Множественные модели миэломы и витро человека для моделирования заболеваний и скрининга на лечение.

Introduction

Multiple myeloma (MM) is a blood cancer that develops in the bone marrow (BM), characterized by the uncontrolled proliferation of plasma cells over producing monoclonal immunoglobulin termed “M” proteins (Fig. 1 ) [ 1 ]. It is the second leading hematological malignancy in the United States. In 1844, London surgeon Dr. Samuel Solly made the first identification and description of MM [ 2 ]. In later years, M protein has been used as a prevalent biomarker to stage MM of either Monoclonal Gammopathy of Undetermined Significance (MGUS), smoldering multiple myeloma (SMM) to overt MM and in rare cases, extramedullary disease (EMD) and/or plasma cell leukemia (PCL) (Fig. 1 ) [ 3 ]. MM often progresses gradually, with initial symptoms including bone pain, fatigue, and frequent infections [ 1 ]. As the disease advances, it can lead to hypercalcemia, renal dysfunction, anemia, and bone lesions, collectively known as CRAB symptoms [ 4 ]. The Revised International Staging System (R-ISS), established by the International Myeloma Working Group (IMWG), can be used for identification of high-risk patients with adverse prognosis: which primarily relies on four factors: beta-2-microglobulin, albumin, lactate dehydrogenase, and genetic abnormalities in myeloma cells [ 4 – 6 ]. Fig. 1 Etiology and treatment of multiple myeloma (MM). MM is a malignancy of plasma cells, arising through a multistep process involving genetic mutations, chromosomal translocations, and risk factors such as age, family history, obesity, and environmental exposures. Most cases develop from precursor conditions like monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM), which progress to symptomatic MM at variable rates. Malignant plasma cells proliferate in the bone marrow (BM), produce abnormal antibodies or M proteins, and cause bone destruction, kidney dysfunction, and immune suppression. Treatment is tailored to disease stage and patient eligibility, and typically includes combinations of proteasome inhibitors, immunomodulatory drugs, steroids, and monoclonal antibodies. High-dose chemotherapy followed by autologous stem cell transplant is standard for eligible patients, while anti-B-cell maturation antigen (BCMA) chimeric antigen receptor (CAR) T-cell therapy, radiation, and supportive care are used for refractory or advanced disease. Created in BioRender. MA, C. (2025) https://BioRender.com/tgjbk7n

Genetic mutations are present in most patients, predominantly at chromosomal level. These often involve multiple translocations that position oncogene adjacent to immunoglobulin heavy or light chain promoters. The frequency of these abnormalities is closely associated with increasing age, suggesting they can arise through numerous cell divisions accumulated over a patient’s lifetime (Fig. 1 ) [ 7 – 9 ]. A subset of chromosomal abnormalities indicates poor outcomes. These include but not limit to chromosomal translocations such as t(4;14), t(14;16), and t(14;20), gain of three or more copies chromosome band 1q21 (1q21 + ), deletion of chromosome arm 1p (del(1p)), and deletion of chromosome band 17p13 (del(17p)) [ 10 – 15 ]. Studies from several different groups show that the prevalence of immunoglobulin heavy chain (IgH) variable (V), diversity (D) and joining (J) (V(D)J) rearrangements increase with disease stage: about 50% in MGUS or SMM, 55–70% for intramedullary MM, 85% in primary PCL and > 90% in human myeloma cell lines [ 16 – 20 ]. In addition to the chromosomal abnormalities, the genes most frequently mutated in multiple myeloma patients include Kirsten rat sarcoma viral oncogene homolog (KRAS) (20–36%, RAS/mitogen-activated protein kinase (MAPK) pathway), neuroblastoma rat sarcoma viral oncogene homolog (NRAS) (20–25%, RAS pathway), tumor protein p53 (TP53) (8–26%, tumor suppressor associated with poor prognosis), DIS3 Homolog (DIS3) (11–16%, RNA exonuclease), family with sequence similarity 46 member C (FAM46C) (11–12%, potential driver gene), B-Raf proto-oncogene, serine/threonine kinase (BRAF) (6–15%, RAS pathway and therapeutic target), tumor necrosis factor receptor Associated Factor 3 (TRAF3) (3–6%, NF-κB signaling), and early growth response (EGR1) (4–6%, possible role in drug resistance) [ 21 – 24 ].

Recent approaches in targeted treatments include proteasome inhibitors (PIs) which act upon the cancer cell protein recycling mechanisms, immunomodulatory drugs (IMiDs) that can amplify immune response, monoclonal antibodies to directly induce apoptosis, and other immunotherapy treatments such as chimeric antigen receptor (CAR) T-cell therapy that targets MM specific markers such as B-cell maturation antigen (BCMA) [ 25 – 34 ]. A five-year survival rate of about 51.9% remains according to patient survival data from recent decades, but with optimal treatment, patients may live more than a decade [ 35 – 38 ]. Contributing to this variation of survivability is likely due to the lack of clinical measurable biomarkers for response to these different treatments [ 39 ].

To increase our understanding of clinical failure and therapeutic potential of novel treatments, MM models ranging from monoculture to computational prediction have evolved [ 40 – 42 ]. These models vary in complexity and recapitulation of the human disease, each serving unique research purposes. In vitro in suspension culture models utilizing human myeloma cell lines offer a foundational platform for high-throughput drug screening and identifying initial leads for further investigation [ 40 ], yet they fail to represent the in vivo conditions such as tissue microenvironment. Current mouse models for multiple myeloma have been essential for the generation of current knowledge regarding MM pathogenesis; however, each have significant limitations that prevent them from fully recapitulating human disease [ 43 – 45 ]. Genetically engineered mouse models capture genetic diversity of MM but may not represent the complete humanized tumor microenvironment, while immunocompetent models preserve immune interactions but rely on mouse-specific biology that differs from human responses [ 46 – 49 ]. Xenograft models demonstrate disease progression and bone marrow homing but lack functional human immune systems, limiting their utility for studying immunotherapy and immune surveillance mechanisms in MM pathogenesis [ 50 ]. To better mimic the bone marrow microenvironment (BMM), researchers have employed techniques such as transwell, microfluidic, and scaffold co-culture systems along many other types of 3D model systems [ 51 – 53 ]. These in vitro co-culture systems allow for studying myeloma-stroma interactions, drug resistance mechanisms, and assessing potential therapeutic regimens. Yet, these conventional 3D systems fail to represent together immunological aspects [ 54 ] and other important tumor microenvironment (TME) factors such as vascularization and perfusion along with shear compression, which are critical in MM progression and treatment resistance [ 46 , 53 , 55 ]. For instance, one factor that influences MM disease progression is vascularization as research suggests inhibition of junction adhesion molecular A (JAM-A), a molecule that regulates endothelial cell junctions and vascular permeability, impaired MM tumor progression [ 56 ]. There is also emerging evidence that shear compression elicits homophilic interactions of MM cells by CD44-mediated surface binding under physiological shear stress and generates cell clusters which exhibited specific therapeutic resistances to PIs that may later develop into EMD [ 57 ].

