Skip to main content
SpringerLink
Log in

Prognostic Indicators in MDS and CMML

  • Chapter
  • First Online:
Pathogenesis and Treatment of Leukemia

  • 442 Accesses

Abstract

Various prognostic scoring systems have been developed over the last 2 decades aiming to better risk stratify myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML). Molecular alterations are increasingly important in the current era of personalized medicine. In this chapter, we review the evolution of prognostic models in MDS and CMML and the development of personalized prognostic assessment in these disorders.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdul Hamid G, Wahab Al-Nehmi A, Shukry S. Diagnosis and classification of myelodysplastic syndrome. London: IntechOpen; 2019.

    Google Scholar 

  2. Platzbecker U, Kubasch AS, Homer-Bouthiette C, Prebet T. Current challenges and unmet medical needs in myelodysplastic syndromes. Leukemia. 2021;35(8):2182–98.

    PubMed  PubMed Central  Google Scholar 

  3. Du M-Y, Xu M, Deng J, Liu L, Guo T, Xia L-H, et al. Evaluation of different scoring systems and gene mutations for the prognosis of myelodysplastic syndrome (MDS) in Chinese population. J Cancer. 2020;11(2):508–19.

    PubMed  PubMed Central  Google Scholar 

  4. Sperling AS, Gibson CJ, Ebert BL. The genetics of myelodysplastic syndrome: from clonal haematopoiesis to secondary leukaemia. Nat Rev Cancer. 2017;17(1):5–19.

    PubMed  Google Scholar 

  5. Neukirchen J, Schoonen WM, Strupp C, Gattermann N, Aul C, Haas R, et al. Incidence and prevalence of myelodysplastic syndromes: data from the Düsseldorf MDS-registry. Leuk Res. 2011;35(12):1591–6.

    PubMed  Google Scholar 

  6. Fenaux P, Haase D, Santini V, Sanz GF, Platzbecker U, Mey U. Myelodysplastic syndromes: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†☆. Ann Oncol. 2021;32(2):142–56.

    PubMed  Google Scholar 

  7. Dao K-HT. Myelodysplastic syndromes: updates and nuances. Med Clin North Am. 2017;101(2):333–50.

    PubMed  PubMed Central  Google Scholar 

  8. Zahid MF, Malik UA, Sohail M, Hassan IN, Ali S, Shaukat MHS. Cytogenetic abnormalities in myelodysplastic syndromes: an overview. Int J Hematol Oncol Stem Cell Res. 2017;11(3):231–9.

    PubMed  PubMed Central  Google Scholar 

  9. Epstein-Peterson ZD, Spitzer B, McCarter J, McGovern E, Levine RL, Tallman MS. De Novo myelodysplastic syndromes in patients 20–50 years old characterized by frequent mutations in TP53 and transcription-related genes. Blood. 2019;134(Supplement_1):2708.

    Google Scholar 

  10. Malcovati L, Papaemmanuil E, Bowen DT, Boultwood J, Della Porta MG, Pascutto C, et al. Clinical significance of SF3B1 mutations in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Blood. 2011;118(24):6239–46.

    PubMed  PubMed Central  Google Scholar 

  11. Santos FP, Kantarjian H, Garcia-Manero G, Ravandi F. The search for better prognostic models in myelodysplastic syndromes. Curr Hematol Malig Rep. 2011;6(1):13–21.

    PubMed  PubMed Central  Google Scholar 

  12. Jankowska AM, Makishima H, Tiu RV, Szpurka H, Huang Y, Traina F, et al. Mutational spectrum analysis of chronic myelomonocytic leukemia includes genes associated with epigenetic regulation: UTX, EZH2, and DNMT3A. Blood. 2011;118(14):3932–41.

    PubMed  PubMed Central  Google Scholar 

  13. Hong M, He G. The 2016 revision to the World Health Organization classification of myelodysplastic syndromes. J Transl Intern Med. 2017;5(3):139–43.

    Google Scholar 

  14. Kubasch AS, Platzbecker U. Patient stratification in myelodysplastic syndromes: how a puzzle may become a map. Hematology. 2020;2020(1):418–25.

    PubMed  PubMed Central  Google Scholar 

  15. Greenberg P, Cox C, Lebeau MM, Fenaux P, Morel P, Sanz G, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89(6):2079–88.

    PubMed  Google Scholar 

  16. Ok CY, Hasserjian RP, Fox PS, Stingo F, Zuo Z, Young KH, et al. Application of the International Prognostic Scoring System-Revised in therapy-related myelodysplastic syndromes and oligoblastic acute myeloid leukemia. Leukemia. 2014;28(1):185–9.

