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.

Abstract: Monitoring of the monoclonal protein (M-protein) by electrophoresis and/or immunofixation (IFE) has long been used to assess treatment response in multiple myeloma (MM). However, with the use of highly effective therapies, the M-protein becomes frequently undetectable, and more sensitive methods had to be explored. We applied IFE and mass spectrometry (EXENT & FLC-MS) in serum samples from newly diagnosed MM patients enrolled in the PETHEMA/GEM2012MENOS65 obtained at baseline (n = 223), and after induction (n = 183), autologous stem cell transplantation (n = 173), and consolidation (n = 173). At baseline, the isotypes identified with both methods fully matched in 82.1% of samples; in the rest but 2 cases, EXENT & FLC-MS provided additional information to IFE with regards to the M-protein(s). Overall, the results of EXENT & FLC-MS and IFE were concordant in & gt;80% of cases, being most discordances due to EXENT & FLC-MS+ but IFE− cases. After consolidation, IFE was not able to discriminate 2 cohorts with different median progression-free survival (PFS), but EXENT & FLC-MS did so; furthermore, among IFE− patients, EXENT & FLC-MS identified 2 groups with significantly different median PFS (P = .0008). In conclusion, compared with IFE, EXENT & FLC-MS is more sensitive to detect the M-protein of patients with MM, both at baseline and during treatment, and provides a more accurate prediction of patients’ outcome. This trial was registered at www.clinicaltrials.gov as #NCT01916252.

Abstract: Background Therapy-related Myelodysplastic Syndromes (t-MDS) are those MDS occurring after cytotoxic and/or radiation therapy administered for a prior neoplastic or non-neoplastic disorder. Their prognosis is generally very poor. The commonly used risk prognostic models for MDS (IPSS and IPSS-R) are not validated in this entity as they were developed after the exclusion of therapy-related cases (Greenberg et al. Blood 1997; Greenberg et al. Blood 2012). Aims The main aims of this study are: a) to report clinical findings and overall survival on 233 patients with t-MDS, and to compare them with a large series of de novo cases; b) to test if IPSS-R is applicable to t-MDS patients. Patients and methods The study is based on the Spanish Registry for MDS, a retrospective database that includes more than 10000 cases. The investigators were asked to fill in a questionnaire regarding prior disease (PD) and prior therapy in those cases reported to be t-MDS. Herein are described the clinical features and overall survival of the first 233 cases with the required information, and compared with patients with de novo MDS from a single center series (n=725). Log Rank test was applied to asses IPSS-R in t-MDS group. Results The 233 reported patients were diagnosed between January 1993 and February 2014. The series includes 104 women (44,6%) and 129 men (55,4%). One hundred and two patients (43.9%) had a primary hematologic malignancy, 119 (51%) had a solid tumor, and 12 (5.1%) received cytotoxic therapy for autoimmune disorders. Ninety eight patients (42.6%) received only chemotherapy (CT), 45 (19.6%) received only radiotherapy (RT), 44 (19.1%) received combined modality treatment (CMT), and 43 (18.6%) received an autologous stem cell transplantation (ASCT). The median time of latency between PD and diagnosis in t-MDS group was 4.56 years (range: 0.03-29.63) in patients previously treated with CT or CMT, significantly lower than the observed after RT (8.54; range 0.83-23.02) or ASCT (8.64; range 2.87-28.32) groups (p=0.023 and p 〈 0.0001, respectively). Median age, hemoglobin concentration, platelet count and absolute neutrophil count at diagnosis were significantly lower in t-MDS compared with de novo MDS, while bone marrow blast cell count was significantly higher. A higher proportion of high risk karyotypes (intermediate, poor and very poor categories of R-IPSS for cytogenetics) in t-MDS than in de novo MDS was observed (45.5% versus 25.6% respectively, p 〈 0.0001). Within t-MDS cases, those patients who had received CT, CMT or ASCT also presented a higher proportion of high risk karyotypes compared with t-MDS after RT (52.1 vs 24.1% respectively, p=0.006). There was a significantly shorter median overall survival in patients treated with CT/CMT or ASCT in comparison with de novo patients or t-MDS patients treated with RT (14.88 versus 25.06 versus 47.18 months, respectively (log rank test 〈 0, 0001). In contrast, no difference between de novo and t-MDS after RT was found (median survival 53.6 months vs. 47.17 months, respectively, n.s). These findings are in concordance with previously published data (Nardi et al, JCO 2012) suggesting that some t-MDS after RT may not be truly t-MDS, but just coincidental. The IPSS-R prognostic score could separate t-MDS patients into five risk groups in terms of overall survival, with a median survival of 76.68 months, 38.73 months, 16.59 months, 13.3 months and 6.37 months, respectively (p 〈 0.0001; Figure 1). A shorter overall survival for each category was found in t-MDS in comparison with de novo MDS, reaching statistical significance in the intermediate and high risk category and showing a trend towards significance in the very low and low risk categories (Figure 2). Conclusions In conclusion, our data supports the notion that t-MDS may contain entities with heterogeneous prognosis due to a diverse biological basis. The capacity of the IPSS-R for separating good prognostic from bad prognostic cases shown in this study could be very useful in the clinical setting in order to offer risk-adapted treatments to patients, although the development of a specific prognostic score for these entities would be needed. 〈 ![if !vml] 〉 〈 ![endif] 〉 Disclosures No relevant conflicts of interest to declare.

