Onida, Francesco

PURPOSE Chronic myelomonocytic leukemia (CMML) is a rare myeloid neoplasm characterized by clinical heterogeneity and is associated with poor outcomes. To date, limited molecular information has been incorporated into disease classification and risk stratification. We aimed to integrate genomic features into the clinical decision-making process for CMML. PATIENTS AND METHODS We analyzed a retrospective cohort of 3013 patients with CMML (training set) and a prospective population of 516 patients (validation set). Using an innovative framework for multimodal data analysis, we developed molecular-based disease taxonomy and prognostication. RESULTS Unsupervised clustering identified nine entities with distinct genomic features and outcomes ( P