    PubMed  Google Scholar 

  17. Bejar R. Clinical and genetic predictors of prognosis in myelodysplastic syndromes. Haematologica. 2014;99(6):956–64.

    PubMed  PubMed Central  Google Scholar 

  18. Malcovati L, Germing U, Kuendgen A, Della Porta MG, Pascutto C, Invernizzi R, et al. Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. J Clin Oncol. 2007;25(23):3503–10.

    PubMed  Google Scholar 

  19. Malcovati L, Porta MGD, Pascutto C, Invernizzi R, Boni M, Travaglino E, et al. Prognostic factors and life expectancy in myelodysplastic syndromes classified according to WHO criteria: a basis for clinical decision making. J Clin Oncol. 2005;23(30):7594–603.

    PubMed  Google Scholar 

  20. Germing U, Lauseker M, Hildebrandt B, Symeonidis A, Cermak J, Fenaux P, et al. Survival, prognostic factors and rates of leukemic transformation in 381 untreated patients with MDS and del(5q): a multicenter study. Leukemia. 2012;26(6):1286–92.

    PubMed  Google Scholar 

  21. Waszczuk-Gajda A, Mądry K, Machowicz R, Drozd-Sokołowska J, Stella-Hołowiecka B, Mital A, et al. Red blood cell transfusion dependency and hyperferritinemia are associated with impaired survival in patients diagnosed with myelodysplastic syndromes: results from the first polish MDS-PALG registry. Adv Clin Exp Med. 2016;25(4):633–41.

    PubMed  Google Scholar 

  22. Della Porta MG, Tuechler H, Malcovati L, Schanz J, Sanz G, Garcia-Manero G, et al. Validation of WHO classification-based Prognostic Scoring System (WPSS) for myelodysplastic syndromes and comparison with the revised International Prognostic Scoring System (IPSS-R). A study of the International Working Group for Prognosis in Myelodysplasia. Leukemia. 2015;29(7):1502–13.

    PubMed  Google Scholar 

  23. Malcovati L, Della Porta MG, Strupp C, Ambaglio I, Kuendgen A, Nachtkamp K, et al. Impact of the degree of anemia on the outcome of patients with myelodysplastic syndrome and its integration into the WHO classification-based Prognostic Scoring System (WPSS). Haematologica. 2011;96(10):1433–40.

    PubMed  PubMed Central  Google Scholar 

  24. Palomo L, Malinverni R, Cabezón M, Xicoy B, Arnan M, Coll R, et al. DNA methylation profile in chronic myelomonocytic leukemia associates with distinct clinical, biological and genetic features. Epigenetics. 2018;13(1):8–18.

    PubMed  PubMed Central  Google Scholar 

  25. Kantarjian H, O'Brien S, Ravandi F, Cortes J, Shan J, Bennett JM, et al. Proposal for a new risk model in myelodysplastic syndrome that accounts for events not considered in the original International Prognostic Scoring System. Cancer. 2008;113(6):1351–61.

    PubMed  Google Scholar 

  26. Garcia-Manero G, Shan J, Faderl S, Cortes J, Ravandi F, Borthakur G, et al. A prognostic score for patients with lower risk myelodysplastic syndrome. Leukemia. 2008;22(3):538–43.

    PubMed  Google Scholar 

  27. Arnan Sangerman M, Pomares H, Alonso E, Galiano M, Encuentra M, Grau J, et al. Validation of low risk prognostic scoring system (LR-PSS) in patients with lower risk IPSS-R myelodysplastic syndrome. Results from a single center. Blood. 2019;134(Supplement_1):4270.

    Google Scholar 

  28. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Solé F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120(12):2454–65.

    PubMed  PubMed Central  Google Scholar 

  29. Zeidan AM, Komrokji RS. There’s risk, and then there’s RISK: the latest clinical prognostic risk stratification models in myelodysplastic syndromes. Curr Hematol Malig Rep. 2013;8(4):351–60.

    PubMed  PubMed Central  Google Scholar 

  30. Gu S, Xia J, Tian Y, Zi J, Ge Z. A novel scoring system integrating molecular abnormalities with IPSS-R can improve the risk stratification in patients with MDS. BMC Cancer. 2021;21(1):134.

    PubMed  PubMed Central  Google Scholar 

  31. Savic A, Marisavljevic D, Kvrgic V, Stanisavljevic N. Validation of the revised international prognostic scoring system for patients with myelodysplastic syndromes. Acta Haematol. 2014;131(4):231–8.

