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. Author manuscript; available in PMC: 2015 Aug 1.
Published in final edited form as: Br J Haematol. 2014 Apr 9;166(3):352–359. doi: 10.1111/bjh.12884

Comparison of the Prognostic Utility of the Revised International Prognostic Scoring System and the French Prognostic Scoring System in Azacitidine-Treated Patients with Myelodysplastic Syndromes

Amer M Zeidan 1,*, Ju-Whei Lee 2, Thomas Prebet 3, Peter Greenberg 4, Zhuoxin Sun 2, Mark Juckett 5, Mitchell R Smith 6, Elisabeth Paietta 7, Janice Gabrilove 8, Harry P Erba 9, Martin S Tallman 10, Steven D Gore 11, on behalf of the Eastern Cooperative Oncology Group (ECOG) and North American Leukemia intergroup
PMCID: PMC4299460  NIHMSID: NIHMS579197  PMID: 24712482

Summary

The revised International Prognostic Scoring System (IPSS-R) was developed in a cohort of untreated myelodysplastic syndromes (MDS) patients. A French Prognostic Scoring System (FPSS) was recently reported to identify differential survival among azacitidine-treated patients with high-risk MDS. We applied the FPSS and IPSS-R to 150 patients previously randomized to azacitidine monotherapy or a combination of azacitidine with entinostat (a histone deacetylase inhibitor). Neither score predicted response but both discriminated patients with different overall survival (OS) (median OS, FPSS: 9.7, 14.7, and 25.3 months, P=0.018; IPSS-R: 12.5, 11.3, 20.8, and 36 months, P=0.005). Statistical analysis suggested no improvement in OS prediction for the FPSS over the IPSS-R in azacitidine-treated patients.

Keywords: Myelodysplastic syndromes (MDS), azacitidine, Revised International Prognostic Scoring System (IPSS-R), French Prognostic Scoring System (FPSS), prognostic models

1. Introduction

Azacitidine is the only drug shown to prolong overall survival (OS) in patients with high risk (HR)-myelodysplastic syndromes (MDS) (Fenaux et al, 2009). Nevertheless, only about half of the patients respond to azacitidine therapy and the vast majority of responders lose their response within two years (Silverman et al, 2002, 2006, Fenaux et al, 2009). No cures are achieved with azacitidine therapy; upon failure the median survival is less than 6 months (Prebet et al, 2011). Therefore, identification of patients with differential baseline probabilities of achieving survival benefit from azacitidine therapy is highly warranted.

The International Prognostic Scoring System (IPSS) (Greenberg et al 1997), the most widely used prognostic tool for MDS, has recently undergone revision (Greenberg et al, 2012). Although the revised IPSS (IPSS-R) showed improved prognostic precision over the IPSS, both the IPSS and the IPSS-R were developed using cohorts of untreated patients (Greenberg et al, 1997, 2012). The IPSS-R has subsequently been shown to have prognostic value among treated MDS patients in retrospective analyses (Lamarque et al, 2012, Voso et al, 2013, Mishra et al, 2013, Savic et al, 2013, Neukirchen et al, 2014).

A new French prognostic scoring system (FPSS) has been shown to separate azacitidine-treated patients with HR-MDS into three prognostic groups with significantly different survivals based on 4 readily available clinical and laboratory parameters: Eastern Cooperative Oncology Group (ECOG) performance status (PS), cytogenetics, presence of circulating blasts, and red blood cell (RBC) transfusion-dependence (Itzykson et al, 2011b, 2012). The FPSS was validated by the same group in a cohort of patients in the AZA001 trial and by two other groups in two small European single-institution cohorts (Breccia et al, 2012, van der Helm et al, 2011). Most patients in the development and validation cohorts were European patients, and the two independent groups used retrospectively collected data for validation (Itzykson et al, 2011b, Itzykson et al, 2012, Breccia et al, 2012, van der Helm et al, 2011).

