DESCRIPTIVE ANALYSIS OF ANTIGEN EXPRESSION PATTERN IN
- 1. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 49 Mohadese H. Boroojerdi, N. Daneshvar, P. Ghoraishizadeh, S. Md Noor, Z. Seman, R. Ramasamy. Descriptive analysis of antigen expression pattern in refractory anemia, refractory anemia with ringed sideroblasts and refractory anemia with excess blast cases using flow cytometry. Journal of Medicine and Biomedical Sciences 2013; 4(2), 49-56. DOI: 10.7813/jmbs.2013/4-2/8 DESCRIPTIVE ANALYSIS OF ANTIGEN EXPRESSION PATTERN IN REFRACTORY ANEMIA, REFRACTORY ANEMIA WITH RINGED SIDEROBLASTS AND REFRACTORY ANEMIA WITH EXCESS BLAST CASES USING FLOW CYTOMETRY Mohadese Hashem Boroojerdi 1 , Nasibeh Daneshvar 2 , Peyman Ghoraishizadeh 1 , Dr. Sabariah Md Noor 1 , Dr. Zainina Seman 1 , Dr. Rajesh Ramasamy 1 1 Pathology Department, Faculty of Medical and Health Science, University Putra Malaysia, 2 Institute of Bioscience, Universiti Putra Malaysia, 43400 Serdang, Selangor (MALAYSIA) E-mails: Mohadese_b84@yahoo.com, Nasibe.daneshvar@yahoo.com, peyman.innovate@gmail.com DOI: 10.7813/jmbs.2013/4-2/8 ABSTRACT Myelodysplastic Syndromes (MDSs) are a slow progressing group of disease that causes peripheral cytopaenia of all cell lineages, with high capabilities to transform into acute myeloid leaukaemia (AML). Establishing a frame of reference for antigen expression pattern in different MDS subtypes, may help to classify the patients for choosing more appropriate therapeutic approaches. In this study, thirty bone marrow (BM) samples from newly-diagnosed MDS patients were analyzed by 4- colour FACS Canto flow cytometer. In this research, the quantitative analysis of antigen expression patterns of granulocytic, monocytic, erythroid and lymphoid lineages also myeloid precursors were performed. The one-way ANOVA was used to test the differences between mean percentages of antigens in MDS subtypes. In this study, we showed the mean percentages of CD71/CD235a/ CD45/CD117, HLA-DR/CD13/CD45/CD11b, CD14/CD33/CD45/CD34, and CD19/ CD20/CD45/CD10 antibody combinations on erythroid, granulocytic, monocytic, and lymphoid lineages, respectively, in different MDS subtypes. The most important finding of this study was the significant difference of CD71 mean percentage in erythroid cells between MDS subtypes. Erythroid lineage in Refractory Anemia with Excess Blast (RAEB) cases was found by lower mean percentage of CD71 (54.74%), as compared to Refractory Anemia (RA) (74.65%) and Refractory Anemia with Ringed Sideroblasts (RARS) cases (74.60%). In this study, the difference of CD71 + erythroid cells was statistically significant only between RARS and RAEB subtypes (p=0.011). Our study, showed the expression range of different CD markers on different lineages of MDS subtypes. These findings can improve understanding the prognosis of various subtypes, and explaining the laboratory and clinical results. Key words: Myelodysplastic Syndromes (MDSs), bone marrow (BM), Flow cytometer 1. INTRODUCTION Myelodysplastic syndromes (MDSs) are a various group of hematological diseases that present production of the blood cells in an ineffective way (1, 2). Several MDS classification and prognostic scoring systems have been proposed (3) (4-6). There are two MDS staging systems such as; International Prognostic Scoring System (IPSS) and World Health Organization (WHO) Prognostic Scoring System (WPSS) (7-9). Percentage of blasts in bone marrow (BM), chromosome abnormalities and, blood counts were three factors that were established by IPSS to stage MDS. In this classification, each patient was taken a score for each of three factors, and lower score was shown better prognosis. As mentioned before, based on IPSS score, MDS patients divided to four groups (low risk, intermediate-1 risk, intermediate-2 risk, high risk). According to this system, cases with higher risk had the lower median survival and higher risk for progressing to Acute Myeloid Leukemia (AML) and cases with lower risk had the higher median survival and lower risk for progressing to AML. MDS cases were classified to five groups based on IPSS scoring. These five groups are: Refractory anemia with ringed sideroblasts (RARS), Refractory anemia (RA), chronic myelomonocytic leukemia
- 2. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 50 (CMML), and Refractory anemia with excess blasts (RAEB), Refractory anemia with excess blasts in transformation (RAEB-t). In general, those with more chromosomal damage, higher number of blasts in the BM, have more chance for progressing to AML (7-9). Chromosome abnormalities and, patient requirement for transfusions were three factors that were established by WPSS to stage MDS. Based on this system, MDS patients divided to five groups (very low risk, low risk, intermediate risk, high risk, very high risk). According to this categorization, cases with lower risk had the higher median survival and lower risk for progressing to AML and vice versa. In this staging system, MDS cases were classified to five groups (Refractory anemia, Refractory anemia with ringed sideroblasts, Refractory cytopenia with multilineage dysplasia, Refractory cytopenia with multilineage dysplasia and ringed sideroblasts, Refractory anemia with excess blasts-1, Refractory anemia with excess blasts-2, Myelodysplastic syndrome, unclassified, Myelodysplastic syndrome associated with isolated del(5q)). In fact, MDS subclassification makes managing and controlling of this disease more possible. Unfortunately, subclassification of MDS remains difficult and imprecise. A single marker that reliably distinguishes MDS from non-MDS would greatly facilitate diagnosis and might aid MDS subclassification. Diagnosis and subclassification of MDS remain difficult by the absence of definitive testing for these purposes(10). In recent decades, immunophenotyping by flow cytometry plays an important role in diagnosis and sub classification of haematological malignancies (11-13). On the other hand, knowing about the prognosis of individual patients will provide useful information that lead to more trustable diagnosis also, better controlling and treatment of the disease. In fact, knowledge about the pattern of antigen expression on various cell lineages in different MDS subtypes can give us a prognostic value of each subtype. Knowing the prognosis of each subtype by immunophenotyping, cause more accurate diagnosis of this malignancy (14) also, might help to choose more appropriate therapeutic approaches (15). 2. MATERIALS AND METHODS In this study, 30 MDS cases with newly diagnosed MDS based on French-American-British (FAB) criteria were investigated (16). Flow cytometric analysis of the samples was performed in the flow cytometry laboratory, Haematology Unit, Pathology Department, Faculty of Medicine and Health Science, University Putra Malaysia (UPM). Sample collection was done from February 2009 to November 2010 at Hospital Kuala Lumpur (HKL) after written informed consent from patients and ethical clearance from the Faculty of Medicine and Health Sciences (UPM) (no: UPM/FPSK/PADS/T7- MJKEtikaPer/F01(LECT(JPAT)_MAC(10)02), and approval letter from HKL (no: HKL /PAT/180/1). The pattern recognition approach that we adopted in this study was the method suggested by Van Lochem et al. (Van Lochem et al 2004). In this study, CD71/CD235a/CD45/CD117, HLA-DR/CD13/CD45/ CD11b, CD14/CD33/CD45/CD34, and CD19/CD20/CD45/CD10 antibody combinations were used to analyze erythroid, granulocytic, monocytic, and lymphoid lineages respectively. The method for labeling the cells was carried out according to Li et al. with some modifications according to the recommendation of the manufacturer to optimize the technique. The process has been described in our published manuscript. A descriptive analysis was done for all variables studied (17). 3. STATISTICAL ANALYSIS The one-way ANOVA was used to test of differences between mean percentages of antigens in MDS subtypes. Tukey test was done in the cases that showed significant differences in one-way ANOVA test. A p-value of ≤0.05 was considered as statistically significant 4. RESULTS The mean percentages of CD13, CD34, CD10, HLA-DR, CD33 and CD11b expression of the granulocyte lineage were 82.85%, 34.72%, 51.15%, 11.05%, 96.35%, and 83.60%, respectively, in RA patients. In addition, patients with RARS showed mean percentages of 86.70%, 32.50%, 39.30%, 16.75%, 93.34%, and 84%, respectively, while patients with RAEB showed mean percentages of 84.28%, 35.41%, 38.22%, 13.96%, 87.85%, and 70.49%, respectively. There was no significant difference in granulocytic antigen expression among MDS subtypes. Table 1 shows the percentages of different antigens on granulocytic lineage in MDS subtypes.
- 3. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 51 Table 1. Percentages of different antigens on granulocytic lineage in MDS subtypes Variable Group Mean percentage SD F/Welch P values CD13 RA 82.85 13.10 0.237 0.790 RARS 86.70 7.76 RAEB 84.28 12.51 CD34 RA 34.72 7.07 0.582 0.566 RARS 32.50 5.38 RAEB 35.41 7.71 CD10 RA 51.15 10.88 1.046 0.365 RARS 39.30 17.53 RAEB 38.22 16.09 HLA-DR RA 11.05 6.23 0.911 0.414 RARS 16.75 10.23 RAEB 13.96 5.54 CD33* RA 96.35 3.53 2.36 0.155 RARS 93.34 2.15 RAEB 87.85 13.93 CD11b* RA 83.60 10.09 2.39 0.157 RARS 84 4.69 RAEB 70.49 22.29 * For cases (CD33 and CD11b) that homogeneity of variance was not assumed Welch statistic has been reported. 4.1.2. ERYTHROID LINEAGE The mean percentages of CD71, CD235a and CD71/CD235a-positive erythroid lineages were 74.65%, 39.27% and 9.02% in RA. Analysis of these antigens’ expressions on RARS and RAEB subtypes showed mean percentages of 74.60%, 38.57%, 7.57% and 54.74%, 33.16%, 5.26%, respectively. The mean percentages of CD71-positive, CD235a-positive and CD71/CD235a-positive nucleated red cells were lower in RAEB as compared to RA and RARS. Only the difference of CD71- positive erythroid cells was statistically significant. Based on Tukey test the difference of CD71- positive erythroid cells was statistically significant only between RARS and RAEB subtypes (p≤0.05). Table 2 shows the mean percentages of different antigen expression on erythroid lineage in MDS subtypes. Table 3 shows the significant differences in one-way ANOVA test by Tukey test. Table 2. Percentages of different antigens on erythroid lineage in MDS subtypes Variable Group Mea percentage SD F/Welch P values CD71 RA 74.65 5.83 5.80 0.008 RARS 74.60 13.22 RAEB 54.74 19.33 CD235a RA 39.27 12.98 0.68 0.515 RARS 38.57 14.02 RAEB 33.16 12.57 CD71/CD235a-positive* RA 9.02 5.486 1.86 0.224 RARS 7.57 4.15 RAEB 5.26 2.45 *For cases (CD71/CD235a) that homogeneity of variance was not assumed (p value was less than 0.05) Welch statistic has been reported. Table 3. Analysis of CD markers that showed significant differences in one-way ANOVA test by Tukey test The mean percentages of monocytic lineage expressed HLA-DR, CD19, HLA-DR/CD11b- positive, CD14, CD33, CD14/CD34-positive and CD13 were 33.05%, 32.40%, 28.50%, 83.47%, 87.77%, 30.72%, and 73.92% in RA. Patients with PARS and RAEB showed mean percentages of 36.89%, 29.70%, 31.50%, 66.65%, 83.22%, 35.84%, 70.92% and 35.90%, 32.85%, 34.29%, 60.63%, 75.40%, 37.48%, 78.16%, respectively. There was no significant difference in monocytic antigen expression among MDS subtypes. Table 4 shows the percentages of different antigens on monocytic lineage in MDS subtypes. Variable Group Group P value CD71 RA RARS 1.00 RAEB 0.087 RARS RA 1.00 RAEB 0.011 RAEB RA 0.087 RARS 0.011
- 4. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 52 Table 4. Percentages of different antigens on monocytic lineage in MDS subtypes Variable Group Mean percentage SD F/Welch P values HLA-DR RA 33.05 7.25 0.25 0.781 RARS 36.89 11.49 RAEB 35.90 13.17 CD19 RA 32.40 11.29 0.30 0.739 RARS 29.70 9.63 RAEB 32.85 10.67 HLA-DR/CD11b-positive RA 28.50 7.30 0.95 0.397 RARS 31.50 8.79 RAEB 34.29 7.57 CD14* RA 83.47 3.84 1.89 0.169 RARS 66.65 19.52 RAEB 60.63 23.81 CD33 RA 87.77 11.04 1.20 0.315 RARS 83.22 9.29 RAEB 75.40 21.19 CD14 / CD34-positive RA 30.72 10.42 0.27 0.763 RARS 35.84 15.02 RAEB 37.48 18.06 CD13 RA 73.92 23.36 0.29 0.748 RARS 70.92 16.88 RAEB 78.16 28.017 *For cases (CD14) that homogeneity of variance was not assumed (p value was less than 0.05)) Welch statistic has been reported. 4.1.4. MYELOID PRECURSORS The mean percentages of CD33, CD34, CD13, HLA-DR, HLA-DR/CD11b-positive, CD117 and CD11b expressed by myeloid precursors were 82.07%, 72.97%, 58.77%, 67.05%, 18.67%, 20.40%, and 29.37%, respectively, in RA. Patients with RARS and RAEB showed mean percentages of 69.36%, 52.70%, 57.21%, 46.49%, 21.97%, 18.61%, 17.79% and 65.72%, 60.95%, 63.64%, 56.54%, 24.38%, 20.68%, 25.98%, respectively. There was no significant difference in myeloid precursor’s antigen expression among MDS subtypes. Table 5 shows the percentages of antigens on myeloid precursors in MDS subtypes. Table 5. Percentages of antigens on myeloid precursors in MDS subtypes Variable Group Mean percentage SD F/Welch P values CD33 RA 82.07 3.10 0.84 0.440 RARS 69.36 25.94 RAEB 65.72 21.88 CD34 RA 72.97 6.37 1.78 0.187 RARS 52.70 20.06 RAEB 60.95 19.68 CD13 RA 58.77 21.31 0.32 0.729 RARS 57.21 21.47 RAEB 63.64 20.27 HLA-DR RA 67.05 18.85 1.77 0.188 RARS 46.49 20.50 RAEB 56.54 19.50 HLA-DR/CD11b-poisitive RA 18.67 13.81 0.29 0.749 RARS 21.97 13.32 RAEB 24.38 14.36 CD117 RA 20.40 9.36 0.14 0.869 RARS 18.61 9.45 RAEB 20.68 10.51 CD11b* RA 29.37 19.23 1.97 0.243 RARS 17.79 5.40 RAEB 25.98 16.73 *For cases (CD11b) that homogeneity of variance was not assumed (p value was less than 0.05) Welch statistic has been reported.
- 5. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 53 The mean range for CD19, CD19/CD10-positive, CD19/CD20-positive and CD20/CD10-positive were 15.47%, 6.84%, 12.43%, and 3.32%, respectively, in RA. The expression of these antigens were 14.35%, 3.75%, 12.97%, and 2.90%, respectively, in RARS and 14.73%, 3.68%, 11.59%, and 3.13%, respectively, in RAEB cases. Table 6 shows the percentages of different antigens on lymphoid lineage in MDS subtypes. Table 7 shows the significant differences in one-way ANOVA test by Tukey test. Table 6. Percentages of different antigens on lymphoid lineage in MDS subtypes Variable Group Mean Percentage SD F/Welch* P values CD19 RA 15.47 7.81 0.061 0.941 RARS 14.35 4.81 RAEB 14.73 5.39 CD19/CD10-positive RA 6.84 2.17 4.108 0.028 RARS 3.75 1.92 RAEB 3.68 2.11 CD19/CD20-positive RA 12.43 2.40 0.401 0.674 RARS 12.97 3.37 RAEB 11.59 4.52 CD20/CD10-positive RA 3.32 2.38 0.105 0.901 RARS 2.90 2.05 RAEB 3.13 1.94 Table 7. Analysis of CD markers that showed significant differences in one-way ANOVA test by Tukey test 5. DISCUSSION MDS has a broad distribution of clinical and pathologic features, and patients vary markedly in their prognosis and response to treatment. Differences in clinical features and treatment response are likely due to molecular heterogeneity that underlies the disease process(18). In this study, we did the quantitative flow cytometric analysis of the most important maturation-associated antigens in BM cells of erythroid, granulocytic, monocytic, lymphoid lineages and myeloid precursors. Thirty newly- diagnosed MDS patients were analysed in this study(19). The aim of this study was quantitative analysis of antigen expression, also, showing some abnormal antigen expression pattern in different MDS subtypes. Several studies have reported the phenotypic changes occurring in the different cell populations in MDS subtypes. In mentioned studies, different antibody panel has been used, and various cell lineages has been analyzed(20, 21) (22, 23). We showed some abnormal expression pattern; in addition, the mean percentage of different antigen in various cell lineages in our study. This study, though the analysis of granulocytic lineage we found that the mean percentage of CD10, CD11b and CD33 was lower in RAEB but the difference was not statistically significant. As mentioned previously, CD13 and CD33 are markers which decrease during maturation. In addition, CD34 that relates to the immature cells was higher in RAEB cases as compared to other MDS subtypes although, the difference was not statistically significant. These results are the same as what has been found by other researchers(20, 21). same as other studies, our findings showed the mean percentage of CD10 on granulocytes was lower in RAEB, while granulocytes in RA cases showed a higher mean percentage of CD10 but the difference was not statistically significant (20, 23). CD 14 is normally expressed on mature monocytes. Decreased of mean percentage of CD14 on monocytic lineage is more prominent in RAEB subtypes (21). In this study, the mean proportion of different CD markers on monocyte lineage was compared among MDS subtypes. The mean proportion of HLA-DR/CD11b combination and CD13 on monocytes was higher in RAEB in comparison to other MDS subtypes. In addition, the mean percentage of CD33 and CD14 on monocytes was lower in RAEB than RA and RARS. But there was no significant difference in monocytic antigen expression Variable Group Group P value CD19/CD10-positive RA RARS 0.038 RAEB 0.027 RARS RA 0.038 RAEB 0.996 RAEB RA 0.027 RARS 0.996
- 6. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 54 among MDS subtypes. We also showed the mean percentages of erythroid precursors CD markers in different subtypes of MDS. The mean percentage of CD71, CD235a and combination of CD71/CD235a on erythroid precursors was lower in RAEB in comparison to RA and RARS. Analysis of erythroid antigens showed that, only the difference of CD71 on erythroid cells was statistically significant between MDS subtypes. This difference was statistically significant only between RARS and RAEB subtypes (p<0.011).Our results also, supported previous studies(23). Changes in antigen expression pattern of the myeloid precursors in MDS cases have been shown several times by different researchers (12, 13, 24-26). These characteristics have been shown with a great frequency in MDS cases that are in a high grade such as RAEB cases (27). Some studies showed enhance of CD34 density on the myeloid precursors in RAEB cases(28) (18). Samuel et al. showed the CD13 expression on myeloid precursors was higher in RAEB cases, as compared to RA and RARS cases (22). Kussick et al, used 4-color flow cytometric analysis, conducted the most extensive evaluation of myeloid precursor’s phenotypic changes in MDS. They found that, the abnormal expression of HLA-DR, CD13, CD33, CD38, and CD117 on myeloid precursor was occurred in 50% or more of cases. In addition, they showed a declined mean percentage of CD33 and HLA-DR and raised mean proportion of CD13 on myeloid precursors in RAEB subtypes(12).Samuel et al. demonstrated similar phenotypic abnormalities with frequencies that were subtype related. These abnormalities were included increased expression of CD13 and CD34 on myeloid precursors. They found that, the most frequent changes in RA, RARS, and RAEB subgroups were increased density of CD34 and CD117 or combination of CD34/CD117. They also indicated abnormal expression of CD34 and CD117 were presented in 50% of RARS, and 100% of RAEB cases (22). In fact, increased density of CD34 and CD117 on myeloid precursors was common findings in AML and has been proposed as valuable aids for detecting minimal residual disease (29, 30). In this study, the mean percentage of CD33 on myeloid precursors was lower in RAEB in comparison to other MDS subtypes. In addition, the mean percentage of CD13 on myeloid precursors was higher in RAEB than RA and RARS. In MDS subtypes, there was an increase in percentage of HLA-DR/CD11b , combination and CD117 on myeloid precursors in RAEB subtypes, as compared to RARS and RA groups. But there was no significant difference in monocytic antigen expression among MDS subtypes. These results support the idea that the maturity of cells in RAEB subtype is lower than in RA and RARS subtypes, as well, may show that RAEB cases have higher risk for progressing to AML, as compared to RA and RARS subtypes. These findings, also, indicates the association of these disorders with AML. In fact, these abnormalities have been detected in high frequency. As a result, it can show the efficacy and importance of these measurements in flow cytometric diagnosis of MDS, as well, may show that phenotypic abnormalities can be subtype related. Indeed, MDS patient’s prognosis is really varied. Although, recent prognostic classifications for example IPSS and WPSS are really useful they are not practical to be used in all subgroups of patients. As a result, more research in the area of the molecular pathogenesis of each subgroups also biomarkers those are responsible for pathogenesis are needed. On the other hand, being familiar with these biomarkers can lead to find the more suitable therapeutic approaches(31). Different studies showed RA and RARS cases higher median survival also lower risk of progressing to AML in compared to RAEB cases. This shows that RA and RARS cases have a better prognosis than RAEB cases (9, 31-34). As mentioned above, different studies also our results about antigen expression pattern of MDS subtypes showed more abnormality and lower maturity in RAEB subtypes, as compared to RA and RARS subtypes. On the other hand, RAEB cases showed the lower median survival and higher risk for progressing to AML as compared to RA and RARS subtypes. These results combined together can support the idea that cases with higher abnormality in antigen expression pattern have higher risk for progression to AML and less median survival. As a result, these cases have worse prognosis. This shows the importance of immunophenotyping and knowing about the antigen expression pattern of MDS subtypes. This study, showed the range of different CD markers on myeloid, erythroid and lymphoid lineages of MDS subtypes. Also, abnormalities in antigen expression pattern of MDS subtypes were shown. In fact, our results can be useful for understanding the prognosis of each subtypes and interpretation of laboratory and clinical findings, also, can help to find more appropriate therapeutic approaches for each patient. Although, the project was successfully completed some limitations were observed. In the end, our study was limited by its small sample size and the lack of samples throughout Malaysia. Having a reference of antigen expression pattern can be useful for estimating disease grade in MDS and can help to predict clinical outcomes of these patients.
- 7. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 55 ACKNOWLEDGMENTS We would like to thank Dr Raudhawati Osman as the head of the haematology unit in Hospital Kuala Lumpur for allowing us to collect and conduct this study and Madam Lee Siew Moi, for the assistance in sample collection. REFERENCES 1. Maynadie M., F. Picard and B. Husson, Immunophenotypic clustering of myelodysplastic syndromes. Blood, 2002. 100: p. 2349-2356. 2. Ogata K. and Y. Yoshida, Clinical implications of blast immunophenotypes in myelodysplastic syndromes. Leuk Lymphoma., 2005. 46: p. 1269-74. 3. Craig F.E. and K.A. Foon, Flow cytometric immunophenotyping from hematologic neoplasms. Blood, 2008. 111(8): p. 3941-3967. 4. Bene M.C., et al., Immunophenotyping of Myelodysplasia. Clinical and Applied Immunology Reviews, 2005. 5(2): p. 133-148. 5. Germing U., et al., No increase in age-specific incidence of myelodysplastic syndromes. Haematologica 2004. 89: p. 905-10. 6. Aul C., N. Gattermann, and W. Schneider, Age-related incidence and other epidemiological aspects of myelodysplastic syndromes. Br J Haematol, 1992. 82: p. 358-67. 7. Park M.J., et al., Is International Prognostic Scoring System (IPSS) still standard in predicting prognosis in patients with myelodysplastic syndrome? External validation of the WHO Classification-Based Prognostic Scoring System (WPSS) and comparison with IPSS. European journal of haematology, 2008. 81(5): p. 364-373. 8. Palmer S.R., et al., Platelet count is an IPSS-independent risk factor predicting survival in refractory anaemia with ringed sideroblasts. British journal of haematology, 2008. 140(6): p. 722-725. 9. Germing U., et al., Prospective validation of the WHO proposals for the classification of myelodysplastic syndromes. Haematologica, 2006. 91(12): p. 1596-1604. 10.Swerdllow S., E. Campo, and N.L. Harris, WHO classification of tumours of haematopoietic and lymphoid tissues. 2008: France: IARC Press, 2008. 11.Della Porta M., et al., Flow cytometry evaluation of erythroid dysplasia in patients with myelodysplastic syndrome. Leukemia, 2006. 20(4): p. 549-555. 12.Kussick S.J., et al., Four-color flow cytometry shows strong concordance with bone marrow morphology and cytogenetics in the evaluation for myelodysplasia. American journal of clinical pathology, 2005. 124(2): p. 170-181. 13.Ogata K., et al., Diagnostic utility of flow cytometry in low-grade myelodysplastic syndromes: a prospective validation study. Haematologica, 2009. 94(8): p. 1066-1074. 14.Truong F., et al., The utility of flow cytometric immunophenotyping in cytopenic patients with a non diagnostic bone marrow: A prospective study. Leukemia Research, 2009. 33: p. 1039-1046. 15.Stetler-Stevenson M., et al., Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood, 2001. 98(4): p. 979-987. 16.Stetler-Stevenson M., et al., Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood, 2001. 98(4): p. 979-987. 17.Lorand-Metze I., et al., Detection of hematopoietic maturation abnormalities by flow cytometry in myelodysplastic syndromes and its utility for the differential diagnosis with non-clonal disorders. Leukemia Research, 2007. 31: p. 147-155. 18.Bennett J.M. A comparative review of classification systems in myelodysplastic syndromes (MDS). in Seminars in oncology. 2005: Elsevier. 19.Lorand-Metze I., et al., Detection of hematopoietic maturation abnormalities by flow cytometry in myelodysplastic syndromes and its utility for the differential diagnosis with non-clonal disorders. Leukemia research, 2007. 31(2): p. 147-155. 20.Stachurski D., et al., Flow cytometric analysis of myelomonocytic cells by a pattern recognition approach is sensitive and specific in diagnosing myelodysplastic syndrome and related marrow diseases: emphasis on a global evaluation and recognition of diagnostic pitfalls. Leukemia research, 2008. 32(2): p. 215-224. 21.Loken M.R. and D.A. Wells, The role of flow cytometry in myelodysplastic syndromes. Journal of the National Comprehensive Cancer Network, 2008. 6(9): p. 935-941.
- 8. © Journal of Medicine and Biomedical Sciences, ISSN: 2078-0273, Vol. 4. No. 2, 2013 56 22.Pirruccello S.J., K.H. Young, and P. Aoun, Myeloblast phenotypic changes in myelodysplasia CD34 and CD117 expression abnormalities are common. American journal of clinical pathology, 2006. 125(6): p. 884-894. 23.Malcovati L., et al., Flow cytometry evaluation of erythroid and myeloid dysplasia in patients with myelodysplastic syndrome. Leukemia, 2005. 19(5): p. 776-783. 24.Wells D.A., et al., Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood, 2003. 102(1): p. 394-403. 25.Del Canizo M.C., et al., Immunophenotypic analysis of myelodysplastic syndromes. Haematologica, 2003. 88(4): p. 402-407. 26.Arroyo J.L., et al., Impact of immunophenotype on prognosis of patients with myelodysplastic syndromes. Its value in patients without karyotypic abnormalities. The Hematology Journal, 2004. 5(3): p. 227-233. 27.Elghetany M.T., Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome. Blood, 2002. 99(1): p. 391-392. 28.Maynadie M., et al., Immunophenotypic clustering of myelodysplastic syndromes. Blood, 2002. 100(7): p. 2349-2356. 29.Beghini A., et al., KIT activating mutations: incidence in adult and pediatric acute myeloid leukemia, and identification of an internal tandem duplication. Haematologica, 2004. 89(8): p. 920-925. 30.Scolnik M.P., et al., CD34 and CD117 are overexpressed in AML and may be valuable to detect minimal residual disease. Leukemia research, 2002. 26(7): p. 615-619. 31.Visconte V., et al., SF3B1, a splicing factor is frequently mutated in refractory anemia with ring sideroblasts. Leukemia, 2011. 26(3): p. 542-545. 32.Verburgh E., et al., A new disease categorization of low-grade myelodysplastic syndromes based on the expression of cytopenia and dysplasia in one versus more than one lineage improves on the WHO classification. Leukemia, 2007. 21(4): p. 668-677. 33.Giagounidis A., et al., Clinical, morphological, cytogenetic, and prognostic features of patients with myelodysplastic syndromes and del (5q) including band q31. Leukemia, 2003. 18(1): p. 113-119. 34.Giagounidis A., et al., Prognosis of patients with del (5q) MDS and complex karyotype and the possible role of lenalidomide in this patient subgroup. Annals of hematology, 2005. 84(9): p. 569-571.