In parallel, computational methods are evolving to bridge the gap between patient therapies and their responses to offer powerful tools for analyzing large-scale genomic, proteomic, and clinical datasets [ 58 , 59 ]. Machine learning and artificial intelligence algorithms can be used to predict patient survival, identify novel disease subtypes, and predict treatment responses [ 60 , 61 ]. For example, a machine learning algorithm was applied to MM clinical and RNAseq data and survival predictions were made for patient ex vivo samples with consideration of the first line of treatment [ 60 ]. In another example, a synergy augmented model (SAM) captured interactions between drugs and ex vivo samples to translate combination effects to clinically relevant time varying concentrations modeled from pharmacokinetic data [ 61 ].

To better replicate a realistic tumor niche for drug testing and personalized therapy, models need to incorporate not just partial immune cell population, but a holistic niche with incorporation of immune, stromal and vascular cells with careful attention to the bone marrow reconstruction with artificial materials that may or may not alter microenvironment [ 62 , 63 ]. For tailored patient specific outcomes, these models must also be able to culture patient samples to account for individual response for applications like drug screening [ 53 ]. Therefore, this review aims to summarize the state of art of current models being used for the MM research and their progress in drug testing where we highlight the need for a humanized immunocompetent model for disease modeling and therapy screening. Finally, we discuss the key challenges and future directions of these models. This review also aims to guide newcomers to MM research in developing or selecting appropriate models to address the biological questions they are interested in.

In suspension cell culture models

In suspension models (Fig. 2 ) for MM research continue to play a significant role in drug development despite their limitations [ 64 , 65 ]. These models primarily utilize human myeloma cell lines cultured in suspension, offering advantages such as low cost, high-throughput screening capability, standardization, and reproducibility [ 65 , 66 ]. Yet, it has been a long-standing question to understand how well these conventional cell lines model patient samples. A group of researchers at the University of California, San Francisco sought out to transcriptionally correlate profiles of 66 MM cell lines to 779 newly diagnosed MM patient tumors. The MM cell line ANBL-6 consistently ranked as the top cell line where it showed the highest median correlation ( R = 0.54) with patient tumors [ 40 ]. Of note, conventionally used cell lines RPMI-8226 and U -266 ranked 48th and 52nd out of the 66 cell lines, respectively. Transcriptomic studies have shown that myeloma cell lines in suspension only reflect a limited segment of the in vivo tumor profile, making them less reliable for predicting clinical outcomes. To address some of these limitations, researchers have developed more advanced models. For instance, transwell co-culture systems have been established to study interactions between multiple cell types. In one such model, human BM stromal HS-5 cells are cultured in the basal chambers while MM RPMI-8226 cells are placed in the apical chamber [ 51 ]. This setup allows for the investigation of paracrine signaling and drug effects in a more complex environment. While these models have paved the way for early studies of drug modeling and preclinical screenings, in suspension models are fundamentally constrained to replicate key aspects of the MM microenvironment, such as oxygen and nutrient gradients, cell-cell and cell-matrix interactions, and the 3D BM architecture [ 67 – 70 ]. For example, the signal transducer and activator of transcription 3 (STAT3) pathway—associated with poor clinical outcome for myeloma patients—was activated in primary myeloma cells cultured in a 3D co-culture system more than when monocultures [ 71 – 73 ]. As a result, while in suspension models remain useful for initial drug screening and basic mechanistic studies, there is a growing recognition of their limitations and a shift towards co-culture 3D models that better recapitulate the in vivo conditions of MM. While MM cells can inherently form 3D clusters due to their unique solid-liquid tumor characteristics, we define 3D MM models more broadly to encompass co-culture systems with stromal, endothelial, or immune cells within extracellular matrices. Fig. 2 Current models for MM research and drug testing in 3D culture platforms. The models span a spectrum from in vivo animal systems to advanced in vitro and computational approaches. Murine models have long remained the gold standard for studying MM pathogenesis, genetics, and therapeutic response, including syngeneic and transgenic that closely mimic human disease heterogeneity and progression. Recognizing the limitations of animal models in recapitulating human microenvironment and immune responses, researchers are increasingly developing 3D cell culture systems-such as spheroids, hydrogels, 3D bioprinting, dynamic modeling, and microfluidics-that enable more physiologically relevant studies of tumor-stroma and drug interactions. These in vitro systems can be tailored for high-throughput drug screening and mechanistic studies by using a patient’s own stroma and disease treatment plan to evaluate patient response and outcome. Computational models and machine learning algorithms now integrate genomic, clinical, and treatment data to predict patient-specific risk, response, and outcomes, offering a powerful tool for precision medicine in MM. Together, these complementary models accelerate translational research and personalized therapy development. Created in BioRender. MA, C. (2025) https://BioRender.com/o5dkm7j

3D MM culture systems

Tissue engineering techniques for developing 3D models of MM provide powerful platforms for advanced research and innovative therapeutic exploration [ 74 ]. By capturing the unique cellular and spatial interactions within the MM niche often unaccounted for in traditional monoculture systems, 3D tissue-engineered models offer a more holistic representation of the disease but there are still many limitations that future researchers need to address. In this section of the review, we will discuss current 3D co-culture models that have been developed and their limitations.

