Abstract: Despite the existence of specific prognostic scoring systems, the International Prognostic Scoring System (IPSS) has been the most used for the evaluation of Chronic Myelomonocytic Leukemia (CMML) although it is not applicable for proliferative variants. Since its publication in 2002, the MD Anderson Prognostic Score (MDAPS) has been the most specific and powerful prognostic tool for CMML. Due to the recent emergence of CMML-specific Prognostic Scoring System (CPSS), we sought to determine its usefulness in our series and it was compared with the MDAPS to identify the index with the best capability to discriminate between high and low risk patients. Aim 1) To assess the prognostic impact of each of the variables composing the prognostic scoring systems: MDAPS and CPSS and 2) to evaluate the discriminative ability of both scores to detect the highest risk patients. Patients and Methods One hundred and twenty-two patients (74M/48F; median age: 76 years, 27-96 years; median follow-up: 1.88 years, 0-11.4 years) diagnosed with CMML (108 CMML-I; 14 CMML-II; 92 dysplastic CMML; 30 proliferative CMML) between 1998 and 2013 from the Hospital Clínic de Barcelona (n=110) and the Hospital Universitari Germans Trias i Pujol (n=12). The prognostic impact in terms of overall survival (OS) and leukemia free survival (LFS) of each of the variables that compose the score systems and both scores were studied by an univariate survival analysis (Kaplan-Meier; Log-Rank). The two prognostic indices were faced in a multivariate analysis (Cox Regression) to assess the discriminative power of each one to detect the highest risk patients. Finally, Receiver Operating Characteristics (ROC) curves were plotted and the area under the ROC curve was calculated as an index for the predictive value of the model. Results All the variables that compose the CPSS (CMML-I vs. II, transfusion requirement, dysplastic vs. proliferative variant and CPSS cytogenetics) had prognostic impact in terms of OS (p & lt;0.001, p & lt;0.001, p & lt;0.001, p =0.001) and LFS (p & lt;0.001, p =0.005, p & lt; 0.001, p =0.004). For the variables composing the MDAPS (Hb & lt;120g/L, total lymphocyte count & gt; 2500/mm3, presence of circulating immature cells and bone marrow blasts ≥ 10%) only the Hb & lt;120g/L and the bone marrow blasts ≥ 10% impacted on OS (p =0.001, p & lt;0.001, respectively) and only the bone marrow blasts ≥ 10% had an impact on the LFS (p & lt;0.001). When the score systems were applied to our series, both had an impact on OS and LFS (OS CPSS p & lt;0.001; LFS CPSS p & lt;0.001; OS MDAPS p & lt;0.001; LFS MDAPS p =0.037). In a multivariate analysis including gender, age, high risk patients defined by the MDAPS (high risk MDAPS) and high risk patients defined by the CPSS (high risk CPSS), only age and high risk CPSS retained its statistical significance for OS (p = 0.023, p =0.001, respectively) and only high risk CPSS for LFS (p =0.001). The greatest area under the curve (AUC), showing the highest predictive value, was observed in the mortality ROC curve of the CPSS (0.77, CI 95%: 0.68-0.86) while the AUC for the MDAPS was smaller (0.58, CI: 0.47-0.69). Conclusions In our series, CPSS seems to be a better tool than MDAPS for the prediction of OS and LFS in CMML. These data reinforce the validity of the CPSS and could serve as an additional validation cohort. Disclosures: No relevant conflicts of interest to declare.

