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.

  • Article
  • Published:

Minimal Residual Disease

Clinical implications of residual normal plasma cells within bone marrow across various disease stages in multiple myeloma

Leukemia (2024)Cite this article

Subjects

Abstract

Residual normal plasma cells (NPCs), which compete with tumor plasma cells, play an important role in multiple myeloma. However, large-scale cohort studies investigating residual NPCs, especially at the minimal residual disease (MRD) phase, are currently lacking. In this study, we conducted a comprehensive investigation into the clinical significance of residual NPCs throughout the entire disease course in 1363 myeloma patients from the NICHE cohort (NCT04645199). Our results revealed that myeloma patients with high baseline NPCs ratio (≥5%) exhibited distinct indolent features, characterized by lower tumor burden, reduced frequencies of cytopenia, immunoparesis, and high-risk cytogenetics. Importantly, high residual NPCs ratio at diagnosis or relapse was independently associated with favorable survival. High absolute percentages of NPCs at undetectable MRD were related with superior clinical benefit and immune reconstitution. At MRD-positive phases, grouping based on NPCs ratio (<50%, 50–90%, ≥90%) demonstrated better risk stratification compared to residual tumor log levels. Based on the time-dependent NPCs ratio trend, we developed a dynamic MRD model that classifies patients into three groups with diverse longitudinal trends, leading to distinct prognoses. Collectively, residual NPCs serves not only as a valuable complementary biomarker for risk stratification but also provides valuable insights on reclassifications and kinetics of MRD.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Clinical benefit of high NPC ratio (≥5%) within bone marrow at diagnosis or relapse.
Fig. 2: Correlation of high NPC percentage with survival and immune reconstitution in MRD-negative patients.
Fig. 3: NPC ratio refine risk classification in patients with detectable MRD.
Fig. 4: Dynamic NPC ratio model predicts distinct survival outcomes by CONNECTOR.

Similar content being viewed by others

Data availability

The data generated in this study are available upon request from the corresponding author.

References

  1. van de Donk NWCJ, Pawlyn C, Yong KL. Multiple myeloma. Lancet. 2021;397:410–27.

    Article  PubMed  Google Scholar 

  2. Rodriguez-Otero P, Paiva B, San-Miguel JF. Roadmap to cure multiple myeloma. Cancer Treat Rev. 2021;100:102284.

    Article  PubMed  Google Scholar 

  3. Cowan AJ, Green DJ, Kwok M, Lee S, Coffey DG, Holmberg LA, et al. Diagnosis and management of multiple myeloma: a review. JAMA. 2022;327:464–77.

    Article  CAS  PubMed  Google Scholar 

  4. Pérez-Persona E, Vidriales MB, Mateo G, García-Sanz R, Mateos MV, de Coca AG, 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;110:2586–92.

    Article  PubMed  Google Scholar 

  5. Paiva B, Vidriales MB, Mateo G, Pérez JJ, Montalbán MA, Sureda A, et al. The persistence of immunophenotypically normal residual bone marrow plasma cells at diagnosis identifies a good prognostic subgroup of symptomatic multiple myeloma patients. Blood. 2009;114:4369–72.

    Article  CAS  PubMed  Google Scholar 

  6. Zhan F, Barlogie B, Arzoumanian V, Huang Y, Williams DR, Hollmig K, et al. Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis. Blood. 2007;109:1692–700.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Paiva B, Vídriales MB, Rosiñol L, Martínez-López J, Mateos MV, Ocio EM, et al. A multiparameter flow cytometry immunophenotypic algorithm for the identification of newly diagnosed symptomatic myeloma with an MGUS-like signature and long-term disease control. Leukemia. 2013;27:2056–61.

    Article  CAS  PubMed  Google Scholar 

  8. Burgos L, Tamariz-Amador LE, Puig N, Cedena MT, Guerrero C, Jelínek T, et al. Definition and clinical significance of the monoclonal gammopathy of undetermined significance-like phenotype in patients with monoclonal gammopathies. J Clin Oncol. 2023;41:3019–31.

  9. An G, Wang H, Qin X, Shi L, Xu Y, Deng S, et al. Polyclonal serum IgM level identifies a subgroup of multiple myeloma patients with low-risk clinicobiological features and superior survival. Leuk Res. 2014;38:666–72.