Abstract: Introduction: The MDS are a group of clonal hematopoietic disorders characterized by blood cytopenias and increased risk of transformation into acute myeloid leukemia (AML). The MDS predominate in old people (median age at diagnosis 〉 70 years) so that a fraction of the observed mortality would be driven by age-related factors shared with the general population rather than the MDS. Distinguishing between the MDS-related and unrelated mortality rates will help better assessment of the population health impact of the MDS and more accurate prognostication. This study was aimed at quantifying the MDS-attributable mortality and its relationship with the IPSSR risk categories. Methods: The database of the GESMD was queried for patients diagnosed with primary MDS after 1980 according to the WHO 2001 classification. Patients with CMML, younger than 16 years or who lacked the basic demographic or follow-up data were excluded. Relative survival and MDS-attributable mortality were calculated by the cohort method and statistically compared by Poisson multivariate regression as described by Dickman (Stat Med 2004; 23: 51). Three main parameters were calculated: the observed (all-cause) mortality, the MDS-attributable mortality (both as percentage of the initial cohort), and the fraction of the observed mortality attributed to the MDS. Results: In total, 7408 patients met the inclusion criteria and constitute the basis for this study. Among these patients, 5307 had enough data to be classified according to the IPSSR. Median age was 74 (IQR: 16-99) years and 58 % were males. The most frequent WHO categories were RAEB, type I or II (29% of cases), RCMD (28%), and RA with ring sideroblasts (16%). Most patients (72%) were classified within the very low and low risk categories of the IPSSR. At the study closing date (December 2014), 1022 patients had progressed to AML, 3198 had died (974 after AML) and 3210 were censored alive. The median actuarial survival for the whole series was 4.8 (95% CI: 4.6-5.1) years and 30% of patients are projected to survive longer than 10 years. The overall MDS-attributable mortality at 5 years from diagnosis was 39%, which accounted for three-quarters of the observed mortality (51%, figure). The corresponding figures at 10 years for the MDS-attributable and observed mortality were 55% and 71%, respectively. According to the IPSSR, the 5-year MDS-attributable mortality rates was 19% for the very low risk category, 39% (low risk), 70% (intermediate risk), 78% (high risk), and 92% (very high risk). On average, the incidence rate ratio for the MDS-attributable mortality increased 1.9 times (95% CI: 1.7-2.3, p 〈 0.001) as the IPSSR worsened from one to the next risk category. The fraction of the observed mortality attributed to the MDS was 0.55 for the very low risk category, 0.79 (low risk), 0.93 (intermediate risk), 0.96 (high risk), and 0.99 (very high risk). After distinguishing between AML-related and unrelated mortality, the 5-year MDS-attributable mortality not related to AML was 10% for the very low risk category, 20% (low risk), 33% (intermediate risk), 42% (high risk), and 44% (very high risk). By comparing these figures with the above ones, we could estimate that about 50% of the MDS-attributable mortality was AML-unrelated and that such fraction kept nearly constant across the five IPSSR categories. Conclusions: About three-quarters of the mortality observed in patients with MDS is caused by the disease, the remaining one-quarter being due to MDS-independent factors shared with the general population. The MDS-attributable mortality increases with the IPSSR risk category, from half the observed mortality in the very low risk to nearly all the mortality observed in the high and very high risk groups. Half the MDS-attributable mortality is driven by factors unrelated to leukemic transformation, a proportion that keeps constant across the five IPSSR risk categories. Disclosures Valcarcel: AMGEN: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; NOVARTIS: Honoraria, Membership on an entity's Board of Directors or advisory committees; GSK: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CELGENE: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Ramos:AMGEN: Consultancy, Honoraria; NOVARTIS: Consultancy, Honoraria; JANSSEN: Honoraria, Membership on an entity's Board of Directors or advisory committees; CELGENE: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Esteve:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Abstract: Background: The International Prognostic Scoring System (IPSS) for MDS has recently been revised (IPSS-R). However both scoring systems were developed after exclusion of therapy-related cases and data on its usefulness in treatment-related MDS (tMDS) is limited. Aims and Methods: We analyzed 1837 pts from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed 1975-2015. Complete data to calculate the IPSS/-R was available in 1511 pts. The impact of prognostic features was analyzed by uni- and multivariable models and estimated by a measure of concordance for censored data (Dxy). Results: Median age was 68 years. 