Abstract: The current classification system for Myelodysplastic Syndromes lumps all therapy-related (tMDS) into one subgroup assuming all tMDS had the same poor prognosis. We have put together a database including 2032 patients with a diagnosis of tMDS from several different IWG centers and the MDS clinical research consortium. With the idea of developing an individual scoring system for tMDS, we decided to start by optimizing the cytogenetic part of the IPSSR. First, we did an extensive review of karyotypes. Finally, 1245 patients had complete data and correct ISCN formula to be used for score development. We could show regarding karyotypes there are very limited differences between primary and tMDS. Mainly the distribution of risk groups differs with complex occurring more (37%) and normal karyotypes occurring less frequent, although still accounting for 30%. There are few exceptions that are relatively special for tMDS, like translocations including 11q23. A few karyotypes are less frequent; therefore, we could not evaluate the value of IPSS-R cytogenetics for all karyotypes. However, if we apply IPSS-R cytogenetics to our patient cohort, we can separate 5 different risk groups as in pMDS. We tested the performance of the score by using the Dxy. As main endpoint we chose transformation-free survival giving better information about the severity of the disease compared to the single endpoints survival and AML transformation that where calculated for completeness as well. The Dxy for the IPSS-R cytogenetic part is 0.31 for transformation-free survival. This indicates an effective prognostic performance although not as good as in pMDS. Several attempts were done to develop a tMDS specific cytogenetic score. The best draft scoring component achieves a Dxy of 0.33. Counting the number of aberrations achieves a score of 0.30. If normal clone present or not is added, the performance of this very simple model is improved with a Dxy of 0.32. As we could show, all these different approaches lead to a comparable performance. One can argue that still regarding a few karyotypes the prognostic impact is slightly different between p and tMDS (e.g. +8). On the other hand, the most practical approach seems to be to adopt the original cytogenetic part of the IPSS-R for further score development since clinicians do not need to use different scoring systems for different MDS subtypes. While the final analyses for the development of a tMDS specific risk score are currently under way, extensive calculations regarding the performance of different scores like WHO- (Dxy 0.24), FAB-classification (Dxy 0.19), WPSS-R (Dxy 0.35), IPSS-R (Dxy 0.37), and IPSS-R+age (Dxy 0.36), show all these systems can separate different risk groups within our cohort. However, these results also show an inferior performance of the scoring systems in t compared to pMDS. There are multiple possible reasons for this. The most important seem to be tMDS patients are often not cured from the primary disease and its disease specific risk of death should ideally be considered. Unfortunately, we don't have that data. And second, we included treated as well as untreated patients. It seems not to be feasible otherwise since the selection bias for old unfit patients would be unacceptable. We could show already in pMDS that the score performances are considerably worse if we analyze treated patients and the score performance in our cohort is better if limited to untreated patients. To conclude, we can say existing classification and scoring systems work in tMDS and can separate groups with clearly different risk for death and transformation. Although we could not develop a tMDS specific cytogenetic score this could be seen positively since it underlines tMDS do not seem to be much different regarding disease specific risk. This should initiate a discussion of a revision of the WHO-classification and encourage clinicians to use the existing tools for risk assessment and treatment decisions. A simple solution could be to use the WHO classification for pMDS and precede each subgroup with a t, like tMDS-SLD, and so on. Such an approach would be of importance for patients falsely classified as tMDS. After all this classification is done according to anamnestic information only and sporadic cases cannot be excluded. Until now, in the first analyzes performed with the final tMDS-database, we did not find any indication that risk factors established in pMDS would lose or change their meaning in tMDS. Figure. Figure. Disclosures Komrokji: Celgene: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees. List:Celgene: Research Funding. Roboz:Orsenix: Consultancy; Eisai: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Astex Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Daiichi Sankyo: Consultancy; Sandoz: Consultancy; Otsuka: Consultancy; Daiichi Sankyo: Consultancy; Eisai: Consultancy; Pfizer: Consultancy; Roche/Genentech: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Celgene Corporation: Consultancy; Cellectis: Research Funding; Orsenix: Consultancy; Aphivena Therapeutics: Consultancy; Otsuka: Consultancy; Jazz Pharmaceuticals: Consultancy; Sandoz: Consultancy; Roche/Genentech: Consultancy; Aphivena Therapeutics: Consultancy; AbbVie: Consultancy; Bayer: Consultancy; Bayer: Consultancy; Astex Pharmaceuticals: Consultancy; Celgene Corporation: Consultancy; AbbVie: Consultancy. Döhner:Jazz: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Celator: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Amgen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Pfizer: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Valent:Pfizer: Honoraria; Novartis: Honoraria; Incyte: Honoraria. Platzbecker:Celgene: Research Funding. Lübbert:TEVA: Other: Study drug; Celgene: Other: Travel Support; Cheplapharm: Other: Study drug; Janssen: Honoraria, Research Funding. Díez-Campelo:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Stauder:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva: Research Funding. Germing:Janssen: Honoraria; Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding.

