Padron, Eric

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 Background Myelodysplastic syndromes (MDS) and chronic myelomonocytic leukemia (CMML) are mainly diagnosed based on morphological changes in the bone marrow. The diagnosis can be challenging in patients (pts) with pancytopenia with minimal dysplasia, and is subject to inter-observer variability. Somatic mutations can be identified in either disease but no genes, in isolation or in combination, are specific for disease phenotype. We developed a geno-clinical model that uses mutational data, peripheral blood values, and clinical variables to predict an MDS vs. CMML diagnosis in pts who presented with cytopenias, in the absence of bone marrow biopsy results. Method We combined genomic and clinical data from 1897 pts treated at our institution (593) and the Munich Leukemia Laboratory (1304). Pts were diagnosed with MDS or CMML according to 2008 WHO criteria. Diagnosis of MDS and CMML was confirmed by independent hematopathologists that were not associated with the study. A genomic panel of 40 genes commonly mutated in myeloid malignancies was included. The initial cohort was randomly (computer generated) divided into learner (80%) and validation (20%) cohorts. Multiple machine learning algorithms were applied to predict the phenotype. Feature extraction algorithms were used to extract genomic/clinical variables that impacted the algorithm decision and to visualize the impact of each variable on phenotype. Prediction performance was evaluated according to the area under the curve of the receiver operator characteristic (ROC-AUC) and confusion/accuracy matrices. Results Of 1897 pts included, 1368 pts had MDS and 529 had CMML. The median age for the entire cohort was 72 years (range, 11-102); 37% were female. The median white blood cell count (WBC) was 5.1x109/L (range, 0.60-176), absolute monocyte count (AMC) 0.19 x109/L (range, 0-96), absolute lymphocyte count (ALC) 0.77x109/L (range, 0-62), absolute neutrophil count (ANC) 2.44x109/L (range, 0-170), hemoglobin (Hgb) 10.2 (range, 3.9-19.6), and platelet (Plt) count 111x103/mL (range 2-1491). The most commonly mutated genes in all pts were: TET2 (33%), ASXL1 (26%), SF3B1 (21%), SRSF2 (16%), RUNX1 (12%), DNMT3A (10%), CBL (7%), U2AF1 (7%), STAG2 (6%), EZH2 (6%), ZRSR2 (6%), NRAS (6%). In CMML, they were: TET2 (51%), ASXL1 (43%), SRSF2 (25%), RUNX1 (18%), CBL (16%), KRAS (12%), NRAS (11%), EZH2 (9%), JAK2 (6%), U2AF1 (5%), SF3B1 (4%), and DNMT3A (3%). In MDS, they were: TET2 (27%), SF3B1 (24%), ASXL1 (21%), SRSF2 (13%), DNMT3A (12%), RUNX1 (10%), STAG2 (8%), U2AF1 (8%), ZRSR2 (7%), TP53 (7%), BCOR (5%), and EZH2 (5%).The median total number of mutations/sample was 2 (range 0-27) for all pts, 2 (range 0-8) for CMML, and 2 (range 0-27) for MDS. A set of 83 genomic/clinical variables were evaluated and several feature extraction algorithms were used to identify the least number of variables that have the most significant impact on the algorithm's decision. These variables included: AMC, ALC, TET2, ANC, ASXL1, SF3B1, Hgb, number of mutations/sample, AEC, age, Plt, splenomegaly, RUNX1, NRAS, CBL, U2AF1, STAG2, DNMT3A, TP53, EZH2, SRSF2, and ZRSR2, Figure 1. When applying the model to the validation cohort, the ROC-AUC was .98 with an accuracy of 94%, with other statistical values as follows: specificities CMML 93%, MDS 96%; sensitivities CMML 96%, MDS 93%; positive predictive values CMML 84%, MDS 98%; negative predictive values CMML 98%, MDS 84%. Individual pt data can also be entered into the model, with a probability of whether the diagnosis is MDS vs. CMML provided along with the impact of each variable on the decision, as shown in Figure 1. When the analysis was restricted to mutations only, the accuracy of the model dropped dramatically (77%, ROC-AUC .85). Conclusions We propose a novel approach using interpretable, individualized modeling to predict MDS vs. CMML phenotypes based on genomic and clinical data without the need for bone marrow biopsy data. This approach can aid clinicians and hematopathologists when encountering pts with cytopenias and a diagnosis suspicious for MDS vs. CMML. The model also provides feature attributions that allow for quantitative understanding of the complex interplay among genotype, clinical variables, and phenotype. Disclosures 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; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees. Meggendorfer:MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Savona:Boehringer Ingelheim: Consultancy; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees, Research Funding. Gerds:CTI Biopharma: Consultancy; Celgene: Consultancy; Apexx Oncology: Consultancy; Incyte: Consultancy. Sallman:Celgene: Research Funding, Speakers Bureau. Komrokji:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Maciejewski:Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Apellis Pharmaceuticals: Consultancy; Ra Pharmaceuticals, Inc: Consultancy; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Nazha:MEI: Consultancy.