    PubMed  Google Scholar 

  32. Tefferi A, Gangat N, Mudireddy M, Lasho TL, Finke C, Begna KH, et al. Mayo alliance prognostic model for myelodysplastic syndromes: integration of genetic and clinical information. Mayo Clin Proc. 2018;93(10):1363–74.

    PubMed  Google Scholar 

  33. Lorand-Metze I, Niero-Melo L, Buzzini R, Bernardo WM. Part 2: Myelodysplastic syndromes – classification systems. Hematol Transfus Cell Ther. 2018;40(3):262–6.

    PubMed  PubMed Central  Google Scholar 

  34. Montalban-Bravo G, Takahashi K, Patel K, Wang F, Xingzhi S, Nogueras GM, et al. Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms. Oncotarget. 2018;9(11):9714–27.

    PubMed  PubMed Central  Google Scholar 

  35. Komrokji RS, Corrales-Yepez M, Al Ali N, Kharfan-Dabaja M, Padron E, Fields T, et al. Validation of the MD Anderson Prognostic Risk Model for patients with myelodysplastic syndrome. Cancer. 2012;118(10):2659–64.

    PubMed  Google Scholar 

  36. Pfeilstöcker M, Tuechler H, Sanz G, Schanz J, Garcia-Manero G, Solé F, et al. Time-dependent changes in mortality and transformation risk in MDS. Blood. 2016;128(7):902–10.

    PubMed  PubMed Central  Google Scholar 

  37. Zeidan AM, Sekeres MA, Wang X-F, Al Ali N, Garcia-Manero G, Steensma DP, et al. Comparing the prognostic value of risk stratifying models for patients with lower-risk myelodysplastic syndromes: is one model better? Am J Hematol. 2015;90(11):1036–40.

    PubMed  Google Scholar 

  38. De Swart L, Smith A, Johnston TW, Haase D, Droste J, Fenaux P, et al. Validation of the revised international prognostic scoring system (IPSS-R) in patients with lower-risk myelodysplastic syndromes: a report from the prospective European LeukaemiaNet MDS (EUMDS) registry. Br J Haematol. 2015;170(3):372–83.

    PubMed  Google Scholar 

  39. Moreno Berggren D, Folkvaljon Y, Engvall M, Sundberg J, Lambe M, Antunovic P, et al. Prognostic scoring systems for myelodysplastic syndromes (MDS) in a population-based setting: a report from the Swedish MDS register. Br J Haematol. 2018;181(5):614–27.

    PubMed  Google Scholar 

  40. Zeidan AM, Al Ali N, Barnard J, Padron E, Lancet JE, Sekeres MA, et al. Comparison of clinical outcomes and prognostic utility of risk stratification tools in patients with therapy-related vs de novo myelodysplastic syndromes: a report on behalf of the MDS Clinical Research Consortium. Leukemia. 2017;31(6):1391–7.

    PubMed  Google Scholar 

  41. Neukirchen J, Lauseker M, Blum S, Giagounidis A, Lübbert M, Martino S, et al. Validation of the revised International Prognostic Scoring System (IPSS-R) in patients with myelodysplastic syndrome: A multicenter study. Leuk Res. 2014;38(1):57–64.

    PubMed  Google Scholar 

  42. Sridharan A, Jain R, Bachhuber MA, Yu Y, Ramesh K, Gundabolu K, et al. Epidemiologic study of myelodysplastic syndromes in a multiethnic, inner city cohort. Exp Hematol Oncol. 2014;3(1):22.

    PubMed  PubMed Central  Google Scholar 

  43. Schanz J, Tüchler H, Solé F, Mallo M, Luño E, Cervera J, et al. New comprehensive cytogenetic scoring system for primary myelodysplastic syndromes (MDS) and oligoblastic acute myeloid leukemia after MDS derived from an international database merge. J Clin Oncol. 2012;30(8):820–9.

    PubMed  PubMed Central  Google Scholar 

  44. Naqvi K, Sasaki K, Montalban-Bravo G, Alfonso Pierola A, Yilmaz M, Short N, et al. Clonal hematopoiesis of indeterminate potential-associated mutations and risk of comorbidities in patients with myelodysplastic syndrome. Cancer. 2019;125(13):2233–41.

    PubMed  Google Scholar 

  45. Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28(2):241–7.

