Отталкиваю меняющееся управление хмурящейся множественной миеломой.

INTRODUCTION

Multiple myeloma (MM) is a hematologic malignancy characterized by the uncontrolled expansion of clonal plasma cells (PCs). 1 Since its first description in the 1840s, 2 the definition and treatment of MM underwent numerous revisions, allowing earlier and more accurate recognition of affected patients. A key milestone was the recognition of monoclonal gammopathy of undetermined significance (MGUS) in the 1970s as a precursor state of MM. 3 However, later observations showed that some patients fulfilled the diagnostic criteria for MM yet had no signs or symptoms of MM, clinically resembling an MGUS state. A case series by Kyle and Greipp (1980) followed six such patients for over 5 years and demonstrated that, for this subgroup, the risks of treatment might outweigh the potential benefits. 4 This led to the establishment of smoldering multiple myeloma (SMM), a disease state that is clinically similar to MGUS but biologically resembles MM. However, this classification was primarily based on disease burden cutoffs reported in retrospective single‐center studies. 4 , 5 This continuous spectrum of monoclonal gammopathies forms the basis of our current understanding of myelomatous disease spectrum, which includes MGUS, SMM, and newly diagnosed MM.

The clinical challenge of SMM lies in its highly variable and heterogeneous natural history. Many patients remain in the smoldering phase for years, while others progress rapidly to MM. 6 Significant strides have been made to characterize and stratify the risk of progression from SMM to MM. This distinction has become even more critical with the advent of novel therapeutic agents. Current treatment strategies are generally divided into two approaches: prevention of progression through single‐agent or doublet therapies, and disease eradication through aggressive, disease‐altering regimens. 7 Despite these advances, existing tools remain limited in their accuracy and practicality for identifying which patients should be treated and which can safely remain in observation. In this review, we trace the evolution of SMM risk stratification models from early clinical observations to advanced genomic profiling, and argue for a shift toward dynamic, biology‐informed approaches.

EARLY DEFINITION OF SMM

Although the term “smoldering multiple myeloma” was coined by Kyle and Greipp in 1980, 4 , 8 it was not until 2003 that the International Myeloma Working Group (IMWG) formally defined the condition. 9 According to this definition, SMM was characterized by the presence of a serum M protein level ≥3 g/dL and/or ≥10% bone marrow plasma cells (BMPCs), in the absence of CRAB features related to the PC disorder—hypercalcemia, renal failure, anemia, or bone lesions—as well as no signs of hyperviscosity, amyloidosis, or recurrent infections. 9 A detectable monoclonal protein in the serum and/or urine, along with monoclonal PCs in the bone marrow, is also required for the diagnosis of SMM. 10 These diagnostic thresholds were derived from retrospective analyses and did not include cytogenetics, contemporary imaging techniques (e.g., magnetic resonance imaging [MRI] or positron emission tomography/computed tomography [PET/CT]), or molecular features (e.g., fluorescence in situ hybridization [FISH]). In 2007, a retrospective study analyzed pooled data on the risk of progression from SMM to MM, reporting an annual progression rate of 10% during the first 5 years, followed by 3% per year over the next 5 years, and 1%–2% annually thereafter, 11 , 12 demonstrating once again that SMM is a disease state with heterogeneous biology. At a time when the standard‐of‐care backbones for MM treatment did not carry the same safety and efficacy ratio as current therapies, the risk of treatment‐related adverse events far outweighed the risk of progression to MM. 13 In the interim, numerous studies examined risk stratification tools to better predict disease progression and guide clinical management of SMM. While many models have provided encouraging results in defining “high‐risk” SMM, agreement between them remains far from uniform. 14

FIRST‐GENERATION RISK MODELS: TUMOR BURDEN‐BASED

Early efforts to stratify patients with SMM focused on predicting progression to MM using easily acquired clinical markers reflective of tumor burden. The first risk stratification model developed by the Mayo Clinic group used two parameters, BMPC percentage ≥ 10% and serum monoclonal (M) protein level ≥ 3 g/dL, to classify patients into risk groups. This model demonstrated a 15‐year progression risk ranging from 39% in the lowest risk group to 87% in the highest. 12

Around the same time, the Spanish PETHEMA group (Programa para el Estudio de la Terapéutica en Hemopatías Malignas) developed a second model using multiparameter flow cytometry. 15 , 16 , 17 They had previously demonstrated that aberrant plasma cells (aPCs) are identified by immunophenotypic analysis with the absence of CD19 and/or CD45, decreased expression of CD38, and overexpression of CD56. These aPCs corresponded to clonal populations, whereas normal‐phenotype PCs were polyclonal. 15 , 18 The ratio of aPCs to total PCs (aPC/PC) was shown to be an important criterion for differentiating MGUS from MM. 18 , 19 In this model, the combination of immunoparesis, defined as the suppression of one or more uninvolved immunoglobulins, and an aPC/PC ratio ≥ 95% was associated with a 5‐year progression risk of up to 72%, compared to just 4% in the lowest risk group. 15

In 2008, the Mayo group updated their risk model to include the serum free light chain (FLC) ratio, defining high‐risk values as ≥8 for κ clones or ≤0.125 for λ clones. 20 , 21 The revised “Mayo 2008” model showed that patients with all three risk factors—BMPC ≥ 10%, M protein ≥3 g/dL, and an abnormal FLC ratio—had a 76% risk of progression at 5 years, compared to 51% in the intermediate group with two factors and 25% for low‐risk group with only one risk factor. 21

While these models showed promise, a prospective head‐to‐head comparison of the Mayo 2008 and PETHEMA models revealed only 29% concordance. 22 On one hand, this discordance reflects that these two models evaluate different high‐risk parameters; thereby, these features may be complementary in capturing distinct aspects of SMM biology. On the other hand, such discordance highlights practical challenges for clinicians in applying these models consistently when counseling patients or when implementing management strategies.