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 Background: Most patients with chronic myelomonocytic leukemia (CMML) are elderly and age >65 years is an adverse prognostic factor (JCO 2013;31:2428). Our objective in the current study was to examine risk factors for survival and treatment outcome in younger patients (age ≤65 years), with emphasis on molecular and cytogenetic markers. Methods: DNA analysis for SRSF2, SF3B1, U2AF1, ASXL1 and SETBP1 mutations was carried out on specimens obtained at time of diagnosis. We used both conventional and revised (see accompanying ASH 2014 abstract) cytogenetic risk models to query their application in the current study population. Treatment data included outcome of allogeneic stem cell transplant (HCT) and treatment with hypomethylating agents (HMA), including azacitidine (AZA) and decitabine (DAC). Results: Survival: 259 patients (median age 59 years, range 18-65; 75% males) with WHO-defined CMML and age≤ 65 years were included in the study: 108 from the French consortium, 81 Mayo Clinic, 47 Moffitt Cancer Center and 23 University of Milan; 217 (84%) patients had CMML-1 and the remainder CMML-2. At a median follow-up of 26 months, 124 (48%) deaths and 79 (31 %) leukemic transformations were documented. Mutational frequencies were 34% (61/178) for ASXL1, 45% (74/165) for SRSF2, and 4% (7/168) for SETBP1. Cytogenetic data was available in 248 of 259 study patients; 56 (23%) patients displayed abnormal karyotype and the revised cytogenetic risk designations included 196 (80%) low, 29 (12%) intermediate, and 23 (9%) high. In univariate analysis, lower hemoglobin (p<0.0001), higher WBC (p=0.005), higher monocyte count (AMC) (P=0.004), lower platelet count (p=0.004), presence of circulating immature myeloid cells (IMC) (p=0.004), presence of circulating blasts (p<0.0001), bone marrow (BM) blasts ≥10% (p=0.005), WHO morphological subtype (p=0.0003), Spanish cytogenetic risk stratification (p=0.0002), revised Mayo cytogenetic risk stratification (p<0.0001), SRSF2 (p=0.03) and ASXL1 (p=0.03) mutational status predicted shortened survival. In multivariable analysis, lower hemoglobin (p<0.0002), presence of circulating blasts (p<0.0001), SRSF2 (p=0.03) and ASXL1mutational status (p=0.04) retained their negative prognostic impact. A similar analysis identified circulating IMC (p<0.0001), presence of circulating blasts (p<0.0001), and BM blast % (p=0.0002) as predictors of blast transformation (BT). Response to hypomethylating agents: Seventy-two (28%) patients received HMA (AZA-46, DAC-26, both-6). In the AZA group, median number of cycles was 5 (range; 1-44), median duration of therapy 5 months (range;1-59) with 4 (6%) receiving DAC on progression and 26 (42%) proceeding with HCT. Over-all response rate (ORR) was 25% (6/24) and included complete response (CR) in 2 (8%) patients, partial response (PR) in 2 (8%) and hematological improvement in 2 (8%). In the DAC group, median number of cycles was 6 (2-25), median duration of therapy 5 months (range;1-49), with 2 (8%) receiving AZA on progression; ORR was 50% (7/14) and included CR in 4 (29%) patients, PR in 1 (7%) and HI in 2 (14%). ASXL1 (p=0.4), SRSF2 (p=0.7) mutational status or karyotype (p=0.5 for Spanish and 0.3 for revised Mayo) did not correlate with response to HMA. Allogeneic stem cell transplant: 50 patients underwent HCT. Conditioning regimen was reported in 41 patients and included myeloablative (MA) in 14 (34%) (median age 51 years, 27-62) and reduced intensity (RIC) in 27 (66%) (median age 48 years, 18-65). Donor source was documented in 40 patients and included matched related in 19 (48%), matched unrelated in 12 (30%), mismatched unrelated in 4, haploidentical in 2 and umbilical cord transplant in one. 27 (63%) patients had chronic GVHD (50% in MA group and 80% in the RIC group). Among 40 evaluable patients, 23 (57%) achieved CR, 8 (21%) relapsed and 8 (21%) died from transplant-related complications. ASXL1 (p=0.9), SRSF2 (p=0.5) mutational status or karyotype (p=0.2 for Spanish and 0.3 for revised Mayo) did not affect transplant outcome. Conclusions: Survival in young CMML patients is poor and even worse in the presence of SRSF2 or ASXL1 mutations, anemia or circulating blasts. Treatment outcome was more impressive with transplant than it was with HMA and neither was influenced by karyotype or ASXL1/SRSF2 mutational status. Disclosures No relevant conflicts of interest to declare.

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.

Abstract Myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPN) are hematologically diverse stem cell malignancies sharing phenotypic features of both myelodysplastic syndromes and myeloproliferative neoplasms. There are currently no standard treatment recommendations for most adult patients with MDS/MPN. To optimize efforts to improve the management and disease outcomes, it is essential to identify meaningful clinical and biologic end points and standardized response criteria for clinical trials. The dual dysplastic and proliferative features in these stem cell malignancies define their uniqueness and challenges. We propose response assessment guidelines to harmonize future clinical trials with the principal objective of establishing suitable treatment algorithms. An international panel comprising laboratory and clinical experts in MDS/MPN was established involving 3 independent academic MDS/MPN workshops (March 2013, December 2013, and June 2014). These recommendations are the result of this collaborative project sponsored by the MDS Foundation.