    PubMed  Google Scholar 

  46. Veryaskina YA, Titov SE, Kovynev IB, Pospelova TI, Zhimulev IF. Prognostic markers of myelodysplastic syndromes. Medicina (Kaunas). 2020;56(8):376.

    PubMed  Google Scholar 

  47. Ogawa S. Genetics of MDS. Blood. 2019;133(10):1049–59.

    PubMed  PubMed Central  Google Scholar 

  48. Makishima H, Yoshizato T, Yoshida K, Sekeres MA, Radivoyevitch T, Suzuki H, et al. Dynamics of clonal evolution in myelodysplastic syndromes. Nat Genet. 2017;49(2):204–12.

    PubMed  Google Scholar 

  49. Awada H, Thapa B, Visconte V. The genomics of myelodysplastic syndromes: origins of disease evolution, biological pathways, and prognostic implications. Cell. 2020;9(11):2512.

    Google Scholar 

  50. Kennedy JA, Ebert BL. Clinical implications of genetic mutations in myelodysplastic syndrome. J Clin Oncol. 2017;35(9):968–74.

    PubMed  PubMed Central  Google Scholar 

  51. Shallis RM, Ahmad R, Zeidan AM. The genetic and molecular pathogenesis of myelodysplastic syndromes. Eur J Haematol. 2018;101(3):260–71.

    PubMed  Google Scholar 

  52. Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391–405.

    PubMed  Google Scholar 

  53. Chen-Liang T-H. Prognosis in myelodysplastic syndromes: the clinical challenge of genomic integration. J Clin Med. 2021;10(10):2052.

    PubMed  PubMed Central  Google Scholar 

  54. Jafari PA, Sadeghian MH, Miri HH, Sadeghi R, Bagheri R, Lavasani S, et al. Prognostic significance of SF3B1 mutations in patients with myelodysplastic syndromes: a meta-analysis. Crit Rev Oncol Hematol. 2020;145:102832.

    PubMed  Google Scholar 

  55. Liang S, Zhou X, Pan H, Yang Y, Shi L, Wang L. Prognostic value of DNMT3A mutations in myelodysplastic syndromes: a meta-analysis. Hematology. 2019;24(1):613–22.

    PubMed  Google Scholar 

  56. Kurtovic-Kozaric A, Przychodzen B, Singh J, Konarska MM, Clemente MJ, Otrock ZK, et al. PRPF8 defects cause missplicing in myeloid malignancies. Leukemia. 2015;29(1):126–36.

    PubMed  Google Scholar 

  57. Wang H, Guo Y, Dong Z, Li T, Xie X, Wan D, et al. Differential U2AF1 mutation sites, burden and co-mutation genes can predict prognosis in patients with myelodysplastic syndrome. Sci Rep. 2020;10(1):18622.

    PubMed  PubMed Central  Google Scholar 

  58. Haase D, Stevenson KE, Neuberg D, Maciejewski JP, Nazha A, Sekeres MA, et al. TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups. Leukemia. 2019;33(7):1747–58.

    PubMed  PubMed Central  Google Scholar 

  59. Chan O, Komrokji RS. Luspatercept in the treatment of lower-risk myelodysplastic syndromes. Future Oncol. 2021;17(12):1473–81.

    PubMed  Google Scholar 

  60. Bewersdorf JP, Zeidan AM. Risk-adapted, individualized treatment strategies of myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). Cancers. 2021;13(7):1610.

    PubMed  PubMed Central  Google Scholar 

  61. Jung S-H, Kim Y-J, Yim S-H, Kim H-J, Kwon Y-R, Hur E-H, et al. Somatic mutations predict outcomes of hypomethylating therapy in patients with myelodysplastic syndrome. Oncotarget. 2016;7(34):55264–75.

    PubMed  PubMed Central  Google Scholar 

  62. Guo Z, Zhang S-K, Zou Z, Fan R-H, Lyu X-D. Prognostic significance of TET2 mutations in myelodysplastic syndromes: a meta-analysis. Leuk Res. 2017;58:102–7.

    PubMed  Google Scholar 

  63. Jin J, Hu C, Yu M, Chen F, Ye L, Yin X, et al. Prognostic value of isocitrate dehydrogenase mutations in myelodysplastic syndromes: a retrospective cohort study and meta-analysis. PLoS One. 2014;9(6):e100206.

    PubMed  PubMed Central  Google Scholar 

  64. Lin Y, Zheng Y, Wang Z-C, Wang S-Y. Prognostic significance of ASXL1 mutations in myelodysplastic syndromes and chronic myelomonocytic leukemia: a meta-analysis. Hematology. 2016;21(8):454–61.

