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International Journal of Hematology

, Volume 108, Issue 6, pp 598–606 | Cite as

Impact of splicing factor mutations on clinical features in patients with myelodysplastic syndromes

  • Naoki Shingai
  • Yuka Harada
  • Hiroko Iizuka
  • Yosuke Ogata
  • Noriko Doki
  • Kazuteru Ohashi
  • Masao Hagihara
  • Norio Komatsu
  • Hironori Harada
Original Article
  • 106 Downloads

Abstract

Splicing factor gene mutations are found in 60–70% of patients with myelodysplastic syndromes (MDS). We investigated the effects of splicing factor gene mutations on the diagnosis, patient characteristics, and prognosis of MDS. A total of 106 patients with MDS were included. The percentage of patients with MDS with ring sideroblasts (14.15%) as per the 2017 WHO classification was significantly higher than that of patients with refractory anemia with ring sideroblasts (2.88%) as per the 2008 WHO classification (P = 0.005). Splicing factor mutations were detected in 32 patients (13 SF3B1, 8 U2AF1, and 11 SRSF2), and the mutations were mutually exclusive. Significant differences were observed in the mean corpuscular volume, platelet count, bone marrow myeloid:erythroid ratio, and megakaryocyte count in patients with different mutations. SRSF2 mutations were associated with a high cumulative incidence of red blood cell transfusion dependence, while SF3B1 mutations were associated with a low cumulative incidence of platelet concentrate transfusion dependence. Presence of SF3B1 mutation was a significant univariate predictor of overall survival, but become nonsignificant in the multivariate model. Although many factors also could affect survival, these results suggest that splicing factor mutations contribute to distinct MDS phenotypes, including patient characteristics and clinical courses.

Keywords

Myelodysplastic syndromes RNA splicing factors Gene mutations Blood transfusion Prognosis 

Notes

Acknowledgements

We thank Makoto Saito, MSc, who helped with the statistical analysis. This study was supported in part by the Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan Grants 15K09460 (H.H) and 16K09831 (Y.H.), the Grant for Joint Research Project of the Institute of Medical Science, the University of Tokyo (H.H.), the Grant from Eiju Foundation (H.H.).

Compliance with ethical standards

Conflict of interest

This study has been funded in part by Celgene K.K.

Supplementary material

12185_2018_2551_MOESM1_ESM.pdf (133 kb)
Supplementary material 1 (PDF 133 KB)

References

  1. 1.
    Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011;478:64–9.CrossRefGoogle Scholar
  2. 2.
    Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D, et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med. 2011;365:1384–95.CrossRefGoogle Scholar
  3. 3.
    Dussiau C, Fontenay M. Mechanisms underlying the heterogeneity of myelodysplastic syndromes. Exp Hematol. 2018;58:17–26.CrossRefGoogle Scholar
  4. 4.
    Makishima H, Yoshizato T, Yoshida K, Sekeres MA, Radivoyevitch T, Suzuki H, et al. Dynamics of clonal evolution in myelodysplastic syndromes. Nat Genet. 2017;49:204–12.CrossRefGoogle Scholar
  5. 5.
    Pellagatti A, Boultwood J. Splicing factor gene mutations in the myelodysplastic syndromes: impact on disease phenotype and therapeutic applications. Adv Biol Regul. 2017;63:59–70.CrossRefGoogle Scholar
  6. 6.
    Steensma DP, Bejar R, Jaiswal S, et al. Clonal hematopoiesis of indeterminate potential asnd its distinction from myelodysplastic syndromes. Blood. 2015;126:9–16.CrossRefGoogle Scholar
  7. 7.
    Kunimoto H, Nakajima H. Epigenetic dysregulation of hematopoietic stem cells and preleukemic state. Int J Hematol. 2017;106:34–44.CrossRefGoogle Scholar
  8. 8.
    Malcovati L, Karimi M, Papaemmanuil E, Ambaglio I, Jädersten M, Jansson M, et al. SF3B1 mutation identifies a distinct subset of myelodysplastic syndrome with ring sideroblasts. Blood. 2015;126:233–41.CrossRefGoogle Scholar
  9. 9.
    Hasserjian RP, Orazi A, Brunning RD, Germing U, Le Beau MM, Porwit A, et al. Myelodysplastic syndromes: overview. In: Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H et al, editors. WHO classification of tumors of hematopoietic and lymphoid tissues. 4th ed. Lyon: IARC; 2017. pp. 97–106.Google Scholar
  10. 10.
    Thol F, Kade S, Schlarmann C, Löffeld P, Morgan M, Krauter J, et al. Frequency and prognostic impact of mutations in SRSF2, U2AF1, and ZRSR2 in patients with myelodysplastic syndromes. Blood. 2012;119:3578–84.CrossRefGoogle Scholar
  11. 11.
    Malcovati L, Papaemmanuil E, Ambaglio I, Elena C, Gallì A, Della Porta MG, et al. Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia. Blood. 2014;124:1513–21.CrossRefGoogle Scholar
  12. 12.
    Li B, Liu J, Jia Y, Wang J, Xu Z, Qin T, et al. Clinical features and biological implications of different U2AF1 mutation types in myelodysplastic syndromes. Genes Chromosom Cancer. 2018;57:80–8.CrossRefGoogle Scholar
  13. 13.
    Brunning RD, Orazi A, Germing U, Le Beau MM. Myelodysplastic syndomes/neoplasms. In: Swerdlow SH, Campo E, Lee Harris N, Jaffe ES, Pileri SA, Stein H, Stein J, Thiele J, Vardiman JW, editors. WHO classification of tumours of haematopoietic and lymphoid tissues. Lyon: IRAC Press; 2008. pp. 87–107.Google Scholar
  14. 14.
    Harada Y, Inoue D, Ding Y, Imagawa J, Doki N, Matsui H, et al. RUNX1/AML1 mutant collaborates with BMI1 overexpression in the development of human and murine myelodysplastic syndromes. Blood. 2013;121:3434–46.CrossRefGoogle Scholar
  15. 15.
    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;120:2454–65.CrossRefGoogle Scholar
  16. 16.
    Kawabata H, Tohyama K, Matsuda A, Araseki K, Hata T, Suzuki T, et al. Validation of the revised International Prognostic Scoring System in patients with myelodysplastic syndrome in Japan: results from a prospective multicenter registry. Int J Hematol. 2017;106:375–84.CrossRefGoogle Scholar
  17. 17.
    Kanda Y. Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant. 2013;48:452–8.CrossRefGoogle Scholar
  18. 18.
    Kobayashi T, Nannya Y, Ichikawa M, Oritani K, Kanakura Y, Tomita A, et al. A nationwide survey of hypoplastic myelodysplastic syndrome (a multicenter retrospective study). Am J Hematol. 2017;92:1324–32.CrossRefGoogle Scholar
  19. 19.
    Strupp C, Nachtkamp K, Hildebrandt B, Giagounidis A, Haas R, Gattermann N, et al. New proposals of the WHO working group (2016) for the diagnosis of myelodysplastic syndromes (MDS): characteristics of refined MDS types. Leuk Res. 2017;57:78–84.CrossRefGoogle Scholar
  20. 20.
    Kanagal-Shamanna R, Hidalgo Lopez JE, Milton DR, Kim HR, Zhao C, Zuo Z, et al. Validation of the 2016 revisions to the WHO classification in lower-risk myelodysplastic syndrome. Am J Hematol. 2017;92:E168–71.CrossRefGoogle Scholar
  21. 21.
    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;28:241–7.CrossRefGoogle Scholar
  22. 22.
    Joshi P, Halene S, Abdel-Wahab O. How do messenger RNA splicing alterations drive myelodysplasia? Blood. 2017;129:2465–70.CrossRefGoogle Scholar
  23. 23.
    Dolatshad H, Pellagatti A, Liberante FG, Llorian M, Repapi E, Steeples V, et al. Cryptic splicing events in the iron transporter ABCB7 and other key target genes in SF3B1-mutant myelodysplastic syndromes. Leukemia. 2016;30:2322–31.CrossRefGoogle Scholar
  24. 24.
    Malcovati L, Cazzola M. Recent advances in the understanding of myelodysplastic syndromes with ring sideroblasts. Br J Haematol. 2016;174:847–58.CrossRefGoogle Scholar
  25. 25.
    Yoshimi A, Abdel-Wahab O. Splicing factor mutations in MDS RARS and MDS/MPN-RS-T. Int J Hematol. 2017;105:720–31.CrossRefGoogle Scholar
