Myelodysplastic syndrome

Prognostic value of monocyte subset distribution in chronic myelomonocytic leukemia: results of a multicenter study

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Flow cytometric profiles and outcome of patients with classical CMML and flow cytometry-defined inflammatory CMML.

References

  1. 1.

    Orazi A, Bennett JM, Germing U, Brunning RD, Bain BJ, Cazzola M, et al. Chronic myelomonocytic leukemia. In: WHO classification of tumours of haematopoietic and lymphoid tissues. Lyon: IARC; 2017. p. 81–6.

  2. 2.

    Selimoglu-Buet D, Wagner-Ballon O, Saada V, Bardet V, Itzykson R, Bencheikh L, et al. Characteristic repartition of monocyte subsets as a diagnostic signature of chronic myelomonocytic leukemia. Blood. 2015;125:3618–26.

    CAS  Article  Google Scholar 

  3. 3.

    Talati C, Zhang L, Shaheen G, Kuykendall A, Ball M, Zhang Q, et al. Monocyte subset analysis accurately distinguishes CMML from MDS and is associated with a favorable MDS prognosis. Blood. 2017;129:1881–3.

    CAS  Article  Google Scholar 

  4. 4.

    Patnaik MM, Timm MM, Vallapureddy R, Lasho TL, Ketterling RP, Gangat N, et al. Flow cytometry based monocyte subset analysis accurately distinguishes chronic myelomonocytic leukemia from myeloproliferative neoplasms with associated monocytosis. Blood Cancer J. 2017;7:e584.

    CAS  Article  Google Scholar 

  5. 5.

    Selimoglu-Buet D, Badaoui B, Benayoun E, Toma A, Fenaux P, Quesnel B, et al. Accumulation of classical monocytes defines a subgroup of MDS that frequently evolves into CMML. Blood. 2017;130:832–5.

    CAS  Article  Google Scholar 

  6. 6.

    Hudson CA, Burack WR, Leary PC, Bennett JM. Clinical utility of classical and nonclassical monocyte percentage in the diagnosis of chronic myelomonocytic leukemia. Am J Clin Pathol. 2018;150:293–302.

    CAS  Article  Google Scholar 

  7. 7.

    Solary E, Itzykson R. How I treat chronic myelomonocytic leukemia. Blood. 2017;130:126–36.

    CAS  Article  Google Scholar 

  8. 8.

    Zahid MF, Barraco D, Lasho TL, Finke C, Ketterling RP, Gangat N, et al. Spectrum of autoimmune diseases and systemic inflammatory syndromes in patients with chronic myelomonocytic leukemia. Leuk Lymphoma. 2017;58:1488–93.

    Article  Google Scholar 

  9. 9.

    Tarfi S, Badaoui B, Freynet N, Morabito M, Lafosse J, Toma A, et al. Disappearance of slan-positive non-classical monocytes for diagnosis of chronic myelomonocytic leukemia with associated inflammatory state. Haematologica. 2020;105:e147–52.

  10. 10.

    Padron E, Garcia-Manero G, Patnaik MM, Itzykson R, Lasho T, Nazha A, et al. An international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies. Blood Cancer J. 2015;5:e333.

    CAS  Article  Google Scholar 

  11. 11.

    Such E, Germing U, Malcovati L, Cervera J, Kuendgen A, Della Porta MG, et al. Development and validation of a prognostic scoring system for patients with chronic myelomonocytic leukemia. Blood. 2013;121:3005–15.

    CAS  Article  Google Scholar 

  12. 12.

    Itzykson R, Kosmider O, Renneville A, Gelsi-Boyer V, Meggendorfer M, Morabito M, et al. Prognostic score including gene mutations in chronic myelomonocytic leukemia. J Clin Oncol. 2013;31:2428–36.

    CAS  Article  Google Scholar 

  13. 13.

    Patnaik MM, Padron E, LaBorde RR, Lasho TL, Finke CM, Hanson CA, et al. Mayo prognostic model for WHO-defined chronic myelomonocytic leukemia: ASXL1 and spliceosome component mutations and outcomes. Leukemia. 2013;27:1504–10.

    CAS  Article  Google Scholar 

  14. 14.

    Elena C, Gallì A, Such E, Meggendorfer M, Germing U, Rizzo E, et al. Integrating clinical features and genetic lesions in the risk assessment of patients with chronic myelomonocytic leukemia. Blood. 2016;128:1408–17.

    CAS  Article  Google Scholar 

  15. 15.

    Hofer TP, van de Loosdrecht AA, Stahl-Hennig C, Cassatella MA, Ziegler-Heitbrock L. 6-Sulfo LacNAc (slan) as a marker for non-classical monocytes. Front Immunol. 2019;10:2052.

    CAS  Article  Google Scholar 

Download references

Groupe Francophone des Myélodysplasies (GFM)

P. Fenaux7, N. Vey10, L. Adès7, A. Guerci11, F. Chermat7, M. Fontenay12, S. Raynaud13, C. Preudhomme14, E. Solary5,8,9, T. Braun15, O. Beynerauzy16, R. Itzykson7, S. Park17, O. Kosmider12, T. Cluzeau13, A. Renneville14

Author information

Affiliations

Authors

Consortia

Contributions

MJ collected clinical and biological annotations on patients, drew the figures, and helped writing the paper. ST, BB, NF, VTQ, and DS-B performed and analyzed the experiments. MD performed statistical analysis. IS and ND performed mutational analysis. MM prepared samples. ML, SM, PF, and ES provided patient samples. ES obtained the grants and critically reviewed the article. DS-B helped conceiving the study, analyzed data, and helped writing the paper. OW-B conceived the study, analyzed data, and wrote the paper. All authors revised and approved the manuscript.

Corresponding author

Correspondence to Orianne Wagner-Ballon.

Ethics declarations

Conflict of interest

OW-B, DS-B, ND, and ES have a patent issued, relevant to the work. The other authors declare no conflict of interest.

Additional information

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

Members of the Groupe Francophone des Myélodysplasies (GFM) are listed at the end of paper.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jestin, M., Tarfi, S., Duchmann, M. et al. Prognostic value of monocyte subset distribution in chronic myelomonocytic leukemia: results of a multicenter study. Leukemia (2020). https://doi.org/10.1038/s41375-020-0955-1

Download citation