better define the behavior of specific clonal neoplastic populations, including their suppression, reemergence, evolution, and changes in the background repertoire.13

A central aspect of NGS-amplicon–based IGH clonality assays for MRD assessment is that primers amplify only rearranged IGH genes. This enables calculations of residual disease as a proportion of detectable disease-associated clonal sequencing reads in reference to total rearranged IGH sequencing reads. Because these originate from amplicons of DNA from mostly B and plasma cells, but not other cells with the IGH gene in germline configuration, this calculated percentage does not directly correlate with the overall level of clone in the entire sample/cellularity, an important parameter for clinical assessment of residual disease levels. With the addition of a known quantity of spike-in clonal DNA (spike-in calibrator) to the monitoring samples, the quantitation of MRD can be normalized to reflect the level of the disease-associated clonotype as a percentage of total sample cellularity. Although multiple studies of NGS-based quantification methods using a calibrator for MRD monitoring have been reported, most remain confined to a proof of principle and/or without details of the systematic validation and performance of the assays,5,11,14, 15, 16, 17 precluding utilization for further validation and broad adoption of MRD quantitation into routine clinical practice. Our aim was to describe the clinical implementation of NGS-based quantitative MRD assessment of B-cell and plasma cell neoplasms, with a focus on longitudinal performance and assessment of samples with very low-level MRD.

Materials and Methods

Patient and Sample Selection

Post-treatment bone marrow aspirate and peripheral blood samples received for disease monitoring were identified between September 1, 2018, and September 1, 2021. All patients had a previously confirmed diagnoses of B-cell or plasma cell neoplasm, and the diagnostic index (disease-associated) clones were characterized on samples with high disease level at the Memorial Sloan Kettering Cancer Center (MSKCC) diagnostic molecular pathology laboratory. Monitoring samples were from patients who had received various treatment regimens and were collected at different timepoints during the course of therapies. The lymphoid neoplasm classification18 and results of morphologic, immunohistochemistry, FC, cytogenetic, and molecular assessment were recorded. Only monitoring samples without frank morphologic involvement were included, encompassing those with negative morphology, and equivocal or low disease level (<5%) as assessed by a combination of morphologic, immunohistochemistry, and FC assessment.

Initial IGH Clonal Characterization by NGS

NGS-based testing for initial characterization of the diagnostic index clones was performed using commercially available LymphoTrack kits (Invivoscribe, Inc., San Diego, CA), as previously described.7 Only cases with diagnostic clones successfully characterized by Leader or FR1 primer sets were included in the current study. Sequences were analyzed using the LymphoTrack MiSeq software version 2.3.1 or version 2.4.3 (Invivoscribe, Inc.).7

MRD Monitoring Assay by NGS

NGS-based MRD testing was performed using the commercially available LymphoTrack assay, as described previously.19 For each monitoring sample, the same PCR primer set (Leader or FR1) that successfully characterized the diagnostic clonal sequences was used for subsequent monitoring. Two replicate reactions of 700 ng of patient's sample DNA each (approximately 100,000 cell equivalents per reaction) were initially tested. Repeat testing was performed in cases with sequencing failure if material was available. Failure was defined by the absence or low-level amplification in the reaction, when generating <50,000 total sequencing reads. A monitoring sample was considered test failure if <2 of 4 replicates showed >50,000 total sequencing reads/replicate. A total of 700 pg (approximately 100 cell equivalents) of the LymphoQuant (spike-in) calibrator (Invivoscribe, Inc.) was used to spike one of the two replicates before the sample underwent amplification. The calibrator consists of a cell line–derived DNA with a single rearrangement (IGHV1-69/IGHJ6). Both replicates were sequenced in parallel in the same pool to allow assessment of the effects of the spike-in calibrator on total IGH sequencing reads and on the detection of low-level diagnostic index clonal sequences. A no-template control and an MRD-positive control were also included in every sequencing run with monitoring samples (further detailed below in the control section). After demultiplexing, a search for sequences with exact match to the index clonal sequence of the corresponding patient was performed using the LymphoTrack MRD data analysis tool (MRDDAT) software version 1.0.3 or version 1.2.0 (Invivoscribe, Inc.). Furthermore, when both replicates (without and with spike-in calibrator) showed concordant results (index clonal sequences detected or not detected), the corresponding MRD status [MRD-positive (MRD+) or undetectable MRD (uMRD)] was assigned, and the level of positivity was calculated. When results were discordant between replicates, repeat testing was performed if material was available. For the repeat cases, MRD+ status was assigned when ≥2 of the 4 replicates detected the index clonal sequence. For samples with >one characterized index sequence due to biallelic or biclonal rearrangements, the normalized disease level was calculated by the average (if biallelic) or by the sum (if biclonal) of the index clonal reads.

MRD was calculated using two approaches.

  • 1.

    Straight percentages of index clonal reads in the total rearranged IGH reads: % index clone = index clonal reads/total rearranged IGH reads

  • 2.

    Normalized disease level with spike-in calibrator: % normalized disease (%MRD cells in total cellularity) = % index clonal reads/% LymphoQuant reads) * 0.1% [given the spike-in of 700 pg DNA into 700,000 pg (700 ng) patient sample = 0.1% spike-in]

Assessment of Performance Characteristics

Reproducibility Assessment

Initial interassay and intra-assay reproducibility were assessed according to validation requirements established by New York State Department of Health (Department of Health, Wadsworth Center, https://www.wadsworth.org/regulatory/clep/clinical-labs/laboratory-standards, last accessed August 25, 2023). Six patients' MRD+ samples were tested following the same protocol used for all the clinical samples as described above. Each sample was tested in triplicate in the same run (intra-assay) and across three separate runs (interassay), on different days and with different sequencing instruments by two technologists (W.Y. and Y.M.). To avoid confounding due to use of common barcodes, all replicates were assigned unique barcodes. Further assessment of interassay reproducibility was performed based on review of all clinical runs containing a spike-in calibrator as part of the control set.

Interlaboratory Reproducibility

To further assess the reproducibility of the assay, aliquots of extracted DNA from 35 unique monitoring samples (n = 35) were tested in the authors’ laboratory and at the Invivoscribe, Inc. (IVS) clinical laboratory (San Diego, CA). Testing was performed following similar protocols including same DNA input, assay kit reagents, type of sequencing instruments, and MRD analysis software.

Control Samples and Quality Control Checks

Each clinical sequencing run included an MRD control prepared from commercially available control standards (Invivoscribe, Inc.): IVS-0019 diluted with IVS-0000 at a 0.05% dilution level. In this construed MRD control sample, three well-characterized clonal sequences known to be present at levels of 3 to 4 × 10−4, 1 × 10−5, and 1 × 10−6 of the total rearranged reads were tracked.

