Skip to main content

Thank you for visiting 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.

Myelodysplastic syndrome

Heterogeneous expression of cytokines accounts for clinical diversity and refines prognostication in CMML


Chronic myelomonocytic leukemia (CMML) is a clinically heterogeneous neoplasm in which JAK2 inhibition has demonstrated reductions in inflammatory cytokines and promising clinical activity. We hypothesize that annotation of inflammatory cytokines may uncover mutation-independent cytokine subsets associated with novel CMML prognostic features. A Luminex cytokine profiling assay was utilized to profile cryopreserved peripheral blood plasma from 215 CMML cases from three academic centers, along with center-specific, age-matched plasma controls. Significant differences were observed between CMML patients and healthy controls in 23 out of 45 cytokines including increased cytokine levels in IL-8, IP-10, IL-1RA, TNF-α, IL-6, MCP-1/CCL2, hepatocyte growth factor (HGF), M-CSF, VEGF, IL-4, and IL-2RA. Cytokine associations were identified with clinical and genetic features, and Euclidian cluster analysis identified three distinct cluster groups associated with important clinical and genetic features in CMML. CMML patients with decreased IL-10 expression had a poor overall survival when compared to CMML patients with elevated expression of IL-10 (P = 0.017), even when adjusted for ASXL1 mutation and other prognostic features. Incorporating IL-10 with the Mayo Molecular Model statistically improved the prognostic ability of the model. These established cytokines, such as IL-10, as prognostically relevant and represent the first comprehensive study exploring the clinical implications of the CMML inflammatory state.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3
Fig. 4


  1. 1.

    Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127:2391–405.

    CAS  Article  Google Scholar 

  2. 2.

    Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DA, Gralnick HR, et al. Proposals for the classification of the acute leukaemias. French-American-British (FAB) co-operative group. Br J Haematol. 1976;33:451–8.

    CAS  Article  Google Scholar 

  3. 3.

    Elena C, Galli 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 

  4. 4.

    Ball M, List AF, Padron E. When clinical heterogeneity exceeds genetic heterogeneity: thinking outside the genomic box in chronic myelomonocytic leukemia. Blood. 2016;128:2381–7.

    CAS  Article  Google Scholar 

  5. 5.

    Merlevede J, Droin N, Qin T, Meldi K, Yoshida K, Morabito M, et al. Mutation allele burden remains unchanged in chronic myelomonocytic leukaemia responding to hypomethylating agents. Nat Commun. 2016;7:10767.

    CAS  Article  Google Scholar 

  6. 6.

    Deininger MWN, Tyner JW, Solary E. Turning the tide in myelodysplastic/myeloproliferative neoplasms. Nat Rev Cancer. 2017;17:425–40.

    CAS  Article  Google Scholar 

  7. 7.

    Beran M, Wen S, Shen Y, Onida F, Jelinek J, Cortes J, et al. Prognostic factors and risk assessment in chronic myelomonocytic leukemia: validation study of the M.D. Anderson Prognostic Scoring System. Leuk Lymphoma. 2007;48:1150–60.

    Article  Google Scholar 

  8. 8.

    Aul C, Gattermann N, Heyll A, Germing U, Derigs G, Schneider W. Primary myelodysplastic syndromes: analysis of prognostic factors in 235 patients and proposals for an improved scoring system. Leukemia. 1992;6:52–9.

    CAS  PubMed  Google Scholar 

  9. 9.

    Worsley A, Oscier DG, Stevens J, Darlow S, Figes A, Mufti GJ, et al. Prognostic features of chronic myelomonocytic leukaemia: a modified Bournemouth score gives the best prediction of survival. Br J Haematol. 1988;68:17–21.

    CAS  Article  Google Scholar 

  10. 10.

    Onida F, Kantarjian HM, Smith TL, Ball G, Keating MJ, Estey EH, et al. Prognostic factors and scoring systems in chronic myelomonocytic leukemia: a retrospective analysis of 213 patients. Blood. 2002;99:840–9.

    CAS  Article  Google Scholar 

  11. 11.

    Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–65.

    CAS  Article  Google Scholar 

  12. 12.

    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 

  13. 13.

    Kohlmann A, Grossmann V, Klein HU, Schindela S, Weiss T, Kazak B, et al. Next-generation sequencing technology reveals a characteristic pattern of molecular mutations in 72.8% of chronic myelomonocytic leukemia by detecting frequent alterations in TET2, CBL, RAS, and RUNX1. J Clin Oncol. 2010;28:3858–65.

    CAS  Article  Google Scholar 

  14. 14.

    Palomo L, Garcia O, Arnan M, Xicoy B, Fuster F, Cabezon M, et al. Targeted deep sequencing improves outcome stratification in chronic myelomonocytic leukemia with low risk cytogenetic features. Oncotarget. 2016;7:57021–35.

    Article  Google Scholar 

  15. 15.

    Gelsi-Boyer V, Trouplin V, Roquain J, Adelaide J, Carbuccia N, Esterni B, et al. ASXL1 mutation is associated with poor prognosis and acute transformation in chronic myelomonocytic leukaemia. Br J Haematol. 2010;151:365–75.

    CAS  Article  Google Scholar 

  16. 16.

    Cui Y, Tong H, Du X, Li B, Gale RP, Qin T, et al. Impact of TET2, SRSF2, ASXL1 and SETBP1 mutations on survival of patients with chronic myelomonocytic leukemia. Exp Hematol Oncol. 2015;4:14.

    Article  Google Scholar 

  17. 17.

    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 

  18. 18.

    Lin Y, Zheng Y, Wang ZC, Wang SY. Prognostic significance of ASXL1 mutations in myelodysplastic syndromes and chronic myelomonocytic leukemia: a meta-analysis. Hematology. 2016;21:454–61.

    CAS  Article  Google Scholar 

  19. 19.

    Padron E, Dezern A, Andrade-Campos M, Vaddi K, Scherle P, Zhang Q, et al. A multi-institution phase I trial of ruxolitinib in patients with chronic myelomonocytic leukemia (CMML). Clin Cancer Res. 2016;22:3746–54.

    CAS  Article  Google Scholar 

  20. 20.

    Geissler K, Jager E, Barna A, Sliwa T, Knobl P, Schwarzinger I, et al. In vitro and in vivo effects of JAK2 inhibition in chronic myelomonocytic leukemia. Eur J Haematol. 2016;97:562–7.

    CAS  Article  Google Scholar 

  21. 21.

    Mascarenhas J, Mughal TI, Verstovsek S. Biology and clinical management of myeloproliferative neoplasms and development of the JAK inhibitor ruxolitinib. Curr Med Chem. 2012;19:4399–413.

    CAS  Article  Google Scholar 

  22. 22.

    Verstovsek S, Kantarjian H, Mesa RA, Pardanani AD, Cortes-Franco J, Thomas DA, et al. Safety and efficacy of INCB018424, a JAK1 and JAK2 inhibitor, in myelofibrosis. N Engl J Med. 2010;363:1117–27.

    CAS  Article  Google Scholar 

  23. 23.

    Tyner JW, Bumm TG, Deininger J, Wood L, Aichberger KJ, Loriaux MM, et al. CYT387, a novel JAK2 inhibitor, induces hematologic responses and normalizes inflammatory cytokines in murine myeloproliferative neoplasms. Blood. 2010;115:5232–40.

    CAS  Article  Google Scholar 

  24. 24.

    Tefferi A, Vaidya R, Caramazza D, Finke C, Lasho T, Pardanani A. Circulating interleukin (IL)-8, IL-2R, IL-12, and IL-15 levels are independently prognostic in primary myelofibrosis: a comprehensive cytokine profiling study. J Clin Oncol. 2011;29:1356–63.

    CAS  Article  Google Scholar 

  25. 25.

    Gillis NK, Ball M, Zhang Q, Ma Z, Zhao Y, Yoder SJ, et al. Clonal haemopoiesis and therapy-related myeloid malignancies in elderly patients: a proof-of-concept, case-control study. Lancet Oncol. 2017;18:112–21.

