Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Diagnosis, risk stratification and management of smouldering multiple myeloma

Abstract

New therapies have markedly improved survival outcomes for patients with multiple myeloma (MM). However, the onset of active disease, as defined by the 2014 International Myeloma Working Group SLiM–CRAB criteria, is often linked to substantial and irreversible morbidity. MM always develops from an asymptomatic precursor state called smouldering multiple myeloma (SMM). The clinical trajectory of SMM varies considerably; low-risk SMM often has an indolent course, similar to monoclonal gammopathy of undetermined significance, whereas nearly half of the subset of patients with high-risk SMM have progression to symptomatic MM within 2 years. Highly active treatments for MM, which remains an incurable disease, are being investigated for the management of SMM, with the aim of delaying or even preventing such progression. Both early therapeutic intervention and active surveillance are reasonable management options for patients with high-risk SMM, with decisions individualized through a detailed risk–benefit discussion with the patient. In this Review, we discuss current approaches for diagnostic evaluation, risk stratification and management of SMM, as well as future challenges and emerging opportunities in the field.

Key points

  • Within the first 5 years following diagnosis, smouldering multiple myeloma (SMM) has a tenfold higher risk of progression to MM compared with monoclonal gammopathy of undetermined significance.

  • The clinical course of SMM is heterogeneous; high-risk SMM is associated with a 40–50% risk of progression to MM within the first 2 years of diagnosis.

  • Advanced imaging, especially PET–CT and MRI, is crucial in the diagnosis of SMM, to rule out overt MM and enhance prognostication.

  • Current risk-stratification tools for SMM, although validated and reproducible, are suboptimal; new approaches based on the disease biology are needed to enhance prognostication.

  • For patients with high-risk SMM, both early therapeutic intervention and active surveillance are reasonable management options, with decisions individualized through a detailed risk–benefit discussion with the patient.

  • Rational end point selection, focused on improving long-term patient outcomes, is key for future clinical trial strategies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Disease spectrum of clonal plasma cell disorders.
Fig. 2: Algorithm for the management for smouldering multiple myeloma.

Similar content being viewed by others

References

  1. Joseph, N. S. et al. Quadruplet therapy for newly diagnosed myeloma: comparative analysis of sequential cohorts with triplet therapy lenalidomide, bortezomib and dexamethasone (RVd) versus daratumamab with RVD (DRVd) in transplant-eligible patients. Blood Cancer J. 14, 159 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Binder, M. et al. Mortality trends in multiple myeloma after the introduction of novel therapies in the United States. Leukemia 36, 801–808 (2022).

    Article  PubMed  Google Scholar 

  3. Huang, J. et al. The epidemiological landscape of multiple myeloma: a global cancer registry estimate of disease burden, risk factors, and temporal trends. Lancet Haematol. 9, e670–e677 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10–45 (2025).

    PubMed  PubMed Central  Google Scholar 

  5. Cowan, A. J. et al. Global burden of multiple myeloma: a systematic analysis for the Global Burden of Disease Study 2016. JAMA Oncol. 4, 1221–1227 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Kumar, S. K. et al. Multiple myeloma. Nat. Rev. Dis. Primers 3, 17046 (2017).

    Article  PubMed  Google Scholar 

  7. Landgren, O. et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood 113, 5412–5417 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Rajkumar, S. V. et al. International Myeloma working group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 15, e538–e548 (2014).

    Article  PubMed  Google Scholar 

  9. Kyle, R. A. et al. Long-term follow-up of monoclonal gammopathy of undetermined significance. N. Engl. J. Med. 378, 241–249 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kyle, R. A. et al. Long-term follow-up of IgM monoclonal gammopathy of undetermined significance. Blood 102, 3759–3764 (2003).