Abstract: Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are chronic myeloid clonal neoplasms. To date, the only potentially curative therapy for these disorders remains allogeneic hematopoietic progenitor cell transplantation (HCT), although patient eligibility is limited due to high morbimortality associated with this procedure coupled with advanced age of most patients. Dopamine receptors (DRs) and serotonin receptors type 1 (HTR1s) were identified as cancer stem cell therapeutic targets in acute myeloid leukemia. Given their close pathophysiologic relationship, expression of HTR1s and DRs was interrogated in MDS and CMML. Both receptors were differentially expressed in patient samples compared to healthy donors. Treatment with HTR1B antagonists reduced cell viability. HTR1 antagonists showed a synergistic cytotoxic effect with currently approved hypomethylating agents in AML cells. Our results suggest that HTR1B constitutes a novel therapeutic target for MDS and CMML. Due to its druggability, the clinical development of new regimens based on this target is promising.

Abstract: Recurrent translocations are uncommon in myelodysplastic syndromes (MDS). Three new recurrent translocations, namely der(12)t(3;12)(q13;p13), t(11;13;22)(q13;q14;q12) and der(17)t(13;17)(q21;p13), identified by conventional cytogenetics (CC) in 4 MDS patients, were further characterized using a panel of commercial and homemade fluorescence in situ hybridization (FISH) probes. The goal of this study was to determine the precise breakpoints and to identify genes that could be related with the neoplastic process. Half of the breakpoints (4/8) were precisely identified and in the remaining half they were narrowed to a region ranging from 14 to 926 kb. All the studied breakpoints had interstitial or terminal deletions ranging from 536 kb to 89 Mb, and only those 7 Mb were detected by CC. The genes located in or around the breakpoints described in our study have not been previously related to MDS. The deleted regions include the 〈 i 〉 ETV6 〈 /i 〉 and 〈 i 〉 RB1 〈 /i 〉 genes, among others, and exclude the 〈 i 〉 TP53 〈 /i 〉 gene. FISH studies were useful to refine the breakpoints of the translocations, but further studies are needed to determine the role of the involved genes in the neoplastic process.

Abstract: Chromosomal translocations are rare in the myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML). With the exception of t(3q), translocations are not explicitly considered in the cytogenetic classification of the IPSS‐R and their impact on disease progression and patient survival is unknown. The present study was aimed at determining the prognostic impact of translocations in the context of the cytogenetic classification of the IPSS‐R. We evaluated 1,653 patients from the Spanish Registry of MDS diagnosed with MDS or CMML and an abnormal karyotype by conventional cytogenetic analysis. Translocations were identified in 168 patients (T group). Compared with the 1,485 patients with abnormal karyotype without translocations (non‐T group), the T group had a larger proportion of patients with refractory anemia with excess of blasts and higher scores in both the cytogenetic and global IPSS‐R. Translocations were associated with a significantly shorter survival and higher incidence of transformation into AML at univariate analysis but both features disapeared after multivariate adjustment for the IPSS‐R cytogenetic category. Patients with single or double translocations other than t(3q) had an outcome similar to those in the non‐T group in the intermediate cytogenetic risk category of the IPSS‐R. In conclusion, the presence of translocations identifies a subgroup of MDS/CMML patients with a more aggressive clinical presentation that can be explained by a higher incidence of complex karyotypes. Single or double translocations other than t(3q) should be explicitly considered into the intermediate risk category of cytogenetic IPSS‐R classification. © 2015 Wiley Periodicals, Inc.