To further assess the performance and stability of the LymphoQuant (spike-in) calibrator across different runs, another constructed MRD positive control: IVS-0013 diluted with IVS-000 at 0.05% was spiked-in at similar proportions to those described for clinical samples. After demultiplexing, sequences with exact matches to the selected clonal sequences and the LymphoQuant (spike-in) calibrator were searched for using the LymphoTrack MRD data analysis tool (MRDDAT) software version 1.0.3, were quantitated as percentage of total IGH sequencing reads, and were monitored as part of standard quality control for the assay.

Flow Cytometry Assessment

The concurrent FC sample was obtained on the same procedure but did not constitute the same pass as that for the NGS. The MRD status of each sample was compared with results of FC assays that were performed as previously described.20, 21, 22 For PCN MRD detection, a target acquisition of at least 3,000,000 cells was used to achieve a detection sensitivity of up to 6 × 10−6. For B-ALL MRD detection, a target acquisition of at least 2,000,000 cells was used to achieve a detection sensitivity of 1 × 10−5. For CLL MRD detection, a target acquisition of at least 1,000,000 cells was used to achieve a detection sensitivity of 1 × 10−4.

Statistical Analysis

Statistical analysis was performed using the R software version 3.4.3 (The R Foundation, https://www.r-project.org).

Results

Detection of Index Clones in Monitoring Samples and Concordance with FC

To assess the utility and performance of the assay, a total of 546 monitoring samples from 294 unique patients were analyzed. The overall distribution of the samples and the patients by disease type are summarized in Figure 1.

Figure 1.

Figure 1

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Distribution of the B-cell and plasma cell neoplasms. A: Distribution of the B-cell and plasma cell neoplasms in 546 monitoring samples. B: Distribution of the B-cell and plasma cell neoplasms in 294 unique patients. B-ALL, B-lymphoblastic leukemia/lymphoma; CLL, chronic lymphocytic leukemia; DLBCL, diffuse large B-cell lymphoma; FL, follicular lymphoma; MCL, mantle cell lymphoma; Others, other types of B-cell neoplasms; PCN, plasma cell neoplasm.

The overall sequencing failure rate was 4.9% (27/546) (Figure 2), all related to lack of amplification in the context of samples with very low total cellularity and/or very low B-cell or plasma cell content (lack of amplifiable template). The corresponding FC studies for this set of samples were also suboptimal in 78% (21/27) of samples with a limited number of acquired events (median 5.4 × 105; range 1.4 × 105 to 8.6 × 106) and low proportion of B and plasma cells (median: 0.2%; range: 0.01% to 2.60%) (Supplemental Table S1). The remaining 519 samples were successfully sequenced, 19 of these (3.7%) required repeat testing due to discrepant MRD results in the two initial replicates. Concurrent FC of these later cases showed very low-level disease involvement (median: 0.00087% of white blood cells), at or below the established technical limits of detection for the assays. For successfully sequenced samples, the median total read count (rearranged reads) per replicate was 341,874 (range: 396 to 1,811,449) and 367,834 (range: 333 to 1,591,328) for standard (without spike-in) and spike-in reactions, respectively. No significant differences were observed between replicates, when analyzed across all samples or when stratified based on primer sets (FR1 or Leader). Among cases positive for residual disease, the index clone was detected at levels that ranged from 0.001% to 84.8% of the total rearranged IGH reads with no significant level differences between the standard and spike-in reactions. Refer to Table 1 for a detailed summary.

Figure 2.

Figure 2

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Summary of 546 monitoring samples tested with next-generation sequencing (NGS)–based measurable residual disease (MRD) assay. Concordant: cases with concordant MRD calling between NGS and flow cytometry (FC); Discordant: cases with discordant MRD calling between NGS and FC. The total 27 “failed” samples include 16 B-lymphoblastic leukemia/lymphoma (B-ALL), 5 mantle cell lymphoma (MCL), 3 plasma cell neoplasm (PCN), 2 chronic lymphocytic leukemia (CLL), and 1 diffuse large B-cell lymphoma (DLBCL) (21 FC-uMRD and 6 FC-MRD+) cases. Red asterisk: 27 cases showed failure pattern of amplification, all corresponding to samples with very low total cellularity and/or very low B-cell or plasma cell content (lack of amplifiable template). Corresponding flow cytometric results for these cases similarly showed suboptimal target sensitivity (failure to reach the minimal number of cells for MRD assessment) in 78% of cases (21/27), further supporting that these were failures associated with very low cellularity post-treatment. uMRD, undetectable measurable residual disease.

Table 1.

Total IGH Sequencing Reads and IGH Clonal Reads of Non–Spike-In and Spike-In Replicates by NGS of Successfully Sequenced Monitoring Samples (Including Samples That Required Repeat Testing After the Initial Attempts)

NGS (non–spike-in versus spike-in) Total IGH sequencing reads
 IGH clonal reads in FC MRD+ cases, %
Range Median Range Median
FR1 (n = 430)
 Non–spike-in 396–1,811,449 352,009 FR1 (n = 139) 0.001–84.75 1.09
 Spike-in 423–1,591,328 380,736 0.001–83.51 0.84
 P value 0.1 0.8
Leader (n = 89)
 Non–spike-in 619–1,439,640 315,337 Leader (n = 41) 0.001–64.21 0.99
 Spike-in 333–1,332,590 296,813 0.003–63.77 0.75
 P value 0.7 0.7
Total (n = 519)
 Non–spike-in 396–1,811,449 341,874 Total (n = 180) 0.001–84.75 1.06
 Spike-in 333–1,591,328 367,834 0.001–83.51 0.81
 P value 0.1 0.7
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FC, flow cytometry; IGH, immunoglobulin heavy chain; MRD, measurable residual disease; NGS, next-generation sequencing.

The binary distribution of cases as MRD+ or uMRD is depicted in Figure 3. The overall concordance rate between NGS and FC was 91% (471/519 samples), including 291 uMRD and 180 MRD+ samples with concordant results by both methods. Among the 48 discordant samples, 27 (5%; 27/519; from 20 patients) were FC-uMRD/NGS-MRD+ and 21 (4%; 21/519; from 17 patients) were FC-MRD+/NGS-uMRD (Supplemental Table S2). By FC, samples in the FC-MRD+/NGS-uMRD category had a median disease level of 0.00067% of white blood cells (range: 0.0001% to 0.14%). For FC-uMRD/NGS-MRD+ samples, median quantitative (normalized) disease level was 0.017% (range: 0.00017% to 1.86%) of total cellularity and corresponding index clonal reads was 1.21% (range: 0.005% to 80.2%) of total rearranged IGH reads. Subsequent disease relapse was seen in 50% (10/20 patients) with FC-uMRD/NGS-MRD+ discrepant samples, and in 35% (6/17 patients) in the FC-MRD+/NGS-uMRD category. Refer to Supplemental Table S3 for detailed clinical follow-up findings in this subset.

