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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.