Abstract: INTRODUCTION: Although specific prognostic models for Chronic Myelomonocytic Leukemia (CMML) exist, few are based on large series of patients. Since its publication in 2002, the MD Anderson prognostic score (MDAPS) has been the most powerful prognostic tool for CMML. Due to the recent emergence of the CMML-specific prognostic scoring system (CPSS) and the Mayo prognostic model, we compared the three scores to assess their usefulness in our series. These three indexes, and not those based on clinical and molecular variables (Mayo Molecular Model and GFM prognostic score), were selected as the most easy-to-apply in normal clinical practice. AIM: 1) To assess the prognostic impact on overall survival (OS) and leukemia-free survival (LFS) of the variables composing the scores: MDAPS, CPSS and Mayo prognostic model; 2) To test the capability of the scores to detect the high-risk CMML patients; 3) To detect the index with the best predictive value for mortality and leukemia transformation, and 4) To implement a new score after selecting the best variables of the three indexes in terms of OS prognostic information. PATIENTS AND METHODS: From January 1997 to August 2013 a retrospective analysis including 146 patients diagnosed with CMML was performed in Hospital Clínic of Barcelona (n=134) and Hospital Germans Trias i Pujol (n=12). The median age was 76 years (range 27-96 years) and 63% were males. One-hundred and twenty-nine (88%) had a CMML-1, 17 (12%) a CMML-2, 102 (70%) a CMML-MD and 44 (30%) a CMML-MP. The median follow-up for surviving patients was 24.5 months and the median OS was 20 months (range 0-159 months). The prognostic impact in terms of OS and LFS of each of the variables that compose the indexes were studied by a univariate survival analysis (Kaplan-Meier; Log-Rank). A multivariate analysis (Cox model) was performed to assess the independent impact of the variables that showed significance in the univariate analysis in order to select the ones with the best prognostic information. The global prognostic scores were analyzed by univariate and multivariate analyses. In addition, ROC curves and the concordance index (C-index) were implemented to select the score with the best predictive power for mortality or leukemia transformation. RESULTS: All the variables that compose the MDAPS (hemoglobin level 〈 12 g/dl, absolute lymphocyte count 〉 2.5 x 109/L, presence of circulating immature myeloid cells (IMCs) and BM blasts ≥ 10%), the CPSS (CMML-MD vs. CMML-MP, CMML-1 vs. CMML-2, RBC transfusion dependency and the Spanish cytogenetic risk classification) and the Mayo prognostic model (absolute monocyte count 〉 10x109/L, presence of IMCs, hemoglobin 〈 10 g/dl and platelet count 〈 100 x 109/L) showed prognostic value on OS with the exception of circulating IMCs. Regarding LFS, only CPSS variables, BM blast ≥ 10% and an absolute monocyte count 〉 10x109/L had an impact. When the scores were applied, all showed an impact on OS and retained their significance in multivariate analysis. By using ROC curves and C-index, CPSS (ROC area: 0.80, CI 95%: 0.72-0.88; C-index: 0.73) showed a slightly better predictive value for mortality. Variables composing the three indexes were compared in a multivariate analysis and only CPSS parameters and platelets 〈 100 x 109/L retained their significance. Based on these findings, by adding platelet count information to CPSS, a new score was implemented (CPSS-P) showing the best risk stratification in our series (Figure 1). CONCLUSIONS: The present study reinforces the validity of CPSS, the MDAPS and the Mayo prognostic model for the assessment of CMML patients. Moreover, by including the platelet count information to the CPSS improved the prediction capacity for OS and LFS in our series. It is of importance to remark that platelet count information could help to better stratify CMML patients, being of special value in the subset of patients with normal karyotype. Figure 1. Associations Between Genetic Mutations and Clinical or Demographic Parameters Figure 1. Associations Between Genetic Mutations and Clinical or Demographic Parameters Disclosures No relevant conflicts of interest to declare.

Abstract: Background: Non-coding RNAs (ncRNAs) have recently emerged as key regulators of diverse cellular processes, including leukemia. ncRNAs are classified according to their size as short (eg, microRNAs) or long ncRNAs. lincRNAs are long ncRNAs located in intergenic regions and have multiple regulatory functions, including gene expression regulation. Interestingly, active crosstalk between microRNAs and lincRNAs has been observed. lincRNAs are known to be deregulated in some cancers but their importance in acute myeloid leukemia (AML) is so far unknown. HOX genes play an important role in hematopoiesis and are deregulated in AML. lincRNAs are especially abundant in the clusters of HOX genes. HOTAIRM1, a myeloid lineage-specific lincRNA, is located at the 3’end of the HOXA cluster and seems to play a regulatory role in myelopoiesis. However, to date the potential prognostic role of HOTAIRM1 expression in AML has not been examined. Aims: To investigate first whether the expression of the lincRNA HOTAIRM1 is associated with the clinical, cytogenetic and molecular characteristics and microRNA expression in AML patients. Secondly, since intermediate risk (IR) AML patients have a highly diverse prognosis, we analyzed the potential prognostic value of HOTAIRM1 expression in IR-AML patients. Methods: To explore the expression level of HOTAIRM1 among different AML subtypes, we analyzed samples from 244 AML patients including CBF-rearranged AML (n=5), APL (n=4), MLL-rearranged AML (n=3), EVI1-rearranged AML (n=3), t(6;9) AML (n=9), AML with monosomal karyotype (n=3), and a large cohort of IR-AML (described below). For the analysis of prognostic value of HOTAIRM1, we analyzed specifically the outcome of 217 IR-AML patients (median age, 52; 51% males) sequentially included in CETLAM trials during the period 1995-2009. Molecular genotyping of this group identified NPM1 mutation (NPM1mut), FLT3-ITD, and biallelic CEBPA mutation (CEBPA mut) in 99 (45%), 79 (36%) and 17 (11%), respectively. The expression of HOTAIRM1 was analyzed using TaqMan® Gene Expression Assays (Applied Biosystems). microRNA and mRNA expression data were obtained in previous studies (Díaz-Beyá, Leukemia 2013). Statistical analyses were performed with BRB Array Tools, SPSS v20 and R v3.0. MaxStat package from R software was used to determine the optimal cutoff point of HOTAIRM1 expression. Results: Among all 244 patients, HOTAIRM1 expression was significantly different among the 7 included genetic subgroups (ANOVA p=0.0024), with the lowest levels observed in APL-AML patients and the highest in the t(6;9)AML patients. Within the IR-AML group, HOTAIRM1 overexpression was observed in NPM1mut patients (p 〈 0.001). The prognostic study showed that high HOTAIRM1 expression was associated with shorter 5-year overall survival (OS) (27+11% vs.47+8%; p=0.009) shorter 5-year disease-free survival (LFS) (22+12% vs. 53+9%; p 〈 0.001), and a higher cumulative incidence of relapse (CIR) at 5 years (55+15% vs. 34+8%; p=0.004). The effect on outcome was maintained within the subgroup with favorable molecular features (i.e., NPM1mut and CEBPAmut without FLT3-ITD) (OS: 75+11% vs. 39+29%; p=0.026). In the multivariate analysis including age, sex, WBC, NPM1mut, FLT3-ITD and number of treatment cycles for CR achievement as covariates, HOTAIRM1 expression emerged as an independent prognostic marker in OS (HR=2.44; 95% CI: 1.51-3.93; p 〈 0.0001), LFS (HR=2.07; 95% CI: 1.31-3.24; p=0.002) and CIR (HR=2.05; 95% CI: 1.18-3.55; p=0.01). Supervised analysis by means of t-test based on multiple permutations revealed a distinctive 33-microRNA signature which correlated with HOTAIRM1 expression including miR-196b (p 〈 0.001) located in the HOXA genomic region. Moreover, we correlated the expression of HOX genes and HOTAIRM1 and observed a positive correlation with HOXA4 gene expression (R2= 0.6; p=0.001). Conclusion: The expression level of the lincRNA HOTAIRM1 varied among different molecularly-defined AML. Interestingly, HOTAIRM1 expression level showed independent prognostic value within the IR-AML group. Moreover, HOTAIRM1 expression strongly correlates with its neighboring HOXA4 gene and harbors a distinctive microRNA signature. Our findings can pave the way for further studies of HOX-related lincRNAs and microRNAs regulatory networks and their influence on clinical outcome. Acknowledgments: ISCIII RH13/00205, SEHH, FIS13/00999 Disclosures No relevant conflicts of interest to declare.