Figure 3.

Figure 3

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Concordance between next-generation sequencing (NGS) and flow cytometry (FC) assays for measurable residual disease (MRD) detection in the 519 successfully sequenced monitoring samples. A: Number of concordant cases (blue) and discordant cases (orange) by NGS and FC assays. B: Percentage of concordant cases (91%, blue) and discordant cases (9%, orange and gray) by NGS and FC assays. n = 471 concordant cases (A); n = 48 discordant cases (A). uMRD, undetectable measurable residual disease.

Quantitation of MRD Level with the Spike-in Calibrator and Correlation with Disease Quantitation by Flow Cytometry

Quantitation of disease level by NGS was determined through normalization calculations using the spike-in calibrator; comparisons were established with the corresponding FC results. Among the 180 samples with concordant MRD+ status by both methods, normalization calculations could be performed on 172 samples. Normalization on the remaining eight samples was precluded by detection of the index clone only in the non-spiked replicates. Repeat testing confirmed the presence of the clone at very low level in two of the four replicates in all cases, but not detected in the spike-in reactions to allow normalization (see Materials and Methods). Among the 172 samples with calculations, the median disease level was 0.047% (range: 0.00026% to 4.2%) by FC and 0.044% (range: 0.00011% to 12.8%) by NGS with no significant differences (P = 0.6) observed between these two assays. When replicates tested with FR1 and Leader primer sets were separately analyzed, there remained no significant difference seen in quantitative MRD levels between NGS and FC (P = 0.5 and P = 0.6, respectively) (Table 2).

Table 2.

Correlation of Quantitative Disease Levels of Concordant MRD+ Cases by NGS and FC

NGS versus FC Quantitative disease level, %
Range Median
FR1 (n = 132)
 NGS, % MRD cells in total cellularity 0.00011–12.8 0.045
 FC, % total WBC 0.00026–4.2 0.033
 P value 0.5
Leader (n = 40)
 NGS, % MRD cells in total cellularity 0.00040–6.0 0.28
 FC, % total WBC 0.0016–3.1 0.91
 P value 0.6
Total (n = 172)
 NGS, % MRD cells in total cellularity 0.00011–12.8 0.044
 FC, % total WBC) 0.00026–4.2 0.047
 P value 0.6
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FC, flow cytometry; MRD, measurable residual disease; NGS, next-generation sequencing; WBC, white blood cells.

Good linear correlation was observed between NGS and FC for all cases (R = 0.67; 95% Cl: 0.58 to 0.74; P < 0.01) (Figure 4A). Better linear correlation was observed for CLL (R = 0.85; 95% Cl: 0.64 to 0.94; P < 0.01) and B-ALL (R = 0.85; 95% Cl: 0.73 to 0.92; P < 0.01) cases (Figure 4, B and C). By contrast, PCN cases had the lowest correlation in disease quantitation by NGS compared with FC (Figure 4D) (95% Cl: 0.42 to 0.69; P < 0.01).

Figure 4.

Figure 4

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Correlation of quantitative disease level between next-generation sequencing (NGS) and low cytometry (FC) assays. A: Correlation of quantitative disease level of all measurable residual disease (MRD)+ samples (R = 0.67; 95% Cl: 0.58 to 0.74; P < 0.01) between NGS and FC. B: Correlation of quantitative disease level of MRD+ chronic lymphocytic leukemia (CLL) samples (R = 0.85; 95% Cl: 0.64 to 0.94; P < 0.01) between NGS and FC. C: Correlation of quantitative disease level of MRD+ B-lymphoblastic leukemia/lymphoma (B-ALL) samples (R = 0.85; 95% Cl: 0.73 to 0.92; P < 0.01) between NGS and FC. D: Correlation of quantitative disease level of MRD+ plasma cell neoplasm (PCN) samples (R = 0.57; 95% Cl: 0.42 to 0.69; P < 0.01) between NGS and FC. n = 172 MRD+ samples (A); n = 19 CLL samples (B); n = 37 B-ALL samples (C); n = 101 samples (D). MSK, Memorial Sloan Kettering; WBC, white blood cells.

Notably, there was very poor correlation between disease quantifications based on % of total rearranged reads versus total cellularity, as well as between % of total rearranged reads versus FC (R = 0.07 and R = 0.06, respectively), reinforcing that normalized quantification aided by the spike-in calibrator is a more meaningful metric that more closely reflects the overall disease level (Supplemental Figure S1).

Sensitivity and Reproducibility of NGS-Based MRD Quantitative Assay

Sensitivity studies for the NGS-based MRD monitoring assay were previously described.19 To monitor sensitivity across clinical runs, the results of a positive control at an MRD level incorporated in each of the clinical runs were reviewed. Across a total of 231 sequencing runs analyzed, the index clonal sequences expected at the levels of 3 to 4 × 10−4, 1 × 10−5, and 1 × 10−6 were detected in 100%, 99.57%, and 71.86% of runs, respectively (Supplemental Figure S2), confirming a highly reproducible technical sensitivity at the level of at least 1 × 10−5 for this assay. The LymphoQuant(spike-in) calibrator, expected at a level of approximately 1 × 10−3, was detected in 100% of the 300 sequencing runs analyzed (Figure 5A). Slight fluctuation in the level of expected clone was found as depicted in Figure 5B. Of note, only a single replicate with the spike-in calibrator was added to each pool, in contrast to clinical monitoring samples for which at least two replicates were sequenced per sample to increase the chance of detecting and confirming the presence of low-level diagnostic index clonal sequences.

Figure 5.

Figure 5

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Sensitivity and reproducibility of next-generation sequencing (NGS)–based measurable residual disease (MRD) monitoring assay: A: Detection of LymphoQuant(spike-in) calibrator in control MRD samples across 300 clinical runs. B: Detection of LymphoQuant(spike-in) calibrator in control MRD samples at expected level of 10−3 with slight fluctuation. C: Reproducibility of MRD monitoring assay with LymphoQuant(spike-in) calibrator. Each of the 6 flow cytometry (FC) MRD+ monitoring samples were tested in 6 replicates in 3 separated runs. D: Correlation of MRD quantitation between Memorial Sloan Kettering (MSK)-NGS and Invivoscribe (IVS)-NGS in a total of 17 MRD+ monitoring samples (R = 0.97; 95% Cl: 0.92 to 0.99; P < 0.01).

In accordance with New York State Department of Health guidance, interassay and intra-assay reproducibility studies using spike-in calibrator were performed on six patients' MRD+ samples with six replicates of each sample tested on three separate runs, and one clonal sequence was tracked for each sample. Median disease level of the six MRD+ samples was 0.069% (range: 0.0054% to 0.14%) by FC. Median quantitative (normalized) disease was 0.024% (range: 0.002% to 0.077%) of total cellularity and median index clonal reads was 0.59% (range: 0.041% to 4.84%) of total rearranged IGH reads by NGS (Supplemental Table S4). Clonal calls were 100% reproducible with low variability in the percentage of normalized disease level (SD: 0.003% to 0.007%) (Figure 5C).

Concordance of Quantitative MRD Detection Across Laboratories

The same DNA aliquots from a subset of monitoring samples (n = 35) were further tested independently by two laboratories of MSK and IVS to assess for interlaboratory reproducibility of the assay. Median total sequencing reads from MSK and IVS laboratories were 329,640 (range: 71,938 to 915,672) and 247,871 (range: 51,711 to 1,105,771), respectively. No significant difference in their amplification efficiency was seen between the two laboratories (P = 0.5) (Table 3). Overall, MRD status showed 100% concordance including MRD+ (n = 17) and uMRD (n = 18) cases between the two laboratories. MRD quantitation calculated using the spike-in calibrator for MRD+ cases was also highly concordant (R = 0.97; 95% Cl: 0.92 to 0.99; P < 0.01) between laboratories (Figure 5D).

Table 3.

Total IGH Sequencing Reads of 35 Monitoring Samples by NGS of MSK and IVS.

NGS (MSK vs IVS) Total IGH sequencing reads
Range Median
Total (n = 35)
 MSK 71,938–925,672 329,640
 IVS 51,711–1,105,771 247,871
 P value 0.5
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FC, flow cytometry; IVS, Invivoscribe; MSK, Memorial Sloan Kettering; NGS, next-generation sequencing.

Discussion

MRD detection is a powerful predictor of post-treatment relapse in patients with B-cell and plasma cell neoplasms.2,8,23, 24, 25 Although the use of NGS-based IGH detection assays is recommended by the National Comprehensive Cancer Network Guidelines for MRD monitoring in selected diseases (ALL, CLL, and PCN), detailed analyses on the technical performance, and correlation with orthogonal MRD assays, such as high sensitivity FC, remain limited.11 Importantly, a disproportionate number of published clinical studies have used proprietary tests of restricted direct access to clinical laboratories,26, 27, 28, 29, 30, 31, 32, 33 limiting the validation, broad implementation, and applicability of assays in routine patient care. The use of spiked normalizers to more precisely quantify residual disease has been explored by various groups; however, formal descriptions and direct comparison to clinically validated orthogonal assays such as FC remain lacking (Supplemental Table S5).34,35 Only recently, a study described the validation in a clinical setting of a quantitative MRD monitoring assay using a spike-in calibrator for B-ALL, but not for other B-cell lineage neoplasms or PCN.36

In previous studies, the authors described their clinical experience of the current commercially available NGS-based assay for diagnostic clonal characterization7 and application for MRD assessment.19 Here the authors describe the further implementation of a spike-in calibrator to allow disease normalization and MRD quantitation in total sample cellularity. From the perspective of a clinician evaluating patients' treatment response, this MRD parameter is much more clinically meaningful, because it can be better correlated with other clinical and pathological information.

To assess the overall utility of the spike-in calibrator, the authors evaluated different aspects of its technical and longitudinal performance, across 546 clinical monitoring samples submitted for routine MRD assessment and through the dedicated analysis of quality assurance controls introduced across >200 clinical runs. First, by comparing the characteristics of two replicates (one standard replicate and one spiked with the calibrator) tested concurrently in each clinical run, the authors demonstrated that the addition of the spike-in calibrator has no significant impact on the total sequencing reads or the % of index clonal reads in total rearranged IGH reads, regardless of the primer set used, suggesting a negligible effect on the overall amplification efficiency of the assay. The authors further demonstrated that the assay maintained high sensitivity and reproducibility, reaching consistent detection of the tracked sequences at the level of at least 1 × 10−5 across all controls. Similarly, the unique spike-in calibrator sequence remained detectable at the expected level in 100% of the control samples. Of note, each sequencing run incorporated one MRD control, whereas clinical samples were tested in replicates, further increasing the detection of sequences present below the level of 1 × 10−5. As such, across the clinical samples, sequencing success rate was high and results were highly concordant to those of the corresponding FC assay, which has demonstrated a sensitivity of 1 × 10−4 to 1 × 10−6.20, 21, 22 By using predefined criteria for total sequencing reads per replicate, approximately 95% of monitoring samples could be successfully sequenced to reach the desired sensitivity, either on the initial attempt or after repeat testing. An important point to emphasize is that almost all test failures could be attributed to low DNA input related to hypocellular samples (post-chemotherapy/post-transplant) or cellular samples with virtually no B or plasma cells based on other ancillary studies, supporting a lack of effective DNA templates for amplification. In this context, although the assay itself is technically deemed a failure, it may further support the absence of disease once correlated with the clinicopathologic context.

Finally, the authors demonstrated a high concordance rate of MRD status (MRD+ or uMRD) between the NGS and FC assays across different B-cell neoplasms and PCN (approximately 91%), similar to that observed in the authors’ previous and more focused study on PCN (approximately 93%).19 Discordances were equally distributed across both assays (5% FC-uMRD/NGS-MRD+ samples; 4% FC-MRD+/NGS-uMRD samples). Notably, the subsequent clinical correlation demonstrated disease relapse in 50% (10/20) of the FC-uMRD/NGS-MRD+ samples compared with 35% (6/17) of those in the FC-MRD+/NGS-uMRD category, suggesting the possible higher sensitivity and/or specificity for the NGS results. Among all the cases with discordant MRD calling between NGS and FC, there were 13 B-ALL samples of FC-uMRD/NGS-MRD+ including four patients with ETV6-RUNX1, ETV6-ABL1, TCF3-PBX1, or KMT2A-AFF1 rearrangement, respectively. The lack of MRD detection by FC in these cases could be due to the low level of index clonal sequences in lineages other than B-lymphoblasts because multilineage involvement and hematopoietic stem cells as the cell-of-origin have been demonstrated in fusion-driven B-ALL.37, 38, 39 The other patients with discordant MRD status by NGS and FC either had immediate subsequent intervention (hematopoietic stem cell transplantation or chemotherapy) or lack of further follow-up, making it difficult to correlate the discordant MRD status by NGS and FC with subsequent disease relapse status (Supplemental Table S3). Although the authors could not be certain of the cause for other discordant cases, these cases mostly exhibited very low MRD level at or near the limit of detection by either assay as well as some technical considerations further mentioned below.

