1 Introduction
Multiple myeloma (MM) is a clonal plasma-cell tumor, with high cytogenetic heterogeneity [
1]. It is the second most common hematological malignancy in many countries [
2‐
4] and has a substantial impact on public health. The specific incidence of MM in China is not clear, but the median age of onset is 57.9–59 years old [
5,
6], which is far lower than that observed in western countries [
7‐
9]. In recent years, new chemotherapies and immune drugs have greatly improved the remission rate and survival time in MM [
4]. However, as the long-term survival rate of patients has increased, the recurrence rate has also risen, resulting in eventual relapse for the majority of patients, for whom there is currently no cure [
10]. Currently, the treatment goals in MM prioritize long-term disease control, disease remission, and improvement of the life quality of the patient. Consequently, there is an urgent need for improved curative outcomes. Additionally, there is an urgent need for sensitive methods for continuous monitoring of minimal residual disease (MRD) and guiding clinical treatment following complete remission [
11].
MM represents a group of malignant tumors originating from B lymphocytes. During the normal formation of B cells and their differentiation into plasma cells, recombination of variable (V), diversity (D), and joining (J) gene segments leads to immunoglobulin heavy chain (IGH) rearrangements. The diversity, randomness, and arbitrary insertion and deletion of nucleotides in the junction region during IGH rearrangements result in each B-cell clone having a unique VDJ sequence resulting from its IGH rearrangement [
12]. Therefore, the clonal IGH rearrangement identified at the time of diagnosis can serve as a valuable clonal biomarker for MRD monitoring during clinical treatment.
The capillary electrophoresis (CE) method, which has gradually become a reference standard for detecting gene rearrangements, particularly for detecting clonal IGH rearrangements, can be used as an auxiliary diagnostic tool for lymphoid tumors. Yet, its application for MRD monitoring is limited [
12]. In contrast, the next-generation sequencing (NGS) technology offers multiple advantages, such as being high-throughput as well as highly time-effective, accurate, and informative. These qualities make NGS highly beneficial in accurately detecting and quantifying clonal rearrangements, especially in the context of MRD monitoring in MM patients post-treatment [
13].
The International Myeloma Working Group (IMWG) recommends next-generation flow cytometry (NGF) and NGS as the main methods for evaluating MRD [
14]. However, the sensitivity, repeatability, and standardization of clinical correlation analysis of these technologies are still under exploration [
15,
16]. In addition, NGS is rarely used to detect MRD in MM patients in China. Thus, this study aimed to assess the applicability of NGS versus NGF to detecting clonal IGH rearrangements in newly diagnosed MM patients and to MRD monitoring post-treatment.
2 Methods
2.1 Patients
This retrospective study included 60 newly diagnosed MM patients from the First Affiliated Hospital of Soochow University between September 2019 and October 2020. All the samples from these patients were collected from the biological sample bank used in a previous study [
17], during which the patients were treated with bortezomib, lenalidomide, and dexamethasone with or without subsequent autologous stem-cell transplantation (ASCT).
This study was approved by the Ethics Committee of the First Affiliated Hospital of Soochow University and followed the Declaration of Helsinki. Informed consent was obtained from each patient.
2.2 Cytogenetic analysis
At diagnosis, interphase fluorescence in situ hybridization (FISH) analysis was performed alongside sorting CD138 plasma cells. The following probes were used: 1q21 for 1q gain, RB1 and D13S319 for 13q deletion or monosomy 13, TP53 for TP53 deletion or monosomy 17, IGH for 14q32 rearrangements, and probes targeting the individual IGH rearrangements t (11;14)(q13; q32) CCND::IGH, t (4;14)(p16; q32) FGFR3::IGH, and t (14;16)(q32; q23) IGH::MAF. The probes were obtained from GP Medical Technologies, Beijing, China. A minimum of 200 interphase nuclei obtained from bone-marrow (BM) cultures were analyzed using a Leica DMRXA fluorescence microscope (Leica, Wetzlar, Germany).
Genomic DNA was isolated from BM aspirates at the time of diagnosis and the follow-up period by using a DNA Extraction Kit (Promega, USA). DNA quantity and quality were evaluated using a Qubit instrument (Thermo Fisher, USA) according to the manufacturer’s instructions.
2.4 PCR and CE
PCR and CE were used to detect IGH clonality [
18]. In brief, the IGH-FR1, FR2, and FR3 gene rearrangements were amplified using PCR with the IdentiCloneTM rearrangement detection kit (Invivoscribe, USA) according to the manufacturer’s instructions. An ABI3730 gene analyzer (Thermo Fisher, USA) was used to analyze the amplicons, following previously described scoring criteria [
19].
2.5 NGF
The flow-cytometry-based strategy used in this study involved 10 characteristic antibodies, including 8 against cell-membrane proteins (CD138-APC, CD38-APC750, CD19-ECD, CD45-KO, CD56-PC7, CD27-PB, CD81-APC700, and CD117-PC5; from Beckman Coulter, USA), and 2 against cytoplasmic proteins (Kappa-FITC and Lambda-PE; from Dako, Denmark), which have previously been described to be able to differentiate between abnormal and normal plasma cells via flow cytometry [
20‐
22]. Sample preparation and detection were performed as previously described [
17]. Briefly, 200 μL–5 mL BM aspirates [(2–20) × 10
6 cells] were stained with the monoclonal antibodies for 30 min at 20–30 °C after lysing red-blood cells by using ammonium chloride and then washed with phosphate-buffered saline. For intracellular light-chain evaluation, cells were stained with anti-kappa and anti-lambda antibodies after adding membrane breakers, followed by washing and incubation. Flow-cytometry events were acquired and analyzed using a Navios Flow cytometer (Beckman Coulter, USA). One million nucleated cells were obtained each time, and if ≥ 20 abnormal plasma cells were detected, the result was considered positive.
2.6 Clonality testing via NGS
NGS-based clonality testing was performed using commercially available LymphoTrack assays (Invivoscribe, USA) targeting IGH-FR1, IGH-FR2, and IGH-FR3. The assays were performed following the manufacturer’s instructions. PCR amplification was performed using a master mix containing primers that had barcoded sequence adaptors. After the PCR products were purified and quantified, they were sequenced using an Ion S5 sequencer platform (Thermo Fisher, USA). Sequencing data in FASTQ format were analyzed using the LymphoTrack software package (InVivoScribe Technologies, San Diego, CA) [
16,
23].
2.7 Criteria for clonality and MRD
The criteria for determining the IGH clonality in newly diagnosed MM patients were as follows: a minimum of five identical sequences obtained through sequencing constituted a clone, and the frequency of the clone needed to be > 5% to be used as a marker for MRD tracking in MM patients, as previously described [
13]. All the sequences identified in BM samples from patients in the remission stage were compared with the clonal sequences derived from the tumor cells in the newly diagnosed samples, serving as index clonal sequences. Samples were considered MRD-positive if the same sequences were detected [
24].
2.8 Validation of the NGS-based method
To assess the performance characteristics of the NGS-based method, a series of experiments were performed. Commercially available assays containing a positive clonal control (IVS0019) and a polyclonal negative control (IVS0000) were used to prepare successive dilutions of DNA, spanning the range of 10–6–10–1, to determine the limit of detection of the method. To assess the specificity of this method, patients exhibiting clonality were identified, providing specific clone information. Three samples with different MRD levels were selected. Accordingly, corresponding patients P1, P2, and P3 had MRD levels of 10–2, 10–3, and 10–4, respectively. The NGS results of these three samples were detected before with definite results, and the selection criteria were to cover the test range of different orders of magnitude of MRD. These samples were used for assessing inter- and intra-assay reproducibility.
2.9 Statistical analysis
All the data were statistically analyzed using GraphPad Prism version 9.3.1 (
https://www.graphpad.com/scientific-software/prism/). Mean values were compared using the independent sample
t-test and analysis of variance, and rates were compared using the χ
2 test. Fisher’s exact test was used to test categorical variables, and the Spearman correlation coefficient was used for statistical comparison. A
p-value < 0.05 was considered to indicate statistical significance. The graphs were generated using GraphPad Prism version 9.3.1 and R version 4.1.0 (R Core Team).
4 Discussion
Therapeutic approaches for treating MM have advanced to include novel drugs, particularly immunotherapies. The combined use of proteasome inhibitors, thalidomide analogs, and CD38-targeting monoclonal antibodies currently represents the mainstay of modern myeloma therapy. New monoclonal antibodies, T-cell activators, and cell therapy are also in the process of entering the clinics. Although a definite cure for MM is still lacking, the introduction of new drugs with different mechanisms and improved treatment approaches has significantly improved the survival of MM patients [
27]. MRD has a strong predictive value in various disease states and treatment conditions [
28,
29]. It can identify the likelihood of relapse and enable early intervention. Evaluation of MRD rates is also used as an endpoint to accelerate drug testing and approval in many trials [
30,
31].
Although many methods, such as NGF and multi-parametric flow cytometry, can be used to detect MRD, there is no standard method. At present, NGF is the most common method used for detecting MRD in clinics. MRD detection based on NGF is fast, efficient, and economical; however, it requires complex visualization and professional data analysis. Furthermore, false-negative MRD detection can occur in some patients due to immunophenotypic changes post-treatment [
32].
The use of NGS for detecting MRD has increasingly been implemented in clinical practice. A study has compared NGS and NGF in detecting MRD and concluded a strong correlation between the two approaches [
33].
MM patients in China tend to be younger than those in Europe or the United States. Furthermore, there is a high demand for effective management of MM in this population. However, the detection of MRD in Chinese MM patients has primarily relied on flow cytometry, which is gradually becoming insufficient to meet patient needs. Although international studies have previously reported on MRD monitoring via second-generation sequencing, there is limited research on the Chinese population.
To the best of our knowledge, this study is the first to compare NGS with NGF in China. To monitor MRD by using NGS, the clonal IGH rearrangement at the time of diagnosis must be known. In this study, both NGS and CE were used to detect the clonal IGH rearrangements in 60 newly diagnosed Chinese MM patients, and the consistency between the two methods was 98.3%. The overall detection rate of the IGH-FR1/FR2/FR3 combination was 70.0% via NGS. Additionally, unique clonal IGH rearrangements were observed in 42 patients. Therefore, NGS could detect clonal rearrangements in most of the newly diagnosed MM patients. Such detection can serve as a molecular biomarker at the time of diagnosis, enabling MRD monitoring during clinical treatment. To evaluate the feasibility of NGS in follow-up MRD monitoring of MM patients, we analyzed the limit and repeatability of NGS in detecting MRD. According to the IMWG guidelines, MM patients are considered MRD-negative if there are no clonal plasma cells in the bone marrow, with a minimum sensitivity of 1 in 10
5 nucleated cells via the NGS method [
14]. The results of the study presented here confirmed that NGS has good sensitivity in MRD detection, and demonstrated a linear curve ranging from 10
–6 to 10
–1, with a correlation coefficient of 0.985. Using this method, it is possible to detect one tumor plasma cell in 1,000,000 nucleated cells, indicating a limit of detection of 10
–6. Thus, NGS exhibits high sensitivity in MRD detection in MM patients. In addition, this approach showed good repeatability in MRD detection in these patients. In samples with different tumor loads, the MRD levels were estimated at 10
–2, 10
–3, and 10
–4 via NGS, and the intra- and inter-assay variation was relatively low.
Currently, the major approaches recommended for MRD assessment in MM patients at home and abroad are the multi-parameter NGF and NGS technologies. There are relatively many reports on multi-parameter NGF in MRD detection in MM patients [
34,
35], whereas the applicability of NGS has seldom been reported in China yet. In this study, 43 samples from 36 patients were evaluated at follow-up by using both NGS and NGF. Our results revealed a consistency rate of 79.1% between the two methods, showing that both methods have high consistency. Interestingly, out of the cases analyzed, 9 showed inconsistent MRD results, with MRD levels being detectable via NGS but undetectable via NGF. It is worth noting that none of the samples identified as MRD-positive via NGF were found to be negative via NGS. Discrepancies between NGF and NGS in detecting MRD can be attributed to differences in sensitivity and detection principles. NGF, which relies on antibodies targeting cell surface proteins, and NGS, which identifies genetic mutations, focus on distinct biological markers [
36,
37]. This divergence in methodological focus can lead to scenarios where MRD is detectable by one technique but remains undetected by the other, reflecting the distinct detection capabilities inherent to each method [
38].
MM patients undergoing CAR-T therapy targeting MM surface antigens, such as CD138 and CD229, may experience blocking of these antigen-binding sites for several months [
39], This necessitates adjustments in the use of NGF for MRD detection. Interestingly, after ASTC, 2 patients tested MRD-negative via NGF. However, the NGS method revealed MRD levels of 2.83 × 10
–5 and 1.10 × 10
–4 in these patients. Notably, both patients demonstrated a very good partial response according to the evaluation of treatment effectiveness following treatment with VRd. Previous studies have also reported [
40] that after induction treatment or transplantation, MRD that turns negative indicates a better clinical prognosis.
Retrospective studies have shown that making treatment decisions based on MRD results (including stopping, intensifying, or changing the treatment) can improve progression-free survival in comparison with patients whose treatment remains unmodified after MRD assessment [
31,
32,
41]. The prognostic value of MRD, as determined by NGS, offers a robust basis for informed treatment adjustments, encompassing de-escalation, intensification, or modification strategies to halt disease progression and improve outcomes [
29,
41]. Furthermore, by uncovering the genetic and immunologic drivers of MRD, NGS facilitates the development of targeted therapies, advancing personalized medicine in MM. This transformative approach not only promises improved therapeutic efficacy and patient well-being but also significantly shifts the MM management paradigm [
41]. Although NGS presents a higher per-sample costs, its superior sensitivity in detecting MRD at very low levels offers potential long-term cost savings. Early and accurate MRD detection can guide more effective treatment adjustments, potentially reducing the overall treatment costs by avoiding unnecessary therapies and hospitalizations.
Emerging technologies, especially the integration of artificial intelligence (AI) and machine learning (ML) are set to enhance MRD detection in MM by processing complex datasets more efficiently, automating the identification of novel MRD markers, and enabling personalized treatment plans through predictive modeling [
42,
43]. Concurrently, the discovery of new biomarkers such as extracellular matrix proteins, angiogenic factors, p53-related protein kinase, circulating tumor cells, and microRNAs is redefining MM diagnosis and treatment [
44‐
48]. The future of MM management is geared towards integrating these technologies and biomarkers into a personalized, predictive, and patient-centered care framework.
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