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Abstract Background: Clonal cytopenia of undetermined significance (CCUS) is defined as persistent unexplained cytopenia with evidence of clonality [myeloid-associated somatic mutations (MTs) or cytogenetic [CG] abnormalities] but without definitive evidence of myeloid neoplasms (MN). The outcomes in CCUS patients (pts) are not well understood. Methods: The CCUS International Study database includes pts from 17 institutions who meet the criteria of CCUS and do not have other causes of cytopenia. Pts with MDS defining CG abnormalities were excluded. We collected baseline clinical data, laboratory parameters, CGs, molecular genetics, treatment, and disease course. Diverse gene panels from different institutions were collated to include a total of 70 myeloid-related somatic genes (30 genes in common). 2018 IWG MDS response criteria were used to determine response rate (RR). Disease progression (DP) is defined by progression to MN. The relationship between independent variables and DP and death was assessed using Cox proportional hazards models. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier method. All statistical analysis was performed using SAS. Results: A total of 258 pts were captured by July 2021. The median age was 71 (IQR 63-77) years, and 66% were male. Among 73 (28%) pts with known malignancy history, 27 (37%) pts received previous chemo- or radiation therapy. 27 (10%) pts had non-malignant hematology comorbidities. 91 (35%) had cardiovascular disease, and 33 (13%) had inflammatory disease. (Table 1) The red blood cell and platelet transfusion-dependent rates were 7.3% and 2.3%, respectively. The median number of MTs was 2 (IQR 1-3), with 221 (86%) pts had ≥1 MT, of which 116 (53%) had ≥ 2 MTs. The most common 5 MTs were TET2 (n=78, 30%), SRSF2 (n=50, 19%), DNMT3A (n=47, 18%), ASXL1 (n=40, 16%), and U2AF1 (n=22, 9%) (Figure 1). The median VAF (mVAF) was 28% (IQR: 9.2%, 43%) with VAF < 10% in 47 (18%) and VAF ≥40% in 78 (30.2%) of the MTs. mVAF of the all genes are shown in Figure 2. Among pts with CG abnormalities (n=62, 24%), trisomy 8 and -Y are the most common karyotypes (n=15 for each, 24%) (Table 2). Eighty one (31%) patients received various treatments for CCUS with modest RR, including growth factors (n=47, 18.2%, RR: 25.5%), supportive care (n=23, 9%, RR: 26%), immunoglobulin/immunosuppressive therapy (n=15, 6%, RR: 40%), and DNMTi (n=8, 3%, RR=13%). The median length of follow-up was 15.6 (IQR 6.9-30.6) months. 24 pts progressed to MN, 14 (58.3%) of which were MDS, 8 (33.3%) CMML, and 2 (8.3%) AML. The 2-year PFS was 86.1% (95% CI: 80-93%) with a median PFS of 16.3 (IQR: 3.7, 21) months. In the multivariable model, positive MT of KRAS (HR: 8.4, 95% CI: 1.9-36.9, p=0.005) and CBL (HR: 16.5, 95% CI: 3.7-73.8, p=0.003) were significantly associated with DP. In the functional pathway analysis, having at least 1 splicing factor MT was significantly associated with DP (HR: 2.6, 95% CI:1.2-5.9, p=0.02). TP53 was not significantly associated with DP (HR: 1.2, 95% CI: 0.2-8.7, p=0.88). Having >1 MT (HR: 3.57, 95% CI: 1.19-10.7, p=0.02) compared to a single MT was significantly associated with DP (Figure 3). Over the follow-up period, 35 pts died. The 2-year OS was 81% (95% CI: 74.9-87.9%). In the multivariable model, MT of KRAS (HR: 6.1, 95% CI: 1.8-20, p=0.003), CBL (HR: 7.3, 95% CI: 1.7-31, p=0.007), and FLT3 (HR: 19.9, 95% CI: 2.5-155, p=0.004) were significantly associated with inferior survival. In the functional pathway analysis, MTs in activated signaling pathway were significantly associated with death (HR: 4.1, 95% CI: 1.8-9, p<0.001). None of the pts with TP53 MT died. Having >1 MT was not statistically significantly associated with higher risk of death (HR: 1.5, 95% CI: 0.7-3.0, p=0.28) (Figure 4). After adjustment for co-MT status and comorbidities, baseline Hb<10 g/dL (HR: 3.7, 95% CI: 1.8-7.9, p<0.001) was significantly associated with greater mortality. Conclusion: This large retrospective study summarizes the CCUS pts' characteristics, with different MT patterns and VAF. We confirmed the impact of having >1 MT on DP, but not OS. Genes involved in activated signaling had a significant impact on both DP and OS. TP53 MT was not associated with worse outcome. Findings may be due to limited cases in the particular genes and different gene panels from multiple institutions. A longer follow-up is planned to further describe the predictors for outcome in this ongoing study. Figure 1 Figure 1. Disclosures Komrokji: Jazz: Consultancy, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; BMSCelgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Acceleron: Consultancy; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy; Taiho Oncology: Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy. Zeidan: BioCryst: Other: Clinical Trial Committees; Kura: Consultancy, Other: Clinical Trial Committees; Ionis: Consultancy; Geron: Other: Clinical Trial Committees; Incyte: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Boehringer Ingelheim: Consultancy, Research Funding; Novartis: Consultancy, Other: Clinical Trial Committees, Travel support, Research Funding; Janssen: Consultancy; Cardiff Oncology: Consultancy, Other: Travel support, Research Funding; Loxo Oncology: Consultancy, Other: Clinical Trial Committees; Aprea: Consultancy, Research Funding; AstraZeneca: Consultancy; Agios: Consultancy; BeyondSpring: Consultancy; Gilead: Consultancy, Other: Clinical Trial Committees; Daiichi Sankyo: Consultancy; Jazz: Consultancy; Astex: Research Funding; BMS: Consultancy, Other: Clinical Trial Committees, Research Funding; Acceleron: Consultancy, Research Funding; Pfizer: Other: Travel support, Research Funding; Genentech: Consultancy; Epizyme: Consultancy; Jasper: Consultancy; Astellas: Consultancy; ADC Therapeutics: Research Funding; AbbVie: Consultancy, Other: Clinical Trial Committees, Research Funding. Madanat: Blue Print Pharmaceutical: Honoraria; Stem line pharmaceutical: Honoraria; Onc Live: Honoraria; Geron Pharmaceutical: Consultancy. Coombs: LOXO: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; AstraZeneca: Honoraria; AbbVie: Honoraria; Genentech: Honoraria; MEI Pharma: Honoraria. Griffiths: Celgene/Bristol-Myers Squibb: Consultancy, Honoraria, Research Funding; Takeda Oncology: Consultancy, Honoraria; Taiho Oncology: Consultancy, Honoraria; Apellis Pharmaceuticals: Research Funding; Novartis: Honoraria; Alexion Pharmaceuticals: Consultancy, Research Funding; Boston Biomedical: Consultancy; Astex Pharmaceuticals: Honoraria, Research Funding; Genentech: Research Funding; Abbvie: Consultancy, Honoraria. Lai: Astellas: Speakers Bureau; Jazz Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees; Jazz Pharma: Speakers Bureau; Macrogenics: Consultancy, Membership on an entity's Board of Directors or advisory committees; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees. Foran: trillium: Research Funding; actinium: Research Funding; kura: Research Funding; boehringer ingelheim: Research Funding; takeda: Research Funding; abbvie: Research Funding; aptose: Research Funding; pfizer: Honoraria; novartis: Honoraria; servier: Honoraria; bms: Honoraria; revolution medicine: Honoraria; OncLive: Honoraria; gamida: Honoraria; certara: Honoraria; sanofi aventis: Honoraria; syros: Honoraria; taiho: Honoraria; h3bioscience: Research Funding; aprea: Research Funding; sellas: Research Funding; stemline: Research Funding. Badar: Pfizer Hematology-Oncology: Membership on an entity's Board of Directors or advisory committees. Desai: Astex: Research Funding; Janssen R&D: Research Funding; Kura Oncology: Consultancy; Takeda: Consultancy; Bristol Myers Squibb: Consultancy; Agios: Consultancy. Ades: ABBVIE: Honoraria; CELGENE/BMS: Honoraria; NOVARTIS: Honoraria; TAKEDA: Honoraria; JAZZ: Honoraria, Research Funding; CELGENE: Research Funding. Brunner: Novartis, Celgene, Takeda, AstraZeneca: Research Funding; Celgene, Forty Seven Inc, Jazz: Other: Advisory Board. Carraway: Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Other: Independent review committee; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Other: Independent review committee; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astex: Other: Independent review committee; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Prebet: BMS: Research Funding; BMS, Curios, Daichi: Consultancy. Patnaik: Kura Oncology: Research Funding; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees. Savona: Karyopharm: Consultancy, Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy, Membership on an entity's Board of Directors or advisory committees; CTI: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS-Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; NOVARTIS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Ryvu: Consultancy, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Taiho: Consultancy, Membership on an entity's Board of Directors or advisory committees; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; ALX Oncology: Research Funding; Astex: Research Funding; Incyte: Research Funding. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution.

Abstract Introduction: Mutations involving isocitrate dehydrogenase 1/2 (IDH) are known oncogenic drivers in hematological malignancies, conferring neomorphic enzymatic activity to IDH 1/2, resulting in the oncometabolite, 2-hydroxyglutarae (2-HG). 2-HG in turn suppresses TET activity, making IDH and TET2 mutations synthetically lethal and almost mutually exclusive. The frequency of IDH mutations in CMML is <10% and their prognostic impact remains unclear. We carried out this study in a large database of molecularly annotated CMML patients to better define the clinical profile and prognostic impact of these mutations. Methods: After IRB approval, CMML patients from the Mayo Clinic, Minnesota and the Moffitt Cancer Center (MCC), Tampa, Florida, were included in the study. All patients had bone marrow (BM) biopsies with cytogenetics and molecular genetics done either at diagnosis, or at first referral. Clinical and mutational data were abstracted and retrospectively analyzed. Overall survival (OS) was calculated from date of CMML diagnosis to date of death/last follow, while AML-free survival (AML-FS) was calculated from date of CMML diagnosis to date of leukemic transformation (LT). Patients that had undergone allogeneic HCT were excluded from the study (n=3). Statistical analysis was carried out using the Blue Sky software. Results: Six hundred and forty four patients were included in the study (Mayo Clinic-357, MCC- 287), median age 71 years (range, 20-95 years), 67.8% being male. Forty-three (6.7%) patients had IDH mutations, 35 (82%) IDH2 and 8 (18%) IDH1; of which, 34 (97%) involved the IDH2R140 hotspot and 5 (62.5%) involved the IDH1R132 hotspot, respectively. The median variant allele fractions (VAF) for IDH1 mutations was 41% (range, 8-46%) and for IDH2 mutations was 46% (range, 7-70%). There were no significant demographic or clinical differences between IDH mutant and wild type CMML patients, with the exception that IDH mutant CMML patients were less likely to be thrombocytopenic (p=0.006), were less likely to have TET2 co-mutations (14% vs 53.2%; p<0.001) and were more likely to have SRSF2 co-mutations (69.8% VS 40.3%; p<0.001). Importantly there were no differences in proliferative or dysplastic subtypes (p=0.3), cytogenetic (p=0.12) and molecular risk stratifications (p=045). There were also no significant demographic or clinical differences between IDH1 vs IDH2 mutant CMML patients. Six (14%) IDH mutant CMML patients had TET2 co-mutations; 5 (83%) with IDH2R140Q (median VAF-28%;all male) and 1 (17%) with IDH1R132H (VAF-44%;female) (Figure 1). Five (11%) IDH2 mutant patients were treated with enasidenib (IDH2 inhibitor), none with a durable response, while none of the IDH1 mutant patients received targeted therapy. At last follow up (median 18 months), 337 (52%) deaths and 119 (18.5%) LT have been documented, with IDH mutant patients having a higher LT rate (30.2% vs 17.6%, p=0.04) compared to wildtype patients. The median OS of the entire cohort was 35 months, with no difference in OS between IDH mutant and wild type patients (34.5 vs 35 months, p=0.12), with IDH1 mutant patients having a shorter OS in comparison to IDH2 mutant patients (31 vs 37 months; p=0.005- Figure 1). IDH mutant CMML patients also had a shorter AML-FS in comparison to wild type patients (36.6 vs 210 months, p=0.005), with there being no differential impact on AML-FS of IDH1 vs IDH2 mutations (p=0.26, Figure 1). Conclusions: IDH mutations are infrequent in CMML (7%), with IDH2 mutations being more common than IDH1 mutations (80 vs 20%). IDH mutations co-occur very infrequently with TET2 mutations (14%), with IDH mutant patients being less likely to have thrombocytopenia and more likely to have SRSF2 co-mutations. IDH mutations negatively impacting AML-FS without a significant impact on OS. Prospective clinical trials testing the safety and efficacy of IDH1/2 inhibitors in CMML are much needed. Figure 1 Figure 1. Disclosures Komrokji: AbbVie: Consultancy; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees; Taiho Oncology: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy, Speakers Bureau; Acceleron: Consultancy; BMSCelgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Geron: Consultancy. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution. Padron: BMS: Research Funding; Stemline: Honoraria; Taiho: Honoraria; Kura: Research Funding; Incyte: Research Funding; Blueprint: Honoraria. Patnaik: StemLine: Research Funding; Kura Oncology: Research Funding.

Abstract CONTEXT AND OBJECTIVE: Refractory anemia with ring sideroblasts and thrombocytosis (RARS-T) is a provisional entity included in the MDS/MPN overlap syndromes, with features of refractory anemia with ring sideroblasts (RARS) and thrombocytosis (platelet count ³ 450 x 10/L), recently renamed MDS/MPN with ring sideroblasts and thrombocytosis. Management of RARS-T is extrapolated from MDS and MPN treatment options namely for alleviating symptoms of anemia and, occasionally, for thrombocytosis. Individual case reports have suggested that lenalidomide may be effective in treating RARS-T. In non-del 5q, low-risk MDS, 25% of patients (pts) became red blood cell transfusion independent (RBC-TI). In the phase II study of lenalidomide in non-del 5q lower risk MDS, 35% of pts with RARS treated with lenalidomide achieved RBC-TI. The primary objective of this study is to evaluate a series of pts with RARS-T and compare hematological improvement (HI) responses among different treatment options. METHODS: This is a retrospective, single-center study. Patients with RARS-T were identified from the MDS Database at Moffitt Cancer Center. Samples for somatic mutation testing were obtained at time of referral. Descriptive statistics were used to describe baseline characteristics and response rates to treatment. The primary endpoint was HI using international working group criteria (IWG) 2006. The median overall survival (OS) was calculated from time of diagnosis. RESULTS: We identified 33 pts with RARS-T. The mean age was 67 years (38-80), 15 pts were male (45.5%), and majority were Caucasians (n=27, 82%). According to international prognostic scoring system (IPSS), 24 patients (73%) were low risk and 9 pts (27%) were int-1 risk. By revised IPSS, 11 pts (33%), 18 (55%), 3 (9%), and 1 (3%) were very low, low, intermediate, and high risk, respectively. The mean WBC was 7.7 (2.8-25.7), the mean Hemoglobin was 9.9 g/dl (7.3-12.4), and the mean platelet count was 682 (372-1644). Among 20 pts who had Next generation sequencing for somatic mutations at time of referral, 19 pts (95%) had SF3B1 mutation and 4 pts (20%) had JAK-2 V617 F mutation. The median OS was 90 mo (95% CI 47-134). For pts with JAK-2 V617F mutations, the median OS was 110 mo versus 74 mo for those with wild type (p=0.24). The rate of AML transformation was 3% (one patient only). Among the 33 pts, 8 pts had no treatment (observation only), 22 pts (67%) received erythroid stimulating agent (ESA) prior to any active therapy, 2 patients received lenalidomide with no prior ESA, and 1 pt ruxolitinib with no prior ESA. Twelve pts (36%) received lenalidomide (7 as first line active therapy after ESA and 2 without prior ESA exposure) and 13 pts (33%) received azacitidine (6 as first line active therapy after ESA). The HI rate for ESA was 36% (12/22) with a median duration of ESA treatment of 33mo. The HI rate for lenalidomide was 50% (6/12) with a median duration of lenalidomide treatment of 10.3mo. Among the 9 pts who received lenalidomide as first line active therapy (2 pts without prior ESA exposure) HI rate was 56%. The HI rate for azacitidine was 15% (2/13) with a median duration of azacitidine treatment of 6.3mo. CONCLUSIONS: Pts with RARS-T had favorable outcomes, with long overall survival and a low rate of AML transformation. Rates of hematological improvement with the use of azacitidine were lower than expected. Lenalidomide is an effective treatment option and should be considered when treating patients with RARS-T. Figure. Figure. Disclosures Sallman: Celgene: Research Funding, Speakers Bureau. List:Celgene: Research Funding. Komrokji:Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau.

Abstract Introduction: CMML is a clinically heterogeneous myeloid neoplasm hallmarked by the coexistence of dysplastic and proliferative patho-clinical features, which can include cytopenias, constitutional symptoms, splenomegaly, and leukocytosis. However, according to the FAB classification schema, CMML may be differentiated in dysplastic and proliferative subgroups only by the presence of leukocytosis in the latter (WBC ≥13 x 109/L). We hypothesize that incorporation of other clinically discriminating features may yield a more informative CMML stratification system. To address this, we propose three distinct CMML categories and explore their clinical relevance leveraging our existing international CMML database (Padron E et al. Blood Cancer J. 20151). Method: 1622 WHO-defined CMML cases diagnosed between 1973 and 2014 were collected from eight large cancer centres that include up to 80 discrete data elements as previously described1. Cases were placed into three clinically distinct groups and the Pearson Chi-Square test and the Kruskal-Wallis test were applied respectively to compare categorical and continuous characteristics. The Kaplan-Meier (KM) method was used to estimate median OS and the log rank test was used to compare survival curves.Cox models whereapplied to obtain univariate and adjusted hazard ratios. Identification of optimal cut-off values for continuous variable was supported by graphical inspection of martingale residuals from the null Cox model. Statistical analyses were done in SPSS v23 and R v.3.3.0. Results: We propose three categories to delineate clinically distinct CMML subtypes: (1) Myelodysplastic (MD)-CMML: WBC≤10 x 109/L, PB-immature myeloid cells (IMC) = 0%, no splenomegaly (2) MD/MP-CMML: WBC 10-20 x 109/L or WBC ≤10 x 109/L but PB-IMC>0% and/or splenomegaly (3) Myeloproliferative (MP)-CMML: WBC >20 x 109/L. A recursive partitioning approach was used to identify the WBC cut points, with splenomegaly and IMC added to more accurately depict the MPN aspect of CMML. Numbers of patients included in the MD-, MD/MP-, and MP-CMML subcategories were 319 (19.7%), 789 (48.6%) and 514 (31.7%), respectively (Table 1). According to the FAB criteria, the MD/MP group included 521 (66%) MD- (WBC ≤13 x 109/L) and 268 (34%) MP-CMML (WBC>13 x 109/L) patients suggesting that the proposed classification clinically reclassifies FAB-defined CMML. Within the MD/MP group, 344 patients (21.2%) had a WBC <10 x 109/L but with IMC>0% and/or splenomegaly. Comparison of overall survival (OS) among proposed groups demonstrated that this classification schema was capable of discriminating the CMML natural history (Figure 1). In comparison to MD/MP-CMML, the unadjusted OS Hazard Ratio (HR) was 0.60 (95% CI 0.49-0.73) for MD-CMML and 1.57 (95% CI 1.36-1.81) for MP-CMML (p<0.001 for both). This difference was retained after adjusting for BM-Blasts (< vs ≥5%), IPSS or CPSS cytogenetics (High-risk vsInterm/low-risk), and elevated levels of LDH (HR was 0.73 with 95% CI 0.57-0.93, p=0.011 for MD-CMML, and 1.33 with 95% CI 1.12-1.58, p=0.001 for MP-CMML, respectively). We next explored whether each proposed group may have distinct variables that uniquely govern its prognosis. Peripheral blood blasts >5%, RBC- and PLT-transfusion-dependence predicted poor OS only in the MD and MD/MP-subcategories (p<0.005), but had no impact in the MP-CMML. Male gender and elevated LDH were only significantly associated with prognosis in the MD/MP group (p<0.05 and p<0.005, respectively) while absolute lymphocyte count >2.5 x 109/L and absolute monocyte count >10 x109/L were uniquely predictive for shorter survival in the MP-CMML subcategory (p<0.005). Notably, we additionally identified gene mutations that uniquely predicted prognosis in each proposed group (Table 2). Conclusions: We demonstrate that our proposed 3-group clinical classification schema is capable of independently stratifying prognosis. Further, our analysis identified clinical and genetic variables that uniquely govern each group's prognosis, suggesting independent clinical behaviour. Further investigations are warranted to validate these groups. Disclosures Jabbour: ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Fenaux:Celgene, Janssen,Novartis, Astex, Teva: Honoraria, Research Funding. Kantarjian:Bristol-Myers Squibb: Research Funding; ARIAD: Research Funding; Amgen: Research Funding; Pfizer Inc: Research Funding; Delta-Fly Pharma: Research Funding; Novartis: Research Funding. Komrokji:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Speakers Bureau.

Abstract Introduction: CMML is a heterogeneous malignancy characterized by peripheral monocytosis and a propensity for AML transformation. Given the low incidence of CMML and its broad range complexity, a large disease-specific data set is necessary to discern independent co-variates predictive of disease behavior. Several prognostic models derived from smaller data sets have been developed to stratify CMML patients into distinct groups that are predictive for OS. However, the validity of these models in a large international data set has never been investigated. Here, we present the largest International CMML data base established to date and report key baseline characteristics predictive for disease outcome. We externally validate and perform a detailed statistical comparison between the International Prognostic Scoring System (IPSS), Revised (R)-IPSS, Global MD Anderson Scoring System (MDASC), MD Anderson Prognostic Score (MDAPS), Dusseldorf Score (DS), Mayo, and Spanish Scoring Systems (CPSS). Methods: Data were abstracted by each contributing institution and deposited for central data review at the Moffitt Cancer Center. Manual review of cases was performed to ensure data quality prior to analysis. Only WHO defined CMML was included. The primary objective of this study was to establish an international CMML data set and validate the above models calculated at the time of presentation to each center. All prognostic models were calculated as previously described. The Kaplan–Meier (KM) method was used to estimate median OS and the log rank test was used to compare KM survival estimates with SPSS version 21.0. Random Forest Survival (RSF) and ROC analysis was done with R. Results: Between July 1981 and June 2014, 1832 CMML patients were captured in the International CMML database. Median age at diagnosis was 70 (16-93) years with a male (67%) predominance. By the WHO classification, the majority of patients had CMML-1 (79.9% vs. 20.1%) and most patients were evenly subcategorized as MPN-CMML (49.8%) versus MDS-CMML (50.2%) by FAB criteria. Splenomegaly was demonstrable in 25%. Most patients had favorable cytogenetics by both IPSS (70%) and CPSS (67.6%) classification schemas. Mean BM blast % was 5.6 and mean monocyte count was 4.85x103/dL. Median OS of the entire data set was 31.6 months. All tested prognostic models were valid and able to predict OS (p<0.0001). To compare relative model performance, 1013 complete cases with sufficient data to calculate all risk models were compared using time dependent receiver operator characteristic (ROC) curves and their area under the curves (AUC). ROC curves were calculated for OS at 36 months. The R-IPSS model had the highest AUC (0.694) while the DS model had the lowest (0.635). The difference in AUC between the R-IPSS and DS models was statistically significant (p = 0.003) whereas there was no significant difference from other models tested. To determine which models were most vulnerable to reclassification from low-risk to higher risk, we calculated a vulnerability score defined by the number of models able to up-stage low-risk disease in more than 15% of cases. Using this metric, the Mayo and MDASC scores were least vulnerable to up-staging by other models. Lastly, we used an orthogonal method for determination of variable importance (RSF for OS at 36 months). RSF is an ensemble method of classification and regression tree methodology. Ranking of variables by their weighted importance determined by this procedure identified the top five variables were hemoglobin, circulating blasts, platelet count, number of cytopenias, and karyotype. A new risk model was developed using the results of the RSF with an AUC of 0.720, which was comparable but not significantly different than other modern scoring systems. Conclusions: This represents the largest international CMML-specific data set derived from eight unique centers of excellence. All modern prognostic models were valid and performed comparably. Despite novel strategies for variable importance discovery, an improved model could not be constructed. This data suggest that the power of clinical variables has reached an asymptote and highlights the need for incorporation of molecular and other novel biological features. This data set is currently being populated with molecular data and is being leveraged to test published CMML molecular prognostic models which will be disclosed at the time of presentation. Disclosures Garcia-Manero: Epizyme, Inc: Research Funding. Fenaux:Celgene, Janssenm, Novartis: Research Funding. Kantarjian:ARIAD, Pfizer, Amgen: Research Funding. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Amgen Corp: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim Corp: Membership on an entity's Board of Directors or advisory committees.