All Title Author
Keywords Abstract

PLOS ONE  2013 

Immunophenotyping in Myelodysplastic Syndromes Can Add Prognostic Information to Well-Established and New Clinical Scores

DOI: 10.1371/journal.pone.0081048

Full-Text   Cite this paper   Add to My Lib


Background myelodysplastic syndromes (MDS) are a heterogeneous group of hematopoietic clonal disorders. So, prognostic variables are important to separate patients with a similar biology and clinical outcome. We compared the importance of risk stratification in primary MDS of IPSS and WPSS with the just described revision of IPSS (IPSS-R), and examined if variables obtained by bone marrow immunophenotyping could add prognostic information to any of the scores. Methods In this prospective study of 101 cases of primary MDS we compared the relation of patients’ overall survival with WHO types, IPSS, IPSS-R, WPSS and phenotypic abnormalities of hematopoietic precursors. We examined aberrancies in myelomonocytic precursors and CD34+ cells. Patients were censored when receiving chemotherapy or BM transplantation. Survival analysis was made by Cox regressions and stability of the models was examined by bootstrap resampling. Results median age: 64 years (15–93). WHO types: 2 cases of 5q- syndrome, 7 of RA, 64 of RCDM and 28 of RAEB. In the univariate Cox analysis, increasing risk category of all scores, degree of anemia, higher percentage of BM blasts, higher number of CD34+ cells and their myeloid fractions besides increasing number of phenotypic abnormalities detected were significantly associated with a shorter survival. In the multivariate analysis comparing the three scores, IPSS-R was the only independent risk factor. Comparing WPSS with phenotypic variables (CD34+/CD13+ cells, CD34+/CD13? cells and “total alterations”) the score and “CD34+/CD13+ cells” remained in the model. When IPSS was tested together with these phenotypic variables, only “CD34+/CD13+ cells”, and “total alterations” remained in the model. Testing IPSS-R with the phenotypic variables studied, only the score and “CD34+/CD13+ cells” entered the model. Conclusions Immunophenotypic analysis of myelomonocytic progenitors provides additional prognostic information to all clinical scores studied. IPSS-R improved risk stratification in MDS compared to the former scores.