    Article  CAS  PubMed  Google Scholar 

  10. Ghosh T, Gonsalves WI, Jevremovic D, Dispenzieri A, Dingli D, Timm MM, et al. The prognostic significance of polyclonal bone marrow plasma cells in patients with relapsing multiple myeloma. Am J Hematol. 2017;92:E507–e12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tschautscher MA, Jevremovic D, Rajkumar V, Dispenzieri A, Lacy MQ, Gertz MA, et al. Prognostic value of minimal residual disease and polyclonal plasma cells in myeloma patients achieving a complete response to therapy. Am J Hematol. 2019;94:751–6.

    Article  CAS  PubMed  Google Scholar 

  12. Munshi NC, Avet-Loiseau H, Rawstron AC, Owen RG, Child JA, Thakurta A, et al. Association of minimal residual disease with superior survival outcomes in patients with multiple myeloma: a meta-analysis. JAMA Oncol. 2017;3:28–35.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Munshi NC, Avet-Loiseau H, Anderson KC, Neri P, Paiva B, Samur M, et al. A large meta-analysis establishes the role of MRD negativity in long-term survival outcomes in patients with multiple myeloma. Blood Adv. 2020;4:5988–99.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lahuerta JJ, Paiva B, Vidriales MB, Cordón L, Cedena MT, Puig N. et al. Depth of response in multiple myeloma: a pooled analysis of three PETHEMA/GEM clinical trials. J Clin Oncol. 2017;35:2900–10.

    Article  CAS  PubMed  Google Scholar 

  15. Paiva B, San-Miguel J, Avet-Loiseau H. MRD in multiple myeloma: does CR really matter? Blood. 2022;140:2423–8.

    Article  CAS  PubMed  Google Scholar 

  16. Usmani SZ, Crowley J, Hoering A, Mitchell A, Waheed S, Nair B, et al. Improvement in long-term outcomes with successive Total Therapy trials for multiple myeloma: are patients now being cured? Leukemia. 2013;27:226–32.

    Article  CAS  PubMed  Google Scholar 

  17. Martinez-Lopez J, Blade J, Mateos MV, Grande C, Alegre A, García-Laraña J, et al. Long-term prognostic significance of response in multiple myeloma after stem cell transplantation. Blood. 2011;118:529–34.

    Article  CAS  PubMed  Google Scholar 

  18. Engelhardt M, Kortüm KM, Goldschmidt H, Merz M. Functional cure and long-term survival in multiple myeloma: how to challenge the previously impossible. Haematologica. Published online February 15, 2024.

  19. Yan W, Qiu C, Du C, Deng S, Sui W, Xu Y, et al. The specific transcriptional signature and clonal selection of MGUS-like profile predict an exceptionally favorable prognosis in patients with newly-diagnosed multiple myeloma. Blood. 2023;142:879.

    Article  Google Scholar 

  20. Paiva B, Manrique I, Dimopoulos MA, Gay F, Min CK, Zweegman S, et al. MRD dynamics during maintenance for improved prognostication of 1280 myeloma patients in TOURMALINE-MM3 and -MM4 trials. Blood. 2023;141:579–91.

  21. Diamond B, Korde N, Lesokhin AM, Smith EL, Shah U, Mailankody S, et al. Dynamics of minimal residual disease in patients with multiple myeloma on continuous lenalidomide maintenance: a single-arm, single-centre, phase 2 trial. Lancet Haematol. 2021;8:e422–e32.

    Article  CAS  PubMed  Google Scholar 

  22. Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15:e538–48.

    Article  PubMed  Google Scholar 

  23. Kumar S, Paiva B, Anderson KC, Durie B, Landgren O, Moreau P, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17:e328–e46.

    Article  PubMed  Google Scholar 

  24. An G, Yan Y, Xu Y, Mao X, Liu J, Fan H, et al. Monitoring the cytogenetic architecture of minimal residual plasma cells indicates therapy-induced clonal selection in multiple myeloma. Leukemia. 2020;34:578–88.

    Article  CAS  PubMed  Google Scholar 

  25. Mao X, Yan W, Mery D, Liu J, Fan H, Xu J, et al. Development and validation of an individualized and weighted Myeloma Prognostic Score System (MPSS) in patients with newly diagnosed multiple myeloma. Am J Hematol. 2024;99:523–33.

  26. Pernice S, Sirovich R, Grassi E, Viviani M, Ferri M, Sassi F, et al. CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system. Bioinformatics. 2023;39:btad201.

  27. Dang M, Wang R, Lee HC, Patel KK, Becnel MR, Han G, et al. Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease. Cancer Cell. 2023;41:1032–47.e4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Zavidij O, Haradhvala NJ, Mouhieddine TH, Sklavenitis-Pistofidis R, Cai S, Reidy M, et al. Single-cell RNA sequencing reveals compromised immune microenvironment in precursor stages of multiple myeloma. Nat Cancer. 2020;1:493–506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Boiarsky R, Haradhvala NJ, Alberge JB, Sklavenitis-Pistofidis R, Mouhieddine TH, Zavidij O, et al. Single cell characterization of myeloma and its precursor conditions reveals transcriptional signatures of early tumorigenesis. Nat Commun. 2022;13:7040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Paiva B, Pérez-Andrés M, Vídriales MB, Almeida J, de las Heras N, Mateos MV, et al. Competition between clonal plasma cells and normal cells for potentially overlapping bone marrow niches is associated with a progressively altered cellular distribution in MGUS vs myeloma. Leukemia. 2011;25:697–706.

    Article  CAS  PubMed  Google Scholar 

  31. Dhodapkar MV. The immune system in multiple myeloma and precursor states: lessons and implications for immunotherapy and interception. Am J Hematol. 2023;98 Suppl 2(Suppl 2):S4-S12.

  32. Poos AM, Prokoph N, Przybilla MJ, Mallm JP, Steiger S, Seufert I, et al. Resolving therapy resistance mechanisms in multiple myeloma by multi-omics subclone analysis. Blood. 2023;142:1633–46.

  33. Misund K, Hofste Op Bruinink D, Coward E, Hoogenboezem RM, Rustad EH, Sanders MA, et al. Clonal evolution after treatment pressure in multiple myeloma: heterogenous genomic aberrations and transcriptomic convergence. Leukemia. 2022;36:1887–97.

  34. Pessoa de Magalhães RJ, Vidriales MB, Paiva B, Fernandez-Gimenez C, García-Sanz R, Mateos MV, et al. Analysis of the immune system of multiple myeloma patients achieving long-term disease control by multidimensional flow cytometry. Haematologica. 2013;98:79–86.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Szalat R, Anderson K, Munshi N. Role of minimal residual disease assessment in multiple myeloma. Haematologica. 2024.

  36. Paiva B, Zherniakova A, Nuñez-Córdoba JM, Rodriguez-Otero P, Shi Q, Munshi NC, et al. Impact of treatment effect on MRD and PFS: an aggregate data analysis from randomized clinical trials in multiple myeloma. Blood Adv. 2023;8:219–23.

    Article  PubMed Central  Google Scholar 

  37. Coffey DG, Maura F, Gonzalez-Kozlova E, Diaz-Mejia JJ, Luo P, Zhang Y, et al. Immunophenotypic correlates of sustained MRD negativity in patients with multiple myeloma. Nat Commun. 2023;14:5335.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Flores-Montero J, Sanoja-Flores L, Paiva B, Puig N, García-Sánchez O, Böttcher S, et al. Next generation flow for highly sensitive and standardized detection of minimal residual disease in multiple myeloma. Leukemia. 2017;31:2094–103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. de Tute RM, Pawlyn C, Cairns DA, Davies FE, Menzies T, Rawstron A, et al. Minimal residual disease after autologous stem-cell transplant for patients with myeloma: prognostic significance and the impact of lenalidomide maintenance and molecular risk. J Clin Oncol. 2022;40:2889–900.

    Article  PubMed  Google Scholar 

  40. Liu J, Yan W, Fan H, Xu J, Li L, Du C, et al. Clinical benefit of autologous stem cell transplantation for multiple myeloma patients achieving undetectable minimal residual disease after induction treatment. Cancer Res Commun. 2023;3:1770–80.

  41. Guerrero C, Termini R, Garcés J-J, Calasanz MJ, Ríos R, Cabezudo E, et al. Kinetics and biology of circulating tumor cells (CTCs) and Measurable Residual Disease (MRD): two dynamic high-risk clones in multiple myeloma (MM). Blood. 2023;142:871.

    Article  Google Scholar 

  42. Costa LJ, Chhabra S, Medvedova E, Dholaria BR, Schmidt TM, Godby KN, et al. Minimal residual disease response-adapted therapy in newly diagnosed multiple myeloma (MASTER): final report of the multicentre, single-arm, phase 2 trial. Lancet Haematol. 2023;10:e890-e901.

  43. Óskarsson J, Rögnvaldsson S, Thorsteinsdottir S, Aspelund T, Gunnarsson SB, Hákonardóttir GK, et al. Determining hemodilution in diagnostic bone marrow aspirated samples in plasma cell disorders by next-generation flow cytometry: proposal for a bone marrow quality index. Blood Cancer J. 2023;13:177.

    Article  PubMed  Google Scholar 

  44. Bai Y, Orfao A, Chim CS. Molecular detection of minimal residual disease in multiple myeloma. Br J Haematol. 2018;181:11–26.

    Article  CAS  PubMed  Google Scholar 

  45. Lakhwani S, Rosiñol L, Puig N, Pico-Picos MA, Medina-González L, Martínez-López J, et al. Recovery of uninvolved heavy/light chain pair immunoparesis in newly diagnosed transplant-eligible myeloma patients complements the prognostic value of minimal residual disease detection. Haematologica. 2024;109:1909–17.

Download references

Acknowledgements

The authors thank the patients and their families, friends, and caregivers and all the study staff and health care providers on coordination; all the faculty and staff at the Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College.

Funding

This work was supported by the National Natural Science Foundation of China (82270218, 82300238, U22A20291, and 81920108006), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2022-I2M-1-022, 2021-I2M-1-041, and 2021-I2M-C&T-B-079).

Author information

Author notes

  1. These authors contributed equally: Wenqiang Yan, Lihui Shi

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China

    Wenqiang Yan, Lihui Shi, Jingyu Xu, Lingna Li, Jian Cui, Yuntong Liu, Jieqiong Zhou, Chenxing Du, Tengteng Yu, Shuaishuai Zhang, Rui Lv, Weiwei Sui, Shuhui Deng, Yan Xu, Dehui Zou, Lugui Qiu, Mu Hao & Gang An

  2. Tianjin Institutes of Health Science, Tianjin, 301600, China

    Wenqiang Yan, Lihui Shi, Jingyu Xu, Lingna Li, Jian Cui, Yuntong Liu, Jieqiong Zhou, Chenxing Du, Tengteng Yu, Shuaishuai Zhang, Rui Lv, Weiwei Sui, Shuhui Deng, Yan Xu, Dehui Zou, Lugui Qiu, Mu Hao & Gang An

  3. Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, China

    Xiaoqing Li & Xin Du

  4. Beijing GoBroad Boren Hospital, Beijing, China

    Lugui Qiu & Gang An

Authors
  1. Wenqiang Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lihui Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jingyu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lingna Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yuntong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jieqiong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chenxing Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tengteng Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shuaishuai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Rui Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Weiwei Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Shuhui Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiaoqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xin Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Dehui Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Lugui Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Mu Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Gang An
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

WY, LS, and GA analyzed data, interpreted results, and drafted the manuscript; JL, JX, LL, JC, YL, CD, XM, TY, and SZ collected data and performed patient follow-up; RL, WS, SD, SY, YX, and GA acquired data and managed patients; XL, MH, XD, YX, and DZ suggested revisions; MH, and LQ, and GA designed the research and approved the final version.

Corresponding authors

Correspondence to Lugui Qiu, Mu Hao or Gang An.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate statement

This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Blood Diseases Hospital. Written informed consent was obtained from all patients to publish this paper.

Additional information

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

Supplementary information

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

Yan, W., Shi, L., Xu, J. et al. Clinical implications of residual normal plasma cells within bone marrow across various disease stages in multiple myeloma. Leukemia (2024). https://doi.org/10.1038/s41375-024-02366-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41375-024-02366-9

Search

Advanced search

Quick links