    PubMed  Google Scholar 

  65. Huang X, Wang X. Effect of enhancer of zeste homolog 2 mutations on the prognosis of patients with myelodysplastic syndrome: a meta-analysis. Medicine (Baltimore). 2020;99(34):e21900.

    PubMed  Google Scholar 

  66. Shou L-H, Cao D, Dong X-H, Fang Q, Wu Y, Zhang Y, et al. Prognostic significance of SETBP1 mutations in myelodysplastic syndromes, chronic myelomonocytic leukemia, and chronic neutrophilic leukemia: a meta-analysis. PLoS One. 2017;12(2):e0171608-e.

    Google Scholar 

  67. Arbab Jafari P, Ayatollahi H, Sadeghi R, Sheikhi M, Asghari A. Prognostic significance of SRSF2 mutations in myelodysplastic syndromes and chronic myelomonocytic leukemia: a meta-analysis. Hematology. 2018;23(10):778–84.

    PubMed  Google Scholar 

  68. Wang X, Song X, Yan X. Effect of RNA splicing machinery gene mutations on prognosis of patients with MDS: a meta-analysis. Medicine (Baltimore). 2019;98(21):e15743.

    PubMed  Google Scholar 

  69. Li B, Zou D, Yang S, Ouyang G, Mu Q. Prognostic significance of U2AF1 mutations in myelodysplastic syndromes: a meta-analysis. J Int Med Res. 2020;48(3):030006051989101.

    Google Scholar 

  70. He W, Zhao C, Hu H. Prognostic effect of RUNX1 mutations in myelodysplastic syndromes: a meta-analysis. Hematology. 2020;25(1):494–501.

    PubMed  Google Scholar 

  71. Hou H-A, Tsai C-H, Lin C-C, Chou W-C, Kuo Y-Y, Liu C-Y, et al. Incorporation of mutations in five genes in the revised International Prognostic Scoring System can improve risk stratification in the patients with myelodysplastic syndrome. Blood Cancer J. 2018;8(4):39.

    PubMed  PubMed Central  Google Scholar 

  72. Nazha A, Narkhede M, Radivoyevitch T, Seastone DJ, Patel BJ, Gerds AT, et al. Incorporation of molecular data into the Revised International Prognostic Scoring System in treated patients with myelodysplastic syndromes. Leukemia. 2016;30(11):2214–20.

    PubMed  Google Scholar 

  73. Chan O, Renneville A, Padron E. Chronic myelomonocytic leukemia diagnosis and management. Leukemia. 2021;35(6):1552–62.

    PubMed  Google Scholar 

  74. Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DAG, Gralnick H, et al. The chronic myeloid leukaemias: guidelines for distinguishing chronic granulocytic, atypical chronic myeloid, and chronic myelomonocytic leukaemia: Proposals by the French - American - British Cooperative Leukaemia Group. Br J Haematol. 1994;87(4):746–54.

    PubMed  Google Scholar 

  75. 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(15):3005–15.

    PubMed  Google Scholar 

  76. Ball M, List AF, Padron E. When clinical heterogeneity exceeds genetic heterogeneity: thinking outside the genomic box in chronic myelomonocytic leukemia. Blood. 2016;128(20):2381–7.

    PubMed  Google Scholar 

  77. Schuler E, Schroeder M, Neukirchen J, Strupp C, Xicoy B, Kündgen A, et al. Refined medullary blast and white blood cell count based classification of chronic myelomonocytic leukemias. Leuk Res. 2014;38(12):1413–9.

    PubMed  Google Scholar 

  78. Moon Y, Kim MH, Kim HR, Ahn J-Y, Huh J, Huh JY, et al. The 2016 WHO versus 2008 WHO Criteria for the diagnosis of chronic myelomonocytic leukemia. Ann Lab Med. 2018;38(5):481–3.

    PubMed  PubMed Central  Google Scholar 

  79. Tremblay D, Rippel N, Feld J, El Jamal SM, Mascarenhas J. Contemporary risk stratification and treatment of chronic myelomonocytic leukemia. Oncologist. 2021;26(5):406–21.

    PubMed  PubMed Central  Google Scholar 

  80. Guru Murthy GS, Dhakal I, Mehta P. Incidence and survival outcomes of chronic myelomonocytic leukemia in the United States. Leuk Lymphoma. 2017;58(7):1648–54.