  26. 26.
    Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nat Genet. 2011;44:53–7.CrossRefGoogle Scholar
  27. 27.
    Ilagan JO, Ramakrishnan A, Hayes B, Murphy ME, Zebari AS, Bradley P, et al. U2AF1 mutations alter splice site recognition in hematological malignancies. Genome Res. 2015;25:14–26.CrossRefGoogle Scholar
  28. 28.
    Cazzola M, Della Porta MG, Malcovati L. The genetic basis of myelodysplasia and its clinical relevance. Blood. 2013;122:4021–34.CrossRefGoogle Scholar
  29. 29.
    Takahashi N, Kameoka J, Takahashi N, Tamai Y, Murai K, Honma R, et al. Causes of macrocytic anemia among 628 patients: mean corpuscular volumes of 114 and 130 fL as critical markers for categorization. Int J Hematol. 2016;104:344–57.CrossRefGoogle Scholar
  30. 30.
    Dolatshad H, Pellagatti A, Fernandez-Mercado M, Yip BH1, Malcovati L, Attwood M, et al. Disruption of SF3B1 results in deregulated expression and splicing of key genes and pathways in myelodysplastic syndrome hematopoietic stem and progenitor cells. Leukemia. 2015;29:1092–103.CrossRefGoogle Scholar
  31. 31.
    Zhu Y, Li X, Chang C, Xu F, He Q, Guo J, et al. SF3B1-mutated myelodysplastic syndrome with ring sideroblasts harbors more severe iron overload and corresponding over-erythropoiesis. Leuk Res. 2016;44:8–16.CrossRefGoogle Scholar
  32. 32.
    Yip BH, Steeples V, Repapi E, Armstrong RN, Llorian M, Roy S, et al. The U2AF1S34F mutation induces lineage-specific splicing alterations in myelodysplastic syndromes. J Clin Investig. 2017;127:2206–21.CrossRefGoogle Scholar
  33. 33.
    Kim E, Ilagan JO, Liang Y, Daubner GM, Lee SC, Ramakrishnan A, et al. SRSF2 mutations contribute to myelodysplasia by mutant-specific effects on exon recognition. Cancer Cell. 2015;27:617–30.CrossRefGoogle Scholar
  34. 34.
    Makishima H, Visconte V, Sakaguchi H, Jankowska AM, Abu Kar S, Jerez A, et al. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood. 2012;119:3203–10.CrossRefGoogle Scholar
  35. 35.
    Inokura K, Fujiwara T, Saito K, Iino T, Hatta S, Okitsu Y, et al. Impact of TET2 deficiency on iron metabolism in erythroblasts. Exp Hematol. 2017;49:56–67.e55.CrossRefGoogle Scholar
  36. 36.
    Kon A, Yamazaki S, Nannya Y, Kataoka K, Ota Y, Nakagawa MM, et al. Physiological Srsf2 P95H expression causes impaired hematopoietic stem cell functions and aberrant RNA splicing in mice. Blood. 2018;131:621–35.CrossRefGoogle Scholar
  37. 37.
    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;30:2214–20.CrossRefGoogle Scholar
  38. 38.
    Zheng X, Zhan Z, Naren D, Li J, Yan T, Gong Y. Prognostic value of SRSF2 mutations in patients with de novo myelodysplastic syndromes: a meta-analysis. PLoS One. 2017;12:e0185053.CrossRefGoogle Scholar
  39. 39.
    Tennant GB, Al-Sabah AI, Burnett AK. Prognosis of myelodysplasic patients: non-parametric multiple regression analysis of populations stratified by mean corpuscular volume and marrow myeloblast number. Br J Haematol. 2002;119:87–96.CrossRefGoogle Scholar
  40. 40.
    Wang H, Wang X, Xu X, Lin G. Mean corpuscular volume predicts prognosis in MDS patients with abnormal karyotypes. Ann Hematol. 2010;89:671–9.CrossRefGoogle Scholar
  41. 41.
    Stengel A, Kern W, Meggendorfer M, Haferlach T, Haferlach C. MDS with deletions in the long arm of chromosome 11 are associated with a high frequency of SF3B1 mutations. Leukemia. 2017;31:1995–7.CrossRefGoogle Scholar
  42. 42.
    Tefferi A, Idossa D, Lasho TL, Mudireddy M, Finke C, Shah S, et al. Mutations and karyotype in myelodysplastic syndromes: TP53 clusters with monosomal karyotype, RUNX1 with trisomy 21, and SF3B1 with inv(3)(q21q26.2) and del(11q). Blood Cancer J. 2017;7:658.CrossRefGoogle Scholar
  43. 43.
    Tefferi A, Lasho TL, Patnaik MM, Saeed L, Mudireddy M, Idossa D, et al. Targeted next-generation sequencing in myelodysplastic syndromes and prognostic interaction between mutations and IPSS-R. Am J Hematol. 2017;92:1311–7.CrossRefGoogle Scholar
  44. 44.
    Nazha A, Al-Issa K, Hamilton BK, Radivoyevitch T, Gerds AT, Mukherjee S, et al. Adding molecular data to prognostic models can improve predictive power in treated patients with myelodysplastic syndromes. Leukemia. 2017;31:2848–50.CrossRefGoogle Scholar

Copyright information

© The Japanese Society of Hematology 2018

Authors and Affiliations

  • Naoki Shingai
    • 1
  • Yuka Harada
    • 1
    • 2
  • Hiroko Iizuka
    • 1
  • Yosuke Ogata
    • 2
  • Noriko Doki
    • 3
  • Kazuteru Ohashi
    • 3
  • Masao Hagihara
    • 4
  • Norio Komatsu
    • 1
  • Hironori Harada
    • 1
    • 5
  1. 1.Department of HematologyJuntendo University School of MedicineTokyoJapan
  2. 2.Department of Clinical Laboratory MedicineBunkyo Gakuin UniversityTokyoJapan
  3. 3.Department of HematologyTokyo Metropolitan Cancer and Infectious Disease Center Komagome HospitalTokyoJapan
  4. 4.Department of HematologyEiju General HospitalTokyoJapan
  5. 5.Laboratory of Oncology, School of Life SciencesTokyo University of Pharmacy and Life SciencesHachiojiJapan

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