[1]  Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DAG, et al. (1982) Proposals for the classification of the myelodysplastic syndromes. Br J Haematol 51: 189–199.
[2]  Swerdlow S, Camp E, Harris N, Jaffe ES, Pileri SA, et al. (2008) WHO classification of tumors of haematopoietic and lymphoid tissues. Lyon: IARC.
[3]  Yin CC, Medeiros LJ, Bueso-Ramos CE (2010) Recent advances in the diagnosis and classification of myeloid neoplasms-comments on the 2008 WHO classification. Int J Lab Hematol 32: 461–476.
[4]  Valent P, Horny HP, Bennett JM, Fonatsch C, Germing U, et al. (2007) Definitions and standards in the diagnosis and treatment of the myelodysplastic syndromes: Consensus statements and report from a working conference. Leuk Res 31: 727–739.
[5]  Lorand-Metze I, Pinheiro MP, Ribeiro E, de Paula EV, Metze K (2004) Factors influencing survival in myelodysplastic syndromes in a Brazilian population: Comparison of FAB and WHO classifications. Leuk Res 28: 587–594.
[6]  Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, et al. (1997) International scoring system for evaluation in MDS. Blood 89: 2079–2088.
[7]  Schanz J, Tüchler H, Solé F, Mallo M, Lu?o E, et al. (2012) 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 30: 820–829.
[8]  Pardanani A, Tefferi AC (2012) Cytogenetic risk stratification in myelodysplastic syndromes: are we there yet? J Clin Oncol 30: 2703–2704.
[9]  Greenberg P, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, et al. (2012) Revised International Prognostic Scoring System for myelodysplastic syndromes. Blood 120: 2454–2465.
[10]  Malcovati L, Della Porta MG, Strupp C, Ambaglio I, Kuendgen A, et al. (2011) 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 96: 1433–1440.
[11]  Thol F, Yun H, Sonntang AK, Damm F, Weissinger EM, et al. (2012) Prognostic significance of combined MN1, ERG, BAALC, and EV11 (MEBE) expression in patients with myelodysplastic syndromes. Ann Hematol 91: 1221–1233.
[12]  Bejar R, Stevenson K, Abdel-Wahab O, Galili N, Nilsson B, et al. (2012) Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med 364: 2496–2506.
[13]  Stetler-Stevenson M (2009) Flow cytometric immunophenotyping: emerging as an important diagnostic tool in the evaluation of cytopenic patients. Leuk Res 33: 1020–1021.
[14]  Lorand-Metze I, Ribeiro E, Lima CSP, Batista LS, Metze K (2007) Detection of hematopoietic maturation abnormalities by flow cytometry in myelodysplastic syndromes and its utility for the differential diagnosis with non-clonal disorders. Leuk Res 31: 147–155.
[15]  van de Loosdrecht AA, Alhan C, Béné MC, Della Porta MG, Dr?ger AM, et al. (2009) Standardization of flow cytometry in myelodysplastic syndromes: report from the first European LeukemiaNet working conference on flow cytometric in myelodysplastic syndromes. Haematologica 94: 1124–1134.
[16]  Matarraz S, Lopez A, Barrena S, Fernandez C, Jensen E, et al. (2008) The immunophenotype of different immature, myeloid and B-cell lineage-committed CD34+ hematopoietic cells allows discrimination between normal/reactive and myelodysplastic syndrome precursors. Leukemia 22: 1175–1183.
[17]  Reis SC, Traina F, Metze K, Saad ST, Lorand-Metze I (2009) Variation of bone marrow CD34+ cell subsets in myelodysplastic syndromes according WHO types. Neoplasma 56: 435–440.
[18]  Chu SC, Wang TF, Li CC, Kao RH, Li DK, et al. (2011) Flow cytometric scoring system as a diagnostic and prognostic tool in myelodysplastic syndromes. Leuk Res 35: 868–873.
[19]  Ribeiro E, Matarraz SS, Santiago M, Lima CSP, Metze K, et al. (2006) Maturation-associated immnophenotypic abnormalities in bone marrow B-lymphocytes in myelodysplastic syndromes. Leuk Res 30: 9–16.
[20]  Reis-Alves SC, Traina F, Saad ST, Metze K, Lorand-Metze I (2010) The impact of several phenotypic features at diagnosis on survival of patients with myelodysplastic syndromes. Neoplasma 57: 530–536.
[21]  Lorand-Metze I, Califani SMV, Ribeiro E, Lima CSP, Metze K (2008) The prognostic value of maturation-associated phenotypic abnormalities in myelodysplastic syndromes. Leuk Res 32: 211–213.
[22]  Ossenkoppele GJ, van de Loosdrecht AA, Schuurhuis GJ (2011) Review of the relevance of aberrant antigen expression by flow cytometry in myeloid neoplasms. Br J Haematol 153: 421–436.
[23]  Westers TM, Ireland R, Kern W, Alhan C, Balleisen JS, et al. (2012) Standardization of flow cytometry in myelodysplastic syndromes: a report from an international consortium and the European LeukemiaNet Working Group (2012) Leukemia. 26: 1730–1741.
[24]  Shaffer LG, Slovak ML, Campbell LJ, eds. (2009) An International System for Human Cytogenetic Nomenclature: Recommendations of the International Standing Committee on Human Cytogenetic Nomenclature. Basel, Switzerland. Karger.
[25]  Oliveira GB, Pereira FG, Metze K, Lorand-Metze I (2001) Spontaneous apoptosis in chronic lymphocytic leukemia and its relationship to clinical and cell kinetic parameters. Cytometry 6: 329–335.
[26]  Bedin V, Adam RL, Sá BCS, Landman G, Metze K (2010) Fractal dimension is an independent prognostic factor for survival in melanoma. BMC Cancer 10: 260.
[27]  Louren?o GJ, Lorand-Metze I, Delamain MT, Miranda ECM, Kameo R, et al. (2010) Polymorphisms of glutathione S-transferase mu 1, theta 1 and pi 1 genes and prognosis in Hodgkin lymphoma. Leuk & Lymph 51: 2215–2221.
[28]  Ferro DP, Falconi MA, Adam RL, Ortega MM, Lima CSP, et al. (2011) Fractal Characteristics of May-Grünwald-Giemsa stained Chromatin are Independent Prognostic Factors for Survival in Multiple Myeloma. PLoS One 6(6): e20706.
[29]  Altman DG, Royston P. (2000) What do we mean by validating a prognostic model? Statist Med 19;453–473.
[30]  Choodari-Oskooei B, Royston P, Parmar MKB (2010) A simulation study of predictive ability measures in a survival model I: explained variation measures. Statist Med 31: 2627–2643.
[31]  Sauerbrei W, Schumacher M (1992) A bootstrap resampling procedure for model building: application to the Cox regression model. Statist Med. 11: 2093–109.
[32]  Elston LB, Sueiro FAR, Cavalcanti JN, Metze K (2009) The Importance of the Mitotic Index as a Prognostic Factor for Survival of Canine Cutaneous Mast Cell Tumors: A Validation Study. Vet Pathol 46: 362–364.
[33]  Rybka MO, Cintra ML, de Souza EM, Metze K (2008) Density of dendritic cells around basal cell carcinoma is related to tumor size, anatomical site and stromal characteristics, and might be responsible for the response to topical therapy. International Journal of Dermatology 47: 1240–1244.
[34]  Adam RL, Silva RC, Pereira FG, Leite NJ, Lorand-Metze I, et al. (2006) The fractal dimension of nuclear chromatin as a prognostic factor in acute precursor B lymphoblastic leukemia. Cell Oncol. 28(1–2): 55–59.
[35]  Pinheiro RF, Metze K, Silva MR, Chauffaille ML (2009) The ambiguous role of interferon regulatory factor-1 (IRF-1) immunoexpression in myelodysplastic syndrome. Leuk Res. 33: 1308–1312.
[36]  Delamain MT, Metze K, Marques JF Jr, Reis AR, de Souza CA, et al. (2006) Optimization of CD34+ collection for autologous transplantation using the evolution of peripheral blood cell counts after mobilization with chemotherapy and G-CSF. Transfus Apher Sci. 34: 33–40.
[37]  Delamain MT, Marques JF Jr, de Souza CA, Lorand-Metze I, Metze K (2008) An algorithm based on peripheral CD34+ cells and hemoglobin concentration provides a better optimization of apheresis than the application of a fixed CD34 threshold. Transfusion. 48: 1133–1137.
[38]  Clark TG, Bradburn MJ, Love SB, Altman DG (2003) Survival Analysis Part IV: Further concepts and methods in survival analysis Br J Cancer. 89: 781–786.
[39]  Smith B, Ryan MAK (2003) Survival analysis using Cox proportional hazards modeling for single and multiple event time data. Cary: SAS Institute, Inc. 254–28. Proceedings of the twenty-eighth annual SAS users group international conference.
[40]  Shao L-l, Zhang L, Hou Y, Yu S, Liu X-g, et al. (2012) Th22 cells as well as Th17 cells expand differentially in patients with early-stage and late-stage myelodysplastic syndrome. PLoS ONE 7(12): e51339.
[41]  Vido JR, Adam RL, Lorand-Metze I, Metze K (2011) Computerized texture analysis of atypical immature myeloid precursors in patients with myelodysplastic syndromes: an entity between blasts and promyelocytes. Diagn Pathol 6: 93.
[42]  Matarraz S, Teodosio C, Fernandez C, Albors M, Jara-Acevedo M, et al. (2012) The proliferation index of specific bone marrow cell compartments from myelodysplastic syndromes is associated with the diagnostic and patient outcome. PLOS ONE 7(8): e44321.
[43]  Ribeiro E, Lima CSP, Metze K, Lorand-Metze I (2004) Flow cytometric analysis of the expression of Fas/Fasl in bone marrow CD34+ cells in myelodysplastic syndromes: relation to disease progression. Leukemia & Lymphoma 45: 309–313.
[44]  Hellstr?m-Lindberg E, Malcovati L (2008) Supportive care and use of hematopoietic growth factors in myelodysplastic syndromes. Semin Hematol 45: 14–22.
[45]  van de Loosdrecht AA, Westers TM, Westra AH, Drager AM, van der Velden V, et al. (2008) Identification of distinct prognostic subgroups in low- and intermediate-1-risk myelodysplastic syndromes by flow cytometry. Blood 111: 1067–1077.
[46]  Maftoun-Banankhah S, Maleki A, Karandikar NJ, Arbini AA, Fuda FS, et al. (2008) Multiparameter flow cytometric analysis reveals low percentage of bone marrow hematogones in myelodysplastic syndromes. Am J Clin Pathol 129: 300–308.
[47]  Wells DA, Benesch M, Loken MR, Vallejo C, Myerson D, et al. (2003) Myeloid and monocytic dispoiesis as determinated by flow cytometry scoring in myelodysplastic syndromes correlates with the IPSS and with outcome after hemopoietic stem cell transplantation. Blood 102: 394–405.
[48]  Gaipa G, Coustan-Smith E, Todisco E, Maglia O, Biondi A, et al. (2002) Characterization of CD34+, CD13+ CD33? Cells, a rare subset of immature human hematopoietic cells. Haematologica 87: 347–356.
[49]  Doehring LC, Heeger C, Aherrahrou Z, Kaczmarek PM, Erdmann J, et al. (2010) Myeloid CD34+CD13+ precursor cells transdifferentiate into chondrocyte-like cells in atherosclerotic intimal calcification Am J Pathol. 177: 473–480.
[50]  Metze K (2011) Pitfalls in the assessment of prognostic factors. Lancet Oncol 12: 1095–1096.
[51]  Metze K (2011) Dichotomizing continuous prognostic factors can cause paradoxical results in survival models. J Am Coll Surg 212: 132–134.
[52]  Metze K (2008) Dichotomization of continuous data - a pitfall in prognostic factor studies. Pathol Res Pract 204: 213–214.
[53]  Senent L, Arenillas L, Lu?o E, Ruiz JC, Sanz G, et al. (2013) Reproducibility of the World Health Organization 2008 criteria for myelodysplastic syndromes. Haematologica 98: 568–575.
[54]  Hiddemann W, Clarkson BD, Büchner TH, Melamed MR, Andreef M (1982) Bone marrow cell count per cubic millimeterbone marrow: a new parameter for quantitating therapy-induced cytoreduction in acute leukemia. Blood 59: 216–225.
[55]  Satoh C, Dan K, Yamashita T, Jo R, Tamura H, et al. (2008) Flow cytometric parameters with little interexaminer variability for diagnosing low-grade myelodysplastic syndromes. Leuk Res 32: 699–707.
[56]  Kern W, Haferlach C, Schnittinger S, Haferlach T (2010) Clinical utility of multiparameter flow cytometry in the diagnosis of 1012 patients with suspected myelodysplastic syndrome. Cancer 116: 4549–4563.


comments powered by Disqus