    PubMed  Google Scholar 

  81. Himmelmann A. Chronic myelomonocytic leukaemia. London: InTech; 2016.

    Google Scholar 

  82. Patnaik MM, Lasho T. Myelodysplastic syndrome/myeloproliferative neoplasm overlap syndromes: a focused review. Hematology Am Soc Hematol Educ Program. 2020;2020(1):460–4.

    PubMed  PubMed Central  Google Scholar 

  83. Patnaik MM, Tefferi A. Chronic Myelomonocytic leukemia: 2020 update on diagnosis, risk stratification and management. Am J Hematol. 2020;95(1):97–115.

    PubMed  Google Scholar 

  84. Germing U, Kündgen A, Gattermann N. Risk assessment in chronic myelomonocytic leukemia (CMML). Leuk Lymphoma. 2004;45(7):1311–8.

    PubMed  Google Scholar 

  85. Mangaonkar AA, Patnaik MM. Advances in chronic myelomonocytic leukemia and future prospects: Lessons learned from precision genomics. Adv Cell Gene Ther. 2019;2(2):e48.

    PubMed  PubMed Central  Google Scholar 

  86. 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(6):1150–60.

    PubMed  Google Scholar 

  87. 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(3):840–9.

    PubMed  Google Scholar 

  88. Ma L, Jiang L, Yang W, Luo Y, Mei C, Zhou X, et al. Real-world data of chronic myelomonocytic leukemia: a Chinese single-center retrospective study. Cancer Med. 2021;10(5):1715–25.

    PubMed  PubMed Central  Google Scholar 

  89. Patnaik MM, Tefferi A. Cytogenetic and molecular abnormalities in chronic myelomonocytic leukemia. Blood Cancer J. 2016;6(2):e393-e.

    Google Scholar 

  90. Itzykson R, Fenaux P, Bowen D, Cross NCP, Cortes J, De Witte T, et al. Diagnosis and treatment of chronic myelomonocytic leukemias in adults: recommendations from the European hematology association and the European LeukemiaNet. Hemasphere. 2018;2(6):e150-e.

    Google Scholar 

  91. 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(10):1408–17.

    PubMed  PubMed Central  Google Scholar 

  92. 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(7):1504–10.

    PubMed  Google Scholar 

  93. 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(19):2428–36.

    PubMed  Google Scholar 

  94. Moreno Berggren D, Kjellander M, Backlund E, Engvall M, Garelius H, Lorenz F, et al. Prognostic scoring systems and comorbidities in chronic myelomonocytic leukaemia: a nationwide population-based study. Br J Haematol. 2021;192(3):474–83.

    PubMed  Google Scholar 

  95. 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 Journal. 2015;5(7):e333-e.

    Google Scholar 

  96. Solary E, Itzykson R. How I treat chronic myelomonocytic leukemia. Blood. 2017;130(2):126–36.

    PubMed  Google Scholar 

  97. Zeidan AM, Hu X, Long JB, Wang R, Ma X, Podoltsev NA, et al. Hypomethylating agent therapy use and survival in older patients with chronic myelomonocytic leukemia in the United States: a large population-based study. Cancer. 2017;123(19):3754–62.

    PubMed  Google Scholar 

  98. Duchmann M, Yalniz FF, Sanna A, Sallman D, Coombs CC, Renneville A, et al. Prognostic role of gene mutations in chronic myelomonocytic leukemia patients treated with hypomethylating agents. EBioMedicine. 2018;31:174–81.

    PubMed  PubMed Central  Google Scholar 

  99. Kwon J. Diagnosis and treatment of chronic myelomonocytic leukemia. Blood Res. 2021;56(S1):S5–S16.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China

    Harinder Gill, Yammy Yung, Cherry Chu & Amber Yip

  2. Department of Medicine, Queen Mary Hospital, Hong Kong, China

    Harinder Gill

Authors
  1. Harinder Gill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yammy Yung
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cherry Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amber Yip
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Harinder Gill .

Editor information

Editors and Affiliations

  1. Department of Medicine, Queen Mary Hospital, Hongkong, Hong Kong

    Harinder Gill

  2. Department of Medicine, Queen Mary Hospital, Hongkong, Hong Kong

    Yok-Lam Kwong

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gill, H., Yung, Y., Chu, C., Yip, A. (2023). Prognostic Indicators in MDS and CMML. In: Gill, H., Kwong, YL. (eds) Pathogenesis and Treatment of Leukemia. Springer, Singapore. https://doi.org/10.1007/978-981-99-3810-0_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3810-0_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3809-4

  • Online ISBN: 978-981-99-3810-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation