A
Research ArticleTitle: Circulating plasma cells (either polyclonal or monoclonal) as markers of aggressive Multiple Myeloma phenotype
IlariaVigliotta1✉Phone+39 051 214 3791Emaililaria.vigliotta@aosp.bo.it
AlessiaVaracalli1
VincenzaSolli2
BarbaraTaurisano2
SilviaArmuzzi2
IgnaziaPistis1
ViolaMeixianVuong2
GaiaMazzocchetti3,4
EnricaBorsi1
AlessiaCroce2
MarinaMartello2
AndreaPoletti2
KatiaMancuso1,2
MichelePuppi1,2
MarcoTalarico1,2
ElenaZamagni1,2
CarolinaTerragna1
1IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”BolognaItaly
2Department of Medical and Surgical SciencesUniversity of BolognaBolognaItaly
3
A
FABIT-Department of Pharmacy and BiotechnologyUniversity of Bologna BolognaItaly4Computational and Chemical BiologyItalian Institute of Technology (IIT)CMP3VdA AostaItaly
Ilaria Vigliotta1,*,†, Alessia Varacalli1,†, Vincenza Solli2, Barbara Taurisano2, Silvia Armuzzi2, Ignazia Pistis1, Viola Meixian Vuong2, Gaia Mazzocchetti3,4, Enrica Borsi1, Alessia Croce2, Marina Martello2, Andrea Poletti2, Katia Mancuso1,2, Michele Puppi1,2, Marco Talarico1,2, Elena Zamagni1,2, and Carolina Terragna1
1IRCCS Azienda Ospedaliero-Universitaria di Bologna, Istituto di Ematologia “Seràgnoli”, Bologna, Italy; ilaria.vigliotta@aosp.bo.it
2Department of Medical and Surgical Sciences - University of Bologna, Bologna, Italy;
3FABIT-Department of Pharmacy and Biotechnology, University of Bologna Bologna, Italy
4Computational and Chemical Biology, Italian Institute of Technology (IIT), CMP3VdA Aosta, Italy
*Correspondence: ilaria.vigliotta@aosp.bo.it; Tel.: +39 051 214 3791
†These authors contributed equally: Ilaria Vigliotta and Alessia Varacalli
A
Key points
• Polyclonal circulating plasma cells are to be taken into account and correlate with an aggressive Multiple Myeloma phenotype
• Plasma cells’ CD56 and CD138 expression is implicated in the bone marrow escaping (hence the formation of circulating plasma cells)
Keywords:
Multiple Myeloma 1
Circulating tumor cells 2
Flow cytometry 3
Liquid biopsy 4
Bone marrow 5, Circulating Plasma cells 6
A
Mutations 7Genomic analyses 8
Clonal heterogeneity 9
Migration 10
Abstract
Multiple myeloma (MM) is a hematological neoplasm characterized by the clonal proliferation of malignant plasma cells (PCs) in the bone marrow (BM). The pathogenesis of MM is complex and multifactorial, with significant heterogeneity among patients, making risk stratification and disease monitoring difficult, often based on invasive sampling. Therefore, there is growing interest in less invasive methods such as liquid biopsy. In this study, the potential of circulating plasma cells (CPCs), including polyclonal CPCs, was evaluated as innovative biomarkers for risk stratification in 107 MM patients. Using multiparametric flow cytometry, the phenotypes of the CPCs in peripheral blood (PB) were analyzed, finding their presence in 92% of cases (monoclonal CPCs 77%, polyclonal CPCs 15%), with a median of 0.02% (range 0.002-9%). CPCs were significantly correlated with various biochemical parameters and the percentage of BM-PCs (p < 0.05), indicating an association with a more aggressive phenotype even in the presence of polyclonal PCs. Genomic analyses identified recurrent mutations in key genes (KRAS and NRAS), potentially involved in CPCs formation. Notably, high CPCs-patients had mutation restricted to KRAS, while patients with a low amount of CPCs carried solely NRAS mutations. Lastly, comparisons between PC phenotypes in BM and PB showed significant heterogeneity, such as light chain inversion or variations in the expression of markers like CD56 and CD138, suggesting the presence of clonal heterogeneity and potential migratory predisposition. Such differences might have prognostic and therapeutic implications, and might contribute to the emergence of CPCs.
A
1. Introduction
Hematological neoplasms known as monoclonal gammopathies are distinguished by the presence of clonal plasma cells (PCs) in the bone marrow (BM). In Multiple Myeloma (MM), PCs can escape the BM niche throughout the peripheral blood (PB), taking the name of circulating plasma cells (CPCs). CPCs, here referred to as monoclonal CPCs (mCPCs) and polyclonal CPCs (pCPCs) in case of non-tumoral phenotype, can often be identified early in the course of the disease and ultimately define the full-blown stage of plasma cell leukemia1–3.
Numerous growth factors, cytokines, and survival signals are implicated in the growth and resistance of malignant PCs, making the disease highly heterogeneous and difficult to treat. Indeed, together with the expansion of BM-PCs clones and/or clusters with pathogenic features, their ability to spread outside of the BM contributing to the development of CPCs might be the soil for resistance features4–6. For this reason, CPCs are of increasing interest as a tool for monitoring disease progression and as a surrogate for measurable residual disease (MRD). The most commonly employed methods for CPCs detection are multiparameter flow cytometry (MFC) and next-generation flow (NGF) cytometry. However, due to the variety of techniques and detection markers, there is still debate about how effective CPCs are as a liquid biopsy alternative analyte for monitoring disease progression, and how to define a diagnostic cut-off for mCPCs to identify patients at high-risk7–12.
Additionally, a deeper understanding of the molecular and cellular mechanisms that enable malignant PCs to escape the BM microenvironment is crucial. However, the precise process by which malignant PCs exit the BM and enter the PB becoming CPCs or mCPCs, remain unclear. This escape is thought to involve the downregulations of adhesion molecules, altered expression of chemokine receptors and enhanced migratory and invasive properties13. CPCs are often considered an immature and quiescent population, characterized by reduced expression of CD81 and CD138, and increased expression of B cell maturation markers (i.e., CD20, CD27 and CD38) and adhesion markers such as CXCR414,15. Notably, the phenotype of CPCs has frequently been observed to mirror that of tumor cells residing in the BM16,17.
In this work, we aimed to characterize the immunophenotype of PCs escaping the BM niche, as well as to identify the feature(s) involved in CPCs formation. Additionally, we sought to develop a reliable and user-friendly MFC-based method to precisely quantify CPCs, with the ability to distinguish between pCPCs and mCPCs.
2. Patients and Methods
2.1. Patients
A
This study included 107 newly diagnosed patients (NDMM). For each patient BM aspirate and PB samples were collected, along with clinical, genomic and biochemical information. Table 1 describes the patients’ baseline characteristics.A
Variable | n= | Mean | Median | Range | Q1-Q3 | Out of Range (male) | Out of Range (female) |
|---|---|---|---|---|---|---|---|
β2M (mg/L) | 61 | 5.78 | 3.9 | 1.6–77.3 | 2.7–5.3 | < 0.7;>1.8 | < 0.7;>1.8 |
κ/λ ratio (by IFE) | 80 | 140.37 | 5.015 | 0-2475.43 | 0.02–65.97 | < 0.26;>1.65 | < 0.26;>1.65 |
M-protein (%) | 77 | 21.47 | 21.9 | 0.01–55.8 | 5.3–33.1 | > 0 | > 0 |
CRP (mg/dL) | 82 | 1.04 | 0.41 | 0.03–9.41 | 0.14–1.08 | > 0.5 | > 0.5 |
Total proteins (g/dL) | 84 | 8.5 | 7.8 | 4.8–16.4 | 6.9–9.72 | < 6.6;>8.3 | < 6.6;>8.3 |
LDH (IU/L) | 81 | 187.23 | 176 | 79–700 | 144–210 | > 248 | > 248 |
Serum Albumin (g/L) | 81 | 37.22 | 37.66 | 25-55.5 | 32-41.7 | < 35;>50 | < 35;>50 |
Calcium (mg/dL) | 86 | 9.59 | 9.5 | 8-13.2 | 9–10 | < 8.6;>10.5 | < 8.6;>10.5 |
PLT (x10*9/L) | 88 | 232.56 | 222 | 50.5–619 | 167.75-269.25 | < 160:>370 | < 160:>370 |
Monocytes (%) | 88 | 6.99 | 6.55 | 1-17.9 | 5.3–8.6 | ||
Lymphocytes (%) | 88 | 29.6 | 28.7 | 7.4–59.8 | 21.25–36.85 | ||
Neutrophils (%) | 88 | 60.71 | 60.3 | 26.9–86 | 52.43–69.75 | ||
WBC (xµl) | 107 | 20597.12 | 18610 | 2386–52528 | 14775–25105 | < 3600;>10500 | < 3600;>10500 |
BM-PCs (% by MFC) | 101 | 9.36 | 4.5 | 0–56 | 1.4–13.5 | ||
Age (y) | 107 | 64.9 | 66.5 | 21–81 | 58-73.25 | ||
Female, with age (y) | 48 | 66 | 36–81 | ||||
Male, with age (y) | 59 | 67 | 21–81 | ||||
CPCs (% by MFC) | 108 | 0.31 | 0.011875 | 0.002-9 | 0.002-0.1 | ||
| PCs = plasma cells; BM = bone marrow; MFC = multiparametric flow cytometry; IFE = immunofixation electrophoresis; WBC = white blood cells; PLT = platelets; LDH = lactate dehydrogenase; CRP = C-reactive protein; β2M = beta2-microglobulin; CPCs = circulating plasma cells | |||||||
The following biochemical data were collected: the percentage of neutrophils, lymphocytes, monocytes and monoclonal component (M-protein), the number of platelets (PLT, x109), calcium (mg/dL), albumin (g/L), lactate dehydrogenase (LDH, IU/L), total proteins (g/dL), C-reactive protein (CRP, mg/dL), and beta-2 microglobulin (β2M, mg/L), and the κ\λ light chain ratio, as shown in Table 1.
2.2. Samples processing
BM aspirates were collected in EDTA tubes and subsequently processed to enrich the CD138 + PCs population, using anti-CD138 human magnetic micro-beads and AutoMACS® Pro II Separator (Miltenyi Biotec, Bergisch Gladbach, Germany), intended to diagnostic purposes. A portion of the sample was destinated to multiparametric flow cytometric (MFC) analyses; briefly, 200 µl of sample, divided into two tubes (100 µl for each tube), were marked with different antibody to discriminated polyclonal to monoclonal PCs and to describe some characteristics of the population, such as the maturation stage(s) and the adhesion propensity. The first tube was labeled with the following antibodies (5 µl for each antibody): CD45-V500, CD138-PE, CD38-PECy7, CD56-APC, CD19-PerCP-Cy5.5, CD20-APC Cy7, CD81-FITC (BD Biosciences, San Jose, CA, USA) and CD117-V450/421 (BioLegend, San Diego, CA, USA). The second tube was labeled with the following antibodies (5 µl for each antibody): CD45-V500, CD138-PE, CD38-PECy7, CD19-PerCP-Cy5.5 (BD Biosciences, San Jose, CA, USA), CD27-V450 (Miltenyi Biotec, Germany), and the two light chains kappa-APC and lambda-FITC (DAKO, Agilent Technologies, CA, USA) employed following intra-cytoplasmic labeling. This protocol required the acquisition of 100,000 events per tube on a FACS Canto II (BD Biosciences, San Jose, CA, USA).
PB samples were also collected in EDTA tubes. For the characterization, identification and counting of CPCs, a home-made protocol was designed, that involved a bulk lysis of a median of 4 mL of sample performed with Buffer Erythrocyte Lysis (QIAGEN GmbH, Hilden, Germany) reaching volume (40–45 mL) and incubating for 15 minutes at room temperature. Then, the sample was centrifuged at 300g for 10 minutes, and the supernatant was discarded by washing the pellet with saline solution to eliminate the excessive lysis buffer. The final product was a pellet of nucleated cells (white blood cells, WBC), that was divided into two tubes and labeled with the panel used for BM labelling, analyzing 3,000,000 cells in each tube. At the end, the supernatant was discarded and the pellet was re-suspended in approximately 2 mL of saline to ensure the proper acquisition of 1,000,000 events per tube/alive WBCs on the flow cytometer. To confidently determine populations, limit of detection (LOD) was set as 20 CPCs/lived WBCs, while limit of quantification (LOQ) was assessed as 50 CPCs/lived WBCs.
2.3. MFC gating strategy
To identify the CPCs population and discriminate polyclonal from monoclonal CPCs, a light chains-driven gating strategy was developed. Briefly, an initial analysis was performed on forward scatter vs. side scatter (FSC/SSC) that allowed the elimination of debris and cellular doublets (here represented by “singlets” plot), thus finally circumscribing P1 gate. “Plasma cells” gate comprises all the circulating PCs, including both polyclonal and aberrant populations. Figure 1 illustrates this analysis, with pCPCs marked in green and mCPCs in red.
Fig. 1
A) Gating strategy to define monoclonal CPCs (in red) and polyclonal CPCs (in green). B) Hierarchy related to the gating strategy used. C) Statistical view of plasma cells, pCPCs and mCPCs, with mean florescence intensity (MFI) of different markers (i.e., CD45-V500, CD138-PE, CD38-PECy7, CD56-APC, CD19-PerCP-Cy5.5, CD20-APC Cy7, CD81-FITC, CD117-V450, CD27-V450, and cytoplasmatic light chains kappa-APC and lambda-FITC).
Six healthy donor samples (2 females, 2 males and 2 pool samples) were employed to describe different-from-normal CPCs, as CD138dim/CD38high/c-Igκ+/c-Igλ+ and CD45+/CD19+/CD56-/CD117-/CD81+/CD27+. Polyclonal CPCs were primarily defined as CD138+/CD38high/c-Igκ+/c-Igλ+ and CD45+/CD19+/CD56-/CD117-/CD20+/CD81+/CD27+ in the majority cases. Moreover, pCPCs were defined by a cytoplasmatic κ/λ ratio between 0.5 and 418,19. On the contrary, mCPCs expression was defined as CD138+/CD38+/c-Igκ or c-Igλ-restricted and CD45-/CD19-/CD56+/CD117-/+/CD20-/+/CD81-/CD27- in the majority cases.
2.4. BM-plasma cells genomic characterization
To describe the genomic profile of BM-PCs, a next-generation sequencing (NGS) assay (UMA panel) was employed20. Briefly, the panel was designed to detect translocations of the immunoglobulin heavy gene (t-IgH), whole-genome genomic copy number alterations (CNAs) and single nucleotide variants (SNVs) in 82 genes. The panel covers a total of 460.4 Kbp and uses the SureSelect system from Agilent Technologies, CA, USA. Library preparation requires 100 ng of genomic DNA and follows a DNA enrichment protocol (Illumina CA, USA). The enriched libraries were sequenced on the Illumina MiSeq platform. The Fastq files obtained were analyzed using a specific bioinformatics pipeline to detect t-IgH, CNAs and SNVs.
2.5. Statistical analyses
107 patients who were enrolled in the study were included in the descriptive analysis. Basic measures of dispersion, including interquartile range (IQR), minimum, and maximum, and central tendency, including mean and median, were employed for continuous variables. These measures provide a robust description of the center and dispersion of the data, preserving their non-parametric nature. To depict the distribution of the categories within the sample, relative and absolute frequencies, represented as percentages, were computed for categorical variables that were distinguished based on specific cut-offs (as shown in Table 1 with normal ranges) and median in the case of PCs. A correlation analysis was conducted to characterize the variables; categorical data were reported as absolute frequency and percentages, and continuous variables were given as median and interquartile range (IQR). Fisher’s exact test and Spearman’s rank test were used to assess the presence of significant associations between qualitative variables and their p-values. The non-parametric Kruskal-Wallis test was used to assess differences in medians between groups. The significance level for this analysis was set at α = 0.05. All data analysis was performed in R version 4.2.1. Benjamini-Hochberg (BH) procedure was applied with a false discovery rate (FDR) threshold of 0.10.
3. Results
3.1. Patients with detectable CPCs exhibit an aggressive disease profile
A total of 209 paired samples from 107 NDMM patients were analyzed: 101 samples from BM and 108 from PB; for each patient, biochemical features, BM-PCs percentages, baseline clinical characteristics, and CPCs data were collected, as shown in Supplementary Table 1.
Overall, three main scenarios were observed among patients: in 83/108 (77%) PB evaluations, mCPCs were found at diagnosis, whereas in 9/108 (8%), no CPCs were measured, and in 16/108 (15%), polyclonal PCs were detected. The median mCPCs was 0.0217% (range 0.002-9, IQR 0.01–0.19), whilst polyclonal CPCs’ median was 0.00855% (range 0.002–0.34, IQR 0.002–0.02). Information related to each sub-group of patients are listed in Table 2. Globally, several baseline biochemical differences were highlighted between the three sub-groups of patients: besides the PB-PCs amount (p < 0.001), also the percentage of BM-PCs (p < 0.001), the platelets (PLT) count (p = 0.008) and the amount of β2M (p < 0.001) were significantly different; in addition, a trend for serum albumin (p = 0.07) was also observed. This observation was confirmed with the Spearman’ test (Fig. 2) and validated via FDR-BH analysis (Supplementary Fig. 1a), highlighting that the percentage of circulating PCs was significantly correlated with β2M (p = 0.035), M-component (p = 0.003), C-reactive protein (CRP, p = 0.03), serum albumin (p < 0.0001) and the percentage of PCs in the marrow (p = 0.001).
CPCs = 0 (n = 9) | pCPCs (n = 16) | mCPCs ≠ 0 (n = 83) | p-value | |
|---|---|---|---|---|
Median [IQR] | Median [IQR] | Median [IQR] | ||
Age (y) | 64.00 [60.00, 71.00] | 61.00 [51.50, 67.00] | 67.00 [58.00, 75.00] | 0.112 |
CPCs (% by MFC) | 0.00 [0.00, 0.00] | 0.01 [0.00, 0.02] | 0.02 [0.01, 0.19] | *<0.001 |
BM-PCs (% by MFC) | 1.25 [0.78, 1.85] | 0.80 [0.25, 4.05] | 7.20 [2.00, 16.15] | *<0.001 |
WBC (xµl) | 18420 [14770, 27230] | 21465 [14117.5, 30720] | 18165 [14827.5, 23915] | 0.582 |
Neutrophils (%) | 64.75 [59.05, 71.12] | 67.25 [58.93, 76.55] | 58.25 [50.45, 67.85] | *0.037 |
Lymphocytes (%) | 28.00 [16.70, 34.57] | 24.60 [16.33, 30.02] | 30.60 [22.42, 37.58] | 0.104 |
Monocytes (%) | 5.90 [5.48, 6.62] | 5.45 [4.18, 9.03] | 6.80 [5.43, 8.60] | 0.215 |
PLT (x10*9/L) | 213.50 [203.00, 242.00] | 296.00 [205.75, 366.50] | 218.50 [153.00, 256.50] | *0.008 |
Calcium (mg/dL) | 9.20 [8.93, 9.70] | 9.25 [8.78, 9.62] | 9.60 [9.17, 10.05] | 0.168 |
Albumin (g/L) | 41.49 [40.65, 44.00] | 40.05 [35.95, 42.44] | 36.90 [31.01, 41.00] | 0.076 |
LDH (IU/L) | 175 [144.50, 198.75] | 179 [147.65, 206.75] | 175 [143.30, 211.50] | 0.92 |
Total proteins (g/dL) | 7.80 [7.75, 8.40] | 7.70 [6.85, 8.42] | 8.00 [6.90, 9.80] | 0.867 |
CRP (mg/dL) | 0.29 [0.18, 0.40] | 0.35 [0.12, 1.08] | 0.43 [0.15, 1.34] | 0.461 |
M-protein (%) | 15.30 [11.90, 20.70] | 11.10 [5.30, 28.05] | 25.00 [5.65, 34.65] | 0.466 |
κ/λ ratio (by IFE) | 19.11 [0.95, 70.50] | 1.96 [0.52, 15.21] | 10.23 [0.01, 80.01] | 0.935 |
β2M (mg/L) | 2.20 [2.10, 2.40] | 2.90 [2.24, 3.30] | 4.60 [2.92, 6.90] | *<0.001 |
| PCs = plasma cells; BM = bone marrow; MFC = multiparameter flow cytometry; WBC = white blood cells; PLT = platelets; LDH = lactate dehydrogenase; CRP = C-reactive protein; β2M = beta2-microglobulin; CPC = circulating plasma cell; IFE = immunofixation electrophoresis; *=significant observation | ||||
Fig. 2
Scatterplot matrix, Spearman’s test (108 counts considered). Statistical significances were highlighted with * (***=0.0001). The absolute correlation between pairs of variables was displayed in the upper panels, and the font size matched the absolute value of the correlation. Along the diagonal was represented the histograms for each variable, and the LOESS (locally estimated scatterplot smoothing) curves were displayed in the lower panels. Median circulating PCs = 0.02%.
This high-risk phenotype was also observed when stratifying by the median CPCs value, with albumin (Supplementary Fig. 2a, p = 0.0029) and calcium levels (Supplementary Fig. 2b, p = 0.00026) showing significant deviations outside the normal ranges (as defined in Table 1) depending on whether CPCs were above or below the median.
Overall, these findings indicate that the presence of CPCs – irrespective of clonality - correlates with a more severe clinical presentation.
3.2. Monoclonal CPCs vs. polyclonal CPCs
Focusing the analysis on patients with detectable CPCs (99/108), either pCPCs (16/99, 16%, 0.00855% CPCs median) or mCPCs (83/99, 84%, 0.0217% CPCs median), the differences in baseline biochemical features were confirmed (Table 3 and Supplementary Fig. 3, p = 0.048). Patients with mCPCs exhibited a higher-risk profile compared to those with pCPCs. Specifically, the two groups differed significantly in terms of age (p = 0.05), BM-PCs percentage (p = 0.01), lymphocytes and neutrophils composition (p < 0.05), PLT count (p = 0.002), β2M (p = 0.002) and calcium levels (p = 0.07), as shown in Table 3. Moreover, median values of BM-PCs percentage (p = 0.0011), β2M (p = 0.00161), serum albumin (p = 0.01838) and platelets count (p = 0.00197) were significantly different between patients with polyclonal CPCs and those with mCPCs (Fig. 3).
pCPCs (n = 16) | mCPCs (n = 83) | p-value | |
|---|---|---|---|
Median [IQR] | Median [IQR] | ||
Age (y) | 61.00 [51.50, 67.00] | 67.00 [58.00, 75.00] | 0.054 |
CPCs (% by MFC) | 0.01 [0.00, 0.02] | 0.02 [0.01, 0.19] | *0.048 |
BM-PCs (% by MFC) | 0.80 [0.25, 4.05] | 7.20 [2.00, 16.15] | *0.001 |
WBC (xµl) | 21465 [14117.5, 30720] | 18165 [14827.5, 23915] | 0.299 |
Neutrophils (%) | 67.25 [58.93, 76.55] | 58.25 [50.45, 67.85] | *0.018 |
Lymphocytes (%) | 24.60 [16.33, 30.02] | 30.60 [22.42, 37.58] | *0.044 |
Monocytes (%) | 5.45 [4.18, 9.03] | 6.80 [5.43, 8.60] | 0.145 |
PLT (x10*9/L) | 296.00 [205.75, 366.50] | 218.50 [153.00, 256.50] | *0.002 |
Calcium (mg/dL) | 9.25 [8.78, 9.62] | 9.60 [9.17, 10.05] | 0.074 |
Serum Albumin (g/L) | 40.05 [35.95, 42.44] | 36.90 [31.01, 41.00] | 0.109 |
LDH (U/L) | 179.00 [147.65, 206.75] | 175.00 [143.30, 211.50] | 0.995 |
Total proteins (g/dL) | 7.70 [6.85, 8.42] | 8.00 [6.90, 9.80] | 0.647 |
CRP (mg/dL) | 0.35 [0.12, 1.08] | 0.43 [0.15, 1.34] | 0.74 |
M-protein (%) | 11.10 [5.30, 28.05] | 25.00 [5.65, 34.65] | 0.316 |
κ/λ ratio (by IFE) | 1.96 [0.52, 15.21] | 10.23 [0.01, 80.01] | 0.826 |
β2M (mg/L) | 2.90 [2.24, 3.30] | 4.60 [2.92, 6.90] | *0.002 |
| PCs = plasma cells; BM = bone marrow; MFC = multiparameter flow cytometry; WBC = white blood cells; PLT = platelets; LDH = lactate dehydrogenase; CRP = C-reactive protein; β2M = beta2-microglobulin; CPC = circulating plasma cell; IFE = immunofixation electrophoresis; *=significant observation | |||
Fig. 3
A) Distribution of BM-PCs percentage (in terms of median) in the two groups of patients (polyclonal PCs, turquoise vs. mCPCs, pink) (p = 0.0011). B) Distribution of B2M (in terms of median) in the two groups of patients (polyclonal PCs vs. mCPCs) (p = 0.00161). C) Median distribution of Serum Albumin between the two groups of patients (polyclonal PCs vs. mCPCs) (p = 0.01838). D) Distribution of Platelets count (in terms of median) in the two groups of patients (polyclonal PCs vs. mCPCs) (p = 0.00197). Graphs are scaled at 90th percentile.
3.2.1. The genomic background of MM patients presenting CPCs
A genomic characterization of the marrow clone was performed on 18/99 samples, using a NGS panel (UMA panel) on CD138 + separated from BM (as described in Materials and Methods section). Of the 18 patients examined, 3/18 (17%) presented pCPCs, while the remaining 83% (15/18) of patients presented mCPCs. Considering the median of CPCs frequency, 7/18 (39%) patients had CPCs percentage below the median, while 11/18 (61%) resulted above the median. According to the new IMWG Consensus Genomic Staging (CGS)21, 3/16 (19%) exhibited both a high number of CPCs and a high-risk genomic profile, 5/16 (31%) patients had detectable high CPCs but a low-risk genomic profile; 3/16 (19%) were classified as high-risk patients with low CPCs and 5/16 (31%) patients were low-risk patients with low CPCs. Two patients were excluded from the analysis owing missing information.
Overall, patients with a low frequency of CPCs (< 0.02%) and a low-risk genomic profile, showed a significatively higher number of alterations attributable to hyperdiploidy (p = 0.00093). Moreover, 7 patients were found to have mutations in the Rat sarcoma virus (Ras)-family genes NRAS and KRAS: in particular, 4 out of 18 patients (22%) carried a KRAS mutation, while 3 out of 18 (17%) had an NRAS mutation. Interestingly, KRAS mutations were exclusively found in patients with higher CPCs (4/4, 100%), whereas NRAS mutations were restricted to patients with lower CPCs levels (3/3, 100%), as shown in Fig. 4.
Fig. 4
BM-PCs genomic alterations across chromosomal arms and molecular features in patients with high and low CPCs. The heatmap displays copy number alterations (CNAs) and molecular markers across patients, grouped by high (top panel) and low (bottom panel) CPCs amount. Chromosomal arms are shown along the x-axis and patients are listed on the y-axis. Blue indicates copy number amplifications (amp), red indicates deletions (del), and grey represents no alteration. Green indicates positive status for specific genes or mutations, while grey in the leftmost columns indicates a negative status.