    Article  CAS  PubMed  Google Scholar 

  11. Rajkumar, S. V., Larson, D. & Kyle, R. A. Diagnosis of smoldering multiple myeloma. N. Engl. J. Med. 365, 474–475 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Raje, N. & Yee, A. J. How we approach smoldering multiple myeloma. J. Clin. Oncol. 38, 1119–1125 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Thorsteinsdóttir, S. et al. Prevalence of smoldering multiple myeloma based on nationwide screening. Nat. Med. 29, 467–472 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kyle, R. A. et al. Clinical course and prognosis of smoldering (asymptomatic) multiple myeloma. N. Engl. J. Med. 356, 2582–2590 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Rajkumar, S. V., Kumar, S., Lonial, S. & Mateos, M. V. Smoldering multiple myeloma current treatment algorithms. Blood Cancer J. 12, 129 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Rajkumar, S. V., Bergsagel, P. L. & Kumar, S. Smoldering multiple myeloma: observation versus control versus cure. Hematol. Oncol. Clin. North Am. 38, 293–303 (2024).

    Article  PubMed  Google Scholar 

  17. Visram, A., Cook, J. & Warsame, R. Smoldering multiple myeloma: evolving diagnostic criteria and treatment strategies. Hematol. Am. Soc. Hematol. Educ. Program 2021, 673–681 (2021).

    Article  Google Scholar 

  18. Lakshman, A. et al. Risk stratification of smoldering multiple myeloma incorporating revised IMWG diagnostic criteria. Blood Cancer J. 8, 59 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mateos, M.-V. et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J. 10, 102 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Visram, A. et al. Monoclonal proteinuria predicts progression risk in asymptomatic multiple myeloma with a free light chain ratio ≥100. Leukemia 36, 1429–1431 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mangiacavalli, S. et al. Monoclonal gammopathy of undetermined significance: a new proposal of workup. Eur. J. Haematol. 91, 356–360 (2013).

    Article  CAS  PubMed  Google Scholar 

  22. Kyle, R. A. 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 24, 1121–1127 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Zamagni, E., Cavo, M., Fakhri, B., Vij, R. & Roodman, D. Bones in multiple myeloma: imaging and therapy. Am. Soc. Clin. Oncol. Educ. Book 38, 638–646 (2018).

    Article  PubMed  Google Scholar 

  24. Merz, M. et al. Predictive value of longitudinal whole-body magnetic resonance imaging in patients with smoldering multiple myeloma. Leukemia 28, 1902–1908 (2014).

    Article  CAS  PubMed  Google Scholar 

  25. Zamagni, E. et al. Standardization of 18F-FDG–PET/CT according to Deauville criteria for metabolic complete response definition in newly diagnosed multiple myeloma. J. Clin. Oncol. 39, 116–125 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Hill, E. et al. Diagnostic performance of (18) F-FDG-PET/CT compared to standard skeletal survey for detecting bone destruction in smouldering multiple myeloma: time to move forward. Br. J. Haematol. 193, 125–128 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Zamagni, E. et al. 18F-FDG PET/CT focal, but not osteolytic, lesions predict the progression of smoldering myeloma to active disease. Leukemia 30, 417–422 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Hillengass, J. et al. Prognostic significance of focal lesions in whole-body magnetic resonance imaging in patients with asymptomatic multiple myeloma. J. Clin. Oncol. 28, 1606–1610 (2010).

    Article  PubMed  Google Scholar 

  29. Caers, J. et al. The role of positron emission tomography-computed tomography and magnetic resonance imaging in diagnosis and follow up of multiple myeloma. Haematologica 99, 629–637 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ludwig, H., Kainz, S., Schreder, M., Zojer, N. & Hinke, A. SLiM CRAB criteria revisited: temporal trends in prognosis of patients with smoldering multiple myeloma who meet the definition of ‘biomarker-defined early multiple myeloma’ — a systematic review with meta-analysis. eClinicalMedicine 58, 101910 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Akhlaghi, T. et al. Evaluating serum free light chain ratio as a biomarker in multiple myeloma. Haematologica 110, 493–497 (2025).

    PubMed  Google Scholar 

  32. Maura, F. et al. Genomics define malignant transformation in myeloma precursor conditions. J. Clin. Oncol. 8, Jco2501733 (2025).

