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Prognostic significance of combined MN1, ERG, BAALC, and EVI1 (MEBE) expression in patients with myelodysplastic syndromes

Annals of Hematology volume 91, pages 1221–1233 (2012)Cite this article

Abstract

Overexpression of MN1, ERG, BAALC, and EVI1 (MEBE) genes in cytogenetically normal acute myeloid leukemia (AML) patients is associated with poor prognosis, but their prognostic effect in patients with myelodysplastic syndromes (MDS) has not been studied systematically. Expression data of the four genes from 140 MDS patients were combined in an additive score, which was validated in an independent patient cohort of 110 MDS patients. A high MEBE score, defined as high expression of at least two of the four genes, predicted a significantly shorter overall survival (OS) (HR 2.29, 95 % CI 1.3–4.09, P = .005) and time to AML progression (HR 4.83, 95 % CI 2.01–11.57, P < .001) compared to a low MEBE score in multivariate analysis independent of karyotype, percentage of bone marrow blasts, transfusion dependence, ASXL1, and IDH1 mutation status. In a validation cohort of 110 MDS patients, a high MEBE score predicted shorter OS (HR 1.77; 95 % CI 1.04–3.0, P = .034) and time to AML progression (HR 3.0, 95 % CI 1.17–7.65, P = .022). A high MEBE expression score is an unfavorable prognostic marker in MDS and is associated with an increased risk for progression to AML. Expression of the MEBE genes is regulated by FLI1 and c-MYC, which are potential upstream targets of the MEBE signature.

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References

  1. Cazzola M, Malcovati L (2010) Prognostic classification and risk assessment in myelodysplastic syndromes. Hematol Oncol Clin North Am 24:459–468

    PubMed  Article  Google Scholar 

  2. Greenberg P, Cox C, LeBeau MM et al (1997) International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood 89:2079–2088

    PubMed  CAS  Google Scholar 

  3. Garcia-Manero G, Shan J, Faderl S et al (2008) A prognostic score for patients with lower risk myelodysplastic syndrome. Leukemia 22:538–543

    PubMed  Article  CAS  Google Scholar 

  4. Thol F, Friesen I, Damm F et al (2011) Prognostic Significance of ASXL1 Mutations in patients with myelodysplastic syndromes. J Clin Oncol 29:2499–2506

    PubMed  Article  CAS  Google Scholar 

  5. Bejar R, Stevenson K, Abdel-Wahab O et al (2011) Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med 364:2496–2506

    PubMed  Article  CAS  Google Scholar 

  6. Heuser M, Beutel G, Krauter J et al (2006) High meningioma 1 (MN1) expression as a predictor for poor outcome in acute myeloid leukemia with normal cytogenetics. Blood 108:3898–3905

    PubMed  Article  CAS  Google Scholar 

  7. Heuser M, Argiropoulos B, Kuchenbauer F et al (2007) MN1 overexpression induces acute myeloid leukemia in mice and predicts ATRA resistance in patients with AML. Blood 110:1639–1647

    PubMed  Article  CAS  Google Scholar 

  8. Langer C, Marcucci G, Holland KB et al (2009) Prognostic importance of MN1 transcript levels, and biologic insights from MN1-associated gene and microRNA expression signatures in cytogenetically normal acute myeloid leukemia: a cancer and leukemia group B study. J Clin Oncol 27:3198–3204

    PubMed  Article  CAS  Google Scholar 

  9. Baldus CD, Tanner SM, Ruppert AS et al (2003) BAALC expression predicts clinical outcome of de novo acute myeloid leukemia patients with normal cytogenetics: a Cancer and Leukemia Group B Study. Blood 102:1613–1618

    PubMed  Article  CAS  Google Scholar 

  10. Baldus CD, Thiede C, Soucek S et al (2006) BAALC expression and FLT3 internal tandem duplication mutations in acute myeloid leukemia patients with normal cytogenetics: prognostic implications. J Clin Oncol 24:790–797

    PubMed  Article  CAS  Google Scholar 

  11. Marcucci G, Maharry K, Whitman SP et al (2007) High expression levels of the ETS-related gene, ERG, predict adverse outcome and improve molecular risk-based classification of cytogenetically normal acute myeloid leukemia: a Cancer and Leukemia Group B Study. J Clin Oncol 25:3337–3343

    PubMed  Article  CAS  Google Scholar 

  12. Groschel S, Lugthart S, Schlenk RF et al (2010) High EVI1 expression predicts outcome in younger adult patients with acute myeloid leukemia and is associated with distinct cytogenetic abnormalities. J Clin Oncol 28:2101–2107

    PubMed  Article  Google Scholar 

  13. Damm F, Oberacker T, Thol F et al (2011) Prognostic importance of histone methyltransferase MLL5 expression in acute myeloid leukemia. J Clin Oncol 29:682–689

    PubMed  Article  CAS  Google Scholar 

  14. Santamaria CM, Chillon MC, Garcia-Sanz R et al (2009) Molecular stratification model for prognosis in cytogenetically normal acute myeloid leukemia. Blood 114:148–152

    PubMed  Article  CAS  Google Scholar 

  15. Heuser M, Yun H, Berg T et al (2011) Cell of origin in AML: susceptibility to MN1-induced transformation is regulated by the MEIS1/AbdB-like HOX protein complex. Cancer Cell 20:39–52

    PubMed  Article  CAS  Google Scholar 

  16. Heuser M, Berg T, Kuchenbauer F et al (2012) Functional role of BAALC in leukemogenesis. Leukemia 26(3):532–536

    PubMed  Article  CAS  Google Scholar 

  17. Baldus CD, Tanner SM, Kusewitt DF et al (2003) BAALC, a novel marker of human hematopoietic progenitor cells. Exp Hematol 31:1051–1056

    PubMed  CAS  Google Scholar 

  18. Tsuzuki S, Taguchi O, Seto M (2011) Promotion and maintenance of leukemia by ERG. Blood 117:3858–3868

    PubMed  Article  CAS  Google Scholar 

  19. Buonamici S, Li D, Chi Y et al (2004) EVI1 induces myelodysplastic syndrome in mice. J Clin Invest 114:713–719

    PubMed  CAS  Google Scholar 

  20. Goyama S, Yamamoto G, Shimabe M et al (2008) Evi-1 is a critical regulator for hematopoietic stem cells and transformed leukemic cells. Cell Stem Cell 3:207–220

    PubMed  Article  CAS  Google Scholar 

  21. Metzeler KH, Dufour A, Benthaus T et al (2009) ERG expression is an independent prognostic factor and allows refined risk stratification in cytogenetically normal acute myeloid leukemia: a comprehensive analysis of ERG, MN1, and BAALC transcript levels using oligonucleotide microarrays. J Clin Oncol 27:5031–5038

    PubMed  Article  CAS  Google Scholar 

  22. Valk PJ, Verhaak RG, Beijen MA et al (2004) Prognostically useful gene-expression profiles in acute myeloid leukemia. N Engl J Med 350:1617–1628

    PubMed  Article  CAS  Google Scholar 

  23. Hofmann WK, Ganser A, Seipelt G et al (1999) Treatment of patients with low-risk myelodysplastic syndromes using a combination of all-trans retinoic acid, interferon alpha, and granulocyte colony-stimulating factor. Ann Hematol 78:125–130

    PubMed  Article  CAS  Google Scholar 

  24. Stadler M, Germing U, Kliche KO et al (2004) A prospective, randomised, phase II study of horse antithymocyte globulin vs rabbit antithymocyte globulin as immune-modulating therapy in patients with low-risk myelodysplastic syndromes. Leukemia 18:460–465

    PubMed  Article  CAS  Google Scholar 

  25. Passweg JR, Giagounidis AA, Simcock M et al (2011) Immunosuppressive therapy for patients with myelodysplastic syndrome: a prospective randomized multicenter phase III trial comparing antithymocyte globulin plus cyclosporine with best supportive care—SAKK 33/99. J Clin Oncol 29:303–309

    PubMed  Article  CAS  Google Scholar 

  26. Porter J (1989) Oral iron chelators: prospects for future development. Eur J Haematol 43:271–285

    PubMed  Article  CAS  Google Scholar 

  27. Chou WC, Huang HH, Hou HA et al (2010) Distinct clinical and biological features of de novo acute myeloid leukemia with additional sex comb-like 1 (ASXL1) mutations. Blood 115:2749–2754

    PubMed  Article  CAS  Google Scholar 

  28. Thol F, Weissinger EM, Krauter J et al (2010) IDH1 mutations in patients with myelodysplastic syndromes are associated with an unfavorable prognosis. Haematologica 95:1668–1674

    PubMed  Article  CAS  Google Scholar 

  29. Thol F, Damm F, Wagner K et al (2010) Prognostic impact of IDH2 mutations in cytogenetically normal acute myeloid leukemia. Blood 116:614–616

    PubMed  Article  CAS  Google Scholar 

  30. Thol F, Damm F, Lüdeking A et al (2011) Incidence and prognostic influence of DNMT3A mutations in acute myeloid leukemia. J Clin Oncol 29:2889–2896

    PubMed  Article  CAS  Google Scholar 

  31. Damm F, Thol F, Kosmider O et al. SF3B1 mutations in myelodysplastic syndromes: clinical associations and prognostic implications. Leukemia 2011; Advance Online Publication

  32. Barjesteh van Waalwijk van Doorn-Khosrovani S, Erpelinck C, van Putten WL et al (2003) High EVI1 expression predicts poor survival in acute myeloid leukemia: a study of 319 de novo AML patients. Blood 101:837–845

  33. Mills KI, Kohlmann A, Williams PM et al (2009) Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. Blood 114:1063–1072

    PubMed  Article  CAS  Google Scholar 

  34. Barrett T, Suzek TO, Troup DB et al (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33:D562–D566

    PubMed  Article  CAS  Google Scholar 

  35. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550

    PubMed  Article  CAS  Google Scholar 

  36. Cui JW, Li YJ, Sarkar A et al (2007) Retroviral insertional activation of the Fli-3 locus in erythroleukemias encoding a cluster of microRNAs that convert Epo-induced differentiation to proliferation. Blood 110:2631–2640

    PubMed  Article  CAS  Google Scholar 

  37. Tiemann U, Sgodda M, Warlich E et al (2011) Optimal reprogramming factor stoichiometry increases colony numbers and affects molecular characteristics of murine induced pluripotent stem cells. Cytometry A 79:426–435

    PubMed  Google Scholar 

  38. Korn EL (1986) Censoring distributions as a measure of follow-up in survival analysis. Stat Med 5:255–260

    PubMed  Article  CAS  Google Scholar 

  39. Cox D (1972) Regression models and life tables. J R Stat Soc B 34:187–202

    Google Scholar 

  40. Thol F, Winschel C, Ludeking A et al (2011) Rare occurrence of DNMT3A mutations in myelodysplastic syndromes. Haematologica 96:1870–1873

    PubMed  Article  Google Scholar 

  41. Wilson NK, Foster SD, Wang X et al (2010) Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7:532–544

    PubMed  Article  CAS  Google Scholar 

  42. Kandilci A, Grosveld GC (2009) Reintroduction of CEBPA in MN1-overexpressing hematopoietic cells prevents their hyperproliferation and restores myeloid differentiation. Blood 114:1596–1606

    PubMed  Article  CAS  Google Scholar 

  43. Salek-Ardakani S, Smooha G, de Boer J et al (2009) ERG is a megakaryocytic oncogene. Cancer Res 69:4665–4673

    PubMed  Article  CAS  Google Scholar 

  44. Thoms JA, Birger Y, Foster S et al (2011) ERG promotes T-acute lymphoblastic leukemia and is transcriptionally regulated in leukemic cells by a stem cell enhancer. Blood 117:7079–7089

    PubMed  Article  CAS  Google Scholar 

  45. Modlich U, Schambach A, Brugman MH et al (2008) Leukemia induction after a single retroviral vector insertion in Evi1 or Prdm16. Leukemia 22:1519–1528

    PubMed  Article  CAS  Google Scholar 

  46. Stein S, Ott MG, Schultze-Strasser S et al (2010) Genomic instability and myelodysplasia with monosomy 7 consequent to EVI1 activation after gene therapy for chronic granulomatous disease. Nat Med 16:198–204

    PubMed  Article  CAS  Google Scholar 

  47. Kataoka K, Sato T, Yoshimi A et al (2011) Evi1 is essential for hematopoietic stem cell self-renewal, and its expression marks hematopoietic cells with long-term multilineage repopulating activity. J Exp Med 208:2403–2416

    PubMed  Article  CAS  Google Scholar 

  48. Pradee T (2011) Over Expression of MN1 accelerates leukemia onset and confers resistance to chemotherapy by suppression of p53 and Bim. ASH Abstract 118:2501

    Google Scholar 

  49. Pellagatti A, Cazzola M, Giagounidis A et al (2010) Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells. Leukemia 24:756–764

    PubMed  Article  CAS  Google Scholar 

  50. Nikpour M, Pellagatti A, Liu A et al (2010) Gene expression profiling of erythroblasts from refractory anaemia with ring sideroblasts (RARS) and effects of G-CSF. Br J Haematol 149:844–854

    PubMed  Article  CAS  Google Scholar 

  51. Papaemmanuil E, Cazzola M, Boultwood J et al (2011) Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med 365:1384–1395

    PubMed  Article  CAS  Google Scholar 

  52. Yoshida K, Sanada M, Shiraishi Y et al (2011) Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478:64–69

    PubMed  Article  CAS  Google Scholar 

  53. Thol F, Kade S, Schlarmann C et al (2012) Frequency and prognostic impact of mutations in SRSF2, U2AF1, and ZRSR2 in patients with myelodysplastic syndromes. Blood. doi:10.1182/blood-2011-12-399337

  54. Damm F, Kosmider O, Gelsi-Boyer V et al (2012) Mutations affecting mRNA splicing define distinct clinical phenotypes and correlate with patient outcome in myelodysplastic syndromes. Blood. doi:10.1182/blood-2011-12-400994

  55. Makishima H, Visconte V, Sakaguchi H et al (2012) Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood. doi:10.1182/blood-2011-12-399774

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Acknowledgments

We thank all patients, contributing doctors, and Tayside Cancer Tissue Bank for their participation in this study. We thank Kerstin Görlich, Elvira Lux, Sylvia Horter, Susanne Luther, Jana König, and Maren Herten for their excellent support in sample and data acquisition. This study was supported by the Deutsche Krebshilfe e.V (grant no. 109003), grant no. DJCLS R 10/22 from the Deutsche-José-Carreras Leukämie-Stiftung e.V, grant no. M 47.1 from the H. W. & J. Hector Stiftung, the Dieter-Schlag-Stiftung grant from 2011, the German Federal Ministry of Education and Research grant 01EO0802, DFG grants HE 5240/4-1 and TH 1779/1-1, and HiLF grant from Hannover Medical School awarded to FT.

Conflict of interest

T.H. owns and is employed by the Munich Leukemia Laboratory GmbH. The other authors indicated no potential conflicts of interest.

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Authors and Affiliations

  1. Department of Hematology, Hemostasis, Oncology, and Stem Cell Transplantation, Hannover Medical School, Carl-Neuberg Str. 1, 30625, Hannover, Germany

    Felicitas Thol, Haiyang Yun, Ann-Kathrin Sonntag, Frederik Damm, Eva M. Weissinger, Jürgen Krauter, Katharina Wagner, Michael Morgan, Martin Wichmann, Arnold Ganser & Michael Heuser

  2. Institute of Cell and Molecular Pathology, Hannover Medical School, Hannover, Germany

    Gudrun Göhring & Brigitte Schlegelberger

  3. Department of Internal Medicine III, University of Frankfurt, Frankfurt, Germany

    Gesine Bug & Oliver Ottmann

  4. Department of Hematology and Oncology, University Hospital Mannheim, Mannheim, Germany

    Wolf-Karsten Hofmann

  5. Department of Experimental Hematology, Hannover Medical School, Hannover, Germany

    Axel Schambach

  6. MLL, Munich Leukemia Laboratory, Munich, Germany

    Torsten Haferlach

  7. St James’s Institute of Oncology, Leeds Teaching Hospitals, Leeds, UK

    David Bowen

  8. Centre for Cancer Research and Cell Biology, Queen’s University Belfast, Belfast, UK

    Ken Mills

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  1. Felicitas Thol
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  2. Haiyang Yun
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Corresponding author

Correspondence to Michael Heuser.

Additional information

Arnold Ganser and Michael Heuser contributed equally to this work.

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Table S1

Comparison of clinical and molecular characteristics of MDS patients with low or high MN1 expression. (DOC 102 kb)

Table S2

Comparison of clinical and molecular characteristics of MDS patients with low or high ERG expression. (DOC 101 kb)

Table S3

Comparison of clinical and molecular characteristics of MDS patients with low or high BAALC expression. (DOC 101 kb)

Table S4

Comparison of clinical and molecular characteristics of MDS patients with low or high EVI1 expression. (DOC 102 kb)

Table S5

Correlation between MN1, ERG, BAALC and EVI1 expression in MDS patients. (DOC 43 kb)

Table S6

List of gene sets enriched or depleted in MDS patients with high MEBE expression score. (DOC 126 kb)

Table S7

Primers used to quantify expression of mouse Mn1, Erg, Baalc, and Evi1. (DOC 42 kb)

Supplemental Figure S1

Prognostic impact of individual gene expression markers in MDS patients in the validation cohort. Kaplan-Meier curves for overall survival and time to AMLtransformation are shown. (A) Overall survival in MDS patients with low or high MN1 expression. (B) Time to AML transformation in MDS patients with low or high MN1 expression. (C) Overall survival in MDS patients with low or high ERG expression. (D) Time to AML transformation in MDS patients with low or high ERG expression. (E) Overall survival in MDS patients with low or high BAALC expression. (F) Time to AML transformation in MDS patients with low or high BAALC expression. (G) Overall survival in MDS patients with high EVI1 vs. low EVI1 expression (see Materials and Methods for definition of high vs. low expression). Time to AML transformation in MDS patients with high EVI1 vs. low EVI1 expression (see Materials and Methods for definition of high vs. low expression). (DOC 205 kb)

Supplemental Figure S2

Prognostic impact of the MEBE expression score in younger and older MDS patients. (A) Overall survival in MDS patients younger than 66 years of age with low or high MEBE score. (B) Time to AML transformation in MDS patients younger than 66 years of age with low or high MEBE score. (C) Overall survival in MDS patients 66 years of age or older with low or high MEBE score. Time to AML transformation in MDS patients 66 years of age or older with low) or high MEBE score. (DOC 124 kb)

Supplemental Figure S3

Prognostic impact of the MEBE expression score determined from bone marrow or peripheral blood in MDS patients. (A) Only bone marrow (BM) samples considered: Overall survival in MDS patients with low or high MEBE score. (B) Only bone marrow samples (BM) considered: Time to AML transformation in MDS patients with low or high MEBE score. (C) Only peripheral blood samples considered: Overall survival in MDS patients with low or high MEBE score. (D) Only peripheral blood samples considered: Time to AML transformation in MDS patients with low or high MEBE score (DOC 122 kb)

Supplemental Figure S4

Prognostic impact of the MEBE expression score in MDS patients with 10-19 percent bone marrow blasts. (A) Overall survival in MDS patients with low or high MEBE score in patients with 10-19 percent bone marrow blasts. (B) Time to AML transformation in MDS patients with low or high MEBE score in patients with 10-19 percent bone marrow blasts. (DOC 70 kb)

Supplemental Figure S5

Correlation of expression classes (high or low) of individual gene expression markers of the MEBE score. Each row represents one patient. Yellow, low expression or low MEBE score. Blue, high expression or high MEBE score. (DOC 142 kb)

Supplemental Figure S6

Effect of constitutive expression of Fli1 and c-Myc on expression levels of Mn1, Erg, Baalc, and Evi1 in mouse bone marrow cells. Fli1, c-Myc, or a control vector was stably expressed in mouse bone marrow cells. Transduced cells were sorted and harvested after 7 days, and cDNA was prepared for expression analysis. Abl was used as a housekeeping gene, and expression ratios of transgene expressing cells vs. control vector-transduced cells are shown (n=3, mean ± SD). Expression of Baalc could be detected by nested PCR only. (DOC 97 kb)

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Thol, F., Yun, H., Sonntag, AK. et al. Prognostic significance of combined MN1, ERG, BAALC, and EVI1 (MEBE) expression in patients with myelodysplastic syndromes. Ann Hematol 91, 1221–1233 (2012). https://doi.org/10.1007/s00277-012-1457-7

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Keywords

  • MDS
  • Prognosis
  • Gene expression
  • Score
  • Splicing