Additionally, significant correlations were observed with focal CNAs involving genes such as IGLL5 (p = 0.039), CCND1 (p = 0.051), EGR1 (p = 0.03798), and DUSP2 (p = 0.03798), as shown in Supplementary Fig. 4. In cases involving CCND1, EGR1, DUSP2 (Supplementary Fig. 4a, b and c), CPCs median was higher in the group that did not present amplification of these genes, suggesting the possibility that they were genes not involved, or as in the case of IGLL5 (Supplementary Fig. 4d), even deleted, in the presence of CPCs.
Finally, patients with a higher propensity to release CPCs into the bloodstream, generally lacked focal amplifications of genes such as HIST1H1D (Supplementary Fig. 5a), PSMB5 (Supplementary Fig. 5b), LTB (Supplementary Fig. 5c) and TRAF2 (Supplementary Fig. 5d). These genes, collectively referred to as “signature genes” in Fig. 4, are involved in critical cellular process, including immune response, cell cycle regulation, and apoptosis in lymphoid cells. Our observations suggest that the increased release of CPCs into the PB may not be directly linked to alterations in these specific pathways, but rather associated with alternative molecular mechanisms.
3.2.2. BM-PCs genomic characterization related to the CPCs’ immunophenotype
To investigate whether the cohort of patients analyzed for genomic alterations (18 out of 99) showed significant differences in the expression of MM markers on CPCs, such as CD138, CD45, CD56, CD27, CD81 and CD117, Fisher's exact test was performed to compare categorical variables for each marker. Each marker was classified as showing negative, dim or positive/high expression, based on MFC analysis, as described in the Materials and Methods section.
Overall, 11 samples exhibited high expression of CD138, while 7 samples showed a dim expression. Analysis showed that patients with CD138high expression on CPCs were associated with trisomy of chromosome 3 (Supplementary Table 2), a cytogenetic abnormality typically linked to improved overall survival (OS)22.
Regarding CD45 expression, 10 samples were negative, 4 showed dim expression, and the remaining 4 were positive. As expected, CPCs with CD45pos expression were also CD138dim and CD81pos, aligning with an immunophenotype typical of normal PC. These patients were more likely to be hyperdiploid (4/4, 100%) (p = 0.01).
For CD81 and CD27, 12 patients had CPCs lacking expression of these markers, while 6 were positive. Notably, patients with amplifications on chromosomes 15 and 19 had loss of CD27 expression (p = 0.013), as shown in Supplementary Table 2.
Expression of c-Kit (CD117) was detected in 6 PB samples, while 12 samples were negative. All CD117-positive samples (6/6, 100%) showed amplification on the q arm of chromosome 11 (p = 0.005), potentially implicating alterations in the MLL gene23.
Lastly, CD56 expression on BM-PCs was assessed: 7 samples were negative, while 11 were positive. CD56-positive expression was associated with the presence of aberrant mCPCs (p = 0.043). In contrast, CD56neg BM-PCs correlated with amplifications on both the p and q arms of chromosome 3 and 19.
3.3. mCPCs’ immunophenotype might differ from BM-PCs, mostly for CD56 expression
Aberrant CPCs (i.e., mCPCs) were detected in 83 out of 108 samples (77%), with a median frequency of 0.0217% (range 0.002-9%). Among these 83 patients, 39 (47%) had lambda-restricted mCPCs, with a median of 0.013% (range 0.002-9%). In contrast, patients with kappa-restricted mCPCs showed a slightly higher median frequency of 0.031% (range 0.002-5.3%). As expected, and shown in Fig. 5, patients releasing a higher number of mCPCs into the bloodstream also exhibited higher M-protein levels (p = 0.005) and lower serum albumin concentrations (p = 0.013), validated with FDR-BH procedure (Supplementary Fig. 1b). These findings confirm the previously recognized aggressive profile associated with MM patients releasing a higher mCPCs burden (Supplementary Fig. 6).
Fig. 5
Scatterplot matrix, Spearman’s test (83 patients considered). Median mCPCs = 0.0217%. * indicates statistical significance (***=0.0001). The upper panels show the absolute correlation between pairs of variables, with the font size corresponding to the correlation's absolute value. Each variable's histogram was represented along the diagonal, while the lower panels show the LOESS curves.
Interestingly, 41 out of 83 (49%) patients displayed a distinct profile from that of their BM-PCs, without any significant difference in mCPCs frequency between patients with concordant and discordant profiles (p = 0.8063, Fig. 6a). In particular, 17 NDMM patients showed a different expression of the marker CD56, also known as neural cell adhesion molecule 1 (NCAM-1). Among them, 2 out of 17 (12%) were CD56-positive in the BM but negative in PB, while the remaining 15 (88%) showed NCAM-1 expression in PB but not in BM, as shown in Fig. 6c. Of these patients, 12 (80%) were found to be κ-restricted showing a higher mCPCs median frequency (0.1%, range 0.002-5.3%), as compared to that overall observed.
Fig. 6
A) Comparison of mCPC counts between patients with identical (blue, n = 36) and differing (red, n = 41) immunophenotypes in PB and BM. No significant difference was observed in median mCPC levels between the two groups (p = 0.8063). B) Table summarizing the association between different clinical and genomic variables and marker expression in BM-PCs and mCPCs, with corresponding p-values. C) NDMM patients with detectable mCPCs were grouped based on the similarity of their immunophenotypic profiles in PB and BM: different profiles (red, 41/83, 50%) and identical profile (blue, 36/83, 43%). The histogram highlights the variation in marker expression in PB, with positive expression in pink and negative expression in green. Differences in cytoplasmic light chain expression are indicated in light blue.
Overall, 14 patients had CD56-negative, while 66 had CD56-positive mCPCs. For 3 samples, information about CD56 expression was missing. CD56-expressing mCPCs were predominantly CD138high, CD45neg, and also CD56-positive in the BM (Fig. 6b). Notably, CD56 expression in the BM was absent in 28 out of 83 samples (34%). Patients with CD56-positive BM-PCs were predominantly λ-restricted (p = 0.06), CD138high, and CD27neg (p = 0.06).
Among the 83 patients with detectable mCPCs, 27 (33%) showed dim expression of CD138, suggesting a more immature profile, compared to that of BM-PCs. However, patients with CD138high mCPCs exhibited a more aggressive disease profile, as indicated by elevated serum calcium levels (Supplementary Fig. 7a, p = 0.04) and higher M-protein concentrations (Supplementary Fig. 7b, p = 0.07). Notably, CD138high mCPCs were consistently CD45neg and CD56pos both in the BM and in the peripheral bloodstream (p = 0.01; Fig. 6b).
Regarding CD117 (c-Kit) expression, it was detected in 26 BM samples and absent in 54 – with 3 missing samples. CD117-positive samples mostly lacked CD81 expression (p = 0.06), while CD117-negative samples were mostly CD20neg (p = 0.05). Moreover, pathological calcium levels were more frequently observed in patients lacking CD117 expression.
Interestingly, 5 cases exhibited discordant light chain expression between CPCs and BM-PCs: all showed c-Igλ in the BM and c-Igκ in the PB-PCs. This finding suggests potential spatial heterogeneity, particularly noteworthy given that all these cases were also diagnosed with concomitant Light chain Amyloidosis.
4. Discussion
Multiple myeloma (MM) is a hematological malignancy driven by the clonal proliferation of malignant plasma cells (PCs) preferably in the bone marrow (BM). Polyclonal circulating plasma cells (CPCs) may be found in the peripheral blood (PB) of healthy individuals, typically in very low numbers and on a transient basis, for example following immune stimulation such as infection or recent vaccination24. To the best of our knowledge, no studies have demonstrated the clinical significance of polyclonal CPCs (pCPCs) as risk markers in plasma cells disorders. In this study, we evaluated the potential of circulating PCs (CPCs) not only restricted to circulating MM cells (mCPCs), present in PB as innovative biomarkers for the stratification of risk in NDMM patients. Using multiparameter flow cytometry, we developed and implemented a protocol for the identification and quantification of CPCs, using classical MM markers – such as CD138, CD38, CD45, CD19, c-κ and c-λ – and additional markers aberrantly expressed by PCs and useful to assess their maturation stage, such as CD117, CD56, CD20, CD81 and CD27.
Overall, the 107 MM patients under study were stratified based on of the presence/absence of CPCs, with mCPCs being found in 77% of cases (83/108), pCPCs in 15% of cases, and 9/108 (8%) patients with no CPCs at all. The median of CPCs found was 0.02% (range 0.002-9%). CPCs were significantly correlated with β2M, monoclonal component, serum albumin, and the percentage of PCs in the BM (p < 0.03) (Fig. 2). These associations suggest that elevated CPCs numbers, whether monoclonal or polyclonal, are indicative of a more aggressive disease phenotype25–28. Similar significant correlations were also observed when considering only aberrant PCs (mCPCs, p < 0.01), indicating a strong influence of biochemical parameters such as albumin and calcium on mCPCs levels, confirming data previously described by our group29. In fact, serum calcium and albumin are known to play key roles in bone metabolism and immune function. Notably, calcium levels may facilitate the mobilization of mCPCs by activating signaling pathways that promote malignant PCs proliferation and migration, including calcium-dependent mechanisms involved in chemotaxis. Similarly, serum albumin levels (an indicator of nutritional and inflammatory status), may influence the systemic microenvironment, potentially affecting the survival and circulation of mCPCs30.
The study also included genomic analyses of BM-PCs, to investigate the impact of the patients’ genomic landscape on the CPCs release and to gain deeper insights into the molecular complexity and heterogeneity of the disease.
A
Using advanced DNA sequencing technologies and the in-house developed UMA panel, we identified genetic alterations and mutations that may influence prognosis, treatment response and risk classification, according to the new IMGW CGS risk stratification. The results showed that patients with a low number of CPCs (< 0.02%) and classified as low-risk according to CGS, were also hyperdiploid (p = 0.00093), supporting the association between low CPCs release and favorable prognostic features. In addition, KRAS mutations were identified in 4 patients with a high CPC count, whereas NRAS mutations were found in 3 patients with lower CPC levels. These findings are consistent with previous studies indicating a negative prognostic impact of KRAS mutations on PFS and OS in MM31,32. Notably, KRAS-mutated plasma cells have been shown to exhibit increased motility, survival and a greater ability to escape the BM niche, potentially due to the activation of the MAPK/ERK pathway, which regulates genes involved in migration, such as CXCR4 and integrins32.Comparative analyses of the immunophenotypic profiles of CPCs and BM-PCs were performed by multiparameter flow cytometry, in 83 out of 108 (77%) samples in which mCPCs were detected. Although nearly half of the patients (41/83, 49%) showed phenotypic discrepancies between PB and BM-PCs, even for a single marker such as CD56, the quantity of mCPCs revealed in PB did not differ based on phenotypic concordance (p = 0.8063). Notably, in 5 cases, mCPCs displayed an inverted light chain compared to their corresponding BM-PCs, suggesting the presence of emerging or distinct clonal populations and highlighting the inter-patient’s heterogeneity typically observed in MM. This phenotypic diversity suggests a high biological variability among malignant cells, which may correspond to different disease subtypes, influencing progression and response to treatments.
Furthermore, differences in markers expression may have clinical relevance, as certain markers are associated with prognosis and PCs maturation status, thereby could be factored into the patients risk profile and eventually choice of treatment. For example, 27 out of 83 (33%) mCPCs had dim expression of CD138. Given that Syndecan-1 (CD138) is a surface protein mainly expressed on PCs which plays a key role in cell adhesion and signaling33, its reduced expression may be attributed to several factors: a) the PB environment lacks the growth factors and adhesion molecules abundant in the BM, potentially leading to decreased CD138 expression; and b) loss or downregulation of CD138 may facilitate detachment of mCPCs from the BM niche, promoting their circulation in PB, a mechanism also observed in other type of cancer34. Additionally, since that CD138 is the classical marker used to sort PCs with immunomagnetic enrichment35, having a consistent cohort (33%) of patients showing a lower expression of this marker might impair sorting-based methods and compromise the downstream analyses. Nevertheless, high CD138 expression (CD138high) remains associated with a more aggressive disease phenotype (Supplementary Fig. 7).
Regarding the CD56 marker, it was preferentially expressed both in BM-PCs and mCPCs. In particular, among the 17 patients which presented discordant CD56 expression between BM and PB, 15 (88%) presented a positive expression of CD56 in mCPCs, that was absent in their corresponding BM-PCs. This higher expression in the periphery may reflect a specific maturation stage of the tumor cells, as well as a possible acquisition of migratory capability. More mature cells often have altered adhesion properties and interaction with the BM microenvironment, potentially facilitating their detachment and dissemination. CD56, a member of the NCAM family, is involved in cell-cell and cell-matrix interactions. Its expression may therefore enhance the ability of malignant cells to migrate and survive outside the BM niche. This phenotypic shift could contribute to disease progression and complicate clinical management in MM patients.
Given the limited size and follow-up of the patient cohort in this analysis, these findings should be considered preliminary and serve as proof of concept. Also, considering the lack of survival clinical data and cytogenetic and/or genomic risk stratifications. Further validation is already in the making with a larger patient population, particularly in conjunction with markers that define clonal maturation and mechanisms of BM escape. To address this, a new MFC panel is currently under development in our laboratory. This panel includes markers related to chemotaxis and adhesion (e.g., CXCR4, CXCL12, CD24), microenvironmental interactions (e.g., CD46, CD152, CD279), and lineage maturation (e.g., CD319, CD200, CD269).
5. Conclusions
This study evaluated the potential of circulating plasma cells (CPCs), including polyclonal circulating plasma cells (pCPCs), as novel biomarkers for risk stratification in 107 patients. CPCs were assessed using multiparametric flow cytometry, detecting their presence in 92% of cases. A comparative phenotypic analysis with bone marrow plasma cells (BM-PCs) revealed significant heterogeneity in 49% of patients, with notable variations in markers such as CD56 and CD138. These differences suggest the emergence of distinct clonal populations or subtypes of disease, which may have important prognostic and therapeutic implications. Importantly, patients with CPCs at diagnosis—whether monoclonal or polyclonal—tended to present with a higher-risk disease profile.
In conclusion, our findings support the use of CPCs, including both mCPCs and polyclonal CPCs, as promising biomarkers in myeloma, providing a foundation for future studies aimed at incorporating this analysis into clinical practice.
Ethics approval and consent to participate:
A
A
The study was conducted in accordance with the Declaration of Helsinki, and approved by the local Ethics Committee (CE-AVEC) of Bologna (protocol code 167/2019/Sper/AUBo approved on March 20, 2019).Consent for publication:
Informed consent was obtained from all subjects involved in the study, within the AIRC2019 (IG2019-22059) project “StreaMMing: the dynamics of Multiple Myeloma minimal residual disease in the peripheral blood stream.
A
A
Data Availability
The data of this study are available at: [https://doi.org/10.5281/zenodo.16259100](https:/doi.org/10.5281/zenodo.16259100)
Conflicts of Interest:
The authors declare no conflicts of interest.
A
Funding:
The work reported in this publication was funded by the Italian Ministry of Health - Ricerca Corrente for APC fees [RC-2025-2797269].
A
Author Contribution
Designed research: I.V., A.V. and C.T.; Performed research: I.V., A.V., B.T., S.A. and I.P.; Performed statistical and bioinformatics analysis: V.S., V.M.V., G.M., A.P.; Analyzed and interpreted data: I.V., A.V. and C.T.; Prepared manuscript: I.V., A.V. and C.T.; E.B., M.M., A.C., K.M., M.P., M.T. and E.Z. provided patients and tissue materials. All co-authors of the present study have read and agreed to the last version of the submitted manuscript.
A
Acknowledgement
We would like to express our gratitude to all the patients, research groups, and study coordinators at the Seràgnoli Institute in Bologna, Italy. The authors would like to acknowledge the Italian Association for Cancer Research (AIRC19-IG22059), BolognaAIL OdV, and Ricerca Corrente (RC2025-2797269).
The following abbreviations are used in this manuscript:
amp | Amplification |
|---|---|
B2M | Beta-2 Microglobulin |
BM | Bone Marrow |
MCPC | Circulating Multiple Myeloma Cell |
CNA | Copy Number Alteration |
CPC | Circulating Plasma cell |
CR | Complete Response |
CRP | C-Reactive Protein |
CXCL12 | C-X-C Motif Chemokine Ligand 12 |
CXCR4 | C-X-C Motif Chemokine Receptor 4 |
del | Deletion |
DNA | Deoxyribonucleic Acid |
IGH | Immunoglobulin Heavy Chain |
IMGW | International Myeloma Working Group |
IQR | Interquartile Range |
ISS | International Staging System |
LDH | Lactate Dehydrogenase |
MFC | Multiparametric Flow Cytometry |
MM | Multiple Myeloma |
MRD | Measurable Residual Disease |
nCR | Near Complete Response |
NDMM | Newly Diagnosed Multiple Myeloma |
NGF | Next-Generation Flow |
NGS | Next-Generation Sequencing |
OS | Overall Survival |
PB | Peripheral Blood |
PC | Plasma Cell |
PD | Progression Disease |
PFS | Progression-Free Survival |
PR | Partial Response |
R-ISS | Revised International Staging System |
sCR | Stringent Complete Response |
SD | Stable Disease |
SMM | Smoldering Multiple Myeloma |
SNV | Single Nucleotide Variant |
t | Translocation |
VGPR | Very Good Partial Response |
Electronic Supplementary Material
Below is the link to the electronic supplementary material
References
1.
An G, Qin X, Acharya C, et al. Multiple myeloma patients with low proportion of circulating plasma cells had similar survival with primary plasma cell leukemia patients. Ann Hematol. 2015;94(2):257–64. 10.1007/s00277-014-2211-0.
2.
Fernández de Larrea C, Kyle RA, Durie BG, et al. Plasma cell leukemia: consensus statement on diagnostic requirements, response criteria and treatment recommendations by the International Myeloma Working Group. Leukemia. 2013;27(4):780–91. 10.1038/leu.2012.336.
3.
Bezdekova R, Jelinek T, Kralova R, et al. Necessity of flow cytometry assessment of circulating plasma cells and its connection with clinical characteristics of primary and secondary plasma cell leukaemia. Br J Haematol. 2021;195(1):95–107. 10.1111/bjh.17713.
4.
Klimienė I, Radzevičius M, Matuzevičienė R, Sinkevič-Belliot K, Kučinskienė ZA, Pečeliūnas V. Adhesion molecule immunophenotype of bone marrow multiple myeloma plasma cells impacts the presence of malignant circulating plasma cells in peripheral blood. Int J Lab Hematol. 2021;43(3):403–8. 10.1111/ijlh.13387.
5.
Wu D, Zhang P, Li F, et al. CD138- multiple myeloma cells express high level of CHK1 which correlated to overall survival in MM patient. Aging. 2020;12(22):23067–81. 10.18632/aging.104066.
6.
Noh JY, Seo H, Lee J, Jung H. Immunotherapy in Hematologic Malignancies: Emerging Therapies and Novel Approaches. Int J Mol Sci. 2020;21(21):8000. Published 2020 Oct 27. 10.3390/ijms21218000
7.
Gonsalves WI, Jevremovic D, Nandakumar B, et al. Enhancing the R-ISS classification of newly diagnosed multiple myeloma by quantifying circulating clonal plasma cells. Am J Hematol. 2020;95(3):310–5. 10.1002/ajh.25709.
8.
Bertamini L, Oliva S, Rota-Scalabrini D, et al. High Levels of Circulating Tumor Plasma Cells as a Key Hallmark of Aggressive Disease in Transplant-Eligible Patients With Newly Diagnosed Multiple Myeloma. J Clin Oncol. 2022;40(27):3120–31. 10.1200/JCO.21.01393.
9.
Li S, Zhang E, Cai Z. Liquid biopsy by analysis of circulating myeloma cells and cell-free nucleic acids: a novel noninvasive approach of disease evaluation in multiple myeloma. Biomark Res. 2023;11(1):27. Published 2023 Mar 8. 10.1186/s40364-023-00469-6
10.
Fujisawa M, Suehara Y, Fukumoto K, et al. Clinical Significance of Circulating Plasma Cells in Myeloma: Multiparameter Flow Cytometric Analysis. Blood. 2015;126(23):5352. 10.1182/blood.V126.23.5352.5352.
11.
Chen P, Zhang L, Cao X, et al. Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis. Cancer. 2024;130(10):1884–93. 10.1002/cncr.35202.
12.
Tembhare PR, Sriram H, Khanka T, et al. Circulating tumor plasma cells and peripheral blood measurable residual disease assessment in multiple myeloma patients not planned for upfront transplant. Hemasphere. 2024;8(4):e63. 10.1002/hem3.63. Published 2024 Apr 1.
13.
Kraj M, Kopeć-Szlęzak J, Pogłód R, Kruk B. Flow cytometric immunophenotypic characteristics of 36 cases of plasma cell leukemia. Leuk Res. 2011;35(2):169–76. 10.1016/j.leukres.2010.04.021.
14.
Caraux A, Klein B, Paiva B, et al. Circulating human B and plasma cells. Age-associated changes in counts and detailed characterization of circulating normal CD138- and CD138 + plasma cells. Haematologica. 2010;95(6):1016–20. 10.3324/haematol.2009.018689.
15.
Lin D, Shen L, Luo M, et al. Circulating tumor cells: biology and clinical significance. Signal Transduct Target Ther. 2021;6(1):404. 10.1038/s41392-021-00817-8. Published 2021 Nov 22.
A
16.Jelinek T, Bezdekova R, Zatopkova M et al. Current applications of multiparameter flow cytometry in plasma cell disorders. Blood Cancer J. 2017;7(10):e617. Published 2017 Oct 20. 10.1038/bcj.2017.90
17.
Robillard N, Wuillème S, Moreau P, Béné MC. Immunophenotype of normal and myelomatous plasma-cell subsets. Front Immunol. 2014;5:137. Published 2014 Mar 31. 10.3389/fimmu.2014.00137
18.
Cordone I, Amodeo R, Bellesi S, et al. Consensus for Flow Cytometry Clinical Report on Multiple Myeloma: A Multicenter Harmonization Process Merging Laboratory Experience and Clinical Needs. Cancers (Basel). 2023;15(7):2060. 10.3390/cancers15072060. Published 2023 Mar 30.
19.
Das N, Dahiya M, Gupta R, et al. Flow cytometric immunophenotyping of plasma cells across the spectrum of plasma cell proliferative disorders: A fresh insight with pattern-based recognition. Cytometry B Clin Cytom. 2022;102(4):292–302. 10.1002/cyto.b.22062.
20.
Poletti A, Taurisano B, Mazzocchetti G, et al. Unique molecular assay (UMA): a next-generation sequencing targeted panel for efficient and comprehensive genomic profiling and risk stratification of multiple myeloma. Haematologica. 2025;110(10):2436–50. 10.3324/haematol.2025.287559.
21.
Avet-Loiseau H, Davies FE, Samur MK, et al. International Myeloma Society/International Myeloma Working Group Consensus Recommendations on the Definition of High-Risk Multiple Myeloma. J Clin Oncol. 2025;43(24):2739–51. 10.1200/JCO-24-01893.
22.
Chretien ML, Corre J, Lauwers-Cances V, et al. Understanding the role of hyperdiploidy in myeloma prognosis: which trisomies really matter? Blood. 2015;126(25):2713–9. 10.1182/blood-2015-06-650242.
23.
Landry C, Londono D, Devlin SM et al. Multiple Copies of MLL Is The Most Commonly Detected Cytogenetic Abnormality In Newly Diagnosed Multiple Myeloma and May Modify Disease Risk. Blood 2013; 122 (21): 1910. 10.1182/blood.V122.21.1910.1910
24.
Blanchard-Rohner G, Pulickal AS, Jol-van der Zijde CM, et al. Appearance of peripheral blood plasma cells and memory B cells in a primary and secondary immune response in humans. Blood. 2009;114(24):4998–5002. 10.1182/blood-2009-03-211052.
25.
Suku P, Dash A, Radhakrishnan A, Malhotra P, Sachdeva MUS. A review on pathobiology of circulating tumour plasma cells: The sine qua non of poor prognosis in plasma cell neoplasms. Oncol Res. 2025;33(5):1055–68. 10.32604/or.2024.055154. Published 2025 Apr 18.
26.
Yan W, Shi L, Xu J, et al. Clinical implications of residual normal plasma cells within bone marrow across various disease stages in multiple myeloma. Leukemia. 2024;38(10):2235–45. 10.1038/s41375-024-02366-9.
27.
Gupta L, Suku P, Dash A, et al. Detection of circulating normal and tumor plasma cells in newly diagnosed patients of multiple myeloma and their associations with clinical and laboratory parameters. Curr Probl Cancer. 2024;48:101025. 10.1016/j.currproblcancer.2023.101025.
28.
Sun F, Cheng Y, Ma C, et al. Polyclonal plasma cell (PolyPC) signature as a key indicator for predicting the progression of MGUS to multiple myeloma. Cancer Biomark. 2025;42(6):18758592251344936. 10.1177/18758592251344936.
29.
Vigliotta I, Solli V, Armuzzi S, et al. Circulating Multiple Myeloma Cells (CMMCs) as Prognostic and Predictive Markers in Multiple Myeloma and Smouldering MM Patients. Cancers (Basel). 2024;16(17):2929. 10.3390/cancers16172929. Published 2024 Aug 23.
30.
Sheinenzon A, Shehadeh M, Michelis R, Shaoul E, Ronen O. Serum albumin levels and inflammation. Int J Biol Macromol. 2021;184:857–62. 10.1016/j.ijbiomac.2021.06.140.
A
31.Perroud C, Thurian D, Andres M, et al. Effect of MAPK activation via mutations in NRAS, KRAS and BRAF on clinical outcome in newly diagnosed multiple myeloma. Hematol Oncol. 2023;41(5):912–21. 10.1002/hon.3208.
32.
Sacco A, Federico C, Todoerti K, et al. Specific targeting of the KRAS mutational landscape in myeloma as a tool to unveil the elicited antitumor activity. Blood. 2021;138(18):1705–20. 10.1182/blood.2020010572.
33.
McCarron MJ, Park PW, Fooksman DR. CD138 mediates selection of mature plasma cells by regulating their survival. Blood. 2017;129(20):2749–59. 10.1182/blood-2017-01-761643.
34.
Cordone I, Masi S, Summa V et al. Overexpression of syndecan-1, MUC-1, and putative stem cell markers in breast cancer leptomeningeal metastasis: a cerebrospinal fluid flow cytometry study. Breast Cancer Res. 2017;19(1):46. Published 2017 Apr 11. 10.1186/s13058-017-0827-4
35.
Lasa M, Gonzalez C, Notarfranchi L, et al. on behalf of the PETHEMA/GEM cooperative group. Ultrasensitive detection of circulating multiple myeloma cells by next-generation flow after immunomagnetic enrichment. Blood. 2025;146(8):964–70. 10.1182/blood.2025029234.
Tables