1% of pts had 5q-syndrome, 13% RCUD, 4% RARS, 27% RCMD/-RS, 18% RAEB 1, 18% RAEB 2, 4% CMML 1, 2% CMML 2, 3% MDS-U, and 7% AML (RAEB-T) according to WHO-classification. Regarding cytogenetics 38% exhibited good, 14% intermediate, and 48% poor-risk according to IPSS, and 2% very good, 36% good, 17% intermediate, 15% poor, and 31% very poor according to IPSS-R. Prognostic risk groups were 12% IPSS low, 34% int 1, 36% int 2, and 18% high, while the IPSS-R was very low in 8%, low in 20%, intermediate in 17%, high in 23%, and very high in 32%. The most frequent primary diseases were NHL 28%, breast cancer 16%, myeloma 6%, prostate cancer 6%, Hodgkins disease 5%, and 4% gastrointestinal tumors. Patients received chemotherapy in 75% and radiotherapy in 47%. Regarding chemotherapeutic drugs, most pts received combination regimens containing alkylating agents in 65%, topoisomerase inhibitors in 44%, antitubulin agents in 26%, and antimetabolites in 26%. Median follow-up from MDS diagnosis was 59 months, median survival 16 months. Since a disease altering treatment is, at least in higher risk disease, which is overrepresented in tMDS, standard of care, we decided to analyze treated as well as untreated pts to avoid a selection bias. This included stem cell transplantation in 16% with a median survival of 24 months. Features with influence on survival and time to AML in univariable analysis included FAB, WHO, IPSS, IPSS-R, cytogenetics, hb, platelets, marrow and peripheral blasts, ferritin, LDH, fibrosis, ß2-microglobulin, and use of alkylating agents for the treatment of primary disease. For hemoglobin, platelets, LDH, fibrosis, and ß2-microglobulin the influence was stronger on survival. Year of diagnosis, age, gender, neutrophil count, WBC, use of chemo or radiotherapy as well as other chemotherapeutic agents had no marked influence on both outcomes. According to our results, both the IPSS (Dxy 0.29 for survival, 0.32 for AML) and IPSS-R (Dxy 0.34, 0.32 for AML) perform moderately in tMDS, but not as well as in primary MDS (pMDS). Therefore, existing prognostic models need to be adjusted to tMDS. However, this appears to be not without difficulties. The scores tested, as well as most prognostic variables themselves perform inferior compared to pMDS. It becomes even more complicated since tMDS in itself is even more heterogeneous than pMDS. Scores and variables perform differently depending on the primary disease or therapy. The IPSS/-R and its variables perform for example better in pts with solid tumors compared to hematologic diseases or in pts who have received radio- instead of chemotherapy, but also in pts after prostate compared to breast cancer. In addition to the integration of further variables, new cutoffs, or the weighting of existing variables, we are currently testing the possibility of separate score versions for different tMDS subgroups. Separate score versions for survival and time to AML would also give differing weights to most features. Hemoglobin, platelets and cytogenetics would get more weight for survival, while marrow blasts would be more important regarding AML. Conclusion: In contrast to early descriptions of tMDS, with poor risk cytogenetics in the vast majority of pts and a uniformly poor prognosis, surprisingly we find good risk karyotypes in a relatively large number of pts. Although, poor risk cytogenetics are still overrepresented, this indicates, different types of tMDS exist. Our analysis shows that many variables exhibit prognostic influence in tMDS and the IPSS or preferably IPSS-R can be applied in these pts. However, the prognostic power of both scores is inferior compared to pMDS, making an optimized tMDS score reasonable. Currently data from further IWG centers is integrated in our database and further analyses are performed to propose a tMDS specific score. Figure 1. Figure 1. Disclosures Komrokji: Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau; Incyte: Consultancy; Celgene: Consultancy, Research Funding. Sekeres:TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Celgene: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Onconova: Consultancy. Valent:Novartis: Consultancy, Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Pfizer: Honoraria; Celgene: Honoraria. Platzbecker:Boehringer: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Esteve:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Abstract: Background: Evaluation of MRD is standard in patients with AML. However, the role of decentralized MRD assessment for risk stratification in AML remains largely unknown, and so it does which methodological aspects are critical to empower the evaluation of MRD with prognostic significance, particularly if using MFC. Aim: To evaluate the role of decentralized MRD assessment using MFC for risk stratification and putative treatment individualization of patients with AML. Methods: This study was performed on 1,076 