With regard to overall MRD quantitation, the correlation between NGS and FC was variable according to disease type with correlation coefficients (R values) of 0.85, 0.85, and 0.57 for CLL, B-ALL, and PCN samples, respectively. Multiple factors may be responsible for this variability including preanalytical, analytical, and biologic factors, some of which may preferentially affect PCN. In the preanalytical phase, a major contributor is the sample collection. Although samples collected for clinical FC and NGS testing are part of the same procedure, the aspirates submitted constitute separate passes, which can suffer from sampling error, variable amount of hemodilution, and high variability associated with patchy disease distribution such as PCN.40 From the assay design and analytical test components, it is well-established that primer-based assays can suffer from amplification biases, which are further complicated by somatic hypermutation and differences in primer annealing. These differences would further affect calculated percentages of the disease clone and ultimate normalization and quantitation by NGS. On the other hand, the underestimation of plasma cell component is a well-known pitfall of FC. Although hemodilution may contribute the highest to this finding that can affect both NGS and FC samples equally, disruption of surface markers and sample viability may disproportionally affect PCN assessment by FC. It is known that the viability of plasma cells decreases significantly over time after sample collection, compared to the B-cell neoplasms, which could be more stable.40 The FC assay at MSKCC can assess for abnormal expression of multiple markers on plasma cells, including CD19, CD27, CD45, CD56, CD81 and CD117, however, small populations of neoplastic plasma cells with minimal/mild or variable abnormalities in these markers may be difficult to quantitate. On the other hand, the NGS-based assay can detect disease-specific index clonal sequences even from disrupted or nonviable plasma cells, due to the stability of the DNA and the unique nature of the clonally rearranged sequences. From an analytical perspective, NGS-based assay has the advantage that once index clones are established in diagnostic samples, longitudinal monitoring is more streamlined and can be analyzed automatically with established pipelines, reducing subjectivity in interpretation. Nonetheless, final analysis may require the consideration of several factors to improve the accuracy of the assessment. In this study, we specifically searched for the diagnostic index sequence originally detected at the time of diagnosis without mismatches. However, minor related clonal sequences with higher degree of mismatching to the index sequence, either due to sequencing errors or physiologic variation, were not considered in the calculations, which could have led to minor underestimations in some cases. At present, however, there are no specific guidelines on how much variability one could allow for monitoring of different disease processes, particularly those that are affected by ongoing somatic hypermutation and intraclonal heterogeneity. Thus, an exact match for the top index clonal sequence(s) was deemed most suitable for this initial study.

Despite the stated differences in quantitation, based on this study, the authors find that NGS-based assay showed high concordance with FC in MRD detection with expected variability in overall quantitation which was multifactorial with pitfalls affecting both platforms. MRD assessment using this assay and further implementation of the spike-in calibrator delivered highly reproducible results with high sensitivity at a similar level to a high-performing and highly sensitive FC assay.

The authors propose that whenever feasible, it is preferable to validate and implement both NGS and FC assays with a high level of technical sensitivities for MRD detection and quantitation. FC assay has the benefits of being more time and cost efficient; however, it requires fresh samples and a high level of expertise for assay validation and results interpretation. NGS-based MRD assay has the advantage that once characterization clones are established in diagnostic samples, subsequent monitoring samples can be analyzed automatically with established pipelines, reducing subjectivity in interpretation, and the test can be performed on fresh and archival samples. However, it requires successfully characterized clonotypes available. Therefore, we propose that for laboratories with the capability to perform both types of assays, assessment by NGS or FC would be complementary for the detection of low-level MRD, while for laboratories faced with the decision to choose one, the choice of test may be determined by practical rather than test performance considerations.41

Disclosure Statement

M.E.A. has served as a consultant and received honoraria from Biocartis US, Inc., Invivoscribe, Inc. Janssen Global Services, Bristol Myers Squibb, AstraZeneca, Roche, and Merck. C.H. has received honoraria from Blueprint Medicines, Hematopathology Advisory Board, and is an employee of Loxo Oncology, Inc. Y.H. and J.M. were employees of Invivoscribe, Inc., which developed and sells the commercial assay used in this paper. K.P.-D. received an honorarium from Invivoscribe not related to this study. M.R. has served as a consultant for BD Biosciences, Agios, and Celgene, as well as received contract research funding for Agios, Roche, BMS, and Bayer. A.D. has received consulting fees from Physicians' Education Resource, Seattle Genetics, Takeda, Roche, EUSA Pharma, Peerview Institute, Corvus Pharmaceuticals, and AbbVie, as well as research support from Roche and Takeda. C.V. has received consulting fees from DocDoc Pte. Ltd. and Paige.AI, Inc.

Footnotes

Supported by the Comprehensive Cancer Center Core grant P30 CA008748 at Memorial Sloan Kettering Cancer Center from the NIH.

Y.L. and C.H. contributed equally to this work.

Supplemental material for this article can be found at http://doi.org/10.1016/j.jmoldx.2023.11.009.

Contributor Information

Ying Liu, Email: liuy6@mskcc.org.

Maria E. Arcila, Email: arcilam@mskcc.org.

Author Contributions

Y.L., C.H., K.N., and M.E.A. designed the study and wrote the manuscript; M.R. and A.D. designed the study; Q.G., M.R., K.P.-D., M.Z., J.Y., C.V., M.D.E., B.D., J.B., A.D., and M.E.A. analyzed the flow cytometry and/or NGS testing data; W.Y., L.M., M.W., Y.M., and M.S. performed technical aspects of the molecular assays; and Y.H. and J.M. provided technical and analysis support for the NGS-based assay. All authors reviewed and provided input for the manuscript.

Supplemental Data

Supplemental Table S1
mmc1.docx (12.6KB, docx)
Supplemental Table S2
mmc2.docx (13.1KB, docx)
Supplemental Table S3
mmc3.docx (37.7KB, docx)
Supplemental Table S4
mmc4.docx (12.9KB, docx)
Supplemental Table S5
mmc5.docx (12.8KB, docx)

Supplemental Figure S1.

Supplemental Figure S1

Open in a new tab

Correlation of quantitative disease level between % of total immunoglobulin heavy chain (IGH) reads and % of total cellularity or flow cytometry (FC) assay. A: Poor correlation of quantitative disease level of all measurable residual disease (MRD)+ samples between % of total IGH reads and % of total cellularity by next-generation sequencing (NGS) (R = 0.07). B: Poor correlation of quantitative disease level of all MRD+ samples between % of total IGH reads by next-generation sequencing (NGS) and FC (R = 0.06). WBC, white blood cells.