    Google Scholar 

  33. Pérez-Persona, E. 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 110, 2586–2592 (2007).

    Article  PubMed  Google Scholar 

  34. Dispenzieri, A. et al. Immunoglobulin free light chain ratio is an independent risk factor for progression of smoldering (asymptomatic) multiple myeloma. Blood 111, 785–789 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cowan, A. et al. Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study. Lancet Haematol. 10, e203–e212 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ravi, P. et al. Evolving changes in disease biomarkers and risk of early progression in smoldering multiple myeloma. Blood Cancer J. 6, e454 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fernández de Larrea, C. et al. Evolving M-protein pattern in patients with smoldering multiple myeloma: impact on early progression. Leukemia 32, 1427–1434 (2018).

    Article  PubMed  Google Scholar 

  38. Visram, A. et al. Assessing the prognostic utility of smoldering multiple myeloma risk stratification scores applied serially post diagnosis. Blood Cancer J. 11, 186 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bianchi, G. et al. High levels of peripheral blood circulating plasma cells as a specific risk factor for progression of smoldering multiple myeloma. Leukemia 27, 680–685 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Sanoja-Flores, L. et al. Next generation flow for minimally-invasive blood characterization of MGUS and multiple myeloma at diagnosis based on circulating tumor plasma cells (CTPC). Blood Cancer J. 8, 117 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Uehara, A. et al. Different patterns of antigen expression in bone marrow and circulating tumor plasma cells in patients with MGUS and smoldering multiple myeloma. Blood 144, 4682 (2024).

    Article  Google Scholar 

  42. Aljama, M. A. et al. Plasma cell proliferative index is an independent predictor of progression in smoldering multiple myeloma. Blood Adv. 2, 3149–3154 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Termini, R. et al. Circulating tumor and immune cells for minimally invasive risk stratification of smoldering multiple myeloma. Clin. Cancer Res. 28, 4771–4781 (2022).

    Article  CAS  PubMed  Google Scholar 

  44. Paiva, B. et al. Detailed characterization of multiple myeloma circulating tumor cells shows unique phenotypic, cytogenetic, functional, and circadian distribution profile. Blood 122, 3591–3598 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Vigliotta, I. et al. Circulating multiple myeloma cells (CMMCs) as prognostic and predictive markers in multiple myeloma and smouldering MM patients. Cancers 16, 2929 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bernhard, G. et al. Circulating tumor DNA as a liquid biopsy in plasma cell dyscrasias. Haematologica 103, e245–e248 (2018).

    Article  Google Scholar 

  47. Martello, M. et al. High level of circulating cell-free tumor DNA at diagnosis correlates with disease spreading and defines multiple myeloma patients with poor prognosis. Blood Cancer J. 14, 208 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Kastritis, E. et al. Patterns of progression among 427 smoldering myeloma patients diagnosed after 2014: importance of monitoring. Blood Adv. https://doi.org/10.1182/bloodadvances.2025016083 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bergsagel, P. L. & Kuehl, W. M. Molecular pathogenesis and a consequent classification of multiple myeloma. J. Clin. Oncol. 23, 6333–6338 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. Dhodapkar, M. V. The immune system in multiple myeloma and precursor states: lessons and implications for immunotherapy and interception. Am. J. Hematol. 98, S4–S12 (2023).

    Article  CAS  PubMed  Google Scholar 

  51. Fonseca, R. et al. International Myeloma Working Group molecular classification of multiple myeloma: spotlight review. Leukemia 23, 2210–2221 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Alberge, J.-B. et al. Genomic landscape of multiple myeloma and its precursor conditions. Nat. Genet. 57, 1493–1503 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Maura, F. & Bergsagel, P. L. Targeting the tumor and the immune system in smoldering multiple myeloma. N. Engl. J. Med. 392, 1858–1860 (2025).

    Article  CAS  PubMed  Google Scholar 

  54. Aktas-Samur, A. et al. Differentiating between MM like smoldering myeloma (SMM) and non-progressor SMM. Blood 144, 1018 (2024).

    Article  Google Scholar 

  55. Bolli, N. et al. Genomic patterns of progression in smoldering multiple myeloma. Nat. Commun. 9, 3363 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Bustoros, M. et al. Genomic profiling of smoldering multiple myeloma identifies patients at a high risk of disease progression. J. Clin. Oncol. 38, 2380–2389 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bustoros, M. et al. Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes. Nat. Commun. 13, 3449 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Oben, B. et al. Whole-genome sequencing reveals progressive versus stable myeloma precursor conditions as two distinct entities. Nat. Commun. 12, 1861 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Boyle, E. M. et al. The molecular make up of smoldering myeloma highlights the evolutionary pathways leading to multiple myeloma. Nat. Commun. 12, 293 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Aktas Samur, A. et al. Genomically smoldering multiple myeloma is not a distinct entity but a collection of monoclonal gammopathy of undetermined significance or multiple myeloma. J. Clin. Oncol. https://doi.org/10.1200/JCO-25-00289 (2025).

  61. Misund, K. et al. MYC dysregulation in the progression of multiple myeloma. Leukemia 34, 322–326 (2020).

    Article  PubMed  Google Scholar 

  62. Medina-Herrera, A. 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. 14, 74 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Dang, M. et al. Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease. Cancer Cell 41, 1032–1047.e4 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Dutta, A. K. et al. MinimuMM-seq: genome sequencing of circulating tumor cells for minimally invasive molecular characterization of multiple myeloma pathology. Cancer Discov. 13, 348–363 (2023).

    Article  CAS  PubMed  Google Scholar 

  66. Lightbody, E. D. et al. SWIFT-seq enables comprehensive single-cell transcriptomic profiling of circulating tumor cells in multiple myeloma and its precursors. Nat. Cancer 6, 1595–1611 (2025).

    Article  CAS  PubMed  Google Scholar 

  67. Walker, B. A. et al. Aberrant global methylation patterns affect the molecular pathogenesis and prognosis of multiple myeloma. Blood 117, 553–562 (2011).

    Article  CAS  PubMed  Google Scholar 

  68. Heuck, C. J. et al. Myeloma is characterized by stage-specific alterations in DNA methylation that occur early during myelomagenesis. J. Immunol. 190, 2966–2975 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Liu, R. et al. Co-evolution of tumor and immune cells during progression of multiple myeloma. Nat. Commun. 12, 2559 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Bailur, J. K. et al. Early alterations in stem-like/resident T cells, innate and myeloid cells in the bone marrow in preneoplastic gammopathy. JCI Insight 5, e127807 (2019).

    Article  PubMed  Google Scholar 

  71. Schinke, C. et al. Characterizing the role of the immune microenvironment in multiple myeloma progression at a single-cell level. Blood Adv. 6, 5873–5883 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Kini Bailur, J. et al. Changes in bone marrow innate lymphoid cell subsets in monoclonal gammopathy: target for IMiD therapy. Blood Adv. 1, 2343–2347 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Guillerey, C. et al. Immunosurveillance and therapy of multiple myeloma are CD226 dependent. J. Clin. Investig. 125, 2077–2089 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Paiva, B. et al. Immune status of high-risk smoldering multiple myeloma patients and its therapeutic modulation under LenDex: a longitudinal analysis. Blood 127, 1151–1162 (2016).

    Article  CAS  PubMed  Google Scholar 

  75. Zavidij, O. et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat. Cancer 1, 493–506 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kawano, Y. et al. Blocking IFNAR1 inhibits multiple myeloma-driven Treg expansion and immunosuppression. J. Clin. Invest. 128, 2487–2499 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Baertsch, M.-A. et al. Spatial dissection of the bone marrow microenvironment in multiple myeloma by high dimensional multiplex tissue imaging. Blood 142, 85 (2023).

    Article  Google Scholar 

  78. Kim, J. et al. Macrophages and mesenchymal stromal cells support survival and proliferation of multiple myeloma cells. Br. J. Haematol. 158, 336–346 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Scavelli, C. et al. Vasculogenic mimicry by bone marrow macrophages in patients with multiple myeloma. Oncogene 27, 663–674 (2008).

    Article  CAS  PubMed  Google Scholar 

  80. Sun, J. et al. Tumor-associated macrophages in multiple myeloma: advances in biology and therapy. J. Immunother. Cancer 10, e003975 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Costello, R. T. et al. Differential expression of natural killer cell activating receptors in blood versus bone marrow in patients with monoclonal gammopathy. Immunology 139, 338–341 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Bernal, M. et al. Changes in activatory and inhibitory natural killer (NK) receptors may induce progression to multiple myeloma: implications for tumor evasion of T and NK cells. Hum. Immunol. 70, 854–857 (2009).

    Article  CAS  PubMed  Google Scholar 

  83. Abe, M. et al. Osteoclasts enhance myeloma cell growth and survival via cell–cell contact: a vicious cycle between bone destruction and myeloma expansion. Blood 104, 2484–2491 (2004).

    Article  CAS  PubMed  Google Scholar 

  84. An, G. et al. Osteoclasts promote immune suppressive microenvironment in multiple myeloma: therapeutic implication. Blood 128, 1590–1603 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Jafari, A., Fairfield, H., Andersen, T. L. & Reagan, M. R. Myeloma–bone marrow adipocyte axis in tumour survival and treatment response. Br. J. Cancer 125, 775–777 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Hernandez, M., Shin, S., Muller, C. & Attané, C. The role of bone marrow adipocytes in cancer progression: the impact of obesity. Cancer Metastasis Rev. 41, 589–605 (2022).

    Article  PubMed  Google Scholar 

  87. Robinson, M. H. et al. Regulation of antigen-specific T cell infiltration and spatial architecture in multiple myeloma and premalignancy. J. Clin. Invest. 133, e167629 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Abdallah, N. H. et al. Mode of progression in smoldering multiple myeloma: a study of 406 patients. Blood Cancer J. 14, 9 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Werly, A. et al. Patterns of progression in a contemporary cohort of 447 patients with smoldering multiple myeloma. Blood Cancer J. 14, 176 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Landgren, O. et al. EVIDENCE meta-analysis: evaluating minimal residual disease as an intermediate clinical end point for multiple myeloma. Blood 144, 359–367 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Mateos, M.-V. et al. Curative strategy (GEM-CESAR) for high-risk smoldering myeloma (SMM): post-hoc analysis of sustained undetectable measurable residual disease (MRD). Blood 140, 292–294 (2022).

    Article  Google Scholar 

  92. Kumar, S. K. et al. Fixed duration therapy with daratumumab, carfilzomib, lenalidomide and dexamethasone for high risk smoldering multiple myeloma — results of the ascent trial. Blood 140, 1830–1832 (2022).

    Article  Google Scholar 

  93. Nadeem, O. et al. Immuno-PRISM: a randomized phase II platform study of bispecific antibodies in high-risk smoldering myeloma. Blood 142, 206 (2023).

    Article  Google Scholar 

  94. Kazandjian, D. et al. Carfilzomib, lenalidomide, and dexamethasone followed by lenalidomide maintenance for prevention of symptomatic multiple myeloma in patients with high-risk smoldering myeloma: a phase 2 nonrandomized controlled trial. JAMA Oncol. 7, 1678–1685 (2021).

    Article  PubMed  Google Scholar 

  95. Broijl, A. et al. Results of the phase II randomized trial EMN15/HOVON147: carfilzomib–lenalidomide–dexamethasone vs lenalidomide-dexamethasone in patients with high-risk smoldering multiple myeloma. Blood 144, 676 (2024).

    Article  Google Scholar 

  96. Nadeem, O. et al. Deeper response predicts better outcomes in high-risk-smoldering-myeloma: results of the I-PRISM phase II clinical trial. Nat. Commun. 16, 358 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Landgren, O. et al. Efficacy and safety of daratumumab in intermediate/high-risk smoldering multiple myeloma: final analysis of CENTAURUS. Blood 145, 1658–1669 (2025).

    Article  CAS  PubMed  Google Scholar 

  98. Sklavenitis-Pistofidis, R. et al. Immune biomarkers of response to immunotherapy in patients with high-risk smoldering myeloma. Cancer Cell 40, 1358–1373.e8 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Rögnvaldsson, S. et al. Iceland screens, treats, or prevents multiple myeloma (iStopMM): a population-based screening study for monoclonal gammopathy of undetermined significance and randomized controlled trial of follow-up strategies. Blood Cancer J. 11, 94 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Mateos, M. V. 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 174, 243–250 (2022).

    Article  CAS  PubMed  Google Scholar 

  101. Mateos, M. V. et al. Lenalidomide plus dexamethasone for high-risk smoldering multiple myeloma. N. Engl. J. Med. 369, 438–447 (2013).

    Article  CAS  PubMed  Google Scholar 

  102. Lonial, S. et al. Randomized trial of lenalidomide versus observation in smoldering multiple myeloma. J. Clin. Oncol. 38, 1126–1137 (2020).

    Article  CAS  PubMed  Google Scholar 

  103. Dimopoulos, M. A. et al. Daratumumab or active monitoring for high-risk smoldering multiple myeloma. N. Engl. J. Med. 392, 1777–1788 (2025).

    Article  CAS  PubMed  Google Scholar 

  104. Witzig, T. E. et al. A phase III randomized trial of thalidomide plus zoledronic acid versus zoledronic acid alone in patients with asymptomatic multiple myeloma. Leukemia 27, 220–225 (2013).

    Article  CAS  PubMed  Google Scholar 

  105. Musto, P. et al. A multicenter, randomized clinical trial comparing zoledronic acid versus observation in patients with asymptomatic myeloma. Cancer 113, 1588–1595 (2008).

    Article  PubMed  Google Scholar 

  106. D’Arena, G. et al. Pamidronate versus observation in asymptomatic myeloma: final results with long-term follow-up of a randomized study. Leukemia Lymphoma 52, 771–775 (2011).

    Article  PubMed  Google Scholar 

  107. Hjorth, M. et al. Initial versus deferred melphalan-prednisone therapy for asymptomatic multiple myeloma stage I-a randomized study. Myeloma Group of Western Sweden. Eur. J. Haematol. 50, 95–102 (1993).

    Article  CAS  PubMed  Google Scholar 

  108. Abdallah, N. et al. Phase III randomized trial of Thal+ZLD versus ZLD in patients with asymptomatic multiple myeloma — updated results after 18-year follow-up. Leukemia 38, 1169–1171 (2024).

    Article  PubMed  Google Scholar 

  109. Lonial, S. et al. Daratumumab monotherapy in patients with treatment-refractory multiple myeloma (SIRIUS): an open-label, randomised, phase 2 trial. Lancet 387, 1551–1560 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. Landgren, C. O. et al. Daratumumab monotherapy for patients with intermediate-risk or high-risk smoldering multiple myeloma: a randomized, open-label, multicenter, phase 2 study (CENTAURUS). Leukemia 34, 1840–1852 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Meeting of the Oncologic Drugs Advisory Committee (ODAC). FDA. youtube.com https://www.youtube.com/live/iSGFdhMgh1E (2025).

  112. US Food and Drug Administration. FDA approves daratumumab and hyaluronidase-fihj for high-risk smoldering multiple myeloma. fda.gov https://www.fda.gov/drugs/resources-information-approved-drugs/fda-approves-daratumumab-and-hyaluronidase-fihj-high-risk-smoldering-multiple-myeloma (2025).

  113. European Medicines Agency. Darzalex. ema.europa.eu https://www.ema.europa.eu/en/medicines/human/variation/darzalex (2025).

  114. National Comprehensive Cancer Network. Clinical Practice Guidelines in Oncology: Multiple Myeloma, version 1 (2026). nccn.org https://www.nccn.org/login?ReturnURL=https://www.nccn.org/professionals/physician_gls/pdf/myeloma.pdf. (accessed 1 July 2025).

  115. Dimopoulos, M. A., Voorhees, P. M. & Rajkumar, S. V. Daratumumab for high-risk smoldering multiple myeloma. N. Engl. J. Med. 393, 616–617 (2025).

    Article  Google Scholar 

  116. Sonneveld, P. et al. Daratumumab, bortezomib, lenalidomide, and dexamethasone for multiple myeloma. N. Engl. J. Med. 390, 301–313 (2023).