    Article  Google Scholar 

  26. 26.

    Padron E, Painter JS, Kunigal S, Mailloux AW, McGraw K, McDaniel JM, et al. GM-CSF-dependent pSTAT5 sensitivity is a feature with therapeutic potential in chronic myelomonocytic leukemia. Blood. 2013;121:5068–77.

    CAS  Article  Google Scholar 

  27. 27.

    Fraley CRA, Murphy TB, Scrucca L. mclust version 4 for R: normal mixture modeling for model-based clustering, classification, and density estimation. Technical Report No. 597, Department of Statistics, University of Washington. 2012.

  28. 28.

    Raftery CFaAE. Model-based clustering, discriminant analysis and density estimation. J Am Stat Assoc. 2002;97:611–31.

    Article  Google Scholar 

  29. 29.

    CB HousemanEA, Yeh RF, Marsit CJ, Karagas MR, Wrensch M, Nelson HH, Wiemels J, Zheng S, Wiencke JK, Kelsey KT. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics. 2008;9:365.

    Article  Google Scholar 

  30. 30.

    Mirantes C, Passegue E, Pietras EM. Pro-inflammatory cytokines: emerging players regulating HSC function in normal and diseased hematopoiesis. Exp Cell Res. 2014;329:248–54.

    CAS  Article  Google Scholar 

  31. 31.

    Bender S, Haubeck HD, Van de Leur E, Dufhues G, Schiel X, Lauwerijns J, et al. Interleukin-1 beta induces synthesis and secretion of interleukin-6 in human chondrocytes. FEBS Lett. 1990;263:321–4.

    CAS  Article  Google Scholar 

  32. 32.

    Guerne PA, Carson DA, Lotz M. IL-6 production by human articular chondrocytes. Modulation of its synthesis by cytokines, growth factors, and hormones in vitro. J Immunol. 1990;144:499–505.

    CAS  PubMed  Google Scholar 

  33. 33.

    Lotz M, Terkeltaub R, Villiger PM. Cartilage and joint inflammation. Regulation of IL-8 expression by human articular chondrocytes. J Immunol. 1992;148:466–73.

    CAS  PubMed  Google Scholar 

  34. 34.

    Pulsatelli L, Dolzani P, Piacentini A, Silvestri T, Ruggeri R, Gualtieri G, et al. Chemokine production by human chondrocytes. J Rheumatol. 1999;26:1992–2001.

    CAS  PubMed  Google Scholar 

  35. 35.

    Honorati MC, Bovara M, Cattini L, Piacentini A, Facchini A. Contribution of interleukin 17 to human cartilage degradation and synovial inflammation in osteoarthritis. Osteoarthr Cartil. 2002;10:799–807.

    CAS  Article  Google Scholar 

  36. 36.

    Meldi K, Qin T, Buchi F, Droin N, Sotzen J, Micol JB, et al. Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia. J Clin Invest. 2015;125:1857–72.

    Article  Google Scholar 

  37. 37.

    Germing U, Strupp C, Aivado M, Gattermann N. New prognostic parameters for chronic myelomonocytic leukemia. Blood. 2002;100:731–2. author reply 2-3

    CAS  Article  Google Scholar 

  38. 38.

    Zhang Q, Zhao K, Shen Q, Han Y, Gu Y, Li X, et al. Tet2 is required to resolve inflammation by recruiting Hdac2 to specifically repress IL-6. Nature. 2015;525:389–93.

    CAS  Article  Google Scholar 

  39. 39.

    Bird L. Inflammation: TET2: the terminator. Nat Rev Immunol. 2015;15:598.

    CAS  Article  Google Scholar 

  40. 40.

    Feng X, Scheinberg P, Wu CO, Samsel L, Nunez O, Prince C, et al. Cytokine signature profiles in acquired aplastic anemia and myelodysplastic syndromes. Haematologica. 2011;96:602–6.

    CAS  Article  Google Scholar 

  41. 41.

    Boulland ML, Meignin V, Leroy-Viard K, Copie-Bergman C, Briere J, Touitou R, et al. Human interleukin-10 expression in T/natural killer-cell lymphomas: association with anaplastic large cell lymphomas and nasal natural killer-cell lymphomas. Am J Pathol. 1998;153:1229–37.

    CAS  Article  Google Scholar 

  42. 42.

    Nemunaitis J, Fong T, Shabe P, Martineau D, Ando D. Comparison of serum interleukin-10 (IL-10) levels between normal volunteers and patients with advanced melanoma. Cancer Invest. 2001;19:239–47.

    CAS  Article  Google Scholar 

  43. 43.

    Mannino MH, Zhu Z, Xiao H, Bai Q, Wakefield MR, Fang Y. The paradoxical role of IL-10 in immunity and cancer. Cancer Lett. 2015;367:103–7.

    CAS  Article  Google Scholar 

  44. 44.

    Mocellin S, Marincola F, Rossi CR, Nitti D, Lise M. The multifaceted relationship between IL-10 and adaptive immunity: putting together the pieces of a puzzle. Cytokine Growth Factor Rev. 2004;15:61–76.

    CAS  Article  Google Scholar 

  45. 45.

    Kasamatsu T, Saitoh T, Minato Y, Shimizu H, Yokohama A, Tsukamoto N, et al. Polymorphisms of IL-10 affect the severity and prognosis of myelodysplastic syndrome. Eur J Haematol. 2016;96:245–51.

    CAS  Article  Google Scholar 

  46. 46.

    Torisu-Itakura H, Lee JH, Huynh Y, Ye X, Essner R, Morton DL. Monocyte-derived IL-10 expression predicts prognosis of stage IV melanoma patients. J Immunother. 2007;30:831–8.

    CAS  Article  Google Scholar 

  47. 47.

    Vahl JM, Friedrich J, Mittler S, Trump S, Heim L, Kachler K, et al. Interleukin-10-regulated tumour tolerance in non-small cell lung cancer. Br J Cancer. 2017;117:1644–55.

    CAS  Article  Google Scholar 

  48. 48.

    Clerici M, Merola M, Ferrario E, Trabattoni D, Villa ML, Stefanon B, et al. Cytokine production patterns in cervical intraepithelial neoplasia: association with human papillomavirus infection. J Natl Cancer Inst. 1997;89:245–50.

    CAS  Article  Google Scholar 

  49. 49.

    Emmerich J, Mumm JB, Chan IH, LaFace D, Truong H, McClanahan T, et al. IL-10 directly activates and expands tumor-resident CD8(+) T cells without de novo infiltration from secondary lymphoid organs. Cancer Res. 2012;72:3570–81.

    CAS  Article  Google Scholar 

  50. 50.

    Geissler K, Ohler L, Fodinger M, Virgolini I, Leimer M, Kabrna E, et al. Interleukin 10 inhibits growth and granulocyte/macrophage colony-stimulating factor production in chronic myelomonocytic leukemia cells. J Exp Med. 1996;184:1377–84.

    CAS  Article  Google Scholar 

  51. 51.

    Pochlauer S, Jager E, Jager U, Geissler K. Recombinant human interleukin-10 in patients with chronic myelomonocytic leukemia. Ann Hematol. 2014;93:1775–6.

    Article  Google Scholar 

Download references


This research was supported in part by the USF GME Research Grant. Research was funded by EP.

Author contributions

Conception and design: EP and ES; administrative support: EP; provision of study materials or patients: ES, EP, VS, and MWD; collection and assembly of data: ADP, SN, NL, J-MZ, BF, SS, MB, MB, JR, CC, and EP; data analysis and interpretation: MWD, EP, BLF, J-MZ, SN, NL, MB, and JR.

Author information



Corresponding author

Correspondence to Eric Padron.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Niyongere, S., Lucas, N., Zhou, JM. et al. Heterogeneous expression of cytokines accounts for clinical diversity and refines prognostication in CMML. Leukemia 33, 205–216 (2019).

Download citation

Further reading


Quick links