Prerpints.org logo
Preprint
Review

This version is not peer-reviewed.

Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications

A peer-reviewed article of this preprint also exists.

Submitted:

13 January 2025

Posted:

14 January 2025

You are already at the latest version

Abstract
Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single cell transcriptomics, genomics and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of these advanced technologies, challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.
Keywords: 
myeloma
;  
multiple myeloma
;  
tumor microenvironment
;  
microenvironment
;  
single cell RNA sequencing
;  
single cell
;  
multiomics
;  
evolution
;  
therapy evasion
;  
personalized medicine
Subject: 
Medicine and Pharmacology  -   Hematology

Introduction

Multiple myeloma (MM) is common blood cancer defined by the buildup of malignant plasma cells (myeloma cells), typically occurring in the bone marrow [1]. In this condition, myeloma cells produce abnormal proteins, e.g., immunoglobulins (Ig), and cause severe complications such as kidney damage, anemia, hypercalcemia and bone weakening. Each year, over 30,000 individuals in the United States and over 150,000 people globally are diagnosed with MM [2]. MM typically involves chromosomal abnormalities and translocations [3,4]. About half of patients have primary translocations involving one of five chromosomal partners (4, 6, 11, 14 or 20), specifically oncogenes juxtaposed to the immunoglobulin heavy chain (IgH) locus, and almost all cases of MM exhibit dysregulation of one of the three cyclin D genes [3,4]. Though some patients can have early onset disease, MM predominantly affects older adults, with an average onset age of 69 years [5]. As life expectancy continues to rise, it is critical to address MM treatment due to the resulting increase in prevalence of MM [6] by better understanding it’s molecular etiology.
The many unanswered questions and challenges in myeloma research such improving dynamic disease risk assessment through MRD or other means, better defining high risk disease, improving our understanding of the molecular basis of myeloma, improving guidance of treatment strategy, e.g., through new molecular markers, and by identifying novel targets, i.e., novel antigen or small molecule inhibitor targets [7,8]. Single-cell molecular profiling offers a detailed understanding of how MM impacts individual cells and can be used to address these many unanswered challenges at unprecedented resolution.

Single Cell Analysis Technologies in Mm

Single-cell profiling provides a more nuanced look at how MM affects cellular processes within myeloma cells and cells in the microenvironment. The understanding of MM development on a cellular level can also potentially provide insights into treatment through the identification of biomarkers that may answer why many patients become resistant to anti-myeloma drugs. With bulk RNA-seq and microarray, the averages for cellular populations are examined by aggregating a large volume of cells from a given sample. However, this can limit our knowledge on rarer cell types, such as cancer stem cells or immune cell subsets, which may have more insight into many of the questions of MM biology [9]. Similarly, the generalization of results across these large bulk samples can have a confounding effect and makes it difficult to determine how MM clones can evolve over time, and how these clones are able to resist treatment. The use of scRNA-seq in this field has been developed over time, firstly with mRNA transcriptomics in 2009, which allowed for the large-scale detection of mixed-cell samples, followed by the introduction of Smart-seq in 2012 (Figure 1), which enhanced the read coverage of scRNA-seq and enabled the analysis of alternative splicing and single nucleotide variant (SNV) detection which was later refined in Smart-seq2 [10,11]. Notably, Fluidigm C1 microfluidic chips in combination with multiplexed qPCR and Smart-seq2 have already been used in MM research [12,13]. MARS-seq, an approach that barcodes cells using a multiwell plate with individual cells in each well, was also commonly used in MM research [14,15]. These techniques now have largely been superseded by the much higher throughput droplet barcoding approaches such as 10X Genomics Chromium platform (Figure 1, Table 1) [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39].
The development of single-cell proteomics protocols for MM research has largely relied on either a basis of mass spectrometry or targeted antibodies. The most popular of these techniques used in MM are generally antibody based including 10X Genomics multiome RNA+Protein [34], CITE-seq [32], and Mission Bio Tapestry DNA+Protein [41]. However the combination of the mass spectrometry approach and flow cytometry has created mass CyTOF, which allows for the study of specific proteins in single cells through isotope-labeled antibody conjugation with specific molecules in or on the cell surface (Figure 1). Protein expression data can be used by itself or as a validation method for differential expression analysis between cell subpopulations defined from scRNA-seq to evaluate the consistency between differentially expressed RNAs and proteins [31].
Increasingly, MM research has begun to use newly developed single cell genomics approaches such as Mission Bio Tapestry which uses novel probe sets to analyze SNVs, SNVs and copy number variants (CNV), and DNA+Protein [41]. Alternatively, some groups have used 10X Genomics protocols to perform single cell genomics profiling [28] or single cell multiplexed qPCR to identify SNV and CNVs [13]. All of these approaches have provided ample resources for the study of genetics, genomics, epigenetics, and proteomics at the single cell level in myeloma.

Subclonality and Tumor Evolution

Genomic Instability Drives Subclonal Diversity in MM

The clonal architecture of MM is highly dynamic, marked by distinct subclones that evolve in response to selective pressures, including therapy and microenvironmental interactions. This subclonal architecture is evident even in early stages of the disease, such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering myeloma and persists throughout disease progression to relapsed/refractory MM (RRMM) [33,43,44]. Subclones often exhibit unique genetic alterations, such as mutations in NRAS, KRAS, and TP53, which occur as secondary events that contribute to disease progression, treatment resistance, and relapse [44,45,46].
Genomic instability is a hallmark of MM and plays a crucial role in driving subclonal diversity and disease progression. It encompasses both genome-wide changes, such as aneuploidy and chromosomal translocations, and micro-level changes, including SNVs and DNA repair defects [33,44,47]. Notably, alterations in the TP53 gene, including deletions and SNVs, are among the most well-studied genomic abnormalities in MM, frequently associated with poor prognosis, increased clonal fitness, and resistance to therapy [30,46,48].
The impact of genomic instability on MM progression is evident in its ability to drive the emergence of drug-resistant subclones. As myeloma cells acquire genomic alterations, they develop mechanisms to evade treatment, including alterations in DNA damage repair pathways, epigenetic reprogramming, and force changes in the bone marrow microenvironment [33,45]. High-risk genomic features, such as biallelic TP53 alterations, gain(1q), and translocations involving the immunoglobulin heavy chain locus, are known to drive treatment resistance [33,43,46]. Moreover, the presence of multiple, genetically distinct subclones complicates treatment, as different subclones may harbor resistance mechanisms against various therapies, necessitating a more personalized approach [19,45] (Figure 2).
Recent single cell multi-omics analyses have revealed that multidrug-resistant subclones often co-exist within the same patient, each with unique resistance mechanisms, such as altered drug transport, metabolic reprogramming, and immune evasion [33,44]. This parallel development of resistance highlights the need for combination therapies that can simultaneously target multiple subclones and their resistance pathways [30,47]. Indeed most patients are now treated with combination therapies such as lenalidomide, bortezomib, and dexamethasone (RVD) as induction therapy regardless of transplant eligibility [49], carfilzomib, melphalan, and prednisone (KMP) for transplant ineligible NDMM patients [50], carfilzomib, cyclophosphamide, lenalidomide, and dexamethasone (KCRD) for NDMM patients [51], bortezomib, dexamethasone, cisplatin, doxorubicin, cyclophosphamide, and etoposide (VD-PACE) for triple class refractory RRMM [52], daratumamab (i.e., a CD38 monoclonal antibody) combination therapies such as D-RVD [53], D-KRD [54], and numerous others depending on patient characteristics especially in RRMM [55]. Quad therapies especially are now standard treatments for MM patients.

Tumor Evolution in Myeloma

Mutagenesis in MM is the result of natural processes of aging, defects in DNA repair mechanisms, and the mutagenic activity of enzymes like activation-induced deaminase (AID) and APOBEC. The specific mutagenesis processes differ depending on the primary genomic lesions, with HRD MM showing signatures associated with aging, and MM with translocations showing signatures related to APOBEC activity and DNA repair defects [56,57]. The main selective pressures for MM evolution include competition for resources and treatment while the mechanisms driving tumor evolution in MM include chromosomal translocations, CNVs, SNVs, epigenetic modifications, interclonal interactions, and microenvironmental influences [56,57].
The earliest events in MM pathogenesis are usually chromosomal translocations involving the immunoglobulin heavy chain (IgH) locus on chromosome 14q32 [58]. These in turn cause expansion of a single Ig clone resulting in a very heavily overrepresented antibody type, usually IgG or IgA (Figure 3). Notable translocations include t(4;14), t(11;14), t(6;14), t(14;16), and t(14;20), which are implicated in distinct molecular mechanisms driving MM progression [4,59]. These translocations lead to the deregulation of oncogenes such as FGFR3, NSD2, CCND1, CCND3, MAF, and MAFB respectively, resulting in aberrant cell cycle control, increased proliferation, resistance to apoptosis, and ultimately contributing to distinct clinical behaviors and prognostic outcomes [4,58,60,61,62,63,64,65,66]. In addition, dysregulation of epigenetics, including DNA methylation and histone modifications, has emerged as a crucial regulator of MM pathogenesis [67].
Over 80 driver genes are repeatedly mutated in MM patients, and a significant percentage of these SNVs are frequently subclonal, indicating they develop later in the course of tumor evolution than the primary initiating event. The acquisition of SNVs in driver genes is not a random process and depends on the primary genomic event initiating oncogenesis in MM and leading to the expansion of more aggressive or drug-resistant clones under selective pressures, such as therapy or microenvironmental factors [68,69,70]. This results in a heterogeneous population of subclones with distinct genetic profiles, each having different growth advantages and responses to treatment. Subclonal heterogeneity is a key factor in treatment resistance and disease relapse, as different subclones may respond differently to therapy, leading to an evolving and adaptive tumor landscape [71,72].
Clonal evolution, where distinct genetic subclones emerge over time, contributes to disease heterogeneity and can lead to variable treatment responses [73]. Clonal evolution can lead to a "phenotypic shift" which can limit the ability to detect measurable residual disease (MRD) using PCR-based methods but has less of an impact on higher resolution flow cytometry and next-generation sequencing MRD techniques [74]. As the disease progresses, specific biomarkers such as chromosomal abnormalities and elevated serum B-Cell Maturation Antigen (sBCMA) levels become instrumental in predicting patient outcomes [75,76]. For example, higher sBCMA levels correlate with severe disease and can signal shorter progression-free and overall survival [77,78]. Cytogenetic aberrations have a greater prognostic impact in MM than mutations in specific genes. The evolution of cytogenetic abnormalities, particularly the acquisition of high-risk aberrations like del(17p), adversely affects patient prognosis [77]. Though the mechanism is still not fully understood gain/amp(1q) is one of the highest risk cytogenetic aberrations and may operate through upregulation of CKS1B, MCL1, BCL9, and/or PBX1 [26,79,80]. Other high risk cytogenetic events include del(1p) resulting in loss of CDKN2C, del(13q) resulting in loss of RB1, and multiple others [81]. Spatial heterogeneity of MM in different bone marrow lesions can lead to the missed detection of prognostically significant cytogenetic aberrations in a significant percentage of cases [82].
In MM, the development of targeted therapies and personalized medicine should be guided by the tumor’s genetic heterogeneity and evolutionary dynamics, which vary due to both intratumor and intertumor heterogeneity (Figure 4A). These complexities highlight the need for treatment strategies that can adapt to MM’s clonal evolution which is dependent on various factors including patient- and tumor-related features [83]. Personalized treatment strategies increasingly incorporate insights into genetic diversity, requiring therapeutic approaches that address both the inter- and intra-patient variability driven by tumor evolution [84]. As a result, dynamic therapy models such as sequential and adaptive therapies have been developed to modulate treatment in response to changes in the tumor’s genetic profile, targeting the tumor’s evolution to potentially achieve sustained disease control. These models consider the shifting genomic landscape, offering a framework for long-term management of MM by adjusting treatments based on real-time disease progression [85].
Up until now there is only one genomic alteration, t(11;14), that is regularly used for precision medicine, i.e., to define patient cohorts that are likely responsive to venetoclax [86]. There have been resent proposals to use Liquid biopsy methods in precision medicine. These including the analysis of circulating tumor cells (CTCs) and cell-free DNA (cfDNA), to provide novel tools for tracking MM’s clonal evolution in a noninvasive manner [87,88] (Figure 4B). Unlike single-site bone marrow biopsies, could offer a less spatially biased view of the subclonal architecture of the disease, allowing for the detection of new mutations or clonal expansions that might otherwise go unnoticed [89,90]. On the other hand, these assays could also miss myeloma cell clones that are predominanty localized to the bone marrow. Though there are clear limitations to liquid biopsies their use could help identify circulating clones irrespective of spatial heterogeneity within the bone marrow [91]. Whether bone marrow biopsies, bone marrow aspirates, or liquid biopsies are used, ongoing clonal monitoring should be used to adapt treatment regimens to the shifting genetic profile of MM in the future [92,93] (Figure 4C).

Interplay Between Subclones and the Microenviroment

The interplay between subclones in MM is characterized by both competitive and cooperative interactions that drive disease evolution [20,30,33,35,45,94,95,96,97,98,99,100,101,102]. Subclonal competition, influenced by genetics and environmental pressures, leads to the expansion of dominant clones and the emergence of treatment-resistant populations [101]. However, subclones may also exhibit cooperative behavior, where interactions between them and with the tumor microenvironment facilitate mutual survival and adaptation [20,35,70].

Subclone Competition

Subclonal competition is driven by the tumor's inherent genetic instability, which results in diverse subclonal populations with different survival advantages [79]. Treatment provides selective pressure and leads to complex clonal dynamics, where the relative abundance of subclones shifts over time in response to therapeutic interventions, resource competition, and proliferative advantages [20,100]. The competitive interactions among subclones in MM can be explained by several theoretical models of evolutionary dynamics. Branching evolution, for instance, suggests that multiple subclones arise independently and co-exist, each competing for resources within the tumor microenvironment [94,96]. A whole exome sequencing study [96] highlights the diversity within the tumor and explains why certain subclones may become dominant in response to specific pressures, such as the availability of resources or immune evasion mechanisms [20,30,35]. In contrast, linear evolution suggests a more straightforward pathway where one dominant clone sequentially gives rise to another, reflecting a continuous adaptation of the dominant subclone to the microenvironmental changes [101].
Subclonal competition also results in the emergence of treatment-resistant clones, particularly in advanced disease stages such as relapsed/refractory multiple myeloma (RRMM). For example, single-cell RNA sequencing (scRNA-seq) has revealed that patients with RRMM often develop resistant clones characterized by specific genetic mutations, such as gain(1q), which is linked to a unique microenvironment, treatment resistance, and poor prognosis. Through joint scRNA-seq study of myeloma cells and the microenvironment, gain(1q) clones were associated with increased numbers of specific subtypes of tumor associated macrophages that express C1QA and CD206, CD56dim natural killer (NK) cells, and a subset of inflammatory dendritic cells (DC) expressing S100A8, S100A9, and CD14, CLEC7A, and VSIR [35]. The expansion of these resistant clones is associated with the upregulation of genes related to survival, cell proliferation, and immune evasion, allowing them to thrive in conditions where other clones cannot [35]. Another study using single cell transcriptomics and epigenetics characterized the subclonal level resistance mechanisms [33]. In this study, resistance mechanisms could cooccur across subclones such as an increase in Heat Shock Proteins (HSP), i.e., HSP90AA1, after treatment with carfilzomib or an increase in NF-κB pathway after treatment with MEK/BRAF inhibitors [33]. Resistant subclones also had different interactions with the microenvironment [33]. The complexity of subclonal competition underscores the need for personalized treatment strategies that can adapt to the evolving clonal landscape throughout a patient's disease course, as the dominance and prevalence of subclones are not static but are instead constantly shifting in response to therapeutic and environmental pressures [94,102].

Subclone Cooperation

Interestingly, subclones in MM do not merely compete; they may also exhibit cooperative behaviors that accelerate disease progression. This phenomenon of clonal cooperation is observed when subclones interact with each other and with the tumor microenvironment to foster mutual survival or the formation of new clones [98]. Studies have shown that plasma cell-extrinsic factors through subclones may contribute to the acquisition of invasive phenotypes and facilitate the progression of the disease from precursor stages, such as monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), to symptomatic multiple myeloma [102]. Additionally, studies have shown that clonal cooperation plays a significant role in tumor formation and the acquisition of invasive phenotypes, indicating that subclones can indeed help each other evolve in certain contexts [100]. However, it is not clear how the subclones work together over time [95] through these complicated dynamics, where cooperation and competition coexist to drive tumor progression. Time series single cell studies will be essential to understand which subclones persist together in a cooperative state.

Inter-Subclone Communication

During MM progression, subclones may communicate, signal, or stimulate one another. The mechanisms for this are still not well defined or understood. One example of possible intersublcone communication is in endogenous IL6 production by some myeloma cells where the IL6 production from the myeloma cells can have a self-stimulatory affect causing increased growth [103,104]. Alternatively, CD44 can promote cellular adhesion amongst myeloma cells and with the extracellular matrix (ECM) resulting in resistance to Lenalidomide [105]. In relapsed/refractory multiple myeloma, subclones that have acquired resistance to treatment often engage in interactions that further alter the tumor microenvironment, such as the production of inflammatory cytokines or adhesion molecules, which can create a pro-inflammatory environment conducive to tumor growth [35]. However, as certain subclones achieve dominance, there may also be a reduction in inter-subclone communication. This reduction in inter-subclone communication may lead to a more homogeneous tumor population, at least temporarily, until new selective pressures prompt further diversification and communication among emerging resistant subclones [101]. Nevertheless, the underexplored dynamic nature of inter-subclone communication highlights the current challenge of developing durable therapeutic strategies [20,33,97] and underscores the adaptability of subclonal populations in response to their changing microenvironment and treatment landscape.

Subclone and Microenvironment Interactions

The myeloma microenvironment is a complex network of immune and stromal cells that can support tumor growth and can contribute to therapy resistance (Figure 5A). Single cell studies have increasingly shown that the immune microenvironment is directly affected by genetic changes in the myeloma cells and co-evolves along with them [31]. With the generation of large single cell datasets of the myeloma microenvironment, the complex relationships and dynamics of the cells are increasingly well understood. This includes how myeloma cells may affect their immune microenvironment. Myeloma cells can induce dysfunction in T cells by expressing immune checkpoint molecules like PD-L1, leading to T cell exhaustion and reduced anti-tumor activity. This is accompanied by a decrease in CD4+ T cells, further weakening the immune response against myeloma cells [106,107,108,109]. Furthermore single cell studies have shown that T-cells from MM patients tend to shift from a cytotoxic toward an effector phenotype and that these changes can be variable between patients representing large amounts of heterogeneity both within and between patients [39]. Regulatory B cells (Bregs) in the myeloma microenvironment facilitate immune tolerance by secreting transforming growth factor-β (TGF-β), interleukin-10 (IL-10), and interleukin-35 (IL-35), suppressing anti-tumor immune responses [20,21,22]. Macrophages, often polarized to an M2 phenotype, i.e. TAMs, in MM, secrete growth factors and cytokines that promote tumor growth and suppress immune responses [35]. DCs are crucial for antigen presentation and T-cell activation, yet their function is impaired in the myeloma microenvironment, further suppressing the immune response [36]. These DC cells can be activated using an anti-CD40 agonist to reduce myeloma cell load and other symptoms [36]. Single cell studies of the immune microenvironment have shown that all these cell types change in proportion between healthy donors, NDMM, and RRMM [20]. Notably, MM patients had increased numbers of NK cells, CD16+ monocytes, and CD8+ memory/effector cells compared to healthy donors [20]. After treatment the CD14+ monocytes also increased significantly in proportion and began expressing chemokines including CCL3, CCL4, and CXCL2 [20]. In contrast, CD16+ monocytes pretreatment mainly expressed IFN alpha response pathway genes including IFI6, IFL44L, and IFITM [20]. Taken together, there are clear immune compositional and phenotypic shifts in MM and during treatment.
The stromal cells in the microenvironment, such as fibroblasts, osteoclasts, and endothelial cells (ECs), provide structural support and contribute to disease progression. Fibroblasts often differentiate into cancer-associated fibroblasts (CAFs), which secrete factors that promote tumor growth and survival [110]. Single cell studies have identified subtypes of CAFs that express platelet markers including MPL and ITGA2B that may mediate growth [42]. Myeloma cells stimulate osteoclast activity, leading to bone resorption and release of growth factors that further promote tumor growth [108,111]. From single cell studies of the primary site of aberrant osteoclast activity, subclones were found to localize to focal lesions and have altered expression such as downregulation of CXCR4 [25]. ECs respond to pro-angiogenic factors, such as VEGF produced by myeloma cells and other microenvironmental cells, by forming new blood vessels. This increased vascularization delivers essential nutrients and oxygen, fostering tumor growth and facilitating metastasis [110,112]. Subtyping analysis and comparison of ECs between MM stages using scRNA-seq, revealed a pre-vascular to angiogenic shift in ECs from MGUS to MM, an increase in IFN signaling in subsets of ECs in MM that may represent a unique MM subtype, and transcriptional changes associated with angiogenesis, migration, lipid metabolism, and increased expression of SOX18 during MM progression [17]. ECs and the resulting angiogenesis signaling and cytokine release can be closely linked in the MM microenvironment.
Cytokine signaling pathways and immune evasion mechanisms are central to the interactions between myeloma cells and their microenvironment. Key cytokines such as interleukin-6 (IL-6), TGF-β, and IL-10, produced by immune and stromal cells within the tumor microenvironment, play a critical role in promoting tumor growth and creating an immunosuppressive niche. IL-6 is particularly important in enhancing myeloma cell proliferation and survival, as well as supporting drug resistance [113,114]. TGF-β suppresses T-cell function, enables immune evasion, and facilitates tumor metastasis [109]. Subtyping of T-cells via scRNA-seq has further demonstrated a a significant increase in GZMK and TIGIT expressing exhausted CD8 T-cells in MM patients who rapidly progressed [32]. Whereas patients who did not progress had increased numbers of immature B cell subsets expressing IGLL1, SOX4, and DNTT [32]. Meanwhile, IL-10, a potent immunosuppressive cytokine, supports tumor progression by enhancing myeloma cell survival, dampening anti-tumor immune responses, and promoting resistance to therapy [115].

Immunotherapies

Considering these challenges, innovative therapeutic strategies have been developed to target the myeloma microenvironment, aiming to disrupt these supportive interactions or facilitate immune response. Chimeric antigen receptor T (CAR-T) cells (Figure 5B), engineered to target specific antigens on MM cells, such as B-cell maturation antigen (BCMA), CD38, or G-protein-coupled receptor family C group 5 member D (GPRC5D), have demonstrated significant efficacy in patients with relapsed or refractory MM [116,117]. Upon antigen recognition, CAR-T cells release cytotoxic molecules, including granzymes and perforins, leading to myeloma cell death and effective tumor elimination. However, the immunosuppressive nature of the myeloma microenvironment presents a substantial challenge to CAR-T cell durability and efficacy. T cells within this environment are often exposed to sustained antigen stimulation and inhibitory cytokines, leading to T-cell exhaustion, a state in which CAR-T cells lose functionality and fail to persist long-term in the patient. A single cell analysis of BCMA CAR-T cell therapy, identified increased numbers of CD8 T-cells and NK cells and decreased CD14+ monocytes in BCMA CAR-T cell therapy responders compared to non-responders with consistently higher expression of PIM kinases in the monocytes, DCs, and NKs from non-responders [34]. Furthermore, this analysis also revealed CD39 as a potential target either before or after BCMA CAR-T cell therapy [34]. Researchers are exploring alternatives to BCMA CAR-T cell therapy via alternative antigens (GPRC5D or CD38) [118] and the development of dual-targeted CAR-T cells that recognize multiple antigens, such as BCMA and CD19 [119]. In addition, a recent study demonstrated that armored BCMA CAR-T cells with dominant-negative TGF-β receptors resist TGF-β suppression, maintaining their cytotoxic function within the immunosuppressive myeloma microenvironment [120]. These strategies will allow provide clinicians more options so that they can more effectively select CAR-T cell therapies as the tumor adapts.
Bispecific antibodies have emerged as another potent tool for targeting myeloma cells using the microenvironment (Figure 5C), offering an “off-the-shelf” option that bypasses the need for cell modification [121]. These antibodies effectively bridge T cells and MM cells by simultaneously binding CD3 on T cells and specific antigens on MM cells, such as BCMA, CD38, Fc receptor-homolog 5 (FcRH5), or GPRC5D. This interaction activates T cells, prompting the release of granzymes and perforins that induce MM cell death. By leveraging the immune system, this therapeutic strategy enhances T cell-mediated cytotoxicity and disrupts the immunosuppressive tumor microenvironment. Teclistamab is a notable bispecific antibody targeting BCMA and CD3 that has demonstrated high response rates in clinical trials, particularly in patients with heavily pre-treated and refractory disease [122]. Unlike CAR-T cell therapies, which require patient-specific genetic modification, bispecific antibodies are more readily available and can be administered directly, providing an immediate and effective response. Studies have shown that bispecific antibodies not only enhance T-cell recruitment to the tumor site but also activate T cells directly within the bone marrow microenvironment, circumventing some of the suppressive elements that CAR-T cells encounter [121,123]. In a single cell multiomic study of BMCA BiTE treated patients T cell receptor (TCR) diversity decreased, there was an increase in a subset of exhausted T cell, and BCMA expression decreased in poor responders after treatment [21]. These advanced therapies aim to reshape the microenvironment, reinvigorating the immune response and overcoming the protective role that stromal and immune cells provide to myeloma cells. By targeting the interactions between the tumor and its microenvironment, these approaches offer the potential for more durable and effective treatments for MM.

Clinical Utility of Single Cell Technologies in the Myeloma

Research over the past decade has shown that providing effective clinical applications and precision medicine to patients with MM is significantly challenging because of the heterogeneity between patients and within tumors [124], as well as the dynamic clonal evolution characteristic of this disease [31]. Single-cell technologies like scDNA-seq, scRNA-seq, and emerging methods such as scATAC-seq and single-cell proteomics offer a superior approach for further studying the complexity and clinical treatment of MGUS, SMM, and MM (Figure 6A). In fact, the single cell resolution technique flow cytometry is already used to measure MRD [125]. The clinical translation of single-cell profiling is facilitated by its capacity to analyze the dynamics and heterogeneity of both tumor and immune cells, delve into the interactions within the tumor microenvironment, and detect treatment responses and drug resistance amid clonal evolution. The growing body of research and expanding datasets underscore the substantial potential of single-cell technologies to improve diagnosis, prognostication, and monitoring of treatment response and residual disease in MM patients.
Single-cell and multi-omics sequencing have greatly enhanced the ability to study spatial genetic heterogeneity within a single location and between distant tumor locations. Datasets of scRNA-seq have been utilized to investigate the dynamics of tumor heterogeneity throughout the progression of MM [12,15,16,26]. For example, scRNA-seq was used to examine the heterogeneity among 40 individuals across the progression spectrum of MM [15]. In the study, asymptomatic individuals with early-stage disease and those with minimal residual disease post-treatment detected rare tumor plasma cells exhibiting molecular characteristics akin to those of active myeloma [15], suggesting potential implications for personalized therapies. Unique B cell lineages can be annotated from scRNA-seq with seven continuous B lymphocyte lineages annotated in one study. MM affects the development of B cells and the heterogeneity of plasma cells that could be targeted via WNK1 [37].
Single-cell sequencing is also a potent tool to evaluate clonality (Figure 6B) and compositional and phenotypic shifts in the microenvironment (Figure 6C), enabling high-resolution mapping of dysregulation occurring between disease stages. Changes in immune cell composition between SMM, NDMM, and MM are associated with disease progression and potential mechanisms of immune evasion [39]. Additionally, myeloma development and progression in the microenvironment are influenced by both clonal competition and cooperation, occurring within the context of dynamic changes in both the cellular and noncellular components of the tumor microenvironment (Figure 6D). By integrating scRNA-seq with various genomic platforms the clonal evolution and the impact of the tumor microenvironment could be assessed using longitudinal samples from patients [19,21]. These same samples could be used to track plasma cell subpopulations at different stages of disease and to discover patient-specific plasma cell profiles and immune cell expression changes [31]. High-throughput single-cell DNA sequencing of circulating CD34+ cells from MM patients revealed clonal hematopoiesis of indeterminate potential (CHIP)-associated mutations in 50% of cases, with the most common mutations in TET2, EZH2, KIT, DNMT3A, and ASXL1 [40]. Theses longitudinal samples showed a selection of high-fitness mutant clones over time, indicating a trend toward suboptimal therapy responses in CHIP-positive patients [40].
MM therapeutics apply considerable selective pressure that influence the clonal evolution of myeloma cells. Single-cell profiling contributes to identifying low-frequency tumor clones in asymptomatic diseases, detecting drug-resistant clones during treatment and identifying signatures of treatment response and resistance. Using single cell profiling, the pattern of clonal evolution has been shown to mirror the response to antimyeloma therapy. While MM treatment eradicates sensitive subclones, it typically promotes the selection of drug-resistant subclones that were either initially present in the tumor mass or emerged during therapy [96]. Treatment resistance occurs either from selection of preexisting subclones, e.g., cells constituting MRD, or acquisition of new subclonal genetic changes [126]. Datasets of scRNA-seq have been applied to study drug resistance mechanisms in highly resistant MM patients. The prospective, multicenter, single-arm clinical trial (NCT04065789) combined longitudinal scRNA-seq and clinical data to study the molecular dynamics of MM resistance mechanisms. This study defined a roadmap for integrating scRNA-seq in clinical trials, identified a signature of highly resistant MM patients, and discovered peptidylprolyl isomerase A (PPIA) as a potent therapeutic target for these tumors [14]. Furthermore, scRNA-seq has been used for MM drug discovery. The computational pipeline “secDrugs” employs pharmacogenomics data to optimize and regularize a greedy algorithm for predicting new drugs targeting drug-resistant myeloma. The algorithm utilized scRNA-seq as a screening method to identify top combination drug candidates, focusing on the enrichment of target genes [27].
Single-cell profiling has proven effective as a transformative technology in research, but numerous challenges must be addressed to operationalize this technology in clinical settings (Figure 6E). First, the costs associated with single-cell assays are prohibitively high for widespread routine clinical use, including sequencing, specialized equipment, and reagents [127]. Second, like any new technology, single-cell technologies' reliability, reproducibility, and clinical relevance need rigorous validation. Third, the lack of standardized bioinformatic pipelines from sample preparation to data analysis leads to variability and makes it difficult to compare findings across studies and to develop standardized tests for clinical use [128]. Furthermore, the data generated from single-cell technologies are complex and require advanced computational tools and analysis expertise. The complexity of data analysis can be a significant barrier in clinical settings, which may not have the necessary computational infrastructure or bioinformatics support. Last, there would be many ethical and regulatory challenges [129], particularly concerning privacy and the management of the large amounts of genetic data generated, and a lengthy process for regulatory evaluation and approvement.

Discussion

This review highlights how single cell technologies have helped to unravel the complexities associated with MM (Table 1), particularly its clonal evolution, microenvironmental interactions, and clinical applications. The dynamic nature of clonal evolution and the substantial genetic and phenotypic heterogeneity present in MM underscore the challenges faced in diagnosing and treating the disease. Understanding the evolution of subclones, the selective pressures from therapeutic interventions, and the tumor microenvironment is crucial in determining effective personalized treatment approaches. Notably, genomic instability and mutations in key oncogenes like TP53, KRAS, and NRAS significantly contribute to treatment resistance, emphasizing the need for novel targeted therapies.
The myeloma microenvironment plays a pivotal role in disease progression and resistance by fostering immune evasion and providing structural and biochemical support for tumor growth. The involvement of immune cells, including exhausted T cells, TAMs, and regulatory B cells, creates an immunosuppressive environment that favors tumor survival. Targeting the myeloma microenvironment through innovative therapeutic approaches like CAR-T cell therapy and BiTEs offers promising avenues for overcoming these challenges and enhancing treatment efficacy.
The incorporation of single-cell technologies into the study of MM, such as scRNA-seq and single cell genomics, has provided new insights into the complexity of MM at the highest levels of granularity, enabling the dissection of intratumor heterogeneity and the identification of rare but clinically relevant cell populations. These technologies allow for a deeper understanding of clonal dynamics, interactions within the tumor microenvironment, and mechanisms of drug resistance, facilitating more precise identification of therapeutic targets. Despite the transformative potential of these technologies, the translation of these single-cell approaches into clinical practice is hindered by several challenges, including high costs, data complexity, the need for advanced bioinformatics, and ethical considerations.
To advance the field, future research should focus on addressing these barriers to the clinical implementation of single-cell technologies. Developing cost-effective methods, standardizing bioinformatic pipelines, and establishing ethical frameworks for data management are crucial steps. Additionally, combining multi-omics approaches with single-cell profiling could further enhance our understanding of MM and provide a comprehensive view of disease progression, which is essential for developing truly personalized treatment strategies. A collaborative effort between researchers, clinicians, and bioinformaticians will be necessary to overcome these obstacles and fully harness the potential of single-cell technologies to improve outcomes for MM patients.

References

  1. Lannes, R.; Samur, M.; Perrot, A.; Mazzotti, C.; Divoux, M.; Cazaubiel, T.; Leleu, X.; Schavgoulidze, A.; Chretien, M.-L.; Manier, S.; et al. In Multiple Myeloma, High-Risk Secondary Genetic Events Observed at Relapse Are Present From Diagnosis in Tiny, Undetectable Subclonal Populations. Journal of Clinical Oncology 2023, 41, 1695–1702. [Google Scholar] [CrossRef] [PubMed]
  2. Cowan, A.J.; Green, D.J.; Kwok, M.; Lee, S.; Coffey, D.G.; Holmberg, L.A.; Tuazon, S.; Gopal, A.K.; Libby, E.N. Diagnosis and Management of Multiple Myeloma: A Review. JAMA 2022, 327, 464–477. [Google Scholar] [CrossRef] [PubMed]
  3. Alexander, D.D.; Mink, P.J.; Adami, H.-O.; Cole, P.; Mandel, J.S.; Oken, M.M.; Trichopoulos, D. Multiple myeloma: A review of the epidemiologic literature. International Journal of Cancer 2007, 120, 40–61. [Google Scholar] [CrossRef]
  4. Bergsagel, P.L.; Kuehl, W.M. Chromosome translocations in multiple myeloma. Oncogene 2001, 20, 5611–5622. [Google Scholar] [CrossRef]
  5. Institute, N.C. Cancer Stat Facts: Myeloma. 2024.
  6. Nightingale, B.; Decker, M.; Ryan, R.; Kaczmarczyk, K.; Jandir, P.; Waykole, T.; Ashkar, R.; Harmon, G.; Mathur, A.; Levitt, M. Multiple Myeloma: A Review of the Literature and a Case Report Highlighting the Immunocompromised State of Myeloma Patients; 2024.
  7. Mohty, M.; Facon, T.; Malard, F.; Harousseau, J.L. A roadmap towards improving outcomes in multiple myeloma. Blood Cancer J 2024, 14, 135. [Google Scholar] [CrossRef]
  8. Dima, D.; Jiang, D.; Singh, D.J.; Hasipek, M.; Shah, H.S.; Ullah, F.; Khouri, J.; Maciejewski, J.P.; Jha, B.K. Multiple Myeloma Therapy: Emerging Trends and Challenges. Cancers (Basel) 2022, 14. [Google Scholar] [CrossRef]
  9. Gohil, S.H.; Iorgulescu, J.B.; Braun, D.A.; Keskin, D.B.; Livak, K.J. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nature Reviews Clinical Oncology 2021, 18, 244–256. [Google Scholar] [CrossRef]
  10. Ramsköld, D.; Luo, S.; Wang, Y.C.; Li, R.; Deng, Q.; Faridani, O.R.; Daniels, G.A.; Khrebtukova, I.; Loring, J.F.; Laurent, L.C.; et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 2012, 30, 777–782. [Google Scholar] [CrossRef]
  11. Picelli, S.; Faridani, O.R.; Björklund, A.K.; Winberg, G.; Sagasser, S.; Sandberg, R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014, 9, 171–181. [Google Scholar] [CrossRef]
  12. Jang, J.S.; Li, Y.; Mitra, A.K.; Bi, L.; Abyzov, A.; van Wijnen, A.J.; Baughn, L.B.; Van Ness, B.; Rajkumar, V.; Kumar, S.; et al. Molecular signatures of multiple myeloma progression through single cell RNA-Seq. Blood Cancer J 2019, 9, 2. [Google Scholar] [CrossRef]
  13. Melchor, L.; Brioli, A.; Wardell, C.P.; Murison, A.; Potter, N.E.; Kaiser, M.F.; Fryer, R.A.; Johnson, D.C.; Begum, D.B.; Hulkki Wilson, S.; et al. Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia 2014, 28, 1705–1715. [Google Scholar] [CrossRef] [PubMed]
  14. Cohen, Y.C.; Zada, M.; Wang, S.-Y.; Bornstein, C.; David, E.; Moshe, A.; Li, B.; Shlomi-Loubaton, S.; Gatt, M.E.; Gur, C.; et al. Identification of resistance pathways and therapeutic targets in relapsed multiple myeloma patients through single-cell sequencing. Nature Medicine 2021, 27, 491–503. [Google Scholar] [CrossRef] [PubMed]
  15. Ledergor, G.; Weiner, A.; Zada, M.; Wang, S.Y.; Cohen, Y.C.; Gatt, M.E.; Snir, N.; Magen, H.; Koren-Michowitz, M.; Herzog-Tzarfati, K.; et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat Med 2018, 24, 1867–1876. [Google Scholar] [CrossRef]
  16. Boiarsky, R.; Haradhvala, N.J.; Alberge, J.B.; Sklavenitis-Pistofidis, R.; Mouhieddine, T.H.; Zavidij, O.; Shih, M.C.; Firer, D.; Miller, M.; El-Khoury, H.; et al. Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nat Commun 2022, 13, 7040. [Google Scholar] [CrossRef]
  17. Cenzano, I.; Cócera, M.; Bantan, A.; Larrayoz, M.; Vilas-Zornoza, A.; San-Martin, P.; Aguirre-Ruiz, P.; Alignani, D.; Lopez, A.; Barrios, M.M.; et al. Transcriptional Remodeling of the Stromal and Endothelial Microenvironment in MGUS to Multiple Myeloma Progression. bioRxiv 2024. [Google Scholar] [CrossRef]
  18. Chen, M.; Wan, Y.; Li, X.; Xiang, J.; Chen, X.; Jiang, J.; Han, X.; Zhong, L.; Xiao, F.; Liu, J.; et al. Dynamic single-cell RNA-seq analysis reveals distinct tumor program associated with microenvironmental remodeling and drug sensitivity in multiple myeloma. Cell Biosci 2023, 13, 19. [Google Scholar] [CrossRef]
  19. Croucher, D.C.; Richards, L.M.; Tsofack, S.P.; Waller, D.; Li, Z.; Wei, E.N.; Huang, X.F.; Chesi, M.; Bergsagel, P.L.; Sebag, M.; et al. Longitudinal single-cell analysis of a myeloma mouse model identifies subclonal molecular programs associated with progression. Nature Communications 2021, 12, 6322. [Google Scholar] [CrossRef]
  20. Cui, J.; Li, X.; Deng, S.; Du, C.; Fan, H.; Yan, W.; Xu, J.; Li, X.; Yu, T.; Zhang, S.; et al. Identification of Therapy-Induced Clonal Evolution and Resistance Pathways in Minimal Residual Clones in Multiple Myeloma through Single-Cell Sequencing. Clin Cancer Res 2024, 30, 3919–3936. [Google Scholar] [CrossRef] [PubMed]
  21. Dang, M.; Lee, H.C.; Bansal, H.; Acevedo-Calado, M.J.; Qin, L.; Tan, W.; Moreno Rueda, L.Y.; Berrios, D.; Pasvolsky, O.; Gaballa, M.R.; et al. Multi-Omic Single-Cell Characterization of Paired and Serial Samples in Responding and Non-Responding Patients Receiving BCMA Bispecific Antibody Therapy for Multiple Myeloma. Blood 2024, 144, 3272–3272. [Google Scholar] [CrossRef]
  22. Dang, M.; Wang, R.; Lee, H.C.; Patel, K.K.; Becnel, M.R.; Han, G.; Thomas, S.K.; Hao, D.; Chu, Y.; Weber, D.M.; et al. Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease. Cancer Cell 2023, 41, 1032–1047. [Google Scholar] [CrossRef]
  23. de Jong, M.M.E.; Kellermayer, Z.; Papazian, N.; Tahri, S.; Hofste Op Bruinink, D.; Hoogenboezem, R.; Sanders, M.A.; van de Woestijne, P.C.; Bos, P.K.; Khandanpour, C.; et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat Immunol 2021, 22, 769–780. [Google Scholar] [CrossRef]
  24. He, H.; Li, Z.; Lu, J.; Qiang, W.; Jiang, S.; Xu, Y.; Fu, W.; Zhai, X.; Zhou, L.; Qian, M.; et al. Single-cell RNA-seq reveals clonal diversity and prognostic genes of relapsed multiple myeloma. Clin Transl Med 2022, 12, e757. [Google Scholar] [CrossRef] [PubMed]
  25. John, L.; Poos, A.M.; Brobeil, A.; Schinke, C.; Huhn, S.; Prokoph, N.; Lutz, R.; Wagner, B.; Zangari, M.; Tirier, S.M.; et al. Resolving the spatial architecture of myeloma and its microenvironment at the single-cell level. Nat Commun 2023, 14, 5011. [Google Scholar] [CrossRef] [PubMed]
  26. Johnson, T.S.; Sudha, P.; Liu, E.; Becker, N.; Robertson, S.; Blaney, P.; Morgan, G.; Chopra, V.S.; Dos Santos, C.; Nixon, M.; et al. 1q amplification and PHF19 expressing high-risk cells are associated with relapsed/refractory multiple myeloma. Nat Commun 2024, 15, 4144. [Google Scholar] [CrossRef] [PubMed]
  27. Kumar, H.; Mazumder, S.; Chakravarti, S.; Sharma, N.; Mukherjee, U.K.; Kumar, S.; Baughn, L.B.; Van Ness, B.G.; Mitra, A.K. secDrug: a pipeline to discover novel drug combinations to kill drug-resistant multiple myeloma cells using a greedy set cover algorithm and single-cell multi-omics. Blood Cancer Journal 2022, 12, 39. [Google Scholar] [CrossRef]
  28. Lannes, R.; Samur, M.; Perrot, A.; Mazzotti, C.; Divoux, M.; Cazaubiel, T.; Leleu, X.; Schavgoulidze, A.; Chretien, M.L.; Manier, S.; et al. In Multiple Myeloma, High-Risk Secondary Genetic Events Observed at Relapse Are Present From Diagnosis in Tiny, Undetectable Subclonal Populations. J Clin Oncol 2023, 41, 1695–1702. [Google Scholar] [CrossRef]
  29. Larrayoz, M.; Arriazu, E.; Zabaleta, A.; Roncal, C.; Tamayo, I.; Llopiz, D.; Jimenez, M.; Castro, C.; Celay, J.; Vicente, C. An IFNγ-Mediated Immune Inflamed Microenvironment State in Multiple Myeloma with TP53 Loss That Can be Therapeutically Exploited. Blood 2024, 144, 670. [Google Scholar] [CrossRef]
  30. Liang, Y.; He, H.; Wang, W.; Wang, H.; Mo, S.; Fu, R.; Liu, X.; Song, Q.; Xia, Z.; Wang, L. Malignant clonal evolution drives multiple myeloma cellular ecological diversity and microenvironment reprogramming. Mol Cancer 2022, 21, 182. [Google Scholar] [CrossRef]
  31. Liu, R.; Gao, Q.; Foltz, S.M.; Fowles, J.S.; Yao, L.; Wang, J.T.; Cao, S.; Sun, H.; Wendl, M.C.; Sethuraman, S.; et al. Co-evolution of tumor and immune cells during progression of multiple myeloma. Nature Communications 2021, 12, 2559. [Google Scholar] [CrossRef]
  32. Pilcher, W.; Thomas, B.E.; Bhasin, S.S.; Jayasinghe, R.G.; Yao, L.; Gonzalez-Kozlova, E.; Dasari, S.; Kim-Schulze, S.; Rahman, A.; Patton, J.; et al. Cross center single-cell RNA sequencing study of the immune microenvironment in rapid progressing multiple myeloma. NPJ Genom Med 2023, 8, 3. [Google Scholar] [CrossRef]
  33. Poos, A.M.; Prokoph, N.; Przybilla, M.J.; Mallm, J.P.; Steiger, S.; Seufert, I.; John, L.; Tirier, S.M.; Bauer, K.; Baumann, A.; et al. Resolving therapy resistance mechanisms in multiple myeloma by multiomics subclone analysis. Blood 2023, 142, 1633–1646. [Google Scholar] [CrossRef] [PubMed]
  34. Rade, M.; Grieb, N.; Weiss, R.; Sia, J.; Fischer, L.; Born, P.; Boldt, A.; Fricke, S.; Franz, P.; Scolnick, J.; et al. Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma. Nat Cancer 2024, 5, 1318–1333. [Google Scholar] [CrossRef]
  35. Tirier, S.M.; Mallm, J.P.; Steiger, S.; Poos, A.M.; Awwad, M.H.S.; Giesen, N.; Casiraghi, N.; Susak, H.; Bauer, K.; Baumann, A.; et al. Subclone-specific microenvironmental impact and drug response in refractory multiple myeloma revealed by single-cell transcriptomics. Nat Commun 2021, 12, 6960. [Google Scholar] [CrossRef] [PubMed]
  36. Verheye, E.; Kancheva, D.; Satilmis, H.; Vandewalle, N.; Fan, R.; Bardet, P.M.R.; Clappaert, E.J.; Verstaen, K.; De Becker, A.; Vanderkerken, K.; et al. A single-cell transcriptomic map of the murine and human multiple myeloma immune microenvironment across disease stages. J Hematol Oncol 2024, 17, 107. [Google Scholar] [CrossRef] [PubMed]
  37. Wang, Y.; Peng, Y.; Yang, C.; Xiong, D.; Wang, Z.; Peng, H.; Wu, X.; Xiao, X.; Liu, J. Single-cell sequencing analysis of multiple myeloma heterogeneity and identification of new theranostic targets. Cell Death Dis 2024, 15, 672. [Google Scholar] [CrossRef]
  38. Yao, L.; Wang, J.T.; Jayasinghe, R.G.; O'Neal, J.; Tsai, C.F.; Rettig, M.P.; Song, Y.; Liu, R.; Zhao, Y.; Ibrahim, O.M.; et al. Single-Cell Discovery and Multiomic Characterization of Therapeutic Targets in Multiple Myeloma. Cancer Res 2023, 83, 1214–1233. [Google Scholar] [CrossRef]
  39. Zavidij, O.; Haradhvala, N.J.; Mouhieddine, T.H.; Sklavenitis-Pistofidis, R.; Cai, S.; Reidy, M.; Rahmat, M.; Flaifel, A.; Ferland, B.; Su, N.K.; et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nature Cancer 2020, 1, 493–506. [Google Scholar] [CrossRef]
  40. Borsi, E.; Vigliotta, I.; Poletti, A.; Mazzocchetti, G.; Solli, V.; Zazzeroni, L.; Martello, M.; Armuzzi, S.; Taurisano, B.; Kanapari, A.; et al. Single-Cell DNA Sequencing Reveals an Evolutionary Pattern of CHIP in Transplant Eligible Multiple Myeloma Patients. Cells 2024, 13. [Google Scholar] [CrossRef]
  41. Koh, Y.; Park, C.; Cho, G.; Ryu, G.; Park, J.; Yoon, H.; Oh, Y.M.; Lee, C.; An, H.; Sun, C.-H. Impact of Clonal Hematopoiesis on the Carcinogenic Process of Multiple Myeloma. 2024.
  42. Avigan, J.I.; Gagler, D.C.; Ghamlouch, H.; Blaney, P.; Zhang, D.; Davies, F.E.; Morgan, G. Bone Marrow Fibroblasts at the Single-Cell Resolution Level in Multiple Myeloma. Blood 2024, 144, 4636–4636. [Google Scholar] [CrossRef]
  43. Botrugno, O.A.; Tonon, G. Genomic Instability and Replicative Stress in Multiple Myeloma: The Final Curtain? Cancers (Basel) 2021, 14. [Google Scholar] [CrossRef]
  44. Beksac, M.; Balli, S.; Akcora Yildiz, D. Drug Targeting of Genomic Instability in Multiple Myeloma. Front Genet 2020, 11, 228. [Google Scholar] [CrossRef] [PubMed]
  45. Rasche, L.; Schinke, C.; Maura, F.; Bauer, M.A.; Ashby, C.; Deshpande, S.; Poos, A.M.; Zangari, M.; Thanendrarajan, S.; Davies, F.E.; et al. The spatio-temporal evolution of multiple myeloma from baseline to relapse-refractory states. Nat Commun 2022, 13, 4517. [Google Scholar] [CrossRef] [PubMed]
  46. Popek-Marciniec, S.; Styk, W.; Wojcierowska-Litwin, M.; Chocholska, S.; Szudy-Szczyrek, A.; Samardakiewicz, M.; Swiderska-Kolacz, G.; Czerwik-Marcinkowska, J.; Zmorzynski, S. Association of Chromosome 17 Aneuploidy, TP53 Deletion, Expression and Its rs1042522 Variant with Multiple Myeloma Risk and Response to Thalidomide/Bortezomib Treatment. Cancers (Basel) 2023, 15. [Google Scholar] [CrossRef] [PubMed]
  47. Misund, K.; Hofste op Bruinink, D.; Coward, E.; Hoogenboezem, R.M.; Rustad, E.H.; Sanders, M.A.; Rye, M.; Sponaas, A.-M.; van der Holt, B.; Zweegman, S.; et al. Clonal evolution after treatment pressure in multiple myeloma: heterogenous genomic aberrations and transcriptomic convergence. Leukemia 2022, 36, 1887–1897. [Google Scholar] [CrossRef] [PubMed]
  48. Flynt, E.; Bisht, K.; Sridharan, V.; Ortiz, M.; Towfic, F.; Thakurta, A. Prognosis, Biology, and Targeting of TP53 Dysregulation in Multiple Myeloma. Cells 2020, 9. [Google Scholar] [CrossRef]
  49. Punke, A.P.; Waddell, J.A.; Solimando, D.A., Jr. Lenalidomide, Bortezomib, and Dexamethasone (RVD) Regimen for Multiple Myeloma. Hosp Pharm 2017, 52, 27–32. [Google Scholar] [CrossRef]
  50. Facon, T.; Lee, J.H.; Moreau, P.; Niesvizky, R.; Dimopoulos, M.; Hajek, R.; Pour, L.; Jurczyszyn, A.; Qiu, L.; Klippel, Z.; et al. Carfilzomib or bortezomib with melphalan-prednisone for transplant-ineligible patients with newly diagnosed multiple myeloma. Blood 2019, 133, 1953–1963. [Google Scholar] [CrossRef]
  51. Pawlyn, C.; Davies, F.; Cairns, D.; Striha, A.; Hockaday, A.; Kishore, B.; Garg, M.; Williams, C.; Karunanithi, K.; Lindsay, J.; et al. Quadruplet KCRD (Carfilzomib, Cyclophosphamide, Lenalidomide and Dexamethasone) Induction for Newly Diagnosed Myeloma Patients. Clinical Lymphoma, Myeloma and Leukemia 2019, 19, e2. [Google Scholar] [CrossRef]
  52. Venkatesh, P.; Bellman, P.; Shatnawi, S.; Shatnawi, Y.; Shaikh, H.; Strouse, C.S.; Atrash, S.; Hashmi, H.; Mushtaq, M.U.; Ahmed, N.; et al. VD-PACE As Salvage and Bridging Therapy to Transplant and Cellular Therapy in Triple Class Relapsed/Refractory Multiple Myeloma. Transplantation and Cellular Therapy, Official Publication of the American Society for Transplantation and Cellular Therapy 2024, 30, S389–S390. [Google Scholar] [CrossRef]
  53. Voorhees, P.M.; Sborov, D.W.; Laubach, J.; Kaufman, J.L.; Reeves, B.; Rodriguez, C.; Chari, A.; Silbermann, R.; Costa, L.J.; Anderson, L.D., Jr.; et al. Addition of daratumumab to lenalidomide, bortezomib, and dexamethasone for transplantation-eligible patients with newly diagnosed multiple myeloma (GRIFFIN): final analysis of an open-label, randomised, phase 2 trial. Lancet Haematol 2023, 10, e825–e837. [Google Scholar] [CrossRef]
  54. Jakubowiak, A.; Usmani, S.Z.; Krishnan, A.; Lonial, S.; Comenzo, R.L.; Wang, J.; de Boer, C.; Deraedt, W.; Weiss, B.M.; Schecter, J.M.; et al. Daratumumab Plus Carfilzomib, Lenalidomide, and Dexamethasone in Patients With Newly Diagnosed Multiple Myeloma. Clin Lymphoma Myeloma Leuk 2021, 21, 701–710. [Google Scholar] [CrossRef] [PubMed]
  55. Dingli, D.; Ailawadhi, S.; Bergsagel, P.L.; Buadi, F.K.; Dispenzieri, A.; Fonseca, R.; Gertz, M.A.; Gonsalves, W.I.; Hayman, S.R.; Kapoor, P.; et al. Therapy for Relapsed Multiple Myeloma: Guidelines From the Mayo Stratification for Myeloma and Risk-Adapted Therapy. Mayo Clinic Proceedings 2017, 92, 578–598. [Google Scholar] [CrossRef] [PubMed]
  56. Ciriello, G.; Magnani, L.; Aitken, S.J.; Akkari, L.; Behjati, S.; Hanahan, D.; Landau, D.A.; Lopez-Bigas, N.; Lupianez, D.G.; Marine, J.C.; et al. Cancer Evolution: A Multifaceted Affair. Cancer Discov 2024, 14, 36–48. [Google Scholar] [CrossRef]
  57. Yaccoby, S. The phenotypic plasticity of myeloma plasma cells as expressed by dedifferentiation into an immature, resilient, and apoptosis-resistant phenotype. Clin Cancer Res 2005, 11, 7599–7606. [Google Scholar] [CrossRef] [PubMed]
  58. Fonseca, R.; Blood, E.A.; Oken, M.M.; Kyle, R.A.; Dewald, G.W.; Bailey, R.J.; Van Wier, S.A.; Henderson, K.J.; Hoyer, J.D.; Harrington, D.; et al. Myeloma and the t(11;14)(q13;q32); evidence for a biologically defined unique subset of patients. Blood 2002, 99, 3735–3741. [Google Scholar] [CrossRef]
  59. Walker, B.A.; Wardell, C.P.; Johnson, D.C.; Kaiser, M.F.; Begum, D.B.; Dahir, N.B.; Ross, F.M.; Davies, F.E.; Gonzalez, D.; Morgan, G.J. Characterization of IGH locus breakpoints in multiple myeloma indicates a subset of translocations appear to occur in pregerminal center B cells. Blood 2013, 121, 3413–3419. [Google Scholar] [CrossRef]
  60. Sobh, A.; Encinas, E.; Patel, A.; Surapaneni, G.; Bonilla, E.; Kaestner, C.; Poullard, J.; Clerio, M.; Vasan, K.; Freeman, T.; et al. NSD2 drives t(4;14) myeloma cell dependence on adenylate kinase 2 by diverting one-carbon metabolism to the epigenome. Blood 2024, 144, 283–295. [Google Scholar] [CrossRef]
  61. Jiang, Y.; Zhang, C.; Lu, L.; Wang, X.; Liu, H.; Jiang, Y.; Hong, L.; Chen, Y.; Huang, H.; Guo, D. The Prognostic Role of Cyclin D1 in Multiple Myeloma: A Systematic Review and Meta-Analysis. Technol Cancer Res Treat 2022, 21, 15330338211065252. [Google Scholar] [CrossRef]
  62. Qiang, Y.W.; Ye, S.; Chen, Y.; Buros, A.F.; Edmonson, R.; van Rhee, F.; Barlogie, B.; Epstein, J.; Morgan, G.J.; Davies, F.E. MAF protein mediates innate resistance to proteasome inhibition therapy in multiple myeloma. Blood 2016, 128, 2919–2930. [Google Scholar] [CrossRef]
  63. Schavgoulidze, A.; Perrot, A.; Cazaubiel, T.; Leleu, X.; Montes, L.; Jacquet, C.; Belhadj, K.; Brechignac, S.; Frenzel, L.; Chalopin, T.; et al. Prognostic impact of translocation t(14;16) in multiple myeloma according to the presence of additional genetic lesions. Blood Cancer J 2023, 13, 160. [Google Scholar] [CrossRef]
  64. Cardona-Benavides, I.J.; de Ramon, C.; Gutierrez, N.C. Genetic Abnormalities in Multiple Myeloma: Prognostic and Therapeutic Implications. Cells 2021, 10. [Google Scholar] [CrossRef] [PubMed]
  65. Ross, F.M.; Chiecchio, L.; Dagrada, G.; Protheroe, R.K.; Stockley, D.M.; Harrison, C.J.; Cross, N.C.; Szubert, A.J.; Drayson, M.T.; Morgan, G.J.; et al. The t(14;20) is a poor prognostic factor in myeloma but is associated with long-term stable disease in monoclonal gammopathies of undetermined significance. Haematologica 2010, 95, 1221–1225. [Google Scholar] [CrossRef] [PubMed]
  66. Bal, S.; Kumar, S.K.; Fonseca, R.; Gay, F.; Hungria, V.T.; Dogan, A.; Costa, L.J. Multiple myeloma with t(11;14): unique biology and evolving landscape. Am J Cancer Res 2022, 12, 2950–2965. [Google Scholar] [PubMed]
  67. Caprio, C.; Sacco, A.; Giustini, V.; Roccaro, A.M. Epigenetic Aberrations in Multiple Myeloma. Cancers (Basel) 2020, 12. [Google Scholar] [CrossRef] [PubMed]
  68. Bolli, N.; Avet-Loiseau, H.; Wedge, D.C.; Van Loo, P.; Alexandrov, L.B.; Martincorena, I.; Dawson, K.J.; Iorio, F.; Nik-Zainal, S.; Bignell, G.R.; et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun 2014, 5, 2997. [Google Scholar] [CrossRef]
  69. Lohr, J.G.; Stojanov, P.; Carter, S.L.; Cruz-Gordillo, P.; Lawrence, M.S.; Auclair, D.; Sougnez, C.; Knoechel, B.; Gould, J.; Saksena, G.; et al. Widespread genetic heterogeneity in multiple myeloma: implications for targeted therapy. Cancer Cell 2014, 25, 91–101. [Google Scholar] [CrossRef]
  70. Forster, S.; Radpour, R.; Ochsenbein, A.F. Molecular and immunological mechanisms of clonal evolution in multiple myeloma. Front Immunol 2023, 14, 1243997. [Google Scholar] [CrossRef]
  71. Rasche, L.; Chavan, S.S.; Stephens, O.W.; Patel, P.H.; Tytarenko, R.; Ashby, C.; Bauer, M.; Stein, C.; Deshpande, S.; Wardell, C.; et al. Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 2017, 8, 268. [Google Scholar] [CrossRef]
  72. Walker, B.A.; Wardell, C.P.; Melchor, L.; Hulkki, S.; Potter, N.E.; Johnson, D.C.; Fenwick, K.; Kozarewa, I.; Gonzalez, D.; Lord, C.J.; et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4;14) and t(11;14) myeloma. Blood 2012, 120, 1077–1086. [Google Scholar] [CrossRef]
  73. Schavgoulidze, A.; Cazaubiel, T.; Perrot, A.; Avet-Loiseau, H.; Corre, J. Multiple Myeloma: Heterogeneous in Every Way. Cancers (Basel) 2021, 13. [Google Scholar] [CrossRef]
  74. Medina, A.; Puig, N.; Flores-Montero, J.; Jimenez, C.; Sarasquete, M.E.; Garcia-Alvarez, M.; Prieto-Conde, I.; Chillon, C.; Alcoceba, M.; Gutierrez, N.C.; et al. Comparison of next-generation sequencing (NGS) and next-generation flow (NGF) for minimal residual disease (MRD) assessment in multiple myeloma. Blood Cancer J 2020, 10, 108. [Google Scholar] [CrossRef] [PubMed]
  75. Ghermezi, M.; Li, M.; Vardanyan, S.; Harutyunyan, N.M.; Gottlieb, J.; Berenson, A.; Spektor, T.M.; Andreu-Vieyra, C.; Petraki, S.; Sanchez, E.; et al. Serum B-cell maturation antigen: a novel biomarker to predict outcomes for multiple myeloma patients. Haematologica 2017, 102, 785–795. [Google Scholar] [CrossRef]
  76. Kadam Amare, P.; Nikalje Khasnis, S.; Hande, P.; Lele, H.; Wable, N.; Kaskar, S.; Nikam Gujar, N.; Gardi, N.; Prabhudesai, A.; Todi, K.; et al. Cytogenetic Abnormalities in Multiple Myeloma: Incidence, Prognostic Significance, and Geographic Heterogeneity in Indian and Western Populations. Cytogenet Genome Res 2022, 162, 529–540. [Google Scholar] [CrossRef] [PubMed]
  77. Sonneveld, P.; Avet-Loiseau, H.; Lonial, S.; Usmani, S.; Siegel, D.; Anderson, K.C.; Chng, W.J.; Moreau, P.; Attal, M.; Kyle, R.A.; et al. Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group. Blood 2016, 127, 2955–2962. [Google Scholar] [CrossRef]
  78. Sanchez, E.; Li, M.; Kitto, A.; Li, J.; Wang, C.S.; Kirk, D.T.; Yellin, O.; Nichols, C.M.; Dreyer, M.P.; Ahles, C.P.; et al. Serum B-cell maturation antigen is elevated in multiple myeloma and correlates with disease status and survival. Br J Haematol 2012, 158, 727–738. [Google Scholar] [CrossRef]
  79. Avigan, Z.M.; Mitsiades, C.S.; Lagana, A. The role of 1q abnormalities in multiple myeloma: Genomic insights, clinical implications, and therapeutic challenges. Semin Hematol 2024. [Google Scholar] [CrossRef]
  80. Trasanidis, N.; Katsarou, A.; Ponnusamy, K.; Shen, Y.A.; Kostopoulos, I.V.; Bergonia, B.; Keren, K.; Reema, P.; Xiao, X.; Szydlo, R.M.; et al. Systems medicine dissection of chr1q-amp reveals a novel PBX1-FOXM1 axis for targeted therapy in multiple myeloma. Blood 2022, 139, 1939–1953. [Google Scholar] [CrossRef]
  81. Sudha, P.; Ahsan, A.; Ashby, C.; Kausar, T.; Khera, A.; Kazeroun, M.H.; Hsu, C.C.; Wang, L.; Fitzsimons, E.; Salminen, O.; et al. Myeloma Genome Project Panel is a Comprehensive Targeted Genomics Panel for Molecular Profiling of Patients with Multiple Myeloma. Clin Cancer Res 2022, 28, 2854–2864. [Google Scholar] [CrossRef]
  82. Merz, M.; Wei, L.; Hu, Q.; Merz, A.M.A.; Wang, J.; Belal, A.; Alberico, R.; Rondeau, C.; Celotto, K.; Block, A.W.; et al. Clinical Significance of Spatial Heterogeneity in Newly Diagnosed and Relapsed Multiple Myeloma. Blood 2021, 138, 1607–1607. [Google Scholar] [CrossRef]
  83. van de Donk, N. Sequencing multiple myeloma therapies with and after antibody therapies. Hematology Am Soc Hematol Educ Program 2020, 2020, 248–258. [Google Scholar] [CrossRef]
  84. Rasche, L.; Kortum, K.M.; Raab, M.S.; Weinhold, N. The Impact of Tumor Heterogeneity on Diagnostics and Novel Therapeutic Strategies in Multiple Myeloma. Int J Mol Sci 2019, 20. [Google Scholar] [CrossRef] [PubMed]
  85. Teo, W.Z.Y.; Ong, I.Y.E.; Tong, J.W.Y.; Ong, W.L.; Lin, A.; Song, F.; Tai, B.C.; Ooi, M.; Seokojo, C.Y.; Chen, Y.; et al. Response-Adapted Therapy for Newly Diagnosed Multiple Myeloma. Curr Hematol Malig Rep 2023, 18, 190–200. [Google Scholar] [CrossRef] [PubMed]
  86. Kumar, S.; Kaufman, J.L.; Gasparetto, C.; Mikhael, J.; Vij, R.; Pegourie, B.; Benboubker, L.; Facon, T.; Amiot, M.; Moreau, P.; et al. Efficacy of venetoclax as targeted therapy for relapsed/refractory t(11;14) multiple myeloma. Blood 2017, 130, 2401–2409. [Google Scholar] [CrossRef] [PubMed]
  87. Biancon, G.; Gimondi, S.; Vendramin, A.; Carniti, C.; Corradini, P. Noninvasive Molecular Monitoring in Multiple Myeloma Patients Using Cell-Free Tumor DNA: A Pilot Study. J Mol Diagn 2018, 20, 859–870. [Google Scholar] [CrossRef]
  88. Garces, J.J.; Cedena, M.T.; Puig, N.; Burgos, L.; Perez, J.J.; Cordon, L.; Flores-Montero, J.; Sanoja-Flores, L.; Calasanz, M.J.; Ortiol, A.; et al. Circulating Tumor Cells for the Staging of Patients With Newly Diagnosed Transplant-Eligible Multiple Myeloma. J Clin Oncol 2022, 40, 3151–3161. [Google Scholar] [CrossRef]
  89. Vrabel, D.; Sedlarikova, L.; Besse, L.; Rihova, L.; Bezdekova, R.; Almasi, M.; Kubaczkova, V.; Brozova, L.; Jarkovsky, J.; Plonkova, H.; et al. Dynamics of tumor-specific cfDNA in response to therapy in multiple myeloma patients. Eur J Haematol 2020, 104, 190–197. [Google Scholar] [CrossRef]
  90. Ferreira, B.; Caetano, J.; Barahona, F.; Lopes, R.; Carneiro, E.; Costa-Silva, B.; Joao, C. Liquid biopsies for multiple myeloma in a time of precision medicine. J Mol Med (Berl) 2020, 98, 513–525. [Google Scholar] [CrossRef]
  91. 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, 27. [Google Scholar] [CrossRef]
  92. Auclair, D. , et al., A Next Generation Liquid Biopsy Approach for Multiple Myeloma. Blood, 2020. 136(Supplement 1): p. 33-33.
  93. Yee, A.J. and N. Raje, Minimal residual disease in multiple myeloma: why, when, where. Hematology, 2021. 2021(1): p. 37-45.
  94. Kaur, G.; Jena, L.; Gupta, R.; Farswan, A.; Gupta, A.; Sriram, K. Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study. Transl Oncol 2022, 23, 101472. [Google Scholar] [CrossRef]
  95. Ansari-Pour, N.; Samur, M.; Flynt, E.; Gooding, S.; Towfic, F.; Stong, N.; Estevez, M.O.; Mavrommatis, K.; Walker, B.; Morgan, G.; et al. Whole-genome analysis identifies novel drivers and high-risk double-hit events in relapsed/refractory myeloma. Blood 2023, 141, 620–633. [Google Scholar] [CrossRef]
  96. Jones, J.R.; Weinhold, N.; Ashby, C.; Walker, B.A.; Wardell, C.; Pawlyn, C.; Rasche, L.; Melchor, L.; Cairns, D.A.; Gregory, W.M.; et al. Clonal evolution in myeloma: the impact of maintenance lenalidomide and depth of response on the genetics and sub-clonal structure of relapsed disease in uniformly treated newly diagnosed patients. Haematologica 2019, 104, 1440–1450. [Google Scholar] [CrossRef] [PubMed]
  97. Lee, L.X.; Li, S.C. Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective. World J Stem Cells 2020, 12, 706–720. [Google Scholar] [CrossRef] [PubMed]
  98. Sandmann, S.; Karsch, K.; Bartel, P.; Exeler, R.; Brix, T.J.; Mai, E.K.; Varghese, J.; Lenz, G.; Khandanpour, C. The Role of Clonal Evolution on Progression, Blood Parameters, and Response to Therapy in Multiple Myeloma. Front Oncol 2022, 12, 919278. [Google Scholar] [CrossRef] [PubMed]
  99. Guo, G.; Raje, N.S.; Seifer, C.; Kloeber, J.; Isenhart, R.; Ha, G.; Yee, A.J.; O'Donnell, E.K.; Tai, Y.T.; Richardson, P.G.; et al. Genomic discovery and clonal tracking in multiple myeloma by cell-free DNA sequencing. Leukemia 2018, 32, 1838–1841. [Google Scholar] [CrossRef]
  100. Salomon-Perzynski, A.; Jamroziak, K.; Glodkowska-Mrowka, E. Clonal Evolution of Multiple Myeloma-Clinical and Diagnostic Implications. Diagnostics (Basel) 2021, 11. [Google Scholar] [CrossRef]
  101. Keats, J.J.; Chesi, M.; Egan, J.B.; Garbitt, V.M.; Palmer, S.E.; Braggio, E.; Van Wier, S.; Blackburn, P.R.; Baker, A.S.; Dispenzieri, A.; et al. Clonal competition with alternating dominance in multiple myeloma. Blood 2012, 120, 1067–1076. [Google Scholar] [CrossRef]
  102. Dutta, A.K.; Fink, J.L.; Grady, J.P.; Morgan, G.J.; Mullighan, C.G.; To, L.B.; Hewett, D.R.; Zannettino, A.C.W. Subclonal evolution in disease progression from MGUS/SMM to multiple myeloma is characterised by clonal stability. Leukemia 2019, 33, 457–468. [Google Scholar] [CrossRef]
  103. Hitzler, J.K.; Martinez-Valdez, H.; Bergsage, D.B.; Minden, M.D.; Messner, H.A. Role of Interleukin-6 in the Proliferation of Human Multiple Myeloma Cell Lines OCI-My 1 to 7 Established From Patients With Advanced Stage of the Disease. Blood 1991, 78, 1996–2004. [Google Scholar] [CrossRef]
  104. Kawano, M.; Hirano, T.; Matsuda, T.; Taga, T.; Horii, Y.; Iwato, K.; Asaoku, H.; Tang, B.; Tanabe, O.; Tanaka, H.; et al. Autocrine generation and requirement of BSF-2/IL-6 for human multiple myelomas. Nature 1988, 332, 83–85. [Google Scholar] [CrossRef]
  105. Bjorklund, C.C.; Baladandayuthapani, V.; Lin, H.Y.; Jones, R.J.; Kuiatse, I.; Wang, H.; Yang, J.; Shah, J.J.; Thomas, S.K.; Wang, M.; et al. Evidence of a role for CD44 and cell adhesion in mediating resistance to lenalidomide in multiple myeloma: therapeutic implications. Leukemia 2014, 28, 373–383. [Google Scholar] [CrossRef]
  106. Swamydas, M.; Murphy, E.V.; Ignatz-Hoover, J.J.; Malek, E.; Driscoll, J.J. Deciphering mechanisms of immune escape to inform immunotherapeutic strategies in multiple myeloma. Journal of Hematology & Oncology 2022, 15, 17. [Google Scholar] [CrossRef]
  107. Lopes, R.; Caetano, J.; Ferreira, B.; Barahona, F.; Carneiro, E.A.; João, C. The Immune Microenvironment in Multiple Myeloma: Friend or Foe? Cancers 2021, 13, 625. [Google Scholar] [CrossRef] [PubMed]
  108. Lu, K.; Wang, W.; Liu, Y.; Xie, C.; Liu, J.; Xing, L. Advancements in microenvironment-based therapies: transforming the landscape of multiple myeloma treatment. Front Oncol 2024, 14, 1413494. [Google Scholar] [CrossRef] [PubMed]
  109. Wang, C.; Wang, W.; Wang, M.; Deng, J.; Sun, C.; Hu, Y.; Luo, S. Different evasion strategies in multiple myeloma. Front Immunol 2024, 15, 1346211. [Google Scholar] [CrossRef]
  110. Bhowmick, K.; von Suskil, M.; Al-Odat, O.S.; Elbezanti, W.O.; Jonnalagadda, S.C.; Budak-Alpdogan, T.; Pandey, M.K. Pathways to therapy resistance: The sheltering effect of the bone marrow microenvironment to multiple myeloma cells. Heliyon 2024, 10, e33091. [Google Scholar] [CrossRef]
  111. Edwards, C.M.; Zhuang, J.; Mundy, G.R. The pathogenesis of the bone disease of multiple myeloma. Bone 2008, 42, 1007–1013. [Google Scholar] [CrossRef]
  112. Kumar, S.; Witzig, T.E.; Timm, M.; Haug, J.; Wellik, L.; Fonseca, R.; Greipp, P.R.; Rajkumar, S.V. Expression of VEGF and its receptors by myeloma cells. Leukemia 2003, 17, 2025–2031. [Google Scholar] [CrossRef]
  113. Musolino, C.; Allegra, A.; Innao, V.; Allegra, A.G.; Pioggia, G.; Gangemi, S. Inflammatory and Anti-Inflammatory Equilibrium, Proliferative and Antiproliferative Balance: The Role of Cytokines in Multiple Myeloma. Mediators of Inflammation 2017, 2017, 1852517. [Google Scholar] [CrossRef]
  114. Brown, C.O.; Salem, K.; Wagner, B.A.; Bera, S.; Singh, N.; Tiwari, A.; Choudhury, A.; Buettner, G.R.; Goel, A. Interleukin-6 counteracts therapy-induced cellular oxidative stress in multiple myeloma by up-regulating manganese superoxide dismutase. Biochem J 2012, 444, 515–527. [Google Scholar] [CrossRef]
  115. Liu, C.-D.; Chang, C.-C.; Huang, W.-H. The perspectives of interleukin-10 in the pathogenesis and therapeutics of multiple myeloma. Tzu Chi Medical Journal 2021, 33, 257–262. [Google Scholar] [CrossRef]
  116. Zhang, X.; Zhang, H.; Lan, H.; Wu, J.; Xiao, Y. CAR-T cell therapy in multiple myeloma: Current limitations and potential strategies. Front Immunol 2023, 14, 1101495. [Google Scholar] [CrossRef] [PubMed]
  117. van de Donk, N.W.C.J.; Themeli, M.; Usmani, S.Z. Determinants of Response and Mechanisms of Resistance of CAR T-cell Therapy in Multiple Myeloma. Blood Cancer Discovery 2021, 2, 302–318. [Google Scholar] [CrossRef] [PubMed]
  118. Sheykhhasan, M.; Ahmadieh-Yazdi, A.; Vicidomini, R.; Poondla, N.; Tanzadehpanah, H.; Dirbaziyan, A.; Mahaki, H.; Manoochehri, H.; Kalhor, N.; Dama, P. CAR T therapies in multiple myeloma: unleashing the future. Cancer Gene Ther 2024, 31, 667–686. [Google Scholar] [CrossRef] [PubMed]
  119. Yang, J.; Guo, H.; Han, L.; Song, Y.; Zhou, K. Dual-targeted CAR T-cell immunotherapies for hematological malignancies: latest updates from the 2023 ASH annual meeting. Exp Hematol Oncol 2024, 13, 25. [Google Scholar] [CrossRef]
  120. Alabanza, L.M.; Xiong, Y.; Vu, B.; Webster, B.; Wu, D.; Hu, P.; Zhu, Z.; Dropulic, B.; Dash, P.; Schneider, D. Armored BCMA CAR T Cells Eliminate Multiple Myeloma and Are Resistant to the Suppressive Effects of TGF-β. Front Immunol 2022, 13, 832645. [Google Scholar] [CrossRef]
  121. Devasia, A.J.; Chari, A.; Lancman, G. Bispecific antibodies in the treatment of multiple myeloma. Blood Cancer Journal 2024, 14, 158. [Google Scholar] [CrossRef]
  122. Cipkar, C.; Chen, C.; Trudel, S. Antibodies and bispecifics for multiple myeloma: effective effector therapy. Hematology 2022, 2022, 163–172. [Google Scholar] [CrossRef]
  123. Letouzé, E.; Moreau, P.; Munshi, N.; Samur, M.; Minvielle, S.; Touzeau, C. Mechanisms of resistance to bispecific T-cell engagers in multiple myeloma and their clinical implications. Blood Advances 2024, 8, 2952–2959. [Google Scholar] [CrossRef]
  124. Ledergor, G.; Weiner, A.; Zada, M.; Wang, S.-Y.; Cohen, Y.C.; Gatt, M.E.; Snir, N.; Magen, H.; Koren-Michowitz, M.; Herzog-Tzarfati, K.; et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nature Medicine 2018, 24, 1867–1876. [Google Scholar] [CrossRef]
  125. Flanders, A.; Stetler-Stevenson, M.; Landgren, O. Minimal residual disease testing in multiple myeloma by flow cytometry: major heterogeneity. Blood 2013, 122, 1088–1089. [Google Scholar] [CrossRef]
  126. Corre, J.; Cleynen, A.; Robiou du Pont, S.; Buisson, L.; Bolli, N.; Attal, M.; Munshi, N.; Avet-Loiseau, H. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia 2018, 32, 2636–2647. [Google Scholar] [CrossRef] [PubMed]
  127. Baysoy, A.; Bai, Z.; Satija, R.; Fan, R. The technological landscape and applications of single-cell multi-omics. Nature Reviews Molecular Cell Biology 2023, 24, 695–713. [Google Scholar] [CrossRef] [PubMed]
  128. Heumos, L.; Schaar, A.C.; Lance, C.; Litinetskaya, A.; Drost, F.; Zappia, L.; Lücken, M.D.; Strobl, D.C.; Henao, J.; Curion, F.; et al. Best practices for single-cell analysis across modalities. Nature Reviews Genetics 2023, 24, 550–572. [Google Scholar] [CrossRef] [PubMed]
  129. Kourelis, T.; Bansal, R.; Berdeja, J.; Siegel, D.; Patel, K.; Mailankody, S.; Htut, M.; Shah, N.; Wong, S.W.; Sidana, S.; et al. Ethical Challenges with Multiple Myeloma BCMA Chimeric Antigen Receptor T Cell Slot Allocation: A Multi-Institution Experience. Transplant Cell Ther 2023, 29, 255–258. [Google Scholar] [CrossRef]
Preprints 146060 g001
Figure 1. Different types of single cell sequencing technologies that have been used for multiple myeloma research. Developments in single-omics such as transcriptomics (ex: 10x Chromium and SMART-seq2) and proteomics (ex: antibody-based vs mass-spectrometry based) have led to integrative analyses in multi-omics, which combine multiple categories of single cell technologies in methods such as CITE-seq and RAID.
Figure 1. Different types of single cell sequencing technologies that have been used for multiple myeloma research. Developments in single-omics such as transcriptomics (ex: 10x Chromium and SMART-seq2) and proteomics (ex: antibody-based vs mass-spectrometry based) have led to integrative analyses in multi-omics, which combine multiple categories of single cell technologies in methods such as CITE-seq and RAID.
Preprints 146060 g001
Preprints 146060 g002
Figure 2. Representation of subclonal diversity and the role of TP53 mutations in multiple myeloma progression and treatment resistance.
Figure 2. Representation of subclonal diversity and the role of TP53 mutations in multiple myeloma progression and treatment resistance.
Preprints 146060 g002
Preprints 146060 g003
Figure 3. Stem Cell Differentiation and Pathogenesis in Multiple Myeloma. Normal tissue: Stem cells differentiate into B lymphocytes, which further mature into plasma cells capable of producing normal antibodies, ensuring a functional immune response. Multiple myeloma: Genotoxic stress or other factors lead to damage in B lymphocytes. This damage disrupts normal differentiation, resulting in the formation of malignant myeloma cells that produce abnormal antibodies. These cells propagate within the bone marrow, contributing to the progression of MM.
Figure 3. Stem Cell Differentiation and Pathogenesis in Multiple Myeloma. Normal tissue: Stem cells differentiate into B lymphocytes, which further mature into plasma cells capable of producing normal antibodies, ensuring a functional immune response. Multiple myeloma: Genotoxic stress or other factors lead to damage in B lymphocytes. This damage disrupts normal differentiation, resulting in the formation of malignant myeloma cells that produce abnormal antibodies. These cells propagate within the bone marrow, contributing to the progression of MM.
Preprints 146060 g003
Preprints 146060 g004
Figure 4. Liquid Biopsy in Multiple Myeloma for Personalized Treatment Strategies. A) Different patients with MM represent the variability in disease presentation and progression. B) Liquid biopsy enables non-invasive sampling and analysis of blood for circulating myeloma cells, genetic and epigenetic alterations, such as amplifications, deletions, chromosomal abnormalities, mutations, and translocations. It includes studies on protein expression and phosphorylation as well as in vitro culture for functional assessments. C) Insights from liquid biopsy guide the development of personalized treatment strategies tailored to the molecular and cellular characteristics of each patient's disease, improving therapeutic outcomes.
Figure 4. Liquid Biopsy in Multiple Myeloma for Personalized Treatment Strategies. A) Different patients with MM represent the variability in disease presentation and progression. B) Liquid biopsy enables non-invasive sampling and analysis of blood for circulating myeloma cells, genetic and epigenetic alterations, such as amplifications, deletions, chromosomal abnormalities, mutations, and translocations. It includes studies on protein expression and phosphorylation as well as in vitro culture for functional assessments. C) Insights from liquid biopsy guide the development of personalized treatment strategies tailored to the molecular and cellular characteristics of each patient's disease, improving therapeutic outcomes.
Preprints 146060 g004
Preprints 146060 g005
Figure 5. MM Microenvironment and Immunotherapeutic Strategies. A). Immune and stromal cells in MM microenvironment: The MM microenvironment includes T cell exhaustion (accompanied by decreased CD4+ T cells), increased regulatory T cells (Tregs), immunosuppressive regulatory B cells (Bregs), M2-polarized macrophages, and dysfunctional dendritic cells. Cancer-associated fibroblasts (CAFs) support tumor growth, myeloma cells stimulate osteoclast activity, and VEGF secreted by myeloma cells (and other microenvironmental cells) drives abnormal blood vessel formation. B). CAR-T cell therapy: CAR-T cells target MM antigens (e.g., BCMA, CD38, GPRC5D), releasing granzymes and perforins to induce MM cell death. C). Bispecific antibody therapy: Bispecific antibodies link T cells to MM cells by binding CD3 on T cells and MM antigens (e.g., BCMA, CD38, FCRH5, GPRC5D), activating T cells to kill MM cells. Created in https://BioRender.com.
Figure 5. MM Microenvironment and Immunotherapeutic Strategies. A). Immune and stromal cells in MM microenvironment: The MM microenvironment includes T cell exhaustion (accompanied by decreased CD4+ T cells), increased regulatory T cells (Tregs), immunosuppressive regulatory B cells (Bregs), M2-polarized macrophages, and dysfunctional dendritic cells. Cancer-associated fibroblasts (CAFs) support tumor growth, myeloma cells stimulate osteoclast activity, and VEGF secreted by myeloma cells (and other microenvironmental cells) drives abnormal blood vessel formation. B). CAR-T cell therapy: CAR-T cells target MM antigens (e.g., BCMA, CD38, GPRC5D), releasing granzymes and perforins to induce MM cell death. C). Bispecific antibody therapy: Bispecific antibodies link T cells to MM cells by binding CD3 on T cells and MM antigens (e.g., BCMA, CD38, FCRH5, GPRC5D), activating T cells to kill MM cells. Created in https://BioRender.com.
Preprints 146060 g005
Preprints 146060 g006
Figure 6. Single-cell technologies and their clinical application. A) Single-cell technologies like scDNA-seq, scRNA-seq, and emerging methods such as scATAC-seq and single-cell proteomics contribute to studying the complexity and potential clinical treatment of MGUS, SMM, and MM. B) The scRNA-seq can be used to identify the dynamics and heterogeneity of both tumor and immune cells by cell-type clustering and expression signature identification. C) Single-cell technologies enable in-depth studies of cellular interactions within the tumor microenvironment. D) Using scDNA-seq can also help to detect treatment responses and drug resistance amid clonal evolution. E) Such early-stage clinical application of single-cell profiling facilitates its further clinical translation of single-cell technologies includes diagnosis, treatment decision, prognostication, and monitoring of treatment response and residual disease in MM patients.
Figure 6. Single-cell technologies and their clinical application. A) Single-cell technologies like scDNA-seq, scRNA-seq, and emerging methods such as scATAC-seq and single-cell proteomics contribute to studying the complexity and potential clinical treatment of MGUS, SMM, and MM. B) The scRNA-seq can be used to identify the dynamics and heterogeneity of both tumor and immune cells by cell-type clustering and expression signature identification. C) Single-cell technologies enable in-depth studies of cellular interactions within the tumor microenvironment. D) Using scDNA-seq can also help to detect treatment responses and drug resistance amid clonal evolution. E) Such early-stage clinical application of single-cell profiling facilitates its further clinical translation of single-cell technologies includes diagnosis, treatment decision, prognostication, and monitoring of treatment response and residual disease in MM patients.
Preprints 146060 g006
Table 1. List of relevant single cell studies used to inform this review. Note that parentheses contain what type of tagging was performed (5’: 5 prime tagging, 3’: 3 prime tagging, NA: not recorded), a “+” denotes multiomics on the same cell, and an “and” denotes multiomics on different cells potentially from the same sample.
Table 1. List of relevant single cell studies used to inform this review. Note that parentheses contain what type of tagging was performed (5’: 5 prime tagging, 3’: 3 prime tagging, NA: not recorded), a “+” denotes multiomics on the same cell, and an “and” denotes multiomics on different cells potentially from the same sample.
Author Date Platform DOI REF
Melchor et al. 8/28/2014 Fluidigm multiplex qPCR 10.1038/leu.2014.13 [13]
Ledergor et al. 12/24/2018 MARS-seq 10.1038/s41591-018-0269-2 [15]
Jang et al. 1/3/2019 Fluidigm C1 + MAP-RSeq 10.1038/s41408-018-0160-x [12]
Zavidij et al. 4/27/2020 10X Genomics RNA (3’) 10.1038/s43018-020-0053-3 [39]
Cohen et al. 2/22/2021 MARS-seq 10.1038/s41591-021-01232-w [14]
Liu et al. 5/7/2021 10X Genomics RNA (3’ and 5’) 10.1038/s41467-021-22804-x [31]
de Jong et al. 5/20/2021 10X Genomics RNA (3’) 10.1038/s41590-021-00931-3 [23]
Croucher et al. 11/3/2021 10X Genomics RNA (NA) 10.1038/s41467-021-26598-w [19]
Tirier et al. 11/19/2021 10X Genomics RNA (3’) 10.1038/s41467-021-26951-z [35]
Kumar et al. 3/9/2022 10X Genomics RNA (NA) 10.1038/s41408-022-00636-2 [27]
He et al. 3/12/2022 10X Genomics RNA (5’) + V(D)J 10.1002/ctm2.757 [24]
Liang et al. 9/22/2022 10X Genomics RNA (3’) 10.1186/s12943-022-01648-z [30]
Boiarsky et al. 11/17/2022 10X Genomics RNA (3’) 10.1038/s41467-022-33944-z [16]
Pilcher et al. 1/26/2023 10X Genomics RNA (3’) and CITE-seq 10.1038/s41525-022-00340-x [32]
Chen et al. 1/30/2023 10X Genomics RNA (3’) 10.1186/s13578-023-00971-2 [18]
Lannes et al. 3/20/2023 10X Genomics DNA (NA) 10.1200/JCO.21.01987 [28]
Yao et al. 4/14/2023 10X Genomics RNA (3’) 10.1158/0008-5472.CAN-22-1769 [38]
John et al. 8/17/2023 10X Genomics RNA (5’) + V(D)J and 10X Genomics ATAC 10.1038/s41467-023-40584-4 [25]
Dang et al. 6/12/202311/5/2024 10X Genomics RNA (5’) + V(D)J (TCR or BCR) 10.1182/blood-2024-21008010.1016/j.ccell.2023.05.007 [21][22]
Poos et al. 11/9/2023 10X Genomics RNA (3’) and 10X Genomics ATAC 10.1182/blood.2023019758 [33]
Borsi et al. 4/9/2024 Mission Bio Tapestry DNA 10.3390/cells13080657 [40]
Rade et al. 4/19/2024 10X Genomics RNA (5’) + V(D)J (TCR) + V(D)J (BCR) + Protein (Ab) 10.1038/s43018-024-00763-8 [34]
Cenzano et al. 4/24/2024 10X Genomics RNA (3’) 10.1101/2024.04.24.589777 [17]
Johnson et al. 5/16/2024 10X Genomics RNA (NA) + ATAC 10.1038/s41467-024-48327-9 [26]
Koh et al. 7/18/2024 Mission Bio Tapestry DNA + Protein 10.21203/rs.3.rs-4672454/v1 [41]
Cui et al. 9/3/2024 10X Genomics RNA (3’) 10.1158/1078-0432.CCR-24-0545 [20]
Wang et al. 9/14/2024 10X Genomics RNA (3’) 10.1038/s41419-024-07027-4 [37]
Larrayoz et al. 11/5/2024 10X Genomics RNA (NA) and V(D)J (TCR) 10.1182/blood-2024-194041 [29]
Avigan et al. 11/5/2024 scRNA-seq 10.1182/blood-2024-194277 [42]
Verheye et al. 11/7/2024 10X Genomics RNA (3’) 10.1186/s13045-024-01629-3 [36]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
bsky
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

Privacy Settings

© 2025 MDPI (Basel, Switzerland) unless otherwise stated