AML patients in complete remission (CR) after 7+3 induction chemotherapy, in whom MRD was evaluated by MFC in local laboratories over a period of 20 years in the PETHEMA group. We conducted a survey of technical aspects of MFC based MRD testing in the laboratories of the 60 participating Hospitals, to determine the impact of methodological heterogeneity in the prognostic value of MFC. Results: We first investigated the most effective MRD cutoff to stratify patients' risk at first remission. Patients were segmented into progressively higher cutoffs, starting at 0.01% followed by 0.05%, 0.1%, 0.5% and 1%. Our results showed that 0.1% reached higher statistical significance to discriminate patients with different relapse-free survival (RFS, HR: 0.77; P = .001) and overall survival (OS, HR: 0.73; P = .001). In multivariate analyses together with patients' age, WBC, genetic risk and post-consolidation therapy, MRD status was selected as an independent prognostic factor for OS. To further define the utility of "real-world" MRD assessment using MFC in risk stratification of AML, recursive partitioning was performed using the prognostic and treatment related factors selected in the multivariate Cox model for OS. Of the four variables evaluated, hematopoietic stem cell transplantation (HSCT, regardless of autologous or allogeneic source) vs no transplant emerged as the best single discriminator for OS, followed by genetic risk, age and MRD status. There were two branching points defined by MRD status; the first in patients ≤60 years with intermediate genetic risk who were not transplanted and the second in patients with adverse genetics who were not transplanted, in whom & lt;0.1% MRD faintly improved the dismal outcome of this subgroup. Overall, patients not referred to HSCT had dismal RFS regardless of MRD levels. Forty-nine of the 60 hospitals (82%) responded to the survey on questions regarding the measurement of MRD using MFC in the PETHEMA LMA 1999, 2007 and 2010 protocols, providing information corresponding to 966 of the 1,076 (90%) patients regarding the number of markers, preparation of samples, instruments, approach (ie, LAIP, DfN or LAIP+DfN), number of cells to define a cluster, etc. The survey revealed significant heterogeneity intra- and inter-protocols that reflected improvement in MFC assessment of MRD over time, in the absence of harmonization nor standardization at the national level. Accordingly, we investigated if the heterogeneity in methodological, interpretation and reporting aspects of MFC based MRD testing were hampering its ability to predict outcome independently of other patient and treatment related factors. Strikingly, our results showed that except for the denominator used to calculate MRD burden (ie, total nucleated cells vs leukocytes), lack of standardization in all other parameters had an impact on the ability of MFC to predict outcomes in AML (Figure). Namely, panels with ≤4 markers or ≤2 combinations failed to identify patients with significantly different RFS according to MRD status, and MFC-based MRD monitoring was prognostic only when & gt;500,000 cells were measured. Only MRD assessment using patient-specific panels was predictive of outcome. Conclusions: We report here one of the largest studies investigating the role of MRD monitoring using MFC. Our results confirmed that detection of MRD identifies patients in CR/CRi with inferior survival, but uncovered that decentralized MRD testing lacks significance when compared to other baseline risk factors and in the context of risk-adapted post-consolidation strategies. Thus, while this study demonstrated that "real-world" decentralized assessment of MRD using MFC does provide prognostic information in AML patients at first remission, our results question its readiness for risk stratification towards clinical decisions outside trials, at least until adequate standardization of this technique is achieved. Figure Disclosures Paiva: SkylineDx: Consultancy; Takeda: Consultancy, Honoraria, Research Funding; Roche: Research Funding; Adaptive: Honoraria; Amgen: Honoraria; Janssen: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; Kite: Consultancy; Celgene: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Research Funding. Alonso Dominguez:Celgene: Research Funding; Incyte: Research Funding; Pfizer: Research Funding. Martinez-Lopez:Janssen: Speakers Bureau; Altum: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; Hosea: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties; Roche: Speakers Bureau; Amgen: Speakers Bureau; Takeda: Speakers Bureau; Vivia Biotech: Honoraria; Novartis: Research Funding; BMS: Research Funding, Speakers Bureau; Incyte: Research Funding, Speakers Bureau. Sossa:Astellas: Honoraria; Roche: Honoraria; Takeda: Honoraria; Novo: Honoraria. San-Miguel:Roche, AbbVie, GlaxoSmithKline, and Karyopharm: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb, Celgene, Novartis, Takeda, Amgen, MSD, Janssen, and Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees.