Abstract: To develop a prognostic scoring system tailored for therapy-related myelodysplastic syndromes (tMDS), we put together a database containing 1933 patients (pts) with tMDS from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed between 1975-2015. Complete data to calculate the IPSS and IPSS-R were available in 1603 pts. Examining different scoring systems, we found that IPSS and IPSS-R do not risk stratify tMDS as well as they do primary MDS (pMDS), thereby supporting the need for a tMDS-specific score (Kuendgen et al., ASH 2015). The current analysis focuses on cytogenetic information as a potential component of a refined tMDS score, based on this large, unique patient cohort. Of the 1933 pts, 477 had normal karyotype (KT), 197 had missing cytogenetics, while 467 had a karyotype not readily interpretable. Incomplete karyotype descriptions will be reedited for the final evaluation. Of the remaining 1269 pts the most frequent cytogenetic abnormalities (abn) were: -7, del(5q), +mar, +8, del(7q), -5, del(20q), -17, -18, -Y, del(12p), -20, and +1 with 〉 30 cases each. Frequencies are shown in Table 1. Some abn were observed mostly or solely within complex KTs, such as monosomies, except -7. Others, like del(20q) or -Y, are mainly seen as single or double abn, while del(5q), -7, or del(7q) are seen in complex as well as non-complex KTs. The cytogenetic profile overlapped with that of pMDS (most frequent abn: del(5q), -7/del(7q), +8, -18/del(18q), del(20q), -5, -Y, -17/del(17p), +21, and inv(3)/t(3q) (Schanz et al, JCO 2011)), with notable differences including overrepresentation of complete monosomies, a higher frequency of -7 or t(11q23), and a more frequent occurrence of cytogenetic subtypes in complex KTs, which was especially evident in del(5q) occurring as a single abn in 16%, compared to 70% within a complex KT. IPSS-R cytogenetic groups were distributed as follows: Very Good (2%), Good (35%), Int (17%), Poor (15%), Very Poor (32%). Regarding the number of abn (including incomplete KT descriptions) roughly 30% had a normal KT, 20% 1, 10% 2, and 40% ≥3 abn, compared to pMDS: 55% normal KT, 29% 1, 10% 2, and 6% ≥3 abn. To be evaluable for prognostic information, abn should occur in a minimum of 10 pts. As a single aberration this was the case for -7, +8, del(5q), del(20q), del(7q), -Y, and t(11;varia) (q23;varia). Of particular interest, there was no apparent prognostic difference between -7 and del(7q); del(5q) as a single abn was associated with a relatively good survival, while the prognosis was poor with the first additional abn; t(11q23) occurred primarily as a single abn and was associated with an extremely poor prognosis, and prognosis of pts with ≥4 abn was dismal independent of composition (Table 1). To develop a more biologically meaningful scoring system containing homogeneous and prognostically stable groups, we will further combine subgroups with different abn leading to the same cytogenetic consequences. For example, deletions, unbalanced translocations, derivative chromosomes, dicentric chromosomes of 17p, and possibly -17 all lead to a loss of genetic material at the short arm of this respective chromosome affecting TP53. Further information might be derived from analyses of the minimal common deleted regions. For some abn, like del(11q), del(3p), and del(9q), this can be refined to one chromosome band only (table 1). Conclusion: Development of a robust scoring system for all subtypes of tMDS is challenging using existing variables. This focused analysis on the cytogenetic score component shows that favorable KTs are evident in a substantial proportion of pts, in contrast to historic data describing unfavorable cytogenetics in the majority of pts. Although complex and monosomal KTs are overrepresented, this suggests the existence of distinct tMDS-subtypes, although some of these cases might not be truly therapy-induced despite a history of cytotoxic treatment. The next steps will be to analyze the prognosis of the different groups, develop a tMDS cytogenetic score, and examine minimal deleted regions to identify candidate genes for development of tMDS, as well as to describe the possible influence of different primary diseases and treatments (radio- vs chemotherapy, different drugs) on induction of cytogenetic subtypes. Our detailed analysis of tMDS cytogenetics should reveal important prognostic information and is likely to help understand mechanisms of MDS development. Disclosures Komrokji: Novartis: Consultancy, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Sole:Celgene: Membership on an entity's Board of Directors or advisory committees. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Steensma:Amgen: Consultancy; Genoptix: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Millenium/Takeda: Consultancy; Ariad: Equity Ownership. Schlenk:Pfizer: Honoraria, Research Funding; Amgen: Research Funding. Valent:Amgen: Honoraria; Deciphera Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Deciphera Pharmaceuticals: Research Funding. Giagounidis:Celgene Corporation: Consultancy. Giagounidis:Celgene Corporation: Consultancy. Platzbecker:Celgene Corporation: Honoraria, Research Funding; TEVA Pharmaceutical Industries: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen-Cilag: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. Lübbert:Janssen-Cilag: Other: Travel Funding, Research Funding; Celgene: Other: Travel Funding; Ratiopharm: Other: Study drug valproic acid.