Supplemental Figure S2.

Supplemental Figure S2

Open in a new tab

Detectable low-level positive controls in 231 next-generation sequencing (NGS) runs. Dilution of IVS-0019 clonal B-cell control DNA into IVS-0000 polyclonal control DNA sample yielded three different clonal sequences at different expected concentrations, and were added to each NGS sequencing run as low-level positive controls. A: Overall detection of the three clones across 231 NGS runs. B: Detectable clonal sequence 1(expected concentration, 3 × 10−4 to 4 × 10−4) as % of immunoglobulin heavy chain (IGH) sequencing reads. C: Detectable clonal sequence 2 (expected concentration, 1 × 10−5) as % of IGH sequencing reads. D: Detectable clonal sequence 3 (expected concentration, 1 × 10−6) as % of IGH sequencing reads.

References

  • 1.Jones C.F., Wieffer H.M., Oudard M. The application of minimal residual disease in hemato-oncology: a review of its current utility in trials, regulatory decisions and clinical practice. Blood. 2017;130:5630. [Google Scholar]
  • 2.Brüggemann M., Kotrova M. Minimal residual disease in adult ALL: technical aspects and implications for correct clinical interpretation. Blood Adv. 2017;1:2456–2466. doi: 10.1182/bloodadvances.2017009845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bassan R., Brüggemann M., Radcliffe H.-S., Hartfield E., Kreuzbauer G., Wetten S. A systematic literature review and meta-analysis of minimal residual disease as a prognostic indicator in adult B-cell acute lymphoblastic leukemia. Haematologica. 2019;104:2028–2039. doi: 10.3324/haematol.2018.201053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Munshi N.C., Avet-Loiseau H., Rawstron A.C., Owen R.G., Child J.A., Thakurta A., Sherrington P., Samur M.K., Georgieva A., Anderson K.C., Gregory W.M. Association of minimal residual disease with superior survival outcomes in patients with multiple myeloma: a meta-analysis. JAMA Oncol. 2017;3:28–35. doi: 10.1001/jamaoncol.2016.3160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Del Giudice I., Raponi S., Della Starza I., De Propris M.S., Cavalli M., De Novi L.A., Cappelli L.V., Ilari C., Cafforio L., Guarini A., Foà R. Minimal residual disease in chronic lymphocytic leukemia: a new goal? Front Oncol. 2019;9:689. doi: 10.3389/fonc.2019.00689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Logan A.C., Gao H., Wang C., Sahaf B., Jones C.D., Marshall E.L., Buño I., Armstrong R., Fire A.Z., Weinberg K.I., Mindrinos M., Zehnder J.L., Boyd S.D., Xiao W., Davis R.W., Miklos D.B. High-throughput VDJ sequencing for quantification of minimal residual disease in chronic lymphocytic leukemia and immune reconstitution assessment. Proc Natl Acad Sci U S A. 2011;108:21194–21199. doi: 10.1073/pnas.1118357109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Arcila M.E., Yu W., Syed M., Kim H., Maciag L., Yao J., Ho C., Petrova K., Moung C., Salazar P., Rijo I., Baldi T., Zehir A., Landgren O., Park J., Roshal M., Dogan A., Nafa K. Establishment of immunoglobulin heavy (IGH) chain clonality testing by next-generation sequencing for routine characterization of B-cell and plasma cell neoplasms. J Mol Diagn. 2019;21:330–342. doi: 10.1016/j.jmoldx.2018.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cheng S., Inghirami G., Cheng S., Tam W. Simple deep sequencing-based post-remission MRD surveillance predicts clinical relapse in B-ALL. J Hematol Oncol. 2018;11:105. doi: 10.1186/s13045-018-0652-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Martinez-Lopez J., Sanchez-Vega B., Barrio S., Cuenca I., Ruiz-Heredia Y., Alonso R., Rapado I., Marin C., Cedena M.T., Paiva B., Puig N., Mateos M.V., Ayala R., Hernández M.T., Jimenez C., Rosinol L., Martinez R., Teruel A.I., Gutierrez N., Martin-Ramos M.L., Oriol A., Bargay J., Blade J., San-Miguel J., Garcia-Sanz R., Lahuerta J.J. Analytical and clinical validation of a novel in-house deep-sequencing method for minimal residual disease monitoring in a phase II trial for multiple myeloma. Leukemia. 2017;31:1446–1449. doi: 10.1038/leu.2017.58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Scheijen B., Meijers R.W.J., Rijntjes J., van der Klift M.Y., Möbs M., Steinhilber J., Reigl T., van den Brand M., Kotrová M., Ritter J.-M., Catherwood M.A., Stamatopoulos K., Brüggemann M., Davi F., Darzentas N., Pott C., Fend F., Hummel M., Langerak A.W., Groenen P.J.T.A., on behalf of the EuroClonality-NGS Working Group Next-generation sequencing of immunoglobulin gene rearrangements for clonality assessment: a technical feasibility study by EuroClonality-NGS. Leukemia. 2019;33:2227–2240. doi: 10.1038/s41375-019-0508-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sánchez R., Ayala R., Martínez-López J. Minimal residual disease monitoring with next-generation sequencing methodologies in hematological malignancies. Int J Mol Sci. 2019;20:2832. doi: 10.3390/ijms20112832. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Dogliotti I., Drandi D., Genuardi E., Ferrero S. New molecular technologies for minimal residual disease evaluation in B-cell lymphoid malignancies. J Clin Med. 2018;7:288. doi: 10.3390/jcm7090288. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Duez M., Giraud M., Herbert R., Rocher T., Salson M., Thonier F. Vidjil: a web platform for analysis of high-throughput repertoire sequencing. PLoS One. 2016;11 doi: 10.1371/journal.pone.0166126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Knecht H., Reigl T., Kotrová M., Appelt F., Stewart P., Bystry V., Krejci A., Grioni A., Pal K., Stranska K., Plevova K., Rijntjes J., Songia S., Svatoň M., Froňková E., Bartram J., Scheijen B., Herrmann D., García-Sanz R., Hancock J., Moppett J., van Dongen JJM, Cazzaniga G., Davi F., Groenen P.J.T.A., Hummel M., Macintyre E.A., Stamatopoulos K., Trka J., Langerak A.W., Gonzalez D., Pott C., Brüggemann M., Darzentas N., on behalf of the EuroClonality-NGS Working Group Quality control and quantification in IG/TR next-generation sequencing marker identification: protocols and bioinformatic functionalities by EuroClonality-NGS. Leukemia. 