3D spheroid/organoid model

Spheroid/organoid models (Fig. 2 ) have emerged as a transformative tool in cancer research enabling the monitoring of cell-cell and cell-matrix interactions [ 75 , 76 ]. The spheroids form through spontaneous cell aggregation in suspension, fostering these cell-cell interactions and allowing cells to secrete their own extracellular matrix (ECM) [ 77 ]. This self-generated ECM not only offers structural support but also enhances the physiological accuracy of the models [ 78 ]. For example, Kirshner et al. established the 3D reconstructed bone marrow (rBM) spheroid in Matrigel/firbonectin with BM mononuclear cells (BMNCs) culture that supports expansion—up to 17-fold—of primary MM cells harboring patient-specific clonotypic IgH V(D)J rearrangements and characteristic chromosomal abnormalities [ 79 ]. This rBM model maintained the malignant clone’s growth, allowing evaluation of first line chemotherapeutic agents (bortezomib and melphalan), which showed distinct targeting of hematopoietic and MM plasma cell compartments. More specifically, the rBM model revealed a drug-resistant CD20 + MM subpopulation with stem cell-like properties. In another example, Waldschmidt et al. developed a conical agarose-based co-culture platform that propagated primary BMNCs ex vivo, particularly focusing on MM cells. Their method uses a non-adherent agarose microwell array (3D CoSeedis™) with conical-shaped wells where MM cells self-aggregate into clusters, while BM-derived stromal cells (such as the HS-5 cell line) are plated separately at the base of the culture plate. The 3D CoSeedis™ platform supports long-term viability and stable growth of primary MM cells for up to 21 days, overcoming the typical failure of MM cells to expand in monoculture. When testing targeted therapy agents, the platform revealed that bortezomib and auranofin, induced significantly reduced cytotoxicity under 3D co-culture conditions compared to monoculture, with further attenuation of drug sensitivity observed in stromal co-culture versus monoculture conditions [ 80 ].

More recently, Luanpitpong et al. developed a high-throughput 3D spheroid model of MM by co-culturing luciferase-labeled MM cells with cancer-associated fibroblast (CAF)-like stromal cells activated by MM-conditioned medium in a 96-well plate format and applied it to evaluate anti-CD138 CAR-NK cell cytotoxicity [ 81 ]. This model partially recapitulates the immunosuppressive TME, showing that CAF-like stromal cells impair natural killer (NK) cell cytotoxicity and infiltration, although CAR-engineered NK cells demonstrated improved infiltration and killing of MM spheroids during evaluation. Khan et al. developed a organoid model derived from induced pluripotent stem cells (iPSCs), which could later pave way for patient derived studies, that supports the growth, engraftment, and survival of primary myeloma cells for up to 12–14 days in vitro [ 82 ]. Using single-cell RNA sequencing (scRNA-seq) with gene set enrichment analysis, they identified upregulation of inflammatory pathways including tumor necrosis factor (TNF), interleukin 6 (IL-6), and interferon responses, alongside activation of chemokine-mediated signaling through the macrophage migration inhibition factor (MIF)-CD74/CD44 and CCL3–CCR1 axes. These pathways are known to drive pro-survival and tumor-promoting signaling cascades such as phosphoinositide 3-kinase/protein kinase B (AKT)/mechanistic target of rapamycin (PI3K/AKT/mTOR), Janus kinase (JAK)/STAT, and MAPK, which contribute to myeloma progression, immune modulation, and remodeling of the BMM [ 83 – 85 ]. More recently the novel combination of bone and lympho-myeloid bone marrow organoids (comBO) demonstrated that comBO organoids successfully support the engraftment and survival of primary MM cells [ 86 ]. The scRNA-seq analysis revealed upregulation of inflammatory pathways and dysregulated signaling interactions between myeloma cells and the BMM. Additionally, the comBO organoids exhibited arrested lymphoid maturation and altered populations of osteoclasts and endothelial cells, reflecting the complex niche disruptions observed in MM patients.

However, there are notable limitations to using 3D cellular spheroid/organoid models for studying MM. A significant drawback is the absence of a vascular network and the integration of the perivascular niche within these models [ 87 ]. Such lack of vascularization limits these models to replicate the nutrient concentration equilibrium maintained in vivo by physiological processes, which is needed for mimicking the TME [ 88 – 90 ]. Moreover, the interaction between myeloma cells and the endosteal bone lining—a key element of myeloma biology—has been largely overlooked in previous studies using 3D cellular spheroid models [ 91 , 92 ]. The endosteal niche creates a specialized microenvironment including processes such as adhesion, migration, and the development of drug resistance [ 93 – 95 ]. Without these elements, simplified 3D spheroid models fall short in capturing the complexity of the TME, limiting their utility for fully understanding of myeloma pathogenesis.

3D scaffold models

Three-dimensional scaffold models (Fig. 2 ) represent a prevalent and valuable approach for studying MM, offering more physiologically relevant cell-matrix interactions, ECM composition, and mechanical properties compared to traditional in suspension cultures and 3D spheroids [ 53 ]. A variety of animal-derived hydrogels have been employed to from scaffolds to support MM cell survival, proliferation, or stromal interactions, including Matrigel [ 96 – 99 ], collagen [ 100 – 102 ], fibronectin [ 103 – 106 ], hyaluronic acid (HA) [ 107 – 110 ], fibrin [ 111 – 113 ], and gelatin [ 71 , 114 – 117 ]. Using gelatin methacrylate (GelMA)-based scaffold system, Huang et al. demonstrated that the STAT3 pathway was activated in MM cells cultured in the rBM 3D tissue engineered system but not in suspension cultures [ 71 , 118 ]. The authors also confirmed that MM cells were functionally dependent on STAT3 signaling, with Stattic treatment inducing significant apoptosis. When U-266 and RPMI-8226 cells cultured in the 3D system were treated with sub-half-maximal inhibitory concentrations (IC50) concentrations of bortezomib (7 nM) combined with Stattic (3–4 μM for U-266, 15–20 μM for RPMI-8226), the combination resulted in greater cytotoxicity compared to either agent alone [ 69 ]. Similar to Huang et al., Khan et al., as previously mentioned, also used GelMA for their organoid model to mimic the BM perivascular niche, achieving high MM cell viability and engraftment [ 82 ]. Using an alternative hydrogel, Narayanan et al. developed a 3D HA-based model system which demonstrated that medium-stiffness hydrogels supported higher myeloma cell survival and proliferation compared to low- or high-stiffness hydrogels or Matrigel-based cultures [ 119 , 120 ]. For example, Perez et al. also used a 3D matrix of Matrigel in addition to fibronectin to study myeloid-derived suppressor cells (MDSCs) in MM, whereas Cucè et al. combined the use of a Matrigel–spheroid model to evaluate the effect of the chemotherapy drug trabectedin on both cell lines and patient derived MM cells and monocytes [ 92 , 121 ]. In the Matrigel model, trabectedin triggered apoptosis in primary MM cells and MM cell lines in both monoculture and 3D co-culture in vitro assays with slight reduction of trabectedin activity in the Matrigel-spheroid system. In addition, trabectedin induced DNA damage response activation, cellular stress with reactive oxygen species production, and cell cycle arrest [ 92 ]. To ensure long-term culture for non-malignant plasma cells while still preserving the patient-derived BMM, Martini et al. in their recent study developed a hydrogel system combing HA with collagen [ 122 ]. Other studies have explored the use of synthetic hydrogels, for example, Jakubikova et al. utilized PuraMatrix™, a self-assembling peptide hydrogel, to develop a 3D patient-derived co-culture model with mesenchymal stem cells (MSCs), showing higher expression of cytokines and ECM molecules that promote MM survival and stem cell expansion [ 123 ]. Drug testing was performed with doxorubicin, dexamethasone, melphalan, bortezomib and lenalidomide where greater resistance was observed in the MSC PuraMatrix™ model which reflects microenvironmental protection. While these studies provide a library of hydrogels that can be used to better mimic or provide extracellular cues in a 3D co-culture system, it should be noted that the ideal hydrogel would be one that closely resembles the BMM as deviation could lead to aberrant interactions between different culture components and the matrix.

In addition, these scaffold models can incorporate perivascular components, such as endothelial cells, making them a more advanced alternative to simpler spheroid models. Reagan et al. stimulated MSCs to undergo osteogenic differentiation within porous silk scaffolds before introducing myeloma cells and created a bone-like microenvironment that could support the co-culture and interaction with MM cells and modeled drug resistance with bortezomib [ 124 ]. Normal donor (ND)- and MM-MSC micro-RNA (miR) signatures were identified and used to uncover osteogenic miRs of interest for osteoblast differentiation [ 124 ]. Fairfield et al. further extended the use of silk scaffolds by developing the first 3D tissue-engineered bone marrow adipose tissue (BMAT) model where MSCs were seeded into silk scaffolds and cultured with adipogenic media to drive differentiation into adipocytes [ 125 ]. This BMAT model revealed upregulation of DNA replication, metabolic pathways, and proliferation events in BM adipocytes compared to 2D culture. Another example is the 3D tissue-engineered bone marrow model (3DTEBM ® ) as previously developed by de la Puente et al., which aims to incorporate myeloma, endothelial, and stromal cells from matched MM patients [ 126 ]. The scaffold was created using fibrinogen that is naturally found in the endothelial system and calcium chloride for crosslinking, with tranexamic acid added for structural stability up to seven days, which enables a layered 3D co-culture of myeloma cells with stromal and endothelial cells, showing increased proliferation rates in myeloma cells by 250% compared to monoculture [ 126 ]. The 3DTEBM ® model also recreated drug and oxygen gradients, mimicking in vivo conditions where cells near vasculature were more proliferative and therapy-sensitive than those in the endosteal niche [ 126 ]. This was later followed by Alhallak et al. where patient derived samples were cultured in the 3DTEBM ® and treated with the same drug regimens the patients were scheduled to receive clinically. In addition to chemotherapies (etoposide, doxorubicin and melphalan) and immunomodulatory agents tested (lenalidomide and pomalidomide), the 3DTEBM ® platform demonstrated strong predictive capability for proteasome inhibitors, with carfilzomib, bortezomib, and ixazomib all showing direct correlation between ex vivo IC50 values and clinical steady-state plasma concentrations (contributing to the overall R 2 = 0.993). The histone deacetylase inhibitor panobinostat similarly showed accurate prediction of clinical efficacy within the fibrin-based system. These targeted agents, which had poor correlation in traditional monoculture (R 2 = 0.019–0.601), demonstrated the platform’s ability to recapitulate the microenvironmental factors that influence targeted therapy resistance mechanisms. Retrospectively, the 3DTEBM ® predicted clinical therapeutic response with 89% accuracy, demonstrating its potential as a clinically actionable tool for guiding personalized therapy decisions in MM within a timeframe of less than a week [ 74 ]. These models represent advances toward the development of patient-derived platforms for therapeutic screening, offering opportunities to personalize multiple myeloma treatment. It is important to highlight that many of these models lack a human immune landscape, which restricts their ability to fully capture the immune-mediated mechanisms of drug response and resistance found in the native BMM.

3D bio-printed models

Bioprinting, a specialized form of 3D printing (Fig. 2 ), can precisely deposit and assemble biologically relevant materials—such as cells, growth factors, and biomaterials—at specific locations to create tissue-like or organ-like structures for medical and research applications [ 127 – 130 ]. One of its key benefits in developing MM models is the ability to construct structures with distinct zones and incorporate multiple cell types within a single model, effectively recreating the cellular and spatial interactions of the MM microenvironment [ 128 ]. Braham et al. created one of the first 3D bio-printed BM models designed to replicate both the endosteal and perivascular niches critical for MM progression [ 131 ]. They first bio-printed a calcium phosphate cement (CPC) scaffold to be seeded with osteogenic MSCs to mimic the endosteal niche, providing a mineralized bone-like environment. For the perivascular niche, they embedded endothelial progenitor cells and MSCs within a Matrigel hydrogel, which was then co-cultured with the CPC scaffold to recreate the vascularized BMM. This dual-niche configuration enabled key signaling interactions between MM cells and their microenvironment, including secretion of cytokines and growth factors such as IL-6, vascular endothelial growth factor (VEGF), and stromal cell-derived factor 1 (SDF-1) or C-X-C motif chemokine ligand 12 (CXCL12) which are known to promote MM cell survival, proliferation, and migration [ 132 – 135 ]. The hybrid scaffold-hydrogel system enabled evaluation of successful infiltration of T cell engineered genes (TEGs) (αβT cells engineered with γδT cell receptors) on primary patient samples (1 newly diagnosed, 1 relapsed, 2 relapsed/refractory) after both 24 and 48 hours, where TEGs showed a significant killing activity. Utilizing a bioink hydrogel mixture containing growth factors, thiol-fibronectin, and a crosslinker, Rodriguez et al. encapsulated primary BMNCs from 45 MM patients into 3D bio-printed constructs, which maintained high cell viability and preserved the representative cellular populations for at least 7 days, as confirmed by adenosine triphosphate quantification, immunohistochemistry, and flow cytometry [ 136 ]. This platform also enabled the rapid and parallel testing of MM drugs—including lenalidomide, cyclophosphamide, pomalidomide, bortezomib, and dexamethasone—demonstrating variable, patient-specific responses that closely mirrored in vivo outcomes. Furthermore, Wu et al. describes the development of a three-dimensional bio-printed system using coaxial extrusion bioprinting, where a structure with an outer mineral-containing sheath and a soft hydrogel core, replicating the physical and biochemical properties of BM in addition to the incorporation of both the endosteal and perivascular niches [ 137 ]. This model demonstrated that patient-derived MM cells survived up to 7 days in the 3D tissue-engineered system, while they died rapidly during in suspension culture, and enabled drug response testing, revealing that MM cells in the 3D tissue environment were more resistant to bortezomib. In brief, the results showed that in 3D co-culture culture, bortezomib reduced MM cell viability more in monoculture than in co-culture with stromal cells, indicating that the presence of stromal cells confers drug resistance (e.g., at 8 nM bortezomib cell viability was ~49% in monoculture and ~27% in co-culture for MM1S cells). The co-addition of tocilizumab, an anti-IL-6 receptor antibody, further increased the sensitivity of MM cells to bortezomib in the 3D co-culture model, lowering the IC50 and significantly reducing cell viability compared to bortezomib alone. [ 137 ]. Most recently, further innovation has been achieved by Jun et al. with the development of a 3D-printed perfusion bioreactor (3D-PBR) where it incorporates MSCs encapsulated in collagen-fibrin gels alongside human umbilical vein endothelial cells (HUVEC) under perfusion conditions [ 138 ]. This 3D-PBR model encapsulated MSCs within a collagen-fibrin gel and differentiated them into specific lineages such as adipocytes and osteoblasts, under the influence of physical cues provided by the bioreactor. Immunohistochemistry showed that cells cultured in the 3D-PBR expressed more physiologically relevant bone marrow perivascular niche markers compared to static culture models [ 138 ]. Bioprinting technologies have made great strides since their development in the 1980s and are becoming a critical asset in constructing 3D in vitro tissue-engineered culture systems [ 139 ]. These bio-printed MM models mentioned are excellent in mimicking the in vivo counterpart in addressing limitations such as vascularization and including patient samples, however, these models would need to incoroporate a immune compoenent to better undersand the MM TME.

Dynamic bone marrow modeling

Some characteristics of the BMM, such as nutrient circulation, waste removal, and shear forces, cannot be replicated in static culture systems [ 140 ]. To address this, Belloni et al. pioneered the use of bioreactor technology in 2013, adapting the Rotary Cell Culture System (RCCS™) (Fig. 2 ) to culture primary myeloma cells [ 141 ]. This dynamic model maintained cells in a “free-fall” state, promoting nutrient mass transfer and low shear stress conditions. Initially designed for tissue explants from MM patients, the RCCS™ system preserved tissue architecture and viability, including disrupted vessels observed in static cultures [ 142 ]. Drug response studies using bortezomib, a proteasome inhibitor demonstrated concordance between β2-microglobulin levels in ex vivo supernatants and patient sera, validating a predictive accuracy of this model [ 142 ]. Challenges such as tissue sensitivity, sample size standardization, and reproducibility were noted. In a follow-up study, Ferrarini team focused on co-culturing isolated primary myeloma cells with allogeneic bone marrow stromal cells (BMSC) and HUVECs in the bioreactor where MM cell survival was for up to 7 days and paralleled the genomic expansion of dominant clones observed in vivo [ 143 ]. There has been recent validation of the RCCS™ for long-term co-culture of primary MM cells with patient-derived stromal and endothelial cells within gelatin scaffolds [ 144 ]. Genomic analysis confirmed that high-risk MM clones expanded similarly in both bioreactor cultures and in vivo. Additionally, this RCCS™ model was used to assess bortezomib’s impact on MM cells and angiogenic vessels while evaluating surrogate markers like VEGF and Angiopoietin-2 levels [ 144 ]. One of the features that is unique about the RCCS™ bioreactor is that the mechanical stress is partially simulated by maintaining cells in a free-fall state, which reduces shear stress and improves nutrient and waste exchange compared to static cultures. RCCS™, however, may not fully replicate the complex mechanical forces present in vivo, such as the precise shear stresses, compressive forces, and cyclic loading experienced by bone tissue during normal physiological activity [ 145 , 146 ].

Organs-on-chips based MM models

Organs-on-Chips (Fig. 2 ) are carefully designed microfluidic devices that include circulation and cellular compartmentalization to support the unique tissue functions [ 147 – 149 ]. In particular, microfluidic Organs-on-Chips enable the creation of biomimetic structures that replicate biological processes in controlled physiological environments [ 150 – 154 ]. For example, Zhang et al. developed an 8-chamber microfluidic device designed to mimic the endosteal surface by generating an ossified tissue scaffold from human osteoblastic cells (hFOB 1.19) [ 155 ]. Primary BMNCs were introduced into each chamber, and real-time monitoring revealed that myeloma cells expanded after 21 days in culture and migrated toward the osteoblast layer [ 155 ]. Compared to static 3D cultures, perfusion in this system improved long-term viability and proliferation of myeloma cells, demonstrating the importance of circulation in maintaining the endosteal niche. By improving the microfluidic designs, Sui et al. developed a device to closely mimic key MM BM features, including sinusoidal circulation, sinusoidal endothelium, and stromal compartment [ 156 ]. The device was used to investigate the CXCL12-mediated egression of MM cells from the BM stroma and to assess how this process affects the barrier function of ECs, where the researchers found that as MM cells egressed from the stroma, the endothelial cells became less organized and more loosely connected, with widened junction pores and increased permeability [ 156 ]. These findings demonstrate that the microfluidic device can effectively model the dynamic physical and biochemical factors influencing MM cell trafficking through the BM niche. The incorporation of sinusoidal flow in the device further enhances its physiological relevance and provides a foundation for developing systemic models to study cancers that reside in or metastasize to the BM.

In 2014, Khin et al. developed an interdisciplinary platform combining microfluidic technology with computational modeling to assess drug sensitivity in multiple myeloma [ 157 ]. The system utilized commercial 3D microfluidic chambers (μ-slide Chemotaxis 3D, Ibidi). A key innovation was the minimal cell requirement of only 1,000–10,000 cells per experiment, which would be critical for primary patient samples. Cells were embedded in 1.5 mg/mL bovine collagen type I matrix and could be co-cultured with patient-derived stromal cells (CD138 - ) alongside MM cells (CD138 + ). The study demonstrated stable linear drug gradients maintained for over 18 hours using fluorescent peptide tracking. Importantly, co-culture with bone marrow stromal cells (HS-5) increased melphalan resistance in NCI-H929 cells, with half-maximal effective concentration (EC50) shifting from 28 μM to 40 μM. This resistance could be circumvented using sub-lethal bortezomib (1 nM), which restored melphalan sensitivity in co-culture conditions. Primary samples from 7 MM patients showed variable responses to melphalan (IC50 shift from 2 μM to 12 μM with co-culture) showing patient heterogeneity of drug dosage response in the device. The platform incorporated mathematical modeling to convert experimental data into dose-exposure-response surfaces, enabling prediction of initial treatment response depth and estimation of progression-free survival. Validation against mouse xenograft models showed good correlation (Pearson r > 0.97).

More recently, to address a common limitation to organ-on-a-chip devices, Goshal et al. has pioneered the field with introducing a vascularized on-a-chip device [ 158 ]. This system recapitulates both the endosteal and perivascular BM niches by incorporating human MSCs, osteoblasts, endothelial cells, pericytes, and additional stromal components in a spatially organized, modular microfluidic device. Additionally, the system allows for the perfusion of donor-matched CAR T-cells, enabling real-time assessment of T-cell migration, survival, differentiation, and cytotoxicity against MM cells within a physiologically relevant microenvironment. Another recent BM organ-on-a-chip device for modeling leukemia found that the BMM promoted leukemia cell survival and dormancy by cytokine signaling through the CXCL12/C-X-C chemokine receptor (CXCR4) axis and adhesive interactions via vascular cell adhesion protein 1 (VCAM-1)/very late activation 4 (VLA-4) [ 159 ]. This organotypic platform was later used to integrate primary human leukemia cells, functional immune components, and structural stromal cells to create an immunocompetent microenvironment that closely mimics the human bone marrow niche [ 160 ]. By incorporating patient-derived leukemia and immune cells, the model enables realistic simulation of tumor-immune interactions and a precision medicine approach for preclinical testing of CAR T-cell therapies. The microfluidic design allows for dynamic control and real-time monitoring of cellular behaviors, including CAR T-cell infiltration and leukemia cell killing which can offer insights into the mechanisms disease resistance and be valuable in pre-screening individuals prior to drug administration, which is applicable to different blood cancers such as MM and solid tumors and beyond. These models have collectively made great strides to study the tumor-immune interactions, however, they only coculture with limited immune cells and thus were not able to define the role of hematopoiesis in the system and thus its impact on disease modeling and therapeutic screening. Thus, a wide landscape of immune cells needs to be incorporated to accurately depict the BMM.

Current challenges and future directions

While all models in this review have laid groundwork for drug testing and preclinical patient sample testing, none of said models are fully humanized and lack components that other models may have. For one, MM is highly dependent on its microenvironment including stromal cells, endothelial cells, immune cells, and extracellular factors, which plays a pivotal role in disease progression, immune evasion, drug resistance, and therapeutic failure. Traditional in suspension systems fail to capture the totality of these interactions and many of current 3D cell culture systems lack an immune environment, limiting their utility in studying MM biology, drug testing or predicting therapeutic responses. Mouse models have been pursued with the most commonly used being the 5TMM models [ 43 ] and Myc-derived mouse models [ 44 ], as well as the severe combined immunodeficiency (SCID) mouse models for humanization [ 45 ]. The readers can refer to this comprehensive review of current murine models [ 161 ], as our review paper will be focused on 3D bioengineered models. Significant progress has been made in addressing individual challenges such as immunocompetency, vascularization, and extracellular cues in 3D culture system models, yet fully integrating these features remains an ongoing endeavor. To advance the field, these challenges must be collectively addressed, and we should strive to develop models that more faithfully recapitulate the complexity of the native TME. We here discuss individually the current challenges to produce biomimetic models and future directions for translating these platforms to preclinical and clinical studies (Fig. 3 ). Fig. 3 Human immunocompetent MM preclinical model for therapeutic translation. Designing an immunocompetent translational 3D human MM system for MM requires careful integration of patient-derived samples, advanced culture techniques, and drug screening methodologies. The process begins with the collection of BM aspirates from MM patients, followed by red blood cell removal to keep all mononuclear cells, including myeloma cells, T-cell, B-cells, macrophages, and stromal components. These cells are then incorporated into a 3D culture system that recapitulates the complex bone marrow microenvironment (BMM), supporting cell-cell and cell-matrix interactions essential for realistic modeling. Drug applications can be tested directly on patient-derived 3D cultures, with responses validated through high-throughput assays, imaging, and molecular analysis which can be then used for further guidance in the treatment regimen. Compared to traditional stand-alone cultures or murine models, patient-specific systems offer superior relevance, speed, and scalability, and represent a critical step toward implementing precision medicine in the management of MM. Created in BioRender. MA, C. (2025) https://BioRender.com/mb6ny2n

Immunocompetent components

Given the critical role of the immune system in MM progression and therapeutic response, developing immunocompetent in vitro models that incorporate functional immune components is essential and in some cases synergistically improve drug treatment efficacy [ 146 , 162 , 163 ]. Such models should aim to more accurately replicate the complex interactions within the BMM, involving adequate representation of T cells for bispecific antibody testing, NK cells and macrophages for monoclonal antibodies facilitating tumor-immune crosstalk, which are largely absent in conventional culture systems. For example, by reflecting clinical relapse patterns observed in relapsed/refractory MM patients, MSC-based immunocompetent models could provide a platform where resistance mechanisms could be studied [ 164 ]. Another promising example is a 3D BM niche model that supported the migration and cytotoxic activity of engineered γδ T cells against primary myeloma cells [ 165 ]. Additionally, studying immune evasion mechanisms—such as PD-1/PD-L1 signaling—within these systems could provide insights into optimizing immunotherapies and studying mechanisms of resistance. While most models mentioned above incorporate some aspect of stromal and endothelial cells along with some immune subsets like macrophages or T lymphocytes, they often lack hematopoietic stem and progenitor cells (HSPCs) or other important components of the hematopoietic niche such as extracellular cues or vascularization (as discussed in below sections) thus failing to capture a broad spectrum of immune cells interacting in a physiologically relevant system [ 166 ]. HSPCs play a critical role in maintaining normal hematopoiesis within the BM and may be outcompeted by MM cells for niche occupancy during disease progression [ 167 ]. Incorporating HSPCs into a culture system, 3D tissue-engineered models could provide new insights into how MM disrupts normal hematopoietic processes or hijacks niche signals for its own survival. A model proposed by Seiber et al. presents a microfluidic platform designed to mimic the BMM and support the long-term culture of HSPCs in vitro [ 168 ]. The researchers developed a 3D tissue-engineered system using a hydroxyapatite-coated zirconium oxide scaffold seeded with human MSCs and cord blood-derived HSPCs, where HSPCs retained their primitive markers (CD34 + CD38 − ) and functional capacity to form granulocyte, erythrocyte, macrophage, and megakaryocyte colonies for up to 28 days [ 168 ]. Another exciting direction for future MM modeling could be the integration of germinal center (GC)-like systems with engineered tissue niches to study how specific genetic lesions drive the transition from MGUS to multiple myeloma [ 169 – 172 ]. The GC is a key site where B cells acquire somatic mutations and chromosomal translocations—many of which are implicated in MM pathogenesis, including IgH translocations (e.g., t(4;14), t(11;14)) and early activating mutations in KRAS, NRAS, or DIS3 [ 21 – 24 ]. By incorporating patient-derived or gene-edited B cells bearing these lesions into GC-mimicking platforms, researchers can observe how such mutations influence plasma cell differentiation, niche engagement, and clonal fitness in real time. As such, preclinical platforms that ensure long-term culture would be also foundational to capturing the chronic and relapsing nature of MM, enabling researchers to investigate long-term disease evolution, immune escape, clonal selection, and therapy resistance. In addition, MM EMD can lead to break out lesions (BoLs) of the bone marrow and a recent publication by Lutz et al. shows these BoLs are their own unique immune hotspot consisting of mainly immunosuppressive cues [ 173 ]. Incorporating these ideas into future models may uncover how specific genetic abnormalities interact with immune and stromal cues to promote malignant transformation, laying the foundation for early detection strategies and preventive interventions in MM.

Vascularization

Another major limitation is the lack of vascularization in most 3D BM models. The BM is a highly vascularized tissue where blood vessels provide oxygen, nutrients, and signaling molecules essential for both normal hematopoiesis and tumor growth [ 174 ]. The absence of vascular networks in current models limits their ability to replicate angiogenesis or perivascular niche interactions that are critical for MM progression [ 56 ]. Developing vascularized models could address this limitation by enabling researchers to study how angiogenesis contributes to tumor growth or how anti-angiogenic therapies affect MM progression. A recent MM 3D BM niche model incorporated endothelial progenitor cells alongside MSCs, forming perivascular structures that enhanced myeloma cell survival and proliferation over extended periods [ 131 ]. Expanding on this approach by integrating dynamic flow systems or bioprinting techniques could improve vascularization in MM models, enabling more accurate studies of angiogenesis and drug delivery.

Extracellular cues

The ECM plays a critical role in supporting tumor growth and regulating cell behavior in the BM niche [ 175 ]. Though many hydrogels have been successfully employed in creating MM models such as fibronectin or laminin, but they may not fully replicate the dynamic remodeling observed in vivo [ 176 ]. Incorporating patient-derived ECM or using tunable biomaterials to adjust stiffness would better mimic the physiological and mechanical properties of the BM niche, as medium-stiffness hydrogels were found to supported higher myeloma cell survival and proliferation [ 120 ]. This would also allow researchers to study how ECM remodeling contributes to drug resistance or how integrin-mediated adhesion influences MM cell survival. In addition, hypoxia is a hallmark of tumors and contributes to drug resistance by altering cellular metabolism and gene expression [ 177 ]. MM models like 3DTEBM ® system already replicate hypoxic conditions that influence drug penetration and efficacy [ 67 ]. The implementation of silicon-collagen xerogels, a dried form of hydrogel, could be tested to assist drug delivery in a 3D culture system as they are emerging in the localized therapy field [ 102 ]. Addressing these capabilities by integrating oxygen sensors or dynamic control of oxygen levels could further improve our understanding of how hypoxia impacts MM progression and response to therapies such as PIs or IMiDs [ 178 , 179 ].

Cellular sources

The choice and diversity of cellular sources are critical for modeling MM within the BM because the disease’s progression, drug resistance, and survival are tightly regulated by interactions between MM cells and various BM-resident cells as demonstrated by the studies previously mentioned [ 74 , 123 , 156 , 164 , 180 ]. Incorporating relevant cellular components such as MSCs, osteoblasts, endothelial cells, and immune cells into 3D culture systems or organ-on-a-chip platforms allows these models to more faithfully recreate the pro-survival signals, cell-cell contacts, and soluble factor exchanges that occur in the native BMM, as primary cells are difficult to culture in only in suspension cell culture [ 181 ]. The importance of using gene-edited iPSCs is exemplified by CRISPR-Cas9-mediated disruption of Beta 2-microglobulin (B2M) and major histocompatibility complex II (CIITA) genes in the MUSIi001-A-2 line, which eliminates human leukocyte antigen (HLA) class I and II expression and generates “universal” iPSCs with reduced immunogenicity [ 182 ]. Integrating such immunoengineered iPSCs into MM models not only allows for the study of allogeneic cell therapies with minimized risk of immune rejection but also provides a powerful platform for investigating immunological mechanisms and screening novel treatments in a setting that closely reflects patient biology. The ability to integrate patient samples in a 3D co-culture system is key for designing a pre-clinical testing device. The development of screening platform represents a paradigm shift toward individualized therapy approaches using high-throughput liquid handling systems and automated imaging platforms—all of which could be implemented in the design of a patient derived 3D co-culture system [ 183 – 185 ]. By developing a model for testing combinational therapies, synergies of drug combinations can be discovered, providing clinical guidance on future treatment regimens [ 186 ].

Computational modeling and systematic analysis

Over the years, mathematical models have been developed to simulate tumor growth and study tumor dynamics in response to therapy and drug resistance [ 187 ]. For example, Ex vivo Mathematical Myeloma Advisor (EMMA) (Fig. 2 ), which integrated digital image analysis, mathematical modeling, and pharmacokinetic data was developed to predict clinical responses to 31 chemotherapeutic agents and IMiDs on patient ex vivo samples [ 157 , 188 ]. Despite its promise, EMMAs predictions for drug combinations assumed additive effects by independently simulating each drug’s response—a simplification that overlooked synergistic or antagonistic interactions. To address this, the model was extended into the Synergy-Augmented Model (SAM) [ 61 ]. SAM incorporated time-varying drug interactions and accounted for modified damage caused by drugs in combination regimens. Using ex vivo sensitivity data from 203 patients and pharmacokinetic parameters, SAM evaluated the synergistic or antagonistic effects of 46 drug combinations, identifying only four as clinically synergistic. The data also found that bench-observed synergism does not always translate to bedside efficacy. Although EMMA and SAM demonstrate significant advancements in predicting clinical outcomes, further improvements are needed for broader applicability. For instance, EMMA correctly classified 96% of a cohort of 52 MM patients according to treatment response but faced limitations in quantifying adherent cancer cells [ 189 ]. It should be noted ex vivo patient samples that were used for predictive measures are generally cultured in short durations, making it difficult to study long term effects of the BMM. In addition, ex vivo cultures may either be bone marrow aspirates where primarily BMNCs [ 68 ] are extracted or a bone marrow biopsy (trephine) where a core of the bone marrow is sampled along with BMNCs. In most cases, stromal cells are often lost in the sample preparation step; therefore, models that do not reconstitute the stromal component of the model will lose those critical interactions. Moreover, incorporating triple-drug regimens—commonly used in MM treatment—would enhance accuracy but increase model complexity. Other studies have explored similar mathematical frameworks for different cancer types [ 190 , 191 ]. Stochastic differential equation models have been used to connect cellular mechanisms of drug resistance with population-level survival outcomes in metastatic melanoma [ 190 ], while computational approaches have analyzed tumor evolution under convective mass flux conditions to address resistance mechanisms [ 191 ]. One overarching limitation with current computational models that needs to be addressed is making the prediction personalized. The computational model of CAR T-cell immunotherapy for leukemia by Liu et al. is a precision medicine approach to this issue as it integrates individual patient data—including leukemia cell growth rates, T cell expansion and cytotoxicity, and antigen expression patterns—into a personalized mathematical framework that quantitatively simulates and predicts each patient’s unique response to therapy (remission, resistance, or relapse) [ 192 ]. By calibrating this model with clinical and -omics data from individual MM patients, a similar approach could be used to identify which specific mechanisms (such as early CAR T-cell expansion kinetics or antigen loss) are driving different outcomes in each case and use this information to forecast late-stage responses based on early data.