    Article  PubMed  Google Scholar 

  117. Usmani, S. Z. et al. Final analysis of carfilzomib, dexamethasone, and daratumumab vs carfilzomib and dexamethasone in the CANDOR study. Blood Adv. 7, 3739–3748 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Dimopoulos, M. A., Voorhees, P. M. & Rajkumar, S. V. Daratumumab for high-risk smoldering multiple myeloma. N. Engl. J. Med. 393, 617 (2025).

    PubMed  Google Scholar 

  119. Kazandjian, D. et al. Genomic profiling to contextualize the results of intervention for smoldering multiple myeloma. Clin. Cancer Res. 30, 4482–4490 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Diamond, B. et al. Biallelic antigen escape is a mechanism of resistance to anti-CD38 antibodies in multiple myeloma. Blood 146, 1575–1585 (2025).

    Article  CAS  PubMed  Google Scholar 

  121. Landgren, O. et al. Racial disparities in the prevalence of monoclonal gammopathies: a population-based study of 12 482 persons from the National Health and Nutritional Examination Survey. Leukemia 28, 1537–1542 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Kazandjian, D. Multiple myeloma epidemiology and survival: a unique malignancy. Semin. Oncol. 43, 676–681 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  123. Bertamini, L. et al. Serum free light chains in a racially diverse population including African Americans and populations from South Africa. Blood 145, 840–849 (2025).

    Article  CAS  PubMed  Google Scholar 

  124. Mateos, M.-V. et al. Curativestategy (GEM-CESAR) for high-risk smoldering myeloma (SMM): carfilzomib, lenalidomide and dexamethasone (KRd) as induction followed by HDT-ASCT, consolidation with Krd and maintenance with Rd. Blood 132, 2142 (2018).

    Article  Google Scholar 

  125. Mateos, M.-V. et al. Curative strategy for high-risk smoldering myeloma: carfilzomib, lenalidomide, and dexamethasone (KRd) followed by transplant, KRd consolidation, and Rd maintenance. J. Clin. Oncol. 42, 3247–3256 (2024).

    Article  CAS  PubMed  Google Scholar 

  126. Nadeem, O. et al. Phase II trial of daratumumab, bortezomib, lenalidomide and dexamethasone in high-risk smoldering multiple myeloma. Blood 144, 3360 (2024).

    Article  Google Scholar 

  127. Cohen, A. D. et al. How to train your T cells: overcoming immune dysfunction in multiple myeloma. Clin. Cancer Res. 26, 1541–1554 (2020).

    Article  CAS  PubMed  Google Scholar 

  128. Raab, M. S. et al. Phase 2 study of teclistamab-based induction regimens in patients with transplant-eligible (TE) newly diagnosed multiple myeloma (NDMM): results from the GMMG-HD10/DSMM-XX (MajesTEC-5) trial. Blood 144, 493 (2024).

    Article  Google Scholar 

  129. Paul, B. et al. Idecabtagene vicleucel (Ide-cel) in patients (Pts) with newly diagnosed multiple myeloma (NDMM) with an inadequate response to front-line autologous stem cell transplantation (ASCT): KarMMa-2 cohort 2c extended follow-up. Blood 144, 3388 (2024).

    Article  Google Scholar 

  130. Martín, A. et al. Pamidronate induces bone formation in patients with smouldering or indolent myeloma, with no significant anti-tumour effect. Br. J. Haematol. 118, 239–242 (2002).

    Article  PubMed  Google Scholar 

  131. Lipof, J., Baran, A. M., Passero, F. C., Burrows, K. & Lipe, B. Denosumab for smoldering multiple myeloma: rates of osteoporosis and change in lowest T-score after 12 months of treatment. J. Clin. Oncol. 41, e20057 (2023).

    Article  Google Scholar 

  132. Roy, S. & Trinchieri, G. Microbiota: a key orchestrator of cancer therapy. Nat. Rev. Cancer 17, 271–285 (2017).

    Article  CAS  PubMed  Google Scholar 

  133. Scher, J. U. & Abramson, S. B. The microbiome and rheumatoid arthritis. Nat. Rev. Rheumatol. 7, 569–578 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Halfvarson, J. et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat. Microbiol. 2, 17004 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  136. Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Peng, Z. et al. The gut microbiome is associated with clinical response to anti-PD-1/PD-L1 immunotherapy in gastrointestinal cancer. Cancer Immunol. Res. 8, 1251–1261 (2020).

    Article  PubMed  Google Scholar 

  138. Davar, D. et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science 371, 595–602 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Baruch, E. N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science 371, 602–609 (2021).

    Article  CAS  PubMed  Google Scholar 

  140. Spencer, J. & Sollid, L. M. The human intestinal B-cell response. Mucosal Immunol. 9, 1113–1124 (2016).

    Article  CAS  PubMed  Google Scholar 

  141. Jian, X. et al. Alterations of gut microbiome accelerate multiple myeloma progression by increasing the relative abundances of nitrogen-recycling bacteria. Microbiome 8, 74 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Pianko, M. J. et al. Minimal residual disease negativity in multiple myeloma is associated with intestinal microbiota composition. Blood Adv. 3, 2040–2044 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Calcinotto, A. et al. Microbiota-driven interleukin-17-producing cells and eosinophils synergize to accelerate multiple myeloma progression. Nat. Commun. 9, 4832 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  144. Shah, U. A. et al. A high-fiber dietary intervention (NUTRIVENTION) in precursor plasma cell disorders improves biomarkers of disease and may delay progression to myeloma. Blood 144, 671 (2024).

    Article  Google Scholar 

  145. Joseph, J. M. et al. Dietary risk factors for monoclonal gammopathy of undetermined significance in a racially diverse population. Blood Adv. 8, 538–548 (2024).

    Article  CAS  PubMed  Google Scholar 

  146. Ghobrial, I. M. et al. Round table discussion on optimal clinical trial design in precursor multiple myeloma. Blood Cancer Discov. 5, 146–152 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  147. Rajkumar, S. V., Landgren, O. & Mateos, M. V. Smoldering multiple myeloma. Blood 125, 3069–3075 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Rodriguez Otero, P. et al. Trial in progress: a phase 2 study of linvoseltamab for the treatment of high-risk smoldering multiple myeloma (LINKER-SMM1). Blood 142, 3393 (2023).

    Article  Google Scholar 

Download references

Acknowledgements

The work of S.V.R. is supported by the NIH (R01 and SPORE).

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to S. Vincent Rajkumar.

Ethics declarations

Competing interests

S.K. has acted as a consultant or adviser (with all payments made to his institution) for AbbVie, Bristol Myers Squibb/Celgene, Genentech/Roche, Janssen Oncology/Johnson & Johnson, K36, Pfizer, Regeneron, Sanofi and Takeda; declares research funding (to his institution) from AbbVie, Allogene Therapeutics, Bristol Myers Squibb/Celgene, CARsgen Therapeutics, GlaxoSmithKline, Janssen Oncology/Johnson & Johnson, MedImmune, Novartis, Regeneron, Roche/Genentech, Sanofi and Takeda; and has received reimbursement for travel, accommodation and expenses from AbbVie, Beigene, Janssen/Johnson & Johnson and Pfizer. S.V.R. is Chair of the International Myeloma Foundation Board of Directors; has received honoraria for CME Lectures for Medscape; declares intellectual property and royalties as an author and section editor for UpToDate (covering topics on plasma cell disorders); and served as principal investigator of the AQUILA trial, and in that capacity has also presented on behalf of Johnson & Johnson to the FDA ODAC in support of the approval of daratumumab for high-risk smouldering multiple myeloma. S.Z. declares no competing interests.

Peer review

Peer review information

Nature Reviews Clinical Oncology thanks O. Landgren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zanwar, S., Kumar, S. & Rajkumar, S.V. Diagnosis, risk stratification and management of smouldering multiple myeloma. Nat Rev Clin Oncol (2026). https://doi.org/10.1038/s41571-026-01119-0

Download citation

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41571-026-01119-0

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research