Sanchez, Matias

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.