Therefore, we sought to determine the relative prognostic discriminatory power of FPSS and IPSS-R in a large cohort of azacitidine-treated patients whose data was collected prospectively in the context of a North American clinical trial.

2. Materials and Methods

2.1 Source population

The North American Leukemia Intergroup Trial E1905 was a randomized phase II trial that compared azacitidine monotherapy given at 50 mg/m2/day on days 1–10 of each cycle (1 cycle=28 days) to a combination of azacitidine given at the same schedule with the histone deacetylase inhibitor entinostat (4 mg/m2/day on days 3 and 10 of each of six cycles) (Prebet et al, 2014). Responding patients were treated for an additional 18 cycles of treatment in the absence of progression. No prior azanucleoside therapy or induction chemotherapy was allowed. Patients with chronic myelomonocytic leukaemia (CMML), MDS with high-risk features (IPSS of intermediate-2 and high) or those with low or intermediate-1 but with severe thrombocytopenia [platelet count < 50 × 109/l] and/or neutropenia [absolute neutrophil count < 0.5 × 109/l], and acute myeloid leukaemia (AML) with myelodysplasia-related changes (AML-MRC) were eligible. Therapy-related MDS or AML were ineligible. Responses were measured according to the International Working Group 2000 Criteria (Cheson et al, 2001).

A total of 150 patients were evenly randomized between the two treatment arms (93 with MDS, 52 with AML-MRC, and 5 with CMML), of whom 149 were analysed (74 patients in the azacitidine monotherapy arm vs. 75 in the combination arm). In the azacitidine monotherapy group, 32% achieved the primary endpoint (haematological normalization [HN], defined as complete response (CR) + partial remission [PR] + trilineage haematological improvement [HI-T]) compared to 27% in the combination regimen group (P=0.80). The median OS for all disease groups was 18 months for the azacitidine group compared to 13 months in the combination group (P=0.09). For patients with MDS and CMML, the median OS was 21.2 months in the azacitidine monotherapy arm vs. 14.7 months in the combination arm (P=0.06).

2.2 Study population

This was a nested retrospective cohort study of patients enrolled in the E1905 trial for whom the FPSS and IPSS-R could be calculated at baseline. The FPSS was calculated based on 4 prognostic factors: ECOG PS, presence of circulating blasts, RBC transfusion-dependence, and cytogenetics (Itzykson et al, 2011b). The IPSS-R was calculated based on karyotype, bone marrow (BM) blast proportion, haemoglobin, platelet and neutrophil levels (Greenberg et al, 2012). Oligoblastic AML was defined as AML up to 30% BM blasts (previously referred to as refractory anaemia with excess blasts in transformation [RAEB-t]).

2.3 Statistical analysis

Patient demographic factors and disease characteristics were compared by Wilcoxon rank sum tests and Fisher’s exact tests as appropriate. Best overall response rate (ORR) was defined as the proportion of patients with CR, PR, and any HI among all patients. OS was defined as time from registration to death from any cause. Follow-up was censored at last contact. Survival distributions were estimated by the Kaplan-Meier method and tested using the log-rank test. Multivariate Cox proportional hazards models were also fitted to evaluate the effect of FPSS and IPSS-R on OS after adjusting for covariates. The FPSS and IPSS-R were compared using Akaike’s information criterion (AIC; a measure indicating the relative quality of a statistical model based on a given set of data) and the C-statistic (a measure indicating overall adequacy of prediction models with censored survival data). P values are all two-sided and a level of 5% was considered statistically significant.

3. Results

3.1 Study cohort

The IPSS-R scores could be determined for 120 patients (IPSS-R cohort, 80%), of whom 59 received azacitidine monotherapy and 61 received azacitidine-entinostat combination. Of those 120 patients, 52 had IPSS HR-MDS, 20 IPSS lower-risk (LR)-MDS, 5 non-proliferative CMML (White blood cell [WBC] count <12 × 109/l), and 43 had AML-MRC. Supplementary Table 1 summarizes baseline patient and disease characteristics for the IPSS-R cohort. The median follow-up for patients who were still alive (n=8) was 48.5 months. None of the patients belonged to the IPSS-R very-low risk group. Statistically significant differences were observed among the IPSS-R risk groups for ECOG PS (P=0.02), disease type (P<0.0001), RBC transfusion-dependence (P=0.007) and platelet-transfusion dependency (P=0.02). When limited to the patient group for whom the IPSS-R was originally developed (MDS and oligoblastic AML [defined with BM blast percentage up to 30%] and excluding those with CMML or with AML-MRC and >30% BM blasts), IPSS-R risk groups could be assigned to 96 patients (intended IPSS-R subcohort).

The FPSS scores could be determined for 116 patients (FPSS cohort, 77.3%), of whom 56 received azacitidine and 60 received the combination regimen. Those patients included 51 with IPSS HR-MDS, 21 with IPSS LR-MDS, 3 with non-proliferative CMML, and 41 with AML-MRC. The baseline characteristics and demographics of the FPSS cohort are shown in Supplementary Table 2. Statistically significant differences were noted among the FPSS risk groups for the baseline WBC count (P=0.02), haemoglobin level (P=0.008), disease type (P=0.005) and platelet transfusion-dependence (P=0.009). The median follow-up for patients who were still alive (n=8) was 48.5 months. When limited to the group of patients for whom the FPSS was originally developed (IPSS HR-MDS and oligoblastic AML and excluding those with IPSS LR-MDS, CMML, or AML-MRC with >30% BM blasts), the FPSS scores could be calculated for 73 patients (intended FPSS subcohort).

3.2 Survival probabilities by the prognostic model risk groups

A. IPSS-R cohort

Figure 1 depicts the Kaplan-Meier curves according to IPSS-R risk group in the IPSS-R cohort (n=120). The very-high (n=70), high (n=26), intermediate (n=14) and low (n=10) IPSS-R groups had significantly different OS (median OS 12.5, 11.3, 20.8 and 36 months, respectively, P=0.005). After adjustment for the ECOG PS and disease type (but not RBC-transfusion dependence and platelet-transfusion dependence because they are highly associated with IPSS-R risk categorization) the prognostic effect of IPSS-R risk category remained statistically significant (P=0.026) in a multivariate Cox model (Supplementary Table 3).

Figure 1.

Figure 1

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Kaplan-Meier estimates of probabilities of overall survival for all the patients for whom the revised International Prognostic Scoring System (IPSS-R) scores could be calculated (n=120, the IPSS-R cohort). CNSR, censored.

B. FPSS cohort

Figure 2 depicts the Kaplan-Meier survival estimates for the FPSS cohort (n=116) according to FPSS risk group. OS was significantly different among the high, intermediate, and low risk FPSS groups (median OS 9.7, 14.7, 25.3 months, respectively, P=0.018). When adjusted for the baseline WBC count, disease type, and platelet transfusion-dependence in a multivariate Cox model, the prognostic effect of FPSS risk groups was no longer statistically significant (P=0.09, Supplementary Table 4).

Figure 2.

Figure 2

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Kaplan-Meier estimates of probabilities of overall survival for all the patients for whom the French Prognostic Scoring System (FPSS) scores could be calculated (n=116, the FPSS cohort). CNSR, censored.

3.3 Survival probabilities by the prognostic models in cohorts limited to the originally modelled populations

A. Intended IPSS-R subcohort

Restricting evaluation to the 96 patients with MDS and oligoblastic AML (intended IPSS-R subcohort), the OS among the very high (n=55), high (n=21), intermediate (n=11) and low (n=9) risk IPSS-R groups remained significantly different (median OS 12.6, 21.5, 19.6, and 40.7 months, respectively, P=0.007, Figure 3). When adjusted for ECOG PS and the disease type in a multivariate Cox regression model, the prognostic effect of IPSS-R risk group remained statistically significant for this subcohort (P=0.025).

Figure 3.