Importantly, these studies beget further analysis, and other higher risk features began to emerge. BMPC ≥ 60% and FLC ratio ≥ 100 were each associated with 95% and 72% risk of progression within 2 years. 20 , 23 MRI findings of two or more focal lesions were strongly predictive of progression. 24 , 25 , 26 , 27 , 28 Additional risk indicators included IgA subtype, 29 Bence Jones proteinuria, 30 high levels of circulating PCs, 31 and an evolving SMM pattern characterized by progressive increases in M protein or BMPC over time. 19 , 32

In fact, due to the high rates of progression associated with some of these risk factors, the IMWG reached a consensus in 2011 that biomarkers linked to a ≥80% probability of progression to MM within 2 years should be regarded as myeloma‐defining events, warranting early therapeutic intervention. 33 Accordingly, based on the findings discussed above, the diagnosis of MM was modified to include the “SLiM” criteria: FLC ratio ≥ 100, ≥2 focal MRI lesions, and BMPC ≥ 60%, which reflect ultra‐high‐risk SMM patients 6 , 33 (Table 1 ). This change allowed for early initiation of treatment in patients with the potential very high‐risk of progression to MM. 33

It is important to recognize that this stage migration has undoubtedly impacted the overall risk of progression in the new SMM cohort that does not meet the SLiM criteria, which now exhibits a lower rate of progression than their historical counterpart that included SMM with SLiM criteria. Moreover, a few cohort studies reported lower rates of progression than those reported by the IMWG group to justify the inclusion of SLiM criteria. For example, a Danish prospective cohort study concluded that an FLC ratio ≥ 100 was associated with only a 30.4% 2‐year progression risk and was not a statistically significant risk factor. 34 Similar rates were reported in a study at Mount Sinai, where 44% of patients with an FLC ratio ≥ 100 and 41% of patients with BMPC ≥ 60% progressed within 2 years. 35 Given these findings, contemporary practice has largely integrated renal dysfunction, as evidenced by proteinuria > 200 mg/24 h, when considering an FLC ratio > 100 within the context of SLiM‐CRAB criteria. 36

Despite these refinements, several limitations persisted. First, risk stratification models had little impact on standard‐of‐care management outside clinical trials, as both high‐ and low‐risk SMM patients were observed similarly. Second, these models relied heavily on surrogate markers of tumor burden, without accounting for the underlying biological drivers of disease behavior. Third, most models were validated only at diagnosis, missing the evolutionary nature of the disease and preventing real‐time dynamic application. 12 , 37 , 38 , 39 As a result, there was a pressing need for more adaptive, biologically informed prognostic tools that reflect the complexity of disease evolution.

MODERN RISK STRATIFICATION TOOLS AND BIOMARKERS

Following the 2014 redefinition of MM and increased recognition of the limitations of earlier risk models, newer tools have been developed to more accurately predict disease progression in SMM. These contemporary models incorporate not only static measures of tumor burden but also its dynamic changes over time and underlying biological features.

A significant advance came in 2018 with the Mayo Clinic's introduction of the simplified 20/2/20 model, which is based on three easily obtainable clinical parameters: an involved/uninvolved FLC ratio ≥ 20, monoclonal protein ≥ 2 g/dL, and BMPC infiltration ≥ 20%. 40 Patients were stratified into low‐ (0 factors), intermediate‐ (1 factor), and high‐risk (≥2 factors) groups, with respective 2‐year progression risks of 9.7%, 26.3%, and 47%. 40 These findings were subsequently validated by the IMWG in a large multicenter study, which confirmed similar progression rates and further supported the model's clinical utility. 41

To enhance the predictive power of the model, the IMWG‐2020 model incorporated high‐risk cytogenetics abnormalities from clinically obtained FISH, including t(4;14), t(14;16), gain(1q), and del(13q). This resulted in a four‐tiered system with 2‐year progression rates of 6% (low), 23% (low‐intermediate), 46% (intermediate), and 63% (high). 41

Several studies have evaluated the use of the 20/2/20 and the IMWG 2020 criteria for the annual restaging of SMM patients, and have reported reliable risk stratification. 6 , 42 While these models have improved baseline risk assessment, their utility was limited by their reliance on bone marrow and cytogenetic testing, which are not routinely repeated during follow‐up. 42 In contrast, one study incorporated evolving M protein as a dynamic variable into the model and demonstrated an 88% 2‐year progression risk among patients in the high‐risk group. 38

Another study investigated an algorithm designed to identify patients with MM and SMM who exhibit either an MGUS‐like or MM‐like phenotype, with the latter being associated with a higher likelihood of progression. 43 The algorithm utilizes flow cytometry to assess both the percentage of BMPC and the proportion of clonal PCs within the BMPC compartment, classifying patients as MGUS‐like, intermediate, or MM‐like. The 2‐year progression rates for each category were 4.5%, 28% and 63%, respectively. 43

Circulating MM cells (CMMCs) have also emerged as a promising noninvasive biomarker for disease progression and prognosis in MM. 44 , 45 , 46 , 47 These cells exhibit a distinct immunophenotypic profile, characterized by downregulation of integrins, adhesion molecules, and activation markers, as well as cytogenetic differences when compared to clonal BMPCs. 47 In patients with SMM, elevated CMMC counts have been associated with an increased risk of progression to symptomatic MM and poorer clinical outcomes. 46 The IMMUNOCELL study introduced a minimally invasive risk model based on CMMCs and immune markers. Features such as CMMCs > 0.015%, altered monocyte and natural killer (NK) cell subsets, and distinct CD4+ T cell memory signatures outperformed the 20/2/20 model in risk prediction, offering a non–bone marrow–based alternative. 48

Finally, the PANGEA model incorporated both static and dynamic variables, including age, creatinine, hemoglobin, M protein levels, FLC ratio, and BMPC percentage, to generate a continuous risk score. This model provided superior flexibility and predictive accuracy, even in the absence of serial bone marrow assessments. 49 Collectively, these modern tools represent a shift toward biologically and temporally responsive models of SMM risk, addressing key aspects of disease pathophysiology and moving the field closer to personalized monitoring and intervention strategies.

GENOMIC ADVANCES IN SMM

Genomic and molecular analyses have become essential tools for uncovering the underlying mechanisms driving disease progression from SMM to overt MM. Early genomic studies of SMM identified cytogenetic changes that correlated with stable versus progressive SMM. 50 Gains in chromosome 1q and deletions of chromosome 13 were consistently observed in evolving SMM and MM, but were absent or uncommon in non‐evolving SMM. 50 A German study further identified chromosomal abnormalities, including del(17p), t(4;14), gain 1q, and hyperdiploidy, as high‐risk features associated with adverse prognosis. 51 Similar findings were reported by the Mayo Clinic group in 2013. 52

The advent of next‐generation sequencing (NGS) and whole genome sequencing (WGS) in recent years has enabled a deeper understanding of the biology underlying disease progression to MM. 53 , 54 , 55 , 56 , 57 The first study to characterize the genomic landscape of SMM found that the mutational profile and structural rearrangements at the smoldering stage closely resemble those seen in MM. However, the authors identified two distinct patterns of progression: a spontaneous evolution model, in which progression is accompanied by changes in subclonal composition, and a static progression model, where the overall subclonal architecture remains stable throughout disease progression. 54 Seven main single base substitution (SBS) mutational signatures were identified as representative of mutational processes involved in MM development. These included aging‐related signatures, germinal center‐associated polymerase eta somatic hypermutation, reactive oxygen species‐related damage, and apolipoprotein B mRNA editing catalytic polypeptide‐like (APOBEC) enzymes. 58 , 59 Notably, mutational activity of APOBEC cytidine deaminases has been repeatedly implicated in the progression to symptomatic and drug‐resistant MM. 54 , 58 , 59 , 60 , 61

