International Journal of Hematology

, Volume 104, Issue 5, pp 566–573 | Cite as

Up-regulation of ribosomal genes is associated with a poor response to azacitidine in myelodysplasia and related neoplasms

  • M. Monika Belickova
  • Michaela Dostalova Merkerova
  • Hana Votavova
  • Jan Valka
  • Jitka Vesela
  • Barbora Pejsova
  • Hana Hajkova
  • Jiri Klema
  • Jaroslav Cermak
  • Anna Jonasova
Original Article

Abstract

Azacitidine (AZA) is a hypomethylating drug used to treat disorders associated with myelodysplasia and related neoplasms. Approximately 50 % of patients do not respond to AZA and have very poor outcomes. There is thus great interest in identifying predictive biomarkers for AZA responsiveness. We searched for specific genes whose expression level was associated with response status. Using microarrays, we analyzed gene expression patterns in bone marrow CD34+ cells in serial samples from 32 patients with myelodysplastic syndromes, chronic myelomonocytic leukemia, and acute myeloid leukemia with myelodysplasia-related changes before and during the AZA therapy. At baseline, a comparison of the responders and non-responders showed 52 differentially expressed genes (P < 0.01). Functional annotation of the deregulated genes revealed categories primarily related to ribosomes and pathways associated with proliferation. The expression level of RPL28 correlated with overall survival. We identified altered expression in 167 genes in responders, 26 genes in non-responders with stable disease, and 13 genes in non-responders with disease progression using paired t test of expression levels in patients before and during treatment. Our data indicate that AZA treatment failure is associated with the up-regulation of ribosomal genes/pathways that are likely related to intensive proteosynthesis in proliferative/neoplastic cells of non-responders.

Keywords

Myelodysplastic syndromes Ribosomal genes Azacitidine Gene expression profiling 

Supplementary material

12185_2016_2058_MOESM1_ESM.docx (568 kb)
Supplementary material 1 (DOCX 567 kb)

References

  1. 1.
    Fenaux P, Mufti GJ, Hellstrom-Lindberg E, Santini V, Finelli C, Giagounidis A, et al. International Vidaza High-Risk MDS Survival Study Group. Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study. Lancet Oncol. 2009;10:223–32.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Silverman LR, Demakos EP, Peterson BL, Kornblith AB, Holland JC, Odchimar-Reissig R, et al. Randomized controlled trial of azacitidine in patients with the myelodysplastic syndrome: a study of the cancer and leukemia group B. J Clin Oncol. 2002;20:2429–40.CrossRefPubMedGoogle Scholar
  3. 3.
    Garcia-Manero G. Myelodysplastic syndromes: 2015 Update on diagnosis, risk-stratification and management. Am J Hematol. 2015;90:831–41.CrossRefPubMedGoogle Scholar
  4. 4.
    Germing U, Neukirchen J. How to treat patients with CMML? Leuk Res. 2013;37:605–6.CrossRefPubMedGoogle Scholar
  5. 5.
    Bennett JM. Chronic myelomonocytic leukemia. Curr Treat Options Oncol. 2002;3:221–3.CrossRefPubMedGoogle Scholar
  6. 6.
    Germing U, Kündgen A, Gattermann N. Risk assessment in chronic myelomonocytic leukemia (CMML). Leuk Lymphoma. 2004;45:1311–8.CrossRefPubMedGoogle Scholar
  7. 7.
    Glover AB, Leyland-Jones B. Biochemistry of azacitidine: a review. Cancer Treat Rep. 1987;71:959–64.PubMedGoogle Scholar
  8. 8.
    Nimer SD. Myelodysplastic syndromes. Blood. 2008;111:4841–51.CrossRefPubMedGoogle Scholar
  9. 9.
    Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DA, Gralnick HR, et al. Proposals for the classification of the myelodysplastic syndromes. Br J Haematol. 1982;51:189–99.CrossRefPubMedGoogle Scholar
  10. 10.
    Swerdlow SH, Campo E, Harris NL, Flandrin G, Galton DA, Gralnick HR, Sultan C, editors. WHO classification of tumours of haematopoietic and lymphoid tissues. IARC: Lyon, France; 2008.Google Scholar
  11. 11.
    Miesner M, Haferlach C, Bacher U, Weiss T, Macijewski K, Kohlmann A, et al. Multilineage dysplasia (MLD) in acute myeloid leukemia (AML) correlates with MDS-related cytogenetic abnormalities and a prior history of MDS or MDS/MPN but has no independent prognostic relevance: a comparison of 408 cases classified as “AML not otherwise specified” (AML-NOS) or “AML with myelodysplasia-related changes” (AML-MRC). Blood. 2010;116:2742–51.CrossRefPubMedGoogle Scholar
  12. 12.
    Zeidan AM, Sekeres MA, Garcia-Manero G, Steensma DP, Zell K, Barnard J, et al. Comparison of risk stratification tools in predicting outcomes of patients with higher-risk myelodysplastic syndromes treated with azanucleosides. Leukemia. 2016;30:649–57.CrossRefPubMedGoogle Scholar
  13. 13.
    Bejar R, Lord A, Stevenson K, Bar-Natan M, Perez-Ladaga A, Zaneveld J, et al. TET2 mutations predict response to hypomethylating agents in myelodysplastic syndrome patients. Blood. 2014;124:2705–12.CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Follo MY, Finelli C, Mongiorgi S, Clissa C, Bosi C, Testoni N, et al. Reduction of phosphoinositide-phospholipase C beta1 methylation predicts the responsiveness to azacitidine in high-risk MDS. Proc Natl Acad Sci USA. 2009;106:16811–6.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Cluzeau T, Robert G, Mounier N, Karsenti JM, Dufies M, Puissant A, et al. BCL2L10 is a predictive factor for resistance to azacitidine in MDS and AML patients. Oncotarget. 2012;3:490–501.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Valencia A, Masala E, Rossi A, Martino A, Sanna A, Buchi F, et al. Expression of nucleoside-metabolizing enzymes in myelodysplastic syndromes and modulation of response to azacitidine. Leukemia. 2014;28:621–8.CrossRefPubMedGoogle Scholar
  17. 17.
    Miltiades P, Lamprianidou E, Vassilakopoulos TP, Papageorgiou SG, Galanopoulos AG, Vakalopoulou S, et al. Expression of CD25 antigen on CD34+ cells is an independent predictor of outcome in late-stage MDS patients treated with azacitidine. Blood Cancer Journal. 2014;4:e187.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Swerdlow SH, Campo E, Harris NL, et al. WHO classification of tumours of haematopoietic and lymphoid tissues. Lyon: IARC Press; 2008.Google Scholar
  19. 19.
    Cheson BD, Greenberg PL, Bennett JM, Lowenberg B, Wijermans PW, Nimer SD, et al. Clinical application and proposal for modification of the International Working Group (IWG) response criteria in myelodysplasia. Blood. 2006;108:419–25.CrossRefPubMedGoogle Scholar
  20. 20.
    Cheson BD, Bennett JM, Kopecky KJ, Büchner T, Willman CL, Estey EH, et al. International working group for diagnosis, standardization of response criteria, treatment outcomes, andreporting standards for therapeutic trials in acute MyeloidLeukemia. Revised recommendations of the International Working Group for Diagnosis, Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for Therapeutic Trials in Acute Myeloid Leukemia. J Clin Oncol. 2003;21:4642–9.CrossRefPubMedGoogle Scholar
  21. 21.
    Chomczynsky P, Sacchi N. Single step Metod of RNA isolation by acid quanidin-isothiocyanate-phenol-chloroform extraction. Anal Biochem. 1987;162:156–9.Google Scholar
  22. 22.
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–8.CrossRefPubMedGoogle Scholar
  23. 23.
    Budczies J, Klauschen F, Sinn BV, Győrffy B, Schmitt WD, Darb-Esfahani S, et al. Cutoff finder: a comprehensive and straightforward web application enabling rapid biomarker cutoff optimization. PLoS One. 2012;7:e51862.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Sridhar K, Ross DT, Tibshirani R, Butte AJ, Greenberg PL. Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression. Blood. 2009;114:4847–58.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Pellagatti A, Hellström-Lindberg E, Giagounidis A, Perry J, Malcovati L, Della Porta MG, et al. Haploinsufficiency of RPS14 in 5q syndrome is associated with deregulation of ribosomaland translation-related genes. Br J Haematol. 2008;142:57–64.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Ruggero D, Pandolfi PP. Does the ribosome translate cancer? Nat Rev Cancer. 2003;3:179–92.CrossRefPubMedGoogle Scholar
  27. 27.
    Donati G, Montanaro L, Derenzini M. Ribosome biogenesis and control of cell proliferation: p53 is not alone. Cancer Res. 2012;72:1602–7.CrossRefPubMedGoogle Scholar
  28. 28.
    De Keersmaecker K, Sulima SO, Dinman JD. Ribosomopathies and the paradox of cellular hypo- to hyperproliferation. Blood. 2015;125:1377–82.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Barlow JL, Drynan LF, Trim NL, Erber WN, Warren AJ, McKenzie AN. New insights into 5q- syndrome as a ribosomopathy. Cell Cycle. 2010;9:4286–93.CrossRefPubMedGoogle Scholar
  30. 30.
    Vasikova A, Belickova M, Budinska E, Cermak J. A distinct expression of various gene subsets in CD34+ cells from patients with early and advanced myelodysplastic syndrome. Leuk Res. 2010;34:1566–72.CrossRefPubMedGoogle Scholar
  31. 31.
    Zhang W, Liu HT. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res. 2002;12:9–18.CrossRefPubMedGoogle Scholar
  32. 32.
    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 Invesed. 2015;125:1857–72.CrossRefGoogle Scholar
  33. 33.
    Largaespada DA, Shaughnessy JD Jr, Jenkins NA, Copeland NG. Retroviral integration at the Evi-2 locus in BXH-2 myeloid leukemia cell lines disrupts Nf1 expression without changes in steady-state Ras-GTP levels. J Virol. 1995;69:5095–102.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Rücker FG, Sander S, Döhner K, Döhner H, Pollack JR, Bullinger L. Molecular profiling reveals myeloid leukemia cell lines to be faithful model systems characterized by distinct genomic aberrations. Leukemia. 2006;20:994–1001.CrossRefPubMedGoogle Scholar
  35. 35.
    Lin CC, Chiu YC, Hou HS, Wen-Chien Chou WC, Tien HF. Clinical and prognostic relevance of expression of homeodomain-only protein homeobox (HOPX) in acute myeloid leukemia. Blood. 2013;122.Abstract.Google Scholar
  36. 36.
    Delgado MD, León J. Myc roles in hematopoiesis and leukemia. Genes Cancer. 2010;1:605–16.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Boon K, Caron HN, van Asperen R, Valentijn L, Hermus MC, van Sluis P, et al. N-myc enhances the expression of a large set of genes functioning in ribosome biogenesis and protein synthesis. EMBO J. 2001;20:383–93.CrossRefGoogle Scholar
  38. 38.
    Colo GP, Rosato RR, Grant S, Costas MA. RAC3 down-regulation sensitizes human chronic myeloid leukemia cells to TRAIL-induced apoptosis. FEBS Lett. 2007;581:5075–81.CrossRefPubMedGoogle Scholar
  39. 39.
    Recher C, Ysebaert L, Beyne-Rauzy O, Mansat-De Mas V, Ruidavets JB, Cariven P, et al. Expression of focal adhesion kinase in acute myeloid leukemia is associated with enhanced blast migration, increased cellularity, and poor prognosis. Cancer Res. 2004;64:3191–7.CrossRefPubMedGoogle Scholar
  40. 40.
    Gagnon-Kugler T, Langlois F, Stefanovsky V, Lessard F, Moss T. Loss of human ribosomal gene CpG methylation enhances cryptic RNA polymerase II transcription and disrupts ribosomal RNA processing. Mol Cell. 2009;35:414–25.CrossRefPubMedGoogle Scholar
  41. 41.
    Moss T. DNA methyltransferase inhibition may limit cancer cell growth by disrupting ribosome biogenesis. Epigenetics. 2011;6:128–33.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© The Japanese Society of Hematology 2016

Authors and Affiliations

  • M. Monika Belickova
    • 1
  • Michaela Dostalova Merkerova
    • 1
  • Hana Votavova
    • 1
  • Jan Valka
    • 1
  • Jitka Vesela
    • 1
  • Barbora Pejsova
    • 1
  • Hana Hajkova
    • 1
  • Jiri Klema
    • 2
  • Jaroslav Cermak
    • 1
  • Anna Jonasova
    • 3
  1. 1.Department of GenomicsInstitute of Hematology and Blood TransfusionPrague 2Czech Republic
  2. 2.Department of Computer ScienceFaculty of Electrical Engineering, Czech Technical UniversityPragueCzech Republic
  3. 3.1st Department of MedicineGeneral University Hospital PraguePragueCzech Republic

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