2019;33:2254–2265. doi: 10.1038/s41375-019-0499-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Yao Q., Bai Y., Orfao A., Chim C.S. Standardized minimal residual disease detection by next-generation sequencing in multiple myeloma. Front Oncol. 2019;9:449. doi: 10.3389/fonc.2019.00449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Ladetto M., Bruggemann M., Monitillo L., Ferrero S., Pepin F., Drandi D., Barbero D., Palumbo A., Passera R., Boccadoro M., Ritgen M., Gokbuget N., Zheng J., Carlton V., Trautmann H., Faham M., Pott C. Next-generation sequencing and real-time quantitative PCR for minimal residual disease detection in B-cell disorders. Leukemia. 2014;28:1299–1307. doi: 10.1038/leu.2013.375. [DOI] [PubMed] [Google Scholar]
  • 17.Sekiya Y., Xu Y., Muramatsu H., Okuno Y., Narita A., Suzuki K., Wang X., Kawashima N., Sakaguchi H., Yoshida N., Hama A., Takahashi Y., Kato K., Kojima S. Clinical utility of next-generation sequencing-based minimal residual disease in paediatric B-cell acute lymphoblastic leukaemia. Br J Haematol. 2017;176:248–257. doi: 10.1111/bjh.14420. [DOI] [PubMed] [Google Scholar]
  • 18.Swerdlow S.H.C.E., Harris N.L., Jaffe E.S., Pileri S.A., Stein H., Thiele J., editors. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues: WHO Classification of Tumours. Revised ed 4 ed. IARC Publications; Lyon, France: 2017. [Google Scholar]
  • 19.Ho C., Syed M., Roshal M., Petrova-Drus K., Moung C., Yao J., Quesada A.E., Benhamida J., Vanderbilt C., Liu Y., Zhu M., Yu W., Maciag L., Wang M., Ma Y., Gao Q., Rustad E.H., Hultcrantz M., Diamond B.T., Zheng-Lin B., Huang Y., Hutt K., Miller J.E., Dogan A., Nafa K., Landgren O., Arcila M.E. Routine evaluation of minimal residual disease in myeloma using next-generation sequencing clonality testing: feasibility, challenges, and direct comparison with high-sensitivity flow cytometry. J Mol Diagn. 2021;23:181–199. doi: 10.1016/j.jmoldx.2020.10.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Gao Q., Liu Y., Aypar U., Baik J., Londono D., Sun X., Zhang J., Zhang Y., Roshal M. Highly sensitive single tube B-lymphoblastic leukemia/lymphoma minimal/measurable residual disease test robust to surface antigen directed therapy. Cytometry B Clin Cytom. 2023;104:279–293. doi: 10.1002/cyto.b.22120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Gao Q., Roshal M. Minimal/measurable disease analysis in chronic lymphocytic leukemia/small lymphocytic lymphoma by flow cytometry. Curr Protoc. 2022;2:e503. doi: 10.1002/cpz1.503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Roshal M., Flores-Montero J.A., Gao Q., Koeber M., Wardrope J., Durie B.G.M., Dogan A., Orfao A., Landgren O. MRD detection in multiple myeloma: comparison between MSKCC 10-color single-tube and EuroFlow 8-color 2-tube methods. Blood Adv. 2017;1:728–732. doi: 10.1182/bloodadvances.2016003715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Borowitz M.J., Wood B.L., Devidas M., Loh M.L., Raetz E.A., Salzer W.L., Nachman J.B., Carroll A.J., Heerema N.A., Gastier-Foster J.M., Willman C.L., Dai Y., Winick N.J., Hunger S.P., Carroll W.L., Larsen E. Prognostic significance of minimal residual disease in high risk B-ALL: a report from Children’s Oncology Group study AALL0232. Blood. 2015;126:964–971. doi: 10.1182/blood-2015-03-633685. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Logan A.C., Zhang B., Narasimhan B., Carlton V., Zheng J., Moorhead M., Krampf M.R., Jones C.D., Waqar A.N., Faham M., Zehnder J.L., Miklos D.B. Minimal residual disease quantification using consensus primers and high-throughput IGH sequencing predicts post-transplant relapse in chronic lymphocytic leukemia. Leukemia. 2013;27:1659–1665. doi: 10.1038/leu.2013.52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Martinez-Lopez J., Lahuerta J.J., Pepin F., González M., Barrio S., Ayala R., Puig N., Montalban M.A., Paiva B., Weng L., Jiménez C., Sopena M., Moorhead M., Cedena T., Rapado I., Mateos M.V., Rosiñol L., Oriol A., Blanchard M.J., Martínez R., Bladé J., San Miguel J., Faham M., García-Sanz R. Prognostic value of deep sequencing method for minimal residual disease detection in multiple myeloma. Blood. 2014;123:3073–3079. doi: 10.1182/blood-2014-01-550020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Al-Sawaf O., Zhang C., Lu T., Liao M.Z., Panchal A., Robrecht S., Ching T., Tandon M., Fink A.M., Tausch E., Schneider C., Ritgen M., Böttcher S., Kreuzer K.A., Chyla B., Miles D., Wendtner C.M., Eichhorst B., Stilgenbauer S., Jiang Y., Hallek M., Fischer K. Minimal residual disease dynamics after venetoclax-obinutuzumab treatment: extended off-treatment follow-up from the randomized CLL14 study. J Clin Oncol. 2021;39:4049–4060. doi: 10.1200/JCO.21.01181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Costa L.J., Chhabra S., Medvedova E., Dholaria B.R., Schmidt T.M., Godby K.N., Silbermann R., Dhakal B., Bal S., Giri S., D'Souza A., Hall A., Hardwick P., Omel J., Cornell R.F., Hari P., Callander N.S. Daratumumab, carfilzomib, lenalidomide, and dexamethasone with minimal residual disease response-adapted therapy in newly diagnosed multiple myeloma. J Clin Oncol. 2022;40:2901–2912. doi: 10.1200/JCO.21.01935. [DOI] [PubMed] [Google Scholar]