Another large study utilizing NGS demonstrated that SMM exhibits a genetic landscape similar to that of newly diagnosed MM, with the serial acquisition of genetic alterations, most of which are already present at the time of SMM diagnosis. 53 When adjusted for the Mayo 2018 risk model, three genomic alterations emerged as independent predictors of progression: MYC aberrations, MAPK pathway mutations (e.g., KRAS , NRAS ), and DNA repair gene alterations (including del(17p), and mutations in TP53 and ATM ). The inclusion of these genomic risk factors enhanced the predictive accuracy of both the Mayo 2008 and Mayo 2018 models. 53 Although many driver mutations are already acquired by SMM diagnosis, a key observation was the presence of clonal evolution, with subclonal myeloma cell fractions emerging over time. 53 This was further investigated by Boyle et al. (2021), who identified KRAS , NRAS , and FAM46C mutations as “MM‐defining” genetic events, linked to subclonal expansion in cases progressing from SMM to MM. 62 In contrast, MYC rearrangements were not associated with adverse outcomes in that study 62 (Figure 1 ).

Building on this, mutational data from the Pentecost Myeloma Research Center (PMRC) cohort showed that although the overall number of non‐synonymous mutations per sample increased significantly from SMM to MM, 63 the proportion of mutations in any of 81 established MM driver genes 64 did not change significantly between SMM and MM. Notably, however, the frequency of KRAS mutations increased from 1.4% in MGUS patients to 18% in SMM patients (adjusted P‐value = 0.062, Fisher's exact test), suggesting a role in early clonal expansion.

To further advance the understanding of precursor states, Oben et al. (2021) used WGS to characterize patients with precursor conditions, identifying two distinct subgroups: a progressive group, characterized by templated insertions, chromothripsis, mutations in driver genes, canonical APOBEC mutational activity, and aneuploidy; and a stable group, marked by a lower mutational burden and a more indolent clinical course. 65

In parallel to these genomic studies, transcriptional profiling efforts have also contributed to risk stratification in precursor conditions. Gene expression profiling (GEP) of purified MM cells led to the development of a 70‐gene model (GEP70), which proved to be an independent predictor of disease progression. 66 The Southwest Oncology Group (SWOG) developed a risk stratification model incorporating GEP70, elevated involved serum FLC > 25 mg/dL, and M protein spike ≥ 3 g/dL to classify patients into three distinct risk groups: high‐risk (2–3 risk factors, 66.7% progression at 2 years), intermediate‐risk (1 risk factor, 21.9% progression at 2 years), and low‐risk (0 risk factors, 3.4% progression at 2 years). 28 Subsequently, SWOG developed an updated signature termed “GEP4,” based on the expression of only four genes ( RRM2 , DTL , TMEM48 , and ASPM ), which demonstrated improved predictive accuracy when combined with FLC ratio and M spike, estimating an 85.7% 2‐year progression risk and outperforming the original GEP70 model. 61 , 67

Our transcriptional analysis of the PMRC cohort revealed no genes differentially expressed between SMM and NDMM after multiple testing correction, further confirming the transcriptional similarity between these two disease stages. 63 Conversely, we developed a classification model based on Cancer Hallmark enrichment scores that was capable of predicting time to progression (TTP) to MM. Patients exhibiting a “late‐relapse MM‐like” signature—characterized by enrichment in MYC targets, E2F targets, oxidative phosphorylation, G2M checkpoint, and DNA repair pathways—had significantly shorter TTP than those with a “MGUS‐like” signature, which featured enrichment in inflammatory signaling, cytokine responses, and tumor microenvironment dependency pathways, highlighting the biological continuity between SMM and overt MM and reinforcing the need for biologically informed risk models.

A growing body of evidence suggests that epigenetic dysregulation, including aberrant miRNA expression, abnormal DNA methylation, histone modification imbalances, and disrupted chromatin architecture, plays a key role in the pathogenesis and progression of MM. 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 One of the best characterized examples is the t(4;14) translocation, which drives overexpression of the histone methyltransferase NSD2 (nuclear receptor‐binding SET domain protein 2, also known as MMSET ) by placing it under the control of the IGH enhancer. This event results in global and focal changes in H3K36 and H3K27 methylation, contributing to widespread epigenetic reprogramming. 76 Additional mechanisms involving mutations in epigenetic regulators (e.g., TET2 , EP300 , KDM6A , and ARID1A ), post‐translational modifications of histones, altered enhancer landscapes, 3D genome disorganization, and even cues from the tumor microenvironment have all been implicated in the transition from precursor states to symptomatic MM. 71

Our epigenetic analysis using single‐cell multiome (paired scATAC‐seq and scRNA‐seq) confirmed that cytogenetic abnormalities are associated with increased chromatin accessibility and transcriptional activation in MM. Interestingly, while healthy PCs and MGUS cells display relatively similar epigenetic landscapes, samples from patients with SMM and MM showed patient‐specific signatures that, in dimensionality reduction plots, did not follow a continuous trajectory, but rather diverged into distinct epigenetic paths. Notably, sequential samples from the same patient, collected at the SMM and MM stages, overlapped in this space, suggesting that the epigenetic reprogramming characteristic of MM may already be fully established during the smoldering phase.

Targeting epigenetic regulators is currently a promising therapeutic approach to eliminate MM cells or even to prevent disease progression from precursor stages. 71 , 74 , 75 However, there remains a significant gap in our ability to predict which patients will benefit from such strategies, as only a few studies have addressed the functional consequences of specific genetic or epigenetic alterations on a per‐patient basis. This lack of individualized insights continues to challenge the development of precision treatment strategies for SMM. Together, these studies underscore the genetic and epigenetic heterogeneity of myeloma and its critical role in refining risk stratification for patients with precursor conditions (Table 2 ) (Figure 2 ).

NAVIGATING THE SMM TREATMENT CROSSROADS

Historically, the standard management paradigm for SMM has been watchful waiting until the development of myeloma‐defining events. 6 However, with the incorporation of the “SLiM” criteria into the definition of MM, along with advances in risk stratification discussed previously, this passive approach has been increasingly challenged. 77 Several groups have since advocated for early therapeutic intervention in SMM, based on the hypothesis that, similar to precursor lesions in solid tumors, early treatment could prevent progression to overt MM, or even achieve long‐term disease control or cure. 77 , 78 Still, this rationale must be considered in the context of a critical distinction: unlike solid tumor precursors, SMM and MM are systemic diseases, which presents unique challenges to early treatment strategies.

Indeed, the case for treating SMM was explored as early as 1993, when a Swedish group investigated melphalan‐prednisone (MP) therapy in patients with SMM. 79 However, this and several subsequent trials that followed 80 , 81 , 82 , 83 , 84 failed to demonstrate sufficient benefit to justify early treatment, primarily due to limited efficacy and treatment‐related toxicity.

With the advent of novel agents, multiple studies have since presented a stronger case for treating high‐risk SMM, with improved tolerability and response rates. 77 Among these, single‐agent lenalidomide or lenalidomide combined with dexamethasone (Rd) has been the most extensively studied intervention. The QuiRedex study, an open‐label, Phase 3 randomized trial, compared Rd with observation in patients with high‐risk SMM and demonstrated improved overall survival (OS) at 3 years (94% vs. 80%, hazard ratio [HR] 0.31). In addition, early intervention extended progression‐free survival (PFS) by more than 7 years. 85 , 86 Concerns were raised regarding the possible inclusion of patients with subclinical MM, as well as the reintroduction of dexamethasone upon progression. 87 However, the median TTP observed in the observation arm was 21 months, which aligns with expected outcomes for high‐risk SMM. 85 The ECOG‐E3A06 trial, another Phase 3 randomized study, compared lenalidomide with observation in patients with intermediate‐ or high‐risk SMM. This trial demonstrated a significant improvement in PFS, although no OS benefit was observed. 88

While earlier strategies aimed primarily at preventing MM‐associated morbidity, more recent trials have investigated intensive treatment regimens with a curative intent in SMM. 77 The GEM‐CESAR Phase II trial investigated a triplet induction regimen—carfilzomib, lenalidomide, and dexamethasone (KRd)—followed by autologous stem cell transplantation, consolidation, and maintenance therapy. 89 This approach resulted in a 94% PFS at 70 months. Eligibility criteria were based on either the 20/2/20 model or the PETHEMA model, with approximately 52% of patients meeting high‐risk criteria solely under the latter. 89 , 90 However, the minimal residual disease (MRD) negativity rate reported in GEM‐CESAR did not exceed that observed with the same KRd regimen in newly diagnosed MM patients in the FORTE trial. 77 , 91 The ASCENT trial, in contrast, used the 20/2/20 model or IMWG‐2020 model to identify high‐risk SMM patients who were treated with daratumumab–KRd for 12 months, followed by 12 months of daratumumab–lenalidomide (DR) maintenance, totaling 24 months of therapy. 92 Notably, neither ASCENT nor GEM‐CESAR mandate MRI at baseline, raising the possibility that some participants may have had undetected focal lesions, potentially representing subclinical MM. 77 It is important to note that follow‐up in these curative‐intent trials remains relatively short, and longer term data are awaited to fully assess the durability of response and survival outcomes.

Most recently, the CENTAURUS and AQUILA trials investigated the use of single‐agent daratumumab in patients with SMM. 93 , 94 The CENTAURUS trial was a randomized Phase II study that enrolled patients with intermediate‐ and high‐risk SMM (based on 20/2/20 model), demonstrating clinical activity with no significant safety concerns. 94 The AQUILA study, a Phase III trial, randomized high‐risk SMM patients to receive daratumumab or undergo observation. The trial reported improved 5‐year OS (93% vs. 86.9%) and PFS (63% vs. 40.8%) in the daratumumab arm. 93 Notably, while daratumumab significantly prolonged PFS, the benefit may have been driven primarily by delays in the development of anemia, rather than by prevention of serious CRAB‐defining events 95 (Table 3 ).

Ongoing trials are investigating the use of immunotherapy, including chimeric antigen receptor‐T cell (CAR‐T) therapy and bispecific T‐cell Engagers (BiTE), in patients with high‐risk SMM. 96 , 97 These therapies have demonstrated excellent and durable responses in relapsed/refractory MM, supporting their potential utility at earlier stages of disease, when the genetic and immunologic landscape may be less complex. The CAR‐PRISM trial investigated the use of ciltacabtagene autoleucel (cilta‐cel) in six patients with high‐risk SMM, defined according to either the Mayo 20/2/20 or IMWG‐2020 risk models. 96 Preliminary results showed sustained MRD negativity at 10⁻⁶ in all six patients, with a median follow‐up of 6 months (range: 2 months to 1 year). On the other hand, Immuno‐PRISM trial evaluated 12 patients with high‐risk SMM randomized to receive the bispecific antibody teclistamab. Results show a rate of complete response (CR) of 42% with a 100% rate of MRD‐negativity among eight evaluable patients, including two patients who were in very good partial response. 97 More recently, preliminary results from the LINKER‐SMM1 study reported that livoseltamab, a BCMA×CD3 bispecific antibody, achieved a 100% overall response rate with high rates of MRD negativity in patients with high‐risk SMM, and a manageable safety profile. 98 The final results of these trials are eagerly awaited as data becomes more mature.

Despite these encouraging findings, most SMM trials remain limited by small sample sizes, lack of randomization, and reliance on surrogate endpoints. 78 Moreover, the inconsistent application of risk stratification models reduces generalizability, especially given the high discordance between models. 14 , 22 , 78 , 99 Several of these trials have also reported treatment‐related toxicities and deaths. In the absence of control arms in many studies, it remains unclear whether the potential harms, costs, and quality‐of‐life limitations outweigh the benefits of early treatment in patients with SMM. 78

CONCLUSION

The past decade has seen a significant shift in our understanding of the biology and pathophysiology of SMM. Recent trials have challenged the long‐standing paradigm of watchful waiting , particularly in high‐risk SMM, although real‐world changes in the standard of care remain unclear. Currently, the FDA has approved the use of lenalidomide with dexamethasone, and the Oncologic Drugs Advisory Committee (ODAC) has voted 6–2 in favor of approving daratumumab for high‐risk SMM. However, the widespread use of these agents in SMM remains controversial and is likely appropriate only under specific circumstances. Our current paradigm for SMM treatment should be as follows: First, enrollment in clinical trials, ideally those paired with companion diagnostics, remains the preferred approach for initiating therapy. Second, in select cases, treatment may be considered after a period of close observation, which allows patients and their care teams time to assess disease behavior and fully engage with the concept of active surveillance. Third, individualized treatment decisions, guided by patient preferences and evolving biomarkers, are essential as we move toward more personalized care. It is critical that we continue to be judicious in the early use of therapeutics even when given for fixed durations in individuals with SMM, until we can more accurately and consistently assess their risk of progression to MM. At the same time, we must remain mindful of the risk of undertreatment, particularly in light of emerging evidence from studies suggesting that less intensive therapies may fail to alter the disease course meaningfully in truly high‐risk individuals. Accordingly, ongoing research is focused on refining risk stratification models through the integration of molecular, genomic, and clinical data, not just at diagnosis, but dynamically over time, to improve prognostication and guide early intervention strategies in SMM. 100 Further studies are needed to define who truly benefits from early treatment without undue harm, to clarify the potential impact on future anti‐MM therapy, and to determine whether (or which) high‐risk SMM patients should be treated with full anti‐MM regimens rather than preventive strategies.

AUTHOR CONTRIBUTIONS

M. Bakri Hammami : Conceptualization; investigation; writing—original draft; methodology; visualization. Rafael R. Canevarolo : Writing—original draft; visualization; writing—review and editing. Ariosto S. Silva : Writing—review and editing; visualization; methodology. Melissa Alsina : Visualization; writing—review and editing. Nagi Kumar : Visualization; writing—review and editing. Rachid Baz : Conceptualization; supervision; writing—review and editing; methodology. Kenneth H. Shain : Conceptualization; writing—review and editing; supervision; resources; methodology.

CONFLICT OF INTEREST STATEMENT

M.B.H., R.R.C., and N.K.: Nothing to disclose.

A.S.S.: Research funding from AbbVie and Karyopharm Therapeutics, outside the submitted work.

M.A.: Consulting fees from Janssen, Bristol Myers Squibb, and Sanofi; and research support from Bristol Myers Squibb and Pfizer.

R.B.: Research funding from Janssen, Karyopharm, Regeneron, AbbVie, and BMS; advisory board participation with BMS, Janssen, and Pfizer.

K.H.S.: Honoraria from Amgen, Bristol Myers Squibb, Janssen, GSK, Adaptive, Sanofi, Regeneron, and Sebia; research funding from AbbVie, Pfizer, and Karyopharm, outside the submitted work.

FUNDING

This work was supported by philanthropic support from the Pentecost Family Myeloma Research Center and the Bowling Family Foundation.

ACKNOWLEDGMENTS

The authors have nothing to report.

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

REFERENCES

1. Hemminki K, Försti A, Houlston R, Sud A. Epidemiology, genetics and treatment of multiple myeloma and precursor diseases. Int J Cancer. 2021;149(12):1980‐1996. 10.1002/ijc.33762

2. Solly S. Remarks on the pathology of mollities ossium; with cases. J R Soc Med. 1844;27:435‐461. 10.1177/095952874402700129

3. Kyle RA. Monoclonal gammopathy of undetermined significance. Am J Med. 1978;64(5):814‐826. 10.1016/0002-9343(78)90522-3

4. Kyle RA, Greipp PR. Smoldering multiple myeloma. N Engl J Med. 1980;302(24):1347‐1349. 10.1056/NEJM198006123022405

5. Landgren O. Shall we treat smoldering multiple myeloma in the near future? Hematology. 2017;2017(1):194‐204. 10.1182/asheducation-2017.1.194

6. Visram A, Cook J, Warsame R. Smoldering multiple myeloma: evolving diagnostic criteria and treatment strategies. Hematology. 2021;2021(1):673‐681. 10.1182/hematology.2021000304

7. Kumar S, Rajkumar SV. Is it an ember or is it a flame? The natural history of smoldering multiple myeloma. Hematologist. 2023;20(2):202325. 10.1182/hem.V20.2.202325

8. Rajkumar SV. MGUS and smoldering multiple myeloma: update on pathogenesis, natural history, and management. Hematology. 2005;2005:340‐345. 10.1182/asheducation-2005.1.340

9. Group TIMW . Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: a report of the International Myeloma Working Group. Br J Haematol. 2003;121(5):749‐757. 10.1046/j.1365-2141.2003.04355.x

10. Durie BGM, Kyle RA, Belch A, et al. Myeloma management guidelines: a consensus report from the Scientific Advisors of the International Myeloma Foundation. Hematol J. 2003;4(6):379‐398.

11. Kyle RA, Durie BGM, Rajkumar SV, et al. Monoclonal gammopathy of undetermined significance (MGUS) and smoldering (asymptomatic) multiple myeloma: IMWG consensus perspectives risk factors for progression and guidelines for monitoring and management. Leukemia. 2010;24(6):1121‐1127. 10.1038/leu.2010.60

12. Kyle RA, Remstein ED, Therneau TM, et al. Clinical course and prognosis of smoldering (asymptomatic) multiple myeloma. N Engl J Med. 2007;356(25):2582‐2590. 10.1056/NEJMoa070389

13. Greipp PR. Smoldering, asymptomatic stage 1, and indolent myeloma. Curr Treat Options Oncol. 2000;1(2):119‐126. 10.1007/s11864-000-0056-3

14. Hill E, Dew A, Morrison C, et al. Assessment of discordance among smoldering multiple myeloma risk models. JAMA Oncol. 2021;7(1):132‐134. 10.1001/jamaoncol.2020.5585

15. Pérez‐Persona E, Vidriales MB, Mateo G, et al. New criteria to identify risk of progression in monoclonal gammopathy of uncertain significance and smoldering multiple myeloma based on multiparameter flow cytometry analysis of bone marrow plasma cells. Blood. 2007;110(7):2586‐2592. 10.1182/blood-2007-05-088443

16. Ocqueteau M, Orfao A, García‐Sanz R, Almeida J, Gonzalez M, San Miguel JF. Expression of the CD117 antigen (c‐Kit) on normal and myelomatous plasma cells. Br J Haematol. 1996;95(3):489‐493. 10.1111/j.1365-2141.1996.tb08993.x

17. Johnsen HE, Bøgsted M, Klausen TW, et al. Multiparametric flow cytometry profiling of neoplastic plasma cells in multiple myeloma. Cytometry B Clin Cytom. 2010;78(5):338‐347. 10.1002/cyto.b.20523

18. Ocqueteau M, Orfao A, Almeida J, et al. Immunophenotypic characterization of plasma cells from monoclonal gammopathy of undetermined significance patients. Implications for the differential diagnosis between MGUS and multiple myeloma. Am J Pathol. 1998;152(6):1655‐1665.

19. Pérez‐Persona E, Mateo G, García‐Sanz R, et al. Risk of progression in smouldering myeloma and monoclonal gammopathies of unknown significance: comparative analysis of the evolution of monoclonal component and multiparameter flow cytometry of bone marrow plasma cells. Br J Haematol. 2010;148(1):110‐114. 10.1111/j.1365-2141.2009.07929.x

20. Larsen JT, Kumar SK, Dispenzieri A, Kyle RA, Katzmann JA, Rajkumar SV. Serum free light chain ratio as a biomarker for high‐risk smoldering multiple myeloma. Leukemia. 2013;27(4):941‐946. 10.1038/leu.2012.296

21. Dispenzieri A, Kyle RA, Katzmann JA, et al. Immunoglobulin free light chain ratio is an independent risk factor for progression of smoldering (asymptomatic) multiple myeloma. Blood. 2008;111(2):785‐789. 10.1182/blood-2007-08-108357

22. Cherry BM, Korde N, Kwok M, et al. Modeling progression risk for smoldering multiple myeloma: results from a prospective clinical study. Leuk Lymphoma. 2013;54(10):2215‐2218. 10.3109/10428194.2013.764419

23. Rajkumar SV, Larson D, Kyle RA. Diagnosis of smoldering multiple myeloma. N Engl J Med. 2011;365(5):474‐475. 10.1056/NEJMc1106428

24. Hillengass J, Fechtner K, Weber MA, et al. Prognostic significance of focal lesions in whole‐body magnetic resonance imaging in patients with asymptomatic multiple myeloma. J Clin Oncol. 2010;28(9):1606‐1610. 10.1200/JCO.2009.25.5356

25. Kastritis E, Terpos E, Moulopoulos L, et al. Extensive bone marrow infiltration and abnormal free light chain ratio identifies patients with asymptomatic myeloma at high risk for progression to symptomatic disease. Leukemia. 2013;27(4):947‐953. 10.1038/leu.2012.309

26. Moulopoulos LA, Dimopoulos MA, Smith TL, et al. Prognostic significance of magnetic resonance imaging in patients with asymptomatic multiple myeloma. J Clin Oncol. 1995;13(1):251‐256. 10.1200/JCO.1995.13.1.251

27. Walker R, Barlogie B, Haessler J, et al. Magnetic resonance imaging in multiple myeloma: diagnostic and clinical implications. J Clin Oncol. 2007;25(9):1121‐1128. 10.1200/JCO.2006.08.5803

28. Dhodapkar MV, Sexton R, Waheed S, et al. Clinical, genomic, and imaging predictors of myeloma progression from asymptomatic monoclonal gammopathies (SWOG S0120). Blood. 2014;123(1):78‐85. 10.1182/blood-2013-07-515239

29. Weber DM, Dimopoulos MA, Moulopoulos LA, Delasalle KB, Smith T, Alexanian R. Prognostic features of asymptomatic multiple myeloma. Br J Haematol. 1997;97(4):810‐814. 10.1046/j.1365-2141.1997.1122939.x

30. Cesana C, Klersy C, Barbarano L, et al. Prognostic factors for malignant transformation in monoclonal gammopathy of undetermined significance and smoldering multiple myeloma. J Clin Oncol. 2002;20(6):1625‐1634. 10.1200/JCO.2002.20.6.1625

31. Bianchi G, Kyle RA, Larson DR, et al. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia. 2013;27(3):680‐685. 10.1038/leu.2012.237

32. Rosiñol L, Bladé J, Esteve J, et al. Smoldering multiple myeloma: natural history and recognition of an evolving type. Br J Haematol. 2003;123(4):631‐636. 10.1046/j.1365-2141.2003.04654.x

33. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538‐e548. 10.1016/S1470-2045(14)70442-5

34. Sørrig R, Klausen TW, Salomo M, et al. Smoldering multiple myeloma risk factors for progression: a Danish population‐based cohort study. Eur J Haematol. 2016;97(3):303‐309. 10.1111/ejh.12728

35. Wu V, Moshier E, Leng S, et al. Risk stratification of smoldering multiple myeloma: predictive value of free light chains and group‐based trajectory modeling. Blood Adv. 2018;2(12):1470‐1479. 10.1182/bloodadvances.2018016998

36. Visram A, Rajkumar SV, Kapoor P, et al. Monoclonal proteinuria predicts progression risk in asymptomatic multiple myeloma with a free light chain ratio >/=100. Leukemia. 2022;36(5):1429‐1431. 10.1038/s41375-022-01529-w

37. Mouhieddine TH. How to identify and manage high‐risk smoldering multiple myeloma. Curr Oncol Rep. 2024;26(11):1398‐1409. 10.1007/s11912-024-01596-5

38. Fernández de Larrea C, Isola I, Pereira A, et al. Evolving M‐protein pattern in patients with smoldering multiple myeloma: impact on early progression. Leukemia. 2018;32(6):1427‐1434. 10.1038/s41375-018-0013-4

39. de Daniel A, Rodríguez‐Lobato LG, Tovar N, et al. The evolving pattern of the monoclonal protein improves the IMWG 2/20/20 classification for patients with smoldering multiple myeloma. HemaSphere. 2024;8(5):e76. 10.1002/hem3.76

40. Lakshman A, Rajkumar SV, Buadi FK, et al. Risk stratification of smoldering multiple myeloma incorporating revised IMWG diagnostic criteria. Blood Cancer J. 2018;8(6):59. 10.1038/s41408-018-0077-4

41. Mateos MV, Kumar S, Dimopoulos MA, et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J. 2020;10(10):102. 10.1038/s41408-020-00366-3

42. Visram A, Rajkumar SV, Kapoor P, et al. Assessing the prognostic utility of smoldering multiple myeloma risk stratification scores applied serially post diagnosis. Blood Cancer J. 2021;11(11):186. 10.1038/s41408-021-00569-2

43. Burgos L, Tamariz‐Amador LE, Puig N, et al. Definition and clinical significance of the monoclonal gammopathy of undetermined significance‐like phenotype in patients with monoclonal gammopathies. J Clin Oncol. 2023;41(16):3019‐3031. 10.1200/JCO.22.01916

44. Gonsalves WI, Jevremovic D, Nandakumar B, et al. Enhancing the R‐ISS classification of newly diagnosed multiple myeloma by quantifying circulating clonal plasma cells. Am J Hematol. 2020;95(3):310‐315. 10.1002/ajh.25709

45. An G, Qin X, Acharya C, et al. Multiple myeloma patients with low proportion of circulating plasma cells had similar survival with primary plasma cell leukemia patients. Ann Hematol. 2015;94(2):257‐264. 10.1007/s00277-014-2211-0

46. Vigliotta I, Solli V, Armuzzi S, et al. Circulating multiple myeloma cells (CMMCs) as prognostic and predictive markers in multiple myeloma and smouldering MM patients. Cancers. 2024;16(17):2929. 10.3390/cancers16172929

47. Paiva B, Paino T, Sayagues JM, et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood. 2013;122(22):3591‐3598. 10.1182/blood-2013-06-510453

48. Termini R, Žihala D, Terpos E, et al. Circulating tumor and immune cells for minimally invasive risk stratification of smoldering multiple myeloma. Clin Cancer Res. 2022;28(21):4771‐4781. 10.1158/1078-0432.CCR-22-1594

49. Cowan A, Ferrari F, Freeman SS, et al. Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study. Lancet Haematol. 2023;10(3):e203‐e212. 10.1016/S2352-3026(22)00386-6

50. Rosiñol L, Carrió A, Bladé J, et al. Comparative genomic hybridisation identifies two variants of smoldering multiple myeloma. Br J Haematol. 2005;130(5):729‐732. 10.1111/j.1365-2141.2005.05673.x

51. Neben K, Jauch A, Hielscher T, et al. Progression in smoldering myeloma is independently determined by the chromosomal abnormalities del(17p), t(4;14), gain 1q, hyperdiploidy, and tumor load. J Clin Oncol. 2013;31(34):4325‐4332. 10.1200/JCO.2012.48.4923

52. Rajkumar SV, Gupta V, Fonseca R, et al. Impact of primary molecular cytogenetic abnormalities and risk of progression in smoldering multiple myeloma. Leukemia. 2013;27(8):1738‐1744. 10.1038/leu.2013.86

53. Bustoros M, Sklavenitis‐Pistofidis R, Park J, et al. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J Clin Oncol. 2020;38(21):2380‐2389. 10.1200/JCO.20.00437

54. Bolli N, Maura F, Minvielle S, et al. Genomic patterns of progression in smoldering multiple myeloma. Nat Commun. 2018;9(1):3363. 10.1038/s41467-018-05058-y

55. Kazandjian D, Diamond B, Papadimitriou M, et al. Genomic profiling to contextualize the results of intervention for smoldering multiple myeloma. Clin Cancer Res. 2024;30(19):4482‐4490. 10.1158/1078-0432.CCR-24-0210

56. Maura F, Bolli N, Rustad EH, Hultcrantz M, Munshi N, Landgren O. Moving from cancer burden to cancer genomics for smoldering myeloma: a review. JAMA Oncol. 2020;6(3):425‐432. 10.1001/jamaoncol.2019.4659

57. Medina‐Herrera A, Vazquez I, Cuenca I, et al. The genomic profiling of high‐risk smoldering myeloma patients treated with an intensive strategy unveils potential markers of resistance and progression. Blood Cancer J. 2024;14(1):74. 10.1038/s41408-024-01053-3

58. Rustad EH, Yellapantula V, Leongamornlert D, et al. Timing the initiation of multiple myeloma. Nat Commun. 2020;11(1):1917. 10.1038/s41467-020-15740-9

59. Maura F, Petljak M, Lionetti M, et al. Biological and prognostic impact of APOBEC‐induced mutations in the spectrum of plasma cell dyscrasias and multiple myeloma cell lines. Leukemia. 2018;32(4):1043‐1047. 10.1038/leu.2017.345

60. Walker BA, Wardell CP, Murison A, et al. APOBEC family mutational signatures are associated with poor prognosis translocations in multiple myeloma. Nat Commun. 2015;6:6997. 10.1038/ncomms7997

61. Lussier T, Schoebe N, Mai S. Risk stratification and treatment in smoldering multiple myeloma. Cells. 2021;11(1):130. 10.3390/cells11010130

62. Boyle EM, Deshpande S, Tytarenko R, et al. The molecular make up of smoldering myeloma highlights the evolutionary pathways leading to multiple myeloma. Nat Commun. 2021;12(1):293. 10.1038/s41467-020-20524-2

63. Silva AS, Canevarolo R, Reddy Sudalagunta P, et al. Epigenetic plasticity drives carcinogenesis and multi‐therapy resistance in multiple myeloma. Res Sq. 2025. In press. 10.21203/rs.3.rs-6306816/v1

64. Maura F, Rajanna AR, Ziccheddu B, et al. Genomic classification and individualized prognosis in multiple myeloma. J Clin Oncol. 2024;42(11):1229‐1240. 10.1200/JCO.23.01277

65. Oben B, Froyen G, Maclachlan KH, et al. Whole‐genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities. Nat Commun. 2021;12(1):1861. 10.1038/s41467-021-22140-0

66. Shaughnessy JD Jr., Zhan F, Burington BE, et al. A validated gene expression model of high‐risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1. Blood. 2007;109(6):2276‐2284. 10.1182/blood-2006-07-038430

67. Khan R, Dhodapkar M, Rosenthal A, et al. Four genes predict high risk of progression from smoldering to symptomatic multiple myeloma (SWOG S0120). Haematologica. 2015;100(9):1214‐1221. 10.3324/haematol.2015.124651

68. Ismail NH, Mussa A, Zakaria NA, et al. The role of epigenetics in the development and progression of multiple myeloma. Biomedicines. 2022;10(11):2767. 10.3390/biomedicines10112767

69. Alzrigat M, Párraga AA, Jernberg‐Wiklund H. Epigenetics in multiple myeloma: From mechanisms to therapy. Semin Cancer Biol. 2018;51:101‐115. 10.1016/j.semcancer.2017.09.007

70. Dimopoulos K, Gimsing P, Grønbæk K. The role of epigenetics in the biology of multiple myeloma. Blood Cancer J. 2014;4:e207. 10.1038/bcj.2014.29

71. Kumar S, Kats LM, Gruber E. Epigenetic reprogramming in multiple myeloma—challenges and opportunities. Int J Cancer. 2026;158(2):423‐432. 10.1002/ijc.35426

72. Yang T, Liu X, Kumar SK, Jin F, Dai Y. Decoding DNA methylation in epigenetics of multiple myeloma. Blood Rev. 2022;51:100872. 10.1016/j.blre.2021.100872

73. Caprio C, Sacco A, Giustini V, Roccaro AM. Epigenetic aberrations in multiple myeloma. Cancers. 2020;12(10):2996. 10.3390/cancers12102996

74. Muylaert C, Van Hemelrijck LA, Maes A, et al. Aberrant DNA methylation in multiple myeloma: a major obstacle or an opportunity? Front Oncol. 2022;12:979569. 10.3389/fonc.2022.979569

75. De Smedt E, Lui H, Maes K, et al. The epigenome in multiple myeloma: impact on tumor cell plasticity and drug response. Front Oncol. 2018;8:566. 10.3389/fonc.2018.00566

76. Popovic R, Martinez‐Garcia E, Giannopoulou EG, et al. Histone methyltransferase MMSET/NSD2 alters EZH2 binding and reprograms the myeloma epigenome through global and focal changes in H3K36 and H3K27 methylation. PLoS Genet. 2014;10(9):e1004566. 10.1371/journal.pgen.1004566

77. Chakraborty R, Al Hadidi S, Scheffer Cliff ER, Mohyuddin GR. Is aggressive treatment of smoldering myeloma the path to curing myeloma? Blood Adv. 2023;7(15):3932‐3935. 10.1182/bloodadvances.2023009658

78. Kakkilaya A, Trando A, Cliff ERS, et al. Evaluating early intervention in smoldering myeloma clinical trials: a systematic review. Oncologist. 2025;30(2):oyae219. 10.1093/oncolo/oyae219

79. Hjorth M, Hellquist L, Holmberg E, Magnusson B, Rödjer S, Westin J. Initial versus deferred melphalan‐prednisone therapy for asymptomatic multiple myeloma stage I—a randomized study. Eur J Haematol. 1993;50(2):95‐102. 10.1111/j.1600-0609.1993.tb00148.x

80. Rajkumar SV, Gertz MA, Lacy MQ, et al. Thalidomide as initial therapy for early‐stage myeloma. Leukemia. 2003;17(4):775‐779. 10.1038/sj.leu.2402866

81. Grignani G, Gobbi P, Formisano R, et al. A prognostic index for multiple myeloma. Br J Cancer. 1996;73(9):1101‐1107. 10.1038/bjc.1996.212

82. Witzig TE, Laumann KM, Lacy MQ, et al. A phase III randomized trial of thalidomide plus zoledronic acid versus zoledronic acid alone in patients with asymptomatic multiple myeloma. Leukemia. 2013;27(1):220‐225. 10.1038/leu.2012.236

83. Barlogie B, van Rhee F, Shaughnessy JD Jr., et al. Seven‐year median time to progression with thalidomide for smoldering myeloma: partial response identifies subset requiring earlier salvage therapy for symptomatic disease. Blood. 2008;112(8):3122‐3125. 10.1182/blood-2008-06-164228

84. Lust JA, Lacy MQ, Zeldenrust SR, et al. Induction of a chronic disease state in patients with smoldering or indolent multiple myeloma by targeting interleukin 1β‐induced interleukin 6 production and the myeloma proliferative component. Mayo Clin Proc. 2009;84(2):114‐122. 10.4065/84.2.114

85. Mateos MV, Hernández MT, Giraldo P, et al. Lenalidomide plus dexamethasone for high‐risk smoldering multiple myeloma. N Engl J Med. 2013;369(5):438‐447. 10.1056/NEJMoa1300439

86. Mateos MV, Hernández MT, Salvador C, et al. Lenalidomide–dexamethasone versus observation in high‐risk smoldering myeloma after 12 years of median follow‐up time: a randomized, open‐label study. Eur J Cancer. 2022;174:243‐250. 10.1016/j.ejca.2022.07.030

87. Hill E, Dew A, Kazandjian D. State of the science in smoldering myeloma: should we be treating in the clinic? Semin Oncol. 2019;46(2):112‐120. 10.1053/j.seminoncol.2019.03.001

88. Lonial S, Jacobus S, Fonseca R, et al. Randomized trial of lenalidomide versus observation in smoldering multiple myeloma. J Clin Oncol. 2020;38(11):1126‐1137. 10.1200/JCO.19.01740

89. Mateos M‐V, Martínez‐López J, Rodríguez‐Otero P, et al. Curative strategy (GEM‐CESAR) for high‐risk smoldering myeloma (SMM): post‐hoc analysis of sustained undetectable measurable residual disease (MRD). Blood. 2022;140(suppl 1):292‐294. 10.1182/blood-2022-159606

90. Joseph NS, Dhodapkar MV, Lonial S. The role of early intervention in high‐risk smoldering myeloma. Am Soc Clin Oncol Educ Book. 2020;40:355‐363. 10.1200/EDBK_278915

91. Mina R, Musto P, Rota‐Scalabrini D, et al. Carfilzomib induction, consolidation, and maintenance with or without autologous stem‐cell transplantation in patients with newly diagnosed multiple myeloma: pre‐planned cytogenetic subgroup analysis of the randomised, phase 2 FORTE trial. Lancet Oncol. 2023;24(1):64‐76. 10.1016/S1470-2045(22)00693-3

92. Kumar SK, Alsina M, Laplant B, et al. Fixed duration therapy with daratumumab, carfilzomib, lenalidomide and dexamethasone for high risk smoldering multiple myeloma‐results of the ascent trial. Blood. 2022;140(suppl 1):1830‐1832. 10.1182/blood-2022-168930

93. Dimopoulos MA, Voorhees PM, Schjesvold F, et al. Daratumumab or active monitoring for high‐risk smoldering multiple myeloma. N Engl J Med. 2025;392(18):1777‐1788. 10.1056/NEJMoa2409029

94. Landgren O, Chari A, Cohen YC, et al. Efficacy and safety of daratumumab in intermediate/high‐risk smoldering multiple myeloma: final analysis of CENTAURUS. Blood. 2025;145(15):1658‐1669. 10.1182/blood.2024025897

95. Lipof JJ, Pan D, Kumar AD, Chung A, Chari A. AQUILA: dousing the embers of smoldering multiple myeloma with daratumumab monotherapy. Hematologist. 2025;22(2):202523. 10.1182/hem.V22.2.202523

96. Nadeem O, Nikiforow S, DeBraganca K, et al. Early safety and efficacy of CAR‐T cell therapy in precursor myeloma: results of the CAR‐PRISM study using ciltacabtagene autoleucel in high‐risk smoldering myeloma. Blood. 2024;144:1027. 10.1182/blood-2024-202676 39207813

97. Nadeem O, Magidson S, Midha S, et al. Immuno‐PRISM: a randomized phase II platform study of bispecific antibodies in high‐risk smoldering myeloma. Blood. 2023;142(suppl 1):206. 10.1182/blood-2023-177954

98. Rodríguez‐Otero P, Bargay Lleonart JJ, Clavero ME, et al. Safety and efficacy of linvoseltamab (LINVO) in patients (Pts) with high‐risk smoldering multiple myeloma (HR‐SMM): first results from the phase 2 LINKER‐SMM1 trial. Clin Lymphoma Myeloma Leuk. 2025;25:S366‐S367. 10.1016/S2152-2650(25)04011-X

99. Soomro MA, Hoffman J, Goodman AM, Sborov DW, Mohyuddin GR. Heterogeneity of enrolment criteria for ongoing smouldering myeloma trials. Br J Haematol. 2022;197(6):e86‐e88. 10.1111/bjh.18102

100. Alberge JB, Dutta AK, Poletti A, et al. Genomic landscape of multiple myeloma and its precursor conditions. Nat Genet. 2025;57:1493‐1503. 10.1038/s41588-025-02196-0

Associated Data

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.