Figure 3

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Kaplan-Meier estimates of probabilities of overall survival for the patients with myelodysplastic syndromes and oligoblastic acute myeloid leukaemia (≤30% bone marrow blasts) for whom the revised International Prognostic Scoring System (IPSS-R) scores could be calculated (n=96, the intended IPSS-R subcohort). CNSR, censored.

B. Intended FPSS subcohort

When limited to the 73 patients with IPSS HR-MDS and oligoblastic AML (intended FPSS subcohort), the difference between OS among the FPSS risk groups was statistically insignificant (n = 73; median OS 12, 14.2 and 16.4 months for the high [n=11], intermediate [n=58] and low [n=4] risk FPSS groups, respectively, P=0.52, Figure 4).

Figure 4.

Figure 4

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Kaplan-Meier estimates of probabilities of overall survival (OS) for the patients with higher-risk myelodysplastic syndromes and oligoblastic acute myeloid leukaemia (≤30% bone marrow blasts) for whom the French Prognostic Scoring System (FPSS) scores could be calculated (n=73, intended FPSS subcohort). CNSR, censored.

3.4 Comparison between survival prediction power of the FPSS and IPSS-R

In the AIC test, smaller values indicate a better model (as long as the AIC value is also lower than the one in the null model i.e. the model without any predictor). The AIC test suggested that the FPSS is not a better survival discriminator than the IPSS-R (687.28 vs. 684.80, respectively, for the 115 patients for whom both FPSS and IPSS-R scores could be calculated).

The model with FPSS (or IPSS-R) together with age as predictors produced a smaller AIC value than the one with FPSS (or IPSS-R) as a single predictor, implying age may be an important addition to the FPSS (and IPSS-R) for predicting OS. This observation was noted during the development of the IPSS-R and was the basis of providing a formula to adjust the IPSS-R for age. Combining FPSS and IPSS-R in the predicting model did not yield a smaller AIC value than either the FPSS or IPSS-R in univariate analysis (687.78), suggesting no extra power in predicting OS with both FPSS and IPSS-R scores combined in the model. Finally, when evaluation was limited to patients with IPSS HR-MDS and oligoblastic AML for whom both the FPSS and IPSS-R could be calculated (n=73), the IPSS-R appeared to be as good a predictor of OS as the FPSS (AIC=380.57 for FPSS and 380.33 for IPSS-R).

In the C-statistic test, results can range from 0 to 1, with 0.5 indicating the model is no better than chance at making a prediction, 1 indicating that the model perfectly predicts the outcome and 0 suggesting that the model is perfect in completely the opposite way. The C-statistic results further supported the conclusion that FPSS is not superior to the IPSS-R in predicting OS. The C-statistic was higher with IPSS-R than with FPSS, but the 95% confidence interval (95% CI) of the C-statistic from the FPSS model was almost completely overlapping with the one computed, based on IPSS-R in the overall cohort (n=115, IPSS-R: 0.594, 95% CI, 0.493–0.695, FPSS: 0.562, 95% CI, 0.491–0.633) and when restricting to patients with IPSS HR-MDS and oligoblastic AML (n=73, IPSS-R: 0.555, 95% CI, 0.480–0.630, FPSS: 0.528, 95% CI, 0.452–0.604).

3.5 Response Analysis by the prognostic model risk groups

A. IPSS-R cohort and intended subcohort

There was no statistically significant association between the ORR and the IPSS-R risk group in the IPSS-R cohort (total n=120, very-high [n=70], high [n=26], intermediate [n=14] and low [n=10] risk groups, with ORRs of 44.3%, 42.3%, 50%, and 80%, respectively, overall P=0.19, Supplementary Table 5). Similarly, no statistically significant differences were found in ORR when the analysis was limited to the patient population with MDS and oligoblastic AML (intended IPSS-R subcohort).

B. FPSS cohort and intended subcohort

There were no statistically significant differences in the ORR between the 3 FPSS risk groups in the overall FPSS cohort (35.3% 48.3%, 58.3% for the high [n=17], intermediate [n=87] and low [n=12] risk FPSS groups, respectively, P=0.45, Supplementary Table 6) or when limited to the patients with HR-MDS and oligoblastic AML (intended FPSS subcohort).

Discussion and conclusion

This is the first comparison of the FPSS and IPSS-R in a large cohort of azacitidine-treated MDS patients whose original data were collected prospectively in context of a clinical trial. Consistent with published data, our results indicate that while neither prognostic tool was able to identify patients with differential likelihood of achieving objective clinical response to azacitidine therapy, both models functioned well in separating azacitidine-treated patients into prognostic survival groups. These data confirm the prognostic utility of the IPSS-R in azacitidine-treated patients.

The fact that both the FPSS and the IPSS-R were prognostic for OS among the entire cohort of patients on a trial that included patients with CMML and AML-MRC with BM blasts >30%, raises the question of whether these prognostic tools should be specifically evaluated in these subgroups of patients. In fact, a recent study similarly demonstrated the prognostic utility of the IPSS-R in patients with AML with and without antecedent MDS (Stolzel et al, 2014). Nonetheless, our results could be driven by data from patients with HR-MDS and oligoblastic AML, who constituted the majority of the patients.

When we limited multivariate analysis to the group of patients for whom the FPSS was developed (IPSS HR-MDS and oligoblastic AML [n=73]), there were no statistically significant differences in OS among the FPSS risk groups. In contrast, when multivariate analysis was limited to the groups of patients for whom the IPSS-R was developed (MDS and oligoblastic AML [n=96]), the difference in OS among the IPSS-R risk groups remained significantly different.

The impact of the effect of treatment arm (azacitidine monotherapy vs. azacitidine-entinostat combination) was also explored (Supplementary Figures 1 and 2). To address this question, we performed a Cox regression analysis evaluating the interaction between treatment group and the risk stratification for the prediction of OS (i.e. testing the 2-way interaction effect). That test did not show any statistically-significant difference between treatment arms and therefore the results of our analysis were reported by combining treatment arms.

Although the IPSS-R was developed using an untreated-patient cohort, our results indicate that the FPSS is not a better survival discriminator than the IPSS-R for azacitidine-treated patients. Still, several limitations are present in our analysis that makes this observation inconclusive. First, the FPSS was developed for patients who received the standard regimen of azacitidine (75 mg/m2/day) for 7 days, while all patients in this analysis received azacitidine at 50 mg/m2/day for 10 days, and approximately half of them received entinostat. Second, evaluation of peripheral blood blasts was not done systematically, and therefore patients excluded due to missing values could have biased the results. Third, the patients evaluated in IPSS-R analysis included patients with IPSS intermediate-1 and low risk scores, which were not included in the original development of the FPSS and therefore could have systematically affected the results by improving the accuracy of the IPSS-R. Fourth, as indicated earlier, the small sample size, especially in the low-risk FPSS group, in the cohorts and subcohorts means that these results should be interpreted with caution.

A prognostic/predictive clinical model or biomarker that identifies the differential likelihood of benefiting from azacitidine therapy at baseline remains a clinical and research priority (Zeidan et al, 2013, Zeidan & Komrokji, 2013). Despite several reports of potential predictive biomarkers, no baseline laboratory or clinical parameters consistently predicted benefit to azacitidine therapy (Steensma, 2012, van der Helm et al, 2011, Itzykson et al, 2011a).

Our data confirm the prognostic value of the IPSS-R and FPSS for OS in azacitidine-treated MDS patients. In our cohort, the FPSS does not seem to offer any advantage over the IPSS-R in prognostication of OS among azacitidine-treated MDS patients. Nonetheless, due to the previously mentioned limitations of this analysis, both prognostic tools warrant further evaluation as baseline predictors that can possibly define subgroups of patients with lower probabilities of clinical benefit from azacitidine therapy who might be candidates for alternative upfront therapeutic interventions.

Supplementary Material

supp TableS1-S6 & FigureS1-S2

Acknowledgments

Disclosures

Amer Zeidan is supported by a Young Investigator Award (YIA) from the American Society of Clinical Oncology (ASCO) and by an ‘Evans Fellow’ award from the MDS Clinical Research Consortium. This study was coordinated by the Eastern Cooperative Oncology Group (Robert L. Comis, M.D., Chair) and supported in part by Public Health Service Grants CA23318, CA66636, CA21115, CA16116, CA20176, CA27525, CA14958, CA31946, CA32102, CA17145, CA27057 and from the National Cancer Institute, National Institutes of Health and the Department of Health and Human Services. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute.

Footnotes

The results of this study were presented in part at the American Society of Oncology (ASCO) Annual meeting, Chicago, 2013, the Twelfth International MDS conference, Berlin, 2013, and the American Society of Hematology (ASH) Meeting, New Orleans, 2013.

Author Contributions

AMZ conceived and designed the study, performed the research, analysed the data and wrote the manuscript. SDG, TP, PG designed the study, performed the research, analysed the data and critically reviewed the manuscript. JWL and ZS designed the study, performed the research, analysed the data, conducted the statistical analysis and critically reviewed the manuscript. MJ, MRS, EP, JG, HPE, and MST provided patients and critically reviewed the manuscript. All authors reviewed the final version and approved submission.

References

  1. Breccia M, Loglisci G, Cannella L, Finsinger P, Mancini M, Serrao A, Santopietro M, Salaroli A, Alimena G. Application of french prognostic score to patients with international prognostic scoring system intermediate-2 or high risk myelodysplastic syndromes treated with 5-azacitidine is able to predict overall survival and rate of response. Leukemia & Lymphoma. 2012;53:985–6. doi: 10.3109/10428194.2011.643408. [DOI] [PubMed] [Google Scholar]
  2. Cheson BD, Bennett JM, Kantarjian H, Schiffer CA, Nimer SD, Lowenberg B, Stone RM, Mittelman M, Sanz GF, Wijermans PW, Greenberg PL. Myelodysplastic syndromes standardized response criteria: Further definition. Blood. 2001;98:1985. doi: 10.1182/blood.v98.6.1985. [DOI] [PubMed] [Google Scholar]
  3. Fenaux P, Mufti GJ, Hellstrom-Lindberg E, Santini V, Finelli C, Giagounidis A, Schoch R, Gattermann N, Sanz G, List A, Gore SD, Seymour JF, Bennett JM, Byrd J, Backstrom J, Zimmerman L, McKenzie D, Beach C, Silverman LR, 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. The Lancet Oncology. 2009;10:223–232. doi: 10.1016/S1470-2045(09)70003-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, Sanz M, Vallespi T, Hamblin T, Oscier D, Ohyashiki K, Toyama K, Aul C, Mufti G, Bennett J. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89:2079–2088. [PubMed] [Google Scholar]
  5. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, Bennett JM, Bowen D, Fenaux P, Dreyfus F, Kantarjian H, Kuendgen A, Levis A, Malcovati L, Cazzola M, Cermak J, Fonatsch C, Le Beau MM, Slovak ML, Krieger O, Luebbert M, Maciejewski J, Magalhaes SM, Miyazaki Y, Pfeilstocker M, Sekeres M, Sperr WR, Stauder R, Tauro S, Valent P, Vallespi T, van de Loosdrecht AA, Germing U, Haase D. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–2465. doi: 10.1182/blood-2012-03-420489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Itzykson R, Kosmider O, Cluzeau T, Mansat-De Mas V, Dreyfus F, Beyne-Rauzy O, Quesnel B, Vey N, Gelsi-Boyer V, Raynaud S, Preudhomme C, Ades L, Fenaux P, Fontenay M, Groupe Francophone des Myelodysplasies (GFM) Impact of TET2 mutations on response rate to azacitidine in myelodysplastic syndromes and low blast count acute myeloid leukemias. Leukemia : Official Journal of the Leukemia Society of America, Leukemia Research Fund, UK. 2011a;25:1147–1152. doi: 10.1038/leu.2011.71. [DOI] [PubMed] [Google Scholar]
  7. Itzykson R, Thepot S, Quesnel B, Dreyfus F, Beyne-Rauzy O, Turlure P, Vey N, Recher C, Dartigeas C, Legros L, Delaunay J, Salanoubat C, Visanica S, Stamatoullas A, Isnard F, Marfaing-Koka A, de Botton S, Chelghoum Y, Taksin AL, Plantier I, Ame S, Boehrer S, Gardin C, Beach CL, Ades L, Fenaux P, Groupe Francophone des Myelodysplasies(GFM) Prognostic factors for response and overall survival in 282 patients with higher-risk myelodysplastic syndromes treated with azacitidine. Blood. 2011b;117:403–411. doi: 10.1182/blood-2010-06-289280. [DOI] [PubMed] [Google Scholar]
  8. Itzykson R, Thepot S, Quesnel B, Dreyfus F, Recher C, Wattel E, Gardin C, Ades L, Fenaux P. Long-term outcome of higher-risk MDS patients treated with azacitidine: An update of the GFM compassionate program cohort. Blood. 2012;119:6172–6173. doi: 10.1182/blood-2012-04-422204. [DOI] [PubMed] [Google Scholar]
  9. Lamarque M, Raynaud S, Itzykson R, Thepot S, Quesnel B, Dreyfus F, Rauzy OB, Turlure P, Vey N, Recher C, Dartigeas C, Legros L, Delaunay J, Visanica S, Stamatoullas A, Fenaux P, Ades L. The revised IPSS is a powerful tool to evaluate the outcome of MDS patients treated with azacitidine: The GFM experience. Blood. 2012;120:5084–5085. doi: 10.1182/blood-2012-09-453555. [DOI] [PubMed] [Google Scholar]
  10. Mishra A, Corrales-Yepez M, Ali NA, Kharfan-Dabaja M, Padron E, Zhang L, Epling-Burnette PK, Pinilla-Ibarz J, Lancet JE, List AF, Komrokji RS. Validation of the revised international prognostic scoring system in treated patients with myelodysplastic syndromes. American Journal of Hematology. 2013;88:566–70. doi: 10.1002/ajh.23454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Neukirchen J, Lauseker M, Blum S, Giagounidis A, Lubbert M, Martino S, Siragusa S, Schlenk RF, Platzbecker U, Hofmann WK, Gotze K, Palumbo GA, Magrin S, Kundgen A, Aul C, Hildebrandt B, Hasford J, Kobbe G, Haas R, Germing U. Validation of the revised international prognostic scoring system (IPSS-R) in patients with myelodysplastic syndrome: A multicenter study. Leukemia Research. 2014;38:57–64. doi: 10.1016/j.leukres.2013.10.013. [DOI] [PubMed] [Google Scholar]
  12. Prebet T, Gore SD, Esterni B, Gardin C, Itzykson R, Thepot S, Dreyfus F, Rauzy OB, Recher C, Ades L, Quesnel B, Beach CL, Fenaux P, Vey N. Outcome of high-risk myelodysplastic syndrome after azacitidine treatment failure. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2011;29:3322–3327. doi: 10.1200/JCO.2011.35.8135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Prebet T, Sun Z, Figueroa SE, Ketterling R, Melnick A, Greenberg P, Herman J, Juckett M, Smith MR, Malick L, Paietta E, Czader M, Litzow M, Gabrilove J, Erba HP, Gore SD, Tallman MS. Prolonged administration of azacitidine with or without entinostat for myelodysplastic syndrome and acute myeloid leukemia with myelodysplasia-related changes: Results of the US leukemia intergroup trial E1905. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2014 doi: 10.1200/JCO.2013.50.3102. [In press] [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Savic A, Marisavljevic D, Kvrgic V, Stanisavljevic N. Validation of the revised international prognostic scoring system for patients with myelodysplastic syndromes. Acta Haematologica. 2013;131:231–238. doi: 10.1159/000354840. [DOI] [PubMed] [Google Scholar]
  15. Silverman LR, Demakos EP, Peterson BL, Kornblith AB, Holland JC, Odchimar-Reissig R, Stone RM, Nelson D, Powell BL, DeCastro CM, Ellerton J, Larson RA, Schiffer CA, Holland JF. Randomized controlled trial of azacitidine in patients with the myelodysplastic syndrome: A study of the cancer and leukemia group B. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2002;20:2429–2440. doi: 10.1200/JCO.2002.04.117. [DOI] [PubMed] [Google Scholar]
  16. Silverman LR, McKenzie DR, Peterson BL, Holland JF, Backstrom JT, Beach CL, Larson RA, Cancer and Leukemia Group B Further analysis of trials with azacitidine in patients with myelodysplastic syndrome: Studies 8421, 8921, and 9221 by the cancer and leukemia group B. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2006;24:3895–3903. doi: 10.1200/JCO.2005.05.4346. [DOI] [PubMed] [Google Scholar]
  17. Steensma DP. Can hypomethylating agents provide a platform for curative therapy in myelodysplastic syndromes? Best Practice & Research. Clinical Haematology. 2012;25:443–451. doi: 10.1016/j.beha.2012.10.007. [DOI] [PubMed] [Google Scholar]
  18. Stolzel F, Kramer M, Mohr B, Wermke M, Bornhauser M, Ehninger G, Schaich M, Platzbecker U. Impact of the revised international prognostic scoring system on the outcome of patients with acute myeloid leukemia with or without antecedent myelodysplastic syndrome. Leukemia. 2014;28:723–725. doi: 10.1038/leu.2013.356. [DOI] [PubMed] [Google Scholar]
  19. van der Helm LH, Alhan C, Wijermans PW, van Marwijk Kooy M, Schaafsma R, Biemond BJ, Beeker A, Hoogendoorn M, van Rees BP, de Weerdt O, Wegman J, Libourel WJ, Luykx-de Bakker SA, Minnema MC, Brouwer RE, Croon-de Boer F, Eefting M, Jie KS, van de Loosdrecht AA, Koedam J, Veeger NJ, Vellenga E, Huls G. Platelet doubling after the first azacitidine cycle is a promising predictor for response in myelodysplastic syndromes (MDS), chronic myelomonocytic leukaemia (CMML) and acute myeloid leukaemia (AML) patients in the dutch azacitidine compassionate named patient programme. British Journal of Haematology. 2011;155:599–606. doi: 10.1111/j.1365-2141.2011.08893.x. [DOI] [PubMed] [Google Scholar]
  20. Voso MT, Fenu S, Latagliata R, Buccisano F, Piciocchi A, Aloe-Spiriti MA, Breccia M, Criscuolo M, Andriani A, Mancini S, Niscola P, Naso V, Nobile C, Piccioni AL, D’Andrea M, D’Addosio A, Leone G, Venditti A. Revised international prognostic scoring system (IPSS) predicts survival and leukemic evolution of myelodysplastic syndromes significantly better than IPSS and WHO prognostic scoring system: Validation by the gruppo romano mielodisplasie italian regional database. Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology. 2013;31:2671–7. doi: 10.1200/JCO.2012.48.0764. [DOI] [PubMed] [Google Scholar]
  21. Zeidan AM, Komrokji RS. There’s risk, and then there’s RISK: The latest clinical prognostic risk stratification models in myelodysplastic syndromes. Current Hematologic Malignancy Reports. 2013;8:351–60. doi: 10.1007/s11899-013-0172-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Zeidan AM, Linhares Y, Gore SD. Current therapy of myelodysplastic syndromes. Blood Reviews. 2013;27:243–259. doi: 10.1016/j.blre.2013.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

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supp TableS1-S6 & FigureS1-S2
NIHMS579197-supplement-supp_TableS1-S6___FigureS1-S2.docx (701.3KB, docx)

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