Standardization

Standardization issues pose challenges for the widespread adoption of 3D BM models in research or clinical settings [ 53 ]. Variability in culture methods, scaffold materials, cell sources, and readout techniques can lead to inconsistent results across studies, making it difficult to compare findings or validate therapeutic targets. Establishing standardized protocols for creating and using 3D BM models will be essential for ensuring reproducibility and facilitating their integration into drug development pipelines [ 53 ]. Incorporating computational modeling and in silico simulations alongside experimental 3D systems may further enhance predictive power for therapeutic response and disease progression [ 157 ]. Collectively, standardizing 3D co-culture systems will also facilitate the rapid adoption of these models by the pharmaceutical industry to enable more precise prediction of treatment efficacy and resistance mechanisms during drug development [ 193 ].

Discussion

While conventional stand alone in suspension cell culture models along with animal models have served as a cornerstone for initial drug screening and mechanistic studies, their limitations in accurately representing the BMM necessitate the use of more advanced systems. As evidenced by the significant proportion of patients who do not respond to standard regimens and the inevitable development of resistance in nearly all patients, the conventional “one-size-fits-all” approach in drug development has reached its limitations. The future of multiple myeloma therapeutics lies not just in discovering new targets, but in precisely matching existing and emerging therapies to individual patient characteristics, optimizing combination strategies based on mechanistic synergies, and predicting resistance patterns before they emerge clinically—all of which demand more sophisticated, physiologically relevant modeling platforms that can recapitulate the complex, patient-specific biology of the bone marrow microenvironment. To achieve these goals, a paradigm shift is required in both preclinical research and clinical translation. 3-dimensional culture systems offer physiological relevance by incorporating crucial aspects of the TME and enable researchers to investigate myeloma-stroma interactions, drug resistance mechanisms, and the effects of novel therapeutic regimens in a more realistic setting. However, challenges persist in achieving a fully humanized, immunocompetent model that can accurately predict clinical outcomes and facilitate the development of more personalized and effective treatments for MM patients. Future research should focus on addressing these limitations by integrating additional immune cell types, improving ECM representation, and incorporating advanced imaging and analytical techniques. With advances in single-cell omics, spatial transcriptomics and high-throughput drug screening pairing, human preclinical MM in vitro models could enable researchers to dissect tumor heterogeneity and tailor combinational regimens to prevent drug resistance and relapse. As MM remains incurable for most patients, these platforms will be invaluable for accurately predicting patient-specific therapeutic responses and for advancing personalized medicine.

Abbreviations

Two-dimensional

Three-dimensional

3D-printed Perfusion Bioreactor

Three-dimensional Tissue Engineered Bone Marrow

Adenosine Triphosphate

B-cell Maturation Antigen

Bone Marrow

Bone Marrow Adipose Tissue

Bone Marrow Microenvironment

Bone Marrow Mononuclear Cells

Bone Marrow Stem Cell

Chimeric Antigen Receptor

Combined Bone and Lympho-Myeloid Bone Marrow Organoids

Calcium Phosphate Cement

Hypercalcemia, Renal Dysfunction, Anemia, Bone Pain

C-X-C Motif Chemokine Ligand

C-X-C Motif Chemokine Receptor

Half-Maximal Effective Concentration

Extracellular Matrix

Early Growth Response 1

Ex vivo Mathematical Myeloma Advisor

Gelatin Methacrylate

Human Umbilical Vein Endothelial Cell

Hematopoietic Stem and Progenitor Cells

Half-Maximal Inhibitory Concentration

Interleukin 6

Immune Modulatory Drugs

Induced Pluripotent Stem Cells

Janus Kinase

Junction Adhesion Molecular A

Kristen Rat Sarcoma Viral Oncogene

Mitogen-Activated Protein Kinase

Myeloid-Derived Suppressor Cells

Monoclonal Gammopathy Undetermined Significance

Multiple Myeloma

Mesenchymal Stem Cells

Natural Killer

Neuroblastoma Rat Sarcoma Viral Oncogene Homolog

Proteasome Inhibitor

Reconstructed Bone Marrow

Rotary Cell Culture System

Single Cell mRNA Sequencing

Synergy-Augmented Model

Smoldering Multiple Myeloma

Signal Transducer and Activator of Transcription

T-cell Engineered Genes

Tumor Microenvironment

Vascular Cell Adhesion Protein 1

Vascular Endothelial Growth Factor

Variable, Diversity, Joining

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

All figures were created with BioRender.com.

Author contributions

C.M. conceptualized the manuscript. M.Y., J.L., Y.C., J.L., J.Z., and C.M. drafted the manuscript. C.M. revised and supervised the work. All authors commented on the manuscript.

Funding

This study was supported by start-up funds from Cleveland Clinic Research (to C.M.), Grant#22-148-27IRG to the Case Comprehensive Cancer Center from the American Cancer Society (to C.M.), Cleveland Clinic Catalyst Grant (to C.M.), and VeloSano Graduate Grant (to M.Y.). J.Z. laboratory is supported by grants from the NIH/NCI (R01CA251141) and the NIH/NINDS (R01NS124081), V Foundation Scholar Award, ASH bridge award, and two VeloSano Grants. The funder had no role in the design, data collection, data analysis, or reporting of this study.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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