Abstract: There is scarce information about the efficacy of ESA in CMML although their use is common in clinical practice. The objective of this study was to analyze the response and OS in a series of 99 pts with CMML treated with ESA and to evaluate the feasibility of the predictive model of response to ESA used in MDS (Hellström-Lindberg, et al. Br J Haematol. 1997; 99: 344-51). Method Between January 1997 and March 2013 99 pts with CMML from the Spanish Registry of MDS and the Düsseldorf-MDS registry were studied. Clinical characteristics, response and OS were analyzed. Predictive model of response to ESA (0 good, 1 intermediate, and 2 poor) based on erythropoetin (EPO) level ( 〈 or ≥ 500 U/L) and red blood cell (RBC) transfusion need ( 〈 2 or ≥ 2 RBC/month) was applied. Results 66 (67%) pts were males. Median age (range) was 75 (52-93) years. CMML subtype: myelodysplastic 58 (59%), myeloproliferative 41 (41%), CMML-I 84/98 (86%), CMML-II 14/98 (14%). CPSS score: Low/Int-1 65/90 (72%), Int-2/High 25/90 (28%). Transfusion dependence on initiation of ESA 24/86 (28%). Score based on predicted model of response to ESA: 0 43/62 (69%), 1 15/62 (24%), 2 4/62 (7%). ESA type: EPO alfa 22/94 (24%), EPO beta 16/94 (17%), EPO theta 3/94 (3%) darbepoetin 53/94 (56%). Concomitant medication: hydroxyurea 19 (39%), iron 18 (37%), steroids 4 (8%), azacitidine 3 (6%), etoposide 2 (4%), G-CSF 1 (2%), romiplostim 1 (2%) and oral chelation 1 (2%). Four pts were excluded for response analysis because they received azacitidine (3) and oral chelation (1). Response: Erythroid response (ER) 55/86 (64%), transfusion independence 5/22 (23%). ER according to CPSS (Low/Int-1 vs. Int-2/High): 71% vs. 43%, p=0,032. Median (min,max) time of ER was 4 months (0,88). Pts with 0 score according to predictive model of response to ESA presented significantly higher ER than pts with 1-2 score (77% vs. 24%, p 〈 0.001) (Table 1). Median (range) follow-up was 2.1 years (0.1-10.5) years and median OS was 3.3 years (95%CI 2.7-4). OS of pts of Low/Int-1 risk group with ER (n= 40) was significantly higher than that of non-responding pts (n=16) (median in years (4.4, 95%CI (0.4-8.3) vs. 2.3, 95%CI (1.5-3), p 〈 0.001). Conclusions A high frequency of ER was observed in this series of pts with CMML, the majority belonging to Low-risk CPSS. Predictive model of response to ESA from MDS was feasible, with a similar ER than that of MDS pts. Pts with low-risk CPSS with ER to ESA had a better OS than non-ER patients Supported by RD12/0036/0029 from RTICC-Instituto Carlos III, Spain Disclosures: Germing: Celgene: Honoraria, Research Funding; Jansen-Cilag: Honoraria; Novartis: Research Funding; GSK: Research Funding; Amgen: Research Funding.

Abstract: INTRODUCTION MDS are a heterogeneous group, and it is necessary an adequate prognostic stratification in order to the best management. The new revised international prognostic scoring system (IPSS-R) has improved prognostic ability for survival and AML evolution comparing with the previous prognostic indexes. But, it is not clear the prognosis of patients included in the intermediate group, 20% of MDS, patients with a median OS of 3 years according to Greenberg et al, are they in the high or in the low risk category? The aims of the present study were to describe characteristics of patients included in this intermediate group of the IPSS-R in the Spanish MDS cohort and to identify which factors could have an impact on survival. A new score prognostic system (GESMDi score) in order to a better stratification should be proposed in this subset of patients that will be useful for determine the best therapeutic approach for them. METHODS: All patients were included in the GESMD, diagnosed of Primary MSD and Intermediate IPSS-R. The Statistical analyzes were performed using SPSS version 21, Cox models and Kaplan-Meier curves were used to demonstrate clinical outcomes. Regarding the new score proposed, GESMDi score, modeling of prognostic risk was based on multivariate analysis of survival time. Cox model for survival was built to derive the relative weights within the score. RESULTS: Data from 957 patients of 69 centers of GESMD were evaluated. Their median age was 73.9 years (p25/p75 66-80), 61.6% males (N=590), and median follow-up 21,4 months (p25-p75 de 11-41). Regarding WHO 2001 classification: 31% were RAEB-1, 21% CMML, 18% RCMD, 14% RAEB-2, 3% RCMD-RS, 3.1% RARS, 2.5% RA, 2% 5q-syndrome, 2% AML, 1% unclassified. Median hemoglobin at diagnosis was 9.8 g/dL (p25/p75:8.3-11.6), median bone marrow (BM) blasts 6% (p25/p75:3-8) and median platelet count 99x109/L (p25/p75:66-180). According to IPSS, 5% of patients were classified as low risk, 78% as intermediate-1, 16% as intermediate-2 and 1% as high risk. Cytogenetic were very good in 2% of patients, good in 76%, intermediate in 17%, poor in 5% and in 1% very poor. IPSS-R score classified patients in 3 different groups, with a punctuation of≤ 3.5 (35.6%), 〉 3.5 and ≤ 4 (35.8%) and 〉 4 and ≤ 4.5 (28.5%). Median OS was 30.1 months, the estimated 1-year and 2-y OS were 79.2% and 57.8%, respectively. In the univariate analysis for OS older age ( 〉 74y, p 〈 0.001), lower Hb level (≤9.5 g/dL, p 〈 0.001), WHO 2001 with excess of blasts classification (p=0.035), lower platelets level (≤30 x 109/L, p=0.01), PB blasts (yes, p=0.001), ferritine level ( 〉 500 ng/ml, p=0.002), and higher IPSS-R score ( 〉 3.5 and ≤ 4 and 〉 4 and ≤ 4.5, p=0.023 and p=0.004, figure 1) had a deleterious impact on survival. In the multivariate analysis, only age, Hb level, PB blast, ferritine level and IPSS-R value retained statistical significant impact on OS (table 1a). In the multivariate analysis, Hazard ratio, a new score system (GESMDi score) was established for all patients. Patients with adverse features were added points in order to stratify the risk of death: age 〈 74y and/or PB blasts (2 points) and Hb level ≤9.5 g/dL and/or ferritine level 〉 500 ng/ml and/or IPSS-R of 〉 3.5 (1 point), table 1a. The GESMDi score was performed in 685 patients with all data available and 7 groups of patients were defined with different median OS (p 〈 0.0001, table 1b). Two final categories were established according to the definition of risk from the Spanish MDS group, low risk patients (estimated OS 〉 30 months) and high risk patients ( 〈 30 months). Patients with scores between 0-3 (70.6% patients, me OS 41.1, 95CI 34.4-47.7) were in the low risk definition while patients with scores between 4-6 (29.3% patients, me OS 17.5 mo, 95CI 13.4-21.5) were classified as high risk patients (p 〈 0.0001, Figure 2). CONCLUSIONS: GESMDi score, a proposed prognostic score system from patients with intermediate IPSS-R, allow us to establish a better prognosis stratification in this heterogeneous MDS population. Treatment and management should be better established for those patients nowadays according to this novel stratification. Table 1 a) Univariate and multivariate analysis for OS among patients with Intermediate IPSS-R b) OS according to the GESMDi score proposed Table 1. a) Univariate and multivariate analysis for OS among patients with Intermediate IPSS-R b) OS according to the GESMDi score proposed Figure 1 OS according to IPSS-R value in the intermediate group (≤3.5, ≤4 and ≤4.5) Figure 1. OS according to IPSS-R value in the intermediate group (≤3.5, ≤4 and ≤4.5) Figure 2 OS according the GESMDi score proposed in the intermediate IPSS-R group: low and high risk patients (n=685) Figure 2. OS according the GESMDi score proposed in the intermediate IPSS-R group: low and high risk patients (n=685) Disclosures Del Cañizo: Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astex: Membership on an entity's Board of Directors or advisory committees; janssen: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Díez Campelo:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astex: Membership on an entity's Board of Directors or advisory committees; Janssen: Research Funding; celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau.