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:

Multiple myeloma gammopathies

Monitoring the cytogenetic architecture of minimal residual plasma cells indicates therapy-induced clonal selection in multiple myeloma

Leukemia volume 34, pages 578–588 (2020)Cite this article

Subjects

Abstract

Recent attempts have focused on identifying fewer magnitude of minimal residual disease (MRD) rather than exploring the biological and genetic features of the residual plasma cells (PCs). Here, a cohort of 193 patients with at least one cytogenetic abnormalities (CA) at diagnosis were analyzed, and interphase fluorescence in situ hybridization (iFISH) analyses were performed in patient-paired diagnostic and posttherapy samples. Persistent CA in residual PCs were observed for the majority of patients (63%), even detectable in 28/63 (44%) patients with MRD negativity (<10–4). The absence of CA in residual PCs was associated with prolonged survival regardless of MRD status. According to the change of the clonal size of specific CA, patients were clustered into five groups, reflecting different patterns of clone selection under therapy pressure. Therapy-induced clonal selection exerted a significant impact on survival (HR = 4.0; P < 0.001). According to the longitudinal cytogenetic studies at relapse, sequential cytogenetic dynamics were observed in most patients, and cytogenetic architecture of residual PCs could to some extent predict the evolutional pattern at relapse. Collectively, the repeat cytogenetic evaluation in residual PCs could not only serves as a good complementary tool for MRD detection, but also provides a better understanding of clinical response and clonal evolution.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Rawstron AC, Child JA, de Tute RM, Davies FE, Gregory WM, Bell SE, et al. Minimal residual disease assessed by multiparameter flow cytometry in multiple myeloma: impact on outcome in the Medical Research Council Myeloma IX Study. J Clin Oncol. 2013;31:2540–7.

    Article  Google Scholar 

  2. Paiva B, Gutierrez NC, Rosinol L, Vidriales MB, Montalban MA, Martinez-Lopez J, et al. High-risk cytogenetics and persistent minimal residual disease by multiparameter flow cytometry predict unsustained complete response after autologous stem cell transplantation in multiple myeloma. Blood. 2012;119:687–91.

    Article  CAS  Google Scholar 

  3. 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  Google Scholar 

  4. Paiva B, van Dongen JJ, Orfao A. New criteria for response assessment: role of minimal residual disease in multiple myeloma. Blood. 2015;125:3059–68.

    Article  CAS  Google Scholar 

  5. Lahuerta JJ, Paiva B, Vidriales MB, Cordon 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  Google Scholar 

  6. Kazmi SM, Nusrat M, Gunaydin H, Cornelison AM, Shah N, Kebriaei P, et al. Outcomes among high-risk and standard-risk multiple myeloma patients treated with high-dose chemotherapy and autologous hematopoietic stem-cell transplantation. Clin Lymphoma, Myeloma Leuk. 2015;15:687–93.

    Article  Google Scholar 

  7. Takamatsu H, Takezako N, Zheng J, Moorhead M, Carlton VEH, Kong KA, et al. Prognostic value of sequencing-based minimal residual disease detection in patients with multiple myeloma who underwent autologous stem-cell transplantation. Ann Oncol. 2017;28:2503–10.

    Article  CAS  Google Scholar 

  8. Flores-Montero J, Sanoja-Flores L, Paiva B, Puig N, Garcia-Sanchez O, Bottcher 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  Google Scholar 

  9. Bolli N, Avet-Loiseau H, Wedge DC, Van Loo P, Alexandrov LB, Martincorena I, et al. Heterogeneity of genomic evolution and mutational profiles in multiple myeloma. Nat Commun. 2014;5:2997.

    Article  Google Scholar 

  10. Brioli A, Melchor L, Cavo M, Morgan GJ. The impact of intra-clonal heterogeneity on the treatment of multiple myeloma. Br J Haematol. 2014;165:441–54.

    Article  Google Scholar 

  11. 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  Google Scholar 

  12. Paiva B, Vidriales MB, Rosinol L, Martinez-Lopez 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  Google Scholar 

  13. An G, Xu Y, Shi L, Shizhen Z, Deng S, Xie Z, et al. Chromosome 1q21 gains confer inferior outcomes in multiple myeloma treated with bortezomib but copy number variation and percentage of plasma cells involved have no additional prognostic value. Haematologica. 2013;99:353–9.

    Article  Google Scholar 

  14. Flores-Montero J, de Tute R, Paiva B, Perez JJ, Bottcher S, Wind H, et al. Immunophenotype of normal vs. myeloma plasma cells: toward antibody panel specifications for MRD detection in multiple myeloma. Cytom B Clin Cytom. 2016;90:61–72.

    Article  CAS  Google Scholar 

  15. An G, Li Z, Tai YT, Acharya C, Li Q, Qin X, et al. The impact of clone size on the prognostic value of chromosome aberrations by fluorescence in situ hybridization in multiple myeloma. Clin Cancer Res. 2015;21:2148–56.

    Article  Google Scholar 

  16. Miller CA, Mcmichael J, Dang HX, Maher CA, Li D, Ley TJ, et al. Visualizing tumor evolution with the fishplot package for R. Bmc Genomics. 2016;17:880.

    Article  Google Scholar 

  17. Kumar SK, Buadi FK, Rajkumar SV. Pros and cons of frontline autologous transplant in multiple myeloma: the debate over timing. Blood. 2019;133:652–9.

  18. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised international staging system for multiple myeloma: a report from International Myeloma Working Group. J Clin Oncol. 2015;33:2863–9.

    Article  CAS  Google Scholar 

  19. Mikhael JR, Dingli D, Roy V, Reeder CB, Buadi FK, Hayman SR, et al. Management of newly diagnosed symptomatic multiple myeloma: updated Mayo Stratification of Myeloma and Risk-Adapted Therapy (mSMART) consensus guidelines 2013. Mayo Clin Proc. 2013;88:360–76.

    Article  Google Scholar 

  20. Sonneveld P, Avet-Loiseau H, Lonial S, Usmani S, Siegel D, Anderson KC, et al. Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group. Blood. 2016;127:2955–62.

    Article  CAS  Google Scholar 

  21. Merz M, Jauch A, Hielscher T, Mai EK, Seckinger A, Hose D, et al. Longitudinal fluorescence in situ hybridization reveals cytogenetic evolution in myeloma relapsing after autologous transplantation. Haematologica. 2017;102:1432–8.

    Article  CAS  Google Scholar 

  22. Binder M, Rajkumar SV, Ketterling RP, Dispenzieri A, Lacy MQ, Gertz MA, et al. Occurrence and prognostic significance of cytogenetic evolution in patients with multiple myeloma. Blood Cancer J. 2016;6:e401. e

    Article  CAS  Google Scholar 

  23. Cook G, Royle KL, O’Connor S, Cairns DA, Ashcroft AJ, Williams CD, et al. The impact of cytogenetics on duration of response and overall survival in patients with relapsed multiple myeloma (long-term follow-up results from BSBMT/UKMF Myeloma X Relapse [Intensive]): a randomised, open-label, phase 3 trial. Br J Haematol. 2019;185:450–67.

  24. Walker BA, Wardell CP, Melchor L, Hulkki S, Potter NE, Johnson DC, et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4;14) and t(11;14) myeloma. Blood. 2012;120:1077–86.

    Article  CAS  Google Scholar 

  25. Manier S, Salem KZ, Park J, Landau DA, Getz G, Ghobrial IM. Genomic complexity of multiple myeloma and its clinical implications. Nat Rev Clin Oncol. 2017;14:100–13.

    Article  CAS  Google Scholar 

  26. San Miguel J. Multiple myeloma: a model for scientific and clinical progress. Hematol Am Soc Hematol Educ Program. 2014;2014:1–7.

    Article  Google Scholar 

  27. Corre J, Cleynen A, Robiou du Pont S, Buisson L, Bolli N, Attal M, et al. Multiple myeloma clonal evolution in homogeneously treated patients. Leukemia. 2018;32:2636–47.

    Article  Google Scholar 

  28. Keats JJ, Chesi M, Egan JB, Garbitt VM, Palmer SE, Braggio E, et al. Clonal competition with alternating dominance in multiple myeloma. Blood. 2012;120:1067–76.

    Article  CAS  Google Scholar 

  29. Weinhold N, Ashby C, Rasche L, Chavan SS, Stein C, Stephens OW, et al. Clonal selection and double-hit events involving tumor suppressor genes underlie relapse in myeloma. Blood. 2016;128:1735–44.

    Article  CAS  Google Scholar 

  30. Paíno T, Paiva B, Sayagués JM, Mota I, Carvalheiro T, Corchete LA, et al. Phenotypic identification of subclones in multiple myeloma with different chemoresistant, cytogenetic and clonogenic potential. Leukemia. 2014;29:1186–94.

    Article  Google Scholar 

  31. Paiva B, Corchete LA, Vidriales MB, Puig N, Maiso P, Rodriguez I, et al. Phenotypic and genomic analysis of multiple myeloma minimal residual disease tumor cells: a new model to understand chemoresistance. Blood. 2016;127:1896–906.

    Article  CAS  Google Scholar 

  32. Bataille R, Jego G, Robillard N, Barille-Nion S, Harousseau JL, Moreau P, et al. The phenotype of normal, reactive and malignant plasma cells. Identification of "many and multiple myelomas" and of new targets for myeloma therapy. Haematologica. 2006;91:1234–40.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The present study was supported by The Fundamental Research Funds for Excellent Talents of Chinese Academy of Medical Sciences (2018), National Natural Science Foundation of China (81920108006, 81630007, 81670202, and 81570181), CAMS Innovation Fund for Medical Sciences (CIFMS 2017-I2M-1–015; 2017-I2M-1-005; 2016-I2M-3-013).

Author information

Author notes

  1. These authors contributed equally: Yuting Yan, Gang An

Authors and Affiliations

  1. State Key Laboratory of Experimental Hematology, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Science & Peking Union Medical College, 300020, Tianjin, China

    Gang An, Yuting Yan, Yan Xu, Xuehan Mao, Jiahui Liu, Huishou Fan, Qi Wang, Chenxing Du, Zengjun Li, Shuhua Yi, Rui Lv, Shuhui Deng, Weiwei Sui, Mingwei Fu, Mu Hao, Wenyang Huang, Dehui Zou, Yaozhong Zhao, Jianxiang Wang, Tao Cheng & Lugui Qiu

  2. Qilu Hospital of Shandong University (Qingdao), Qingdao, China

    Gang An & Chenglu Yuan

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

    Gang An & Xin Du

  4. LeBow Institute for Myeloma Therapeutics and Jerome Lipper Center for Multiple Myeloma Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA

    Yu-Tzu Tai & Nikhil C. Munshi

Authors
  1. Gang An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuting Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuehan Mao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiahui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huishou Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qi Wang
    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. Zengjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shuhua Yi
    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. Shuhui Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Weiwei Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Mingwei Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Mu Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Wenyang Huang
    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. Yaozhong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chenglu Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Xin Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jianxiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Tao Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Yu-Tzu Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Nikhil C. Munshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Lugui Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Gang An, Nikhil C. Munshi or Lugui Qiu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, G., Yan, Y., Xu, Y. et al. Monitoring the cytogenetic architecture of minimal residual plasma cells indicates therapy-induced clonal selection in multiple myeloma. Leukemia 34, 578–588 (2020). https://doi.org/10.1038/s41375-019-0590-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41375-019-0590-x

This article is cited by

Search

Advanced search

Quick links