  • 28.Frank M.J., Hossain N.M., Bukhari A., Dean E., Spiegel J.Y., Claire G.K., Kirsch I., Jacob A.P., Mullins C.D., Lee L.W., Kong K.A., Craig J., Mackall C.L., Rapoport A.P., Jain M.D., Dahiya S., Locke F.L., Miklos D.B. Monitoring of circulating tumor DNA improves early relapse detection after axicabtagene ciloleucel infusion in large B-cell lymphoma: results of a prospective multi-institutional trial. J Clin Oncol. 2021;39:3034–3043. doi: 10.1200/JCO.21.00377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Martinez-Lopez J., Alonso R., Wong S.W., Rios R., Shah N., Ruiz-Heredia Y., Sanchez-Pina J.M., Sanchez R., Bahri N., Zamanillo I., Poza M., Buenache N., Encinas C., Juarez L., Miras F., Collado L., Barrio S., Martin T., Cedena M.T., Wolf J. Making clinical decisions based on measurable residual disease improves the outcome in multiple myeloma. J Hematol Oncol. 2021;14:126. doi: 10.1186/s13045-021-01135-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Muffly L., Sundaram V., Chen C., Yurkiewicz I., Kuo E., Burnash S., Spiegel J.Y., Arai S., Frank M.J., Johnston L.J., Lowsky R., Meyer E.H., Negrin R.S., Rezvani A.R., Sidana S., Shiraz P., Shizuru J.A., Weng W.K., Liedtke M., Vempaty H.T., Miklos D.B. Concordance of peripheral blood and bone marrow measurable residual disease in adult acute lymphoblastic leukemia. Blood Adv. 2021;5:3147–3151. doi: 10.1182/bloodadvances.2021004234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Richardson P.G., Jacobus S.J., Weller E.A., Hassoun H., Lonial S., Raje N.S., et al. Triplet therapy, transplantation, and maintenance until progression in myeloma. N Engl J Med. 2022;387:132–147. doi: 10.1056/NEJMoa2204925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Short N.J., Kantarjian H., Ravandi F., Konopleva M., Jain N., Kanagal-Shamanna R., Patel K.P., Macaron W., Kadia T.M., Wang S., Jorgensen J.L., Khoury J.D., Yilmaz M., Kebriaei P., Takahashi K., Garcia-Manero G., Daver N., Post S.M., Huang X., Kornblau S.M., Pelletier S., Flores W., Matthews J., Garris R., Jabbour E. High-sensitivity next-generation sequencing MRD assessment in ALL identifies patients at very low risk of relapse. Blood Adv. 2022;6:4006–4014. doi: 10.1182/bloodadvances.2022007378. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Wood B., Wu D., Crossley B., Dai Y., Williamson D., Gawad C., Borowitz M.J., Devidas M., Maloney K.W., Larsen E., Winick N., Raetz E., Carroll W.L., Hunger S.P., Loh M.L., Robins H., Kirsch I. Measurable residual disease detection by high-throughput sequencing improves risk stratification for pediatric B-ALL. Blood. 2018;131:1350–1359. doi: 10.1182/blood-2017-09-806521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ching T., Duncan M.E., Newman-Eerkes T., McWhorter M.M.E., Tracy J.M., Steen M.S., Brown R.P., Venkatasubbarao S., Akers N.K., Vignali M., Moorhead M.E., Watson D., Emerson R.O., Mann T.P., Cimler B.M., Swatkowski P.L., Kirsch I.R., Sang C., Robins H.S., Howie B., Sherwood A. Analytical evaluation of the clonoSEQ Assay for establishing measurable (minimal) residual disease in acute lymphoblastic leukemia, chronic lymphocytic leukemia, and multiple myeloma. BMC Cancer. 2020;20:612. doi: 10.1186/s12885-020-07077-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Hengeveld P.J., van der Klift M.Y., Kolijn P.M., Davi F., Kavelaars F.G., de Jonge E., Robrecht S., Assmann J., van der Straten L., Ritgen M., Westerweel P.E., Fischer K., Goede V., Hallek M., Levin M.D., Langerak A.W. Detecting measurable residual disease beyond 10-4 by an IGHV leader-based NGS approach improves prognostic stratification in CLL. Blood. 2023;141:519–528. doi: 10.1182/blood.2022017411. [DOI] [PubMed] [Google Scholar]
  • 36.Giusti G.N.N., Jotta P.Y., Lopes C.O., Ganazza M.A., de Azevedo A.C., Brandalise S.R., Meidanis J., Yunes J.A. Test trial of spike-in immunoglobulin heavy-chain (IGH) controls for next generation sequencing quantification of minimal residual disease in acute lymphoblastic leukaemia. Br J Haematol. 2020;189:e150–e154. doi: 10.1111/bjh.16571. [DOI] [PubMed] [Google Scholar]
  • 37.Schenk T.M., Keyhani A., Bottcher S., Kliche K.O., Goodacre A., Guo J.Q., Arlinghaus R.B., Kantarjian H.M., Andreeff M. Multilineage involvement of Philadelphia chromosome positive acute lymphoblastic leukemia. Leukemia. 1998;12:666–674. doi: 10.1038/sj.leu.2400986. [DOI] [PubMed] [Google Scholar]
  • 38.Daser A., Rabbitts T.H. Extending the repertoire of the mixed-lineage leukemia gene MLL in leukemogenesis. Genes Dev. 2004;18:965–974. doi: 10.1101/gad.1195504. [DOI] [PubMed] [Google Scholar]
  • 39.Yao J., Xu L., Aypar U., Meyerson H.J., Londono D., Gao Q., Baik J., Dietz J., Benayed R., Sigler A., Yabe M., Dogan A., Arcila M.E., Roshal M., Zhang Y., Mauro M.J., Xiao W. Myeloid/lymphoid neoplasms with eosinophilia/basophilia and ETV6-ABL1 fusion: cell-of-origin and response to tyrosine kinase inhibition. Haematologica. 2020;106:614–618. doi: 10.3324/haematol.2020.249649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Smock K.J., Perkins S.L., Bahler D.W. Quantitation of plasma cells in bone marrow aspirates by flow cytometric analysis compared with morphologic assessment. Arch Pathol Lab Med. 2007;131:951–955. doi: 10.5858/2007-131-951-QOPCIB. [DOI] [PubMed] [Google Scholar]
  • 41.Rawstron A.C., Fazi C., Agathangelidis A., Villamor N., Letestu R., Nomdedeu J., et al. A complementary role of multiparameter flow cytometry and high-throughput sequencing for minimal residual disease detection in chronic lymphocytic leukemia: an European Research Initiative on CLL study. Leukemia. 2016;30:929–936. doi: 10.1038/leu.2015.313. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Table S1
mmc1.docx (12.6KB, docx)
Supplemental Table S2
mmc2.docx (13.1KB, docx)
Supplemental Table S3
mmc3.docx (37.7KB, docx)
Supplemental Table S4
mmc4.docx (12.9KB, docx)
Supplemental Table S5
mmc5.docx (12.8KB, docx)

Articles from The Journal of Molecular Diagnostics : JMD are provided here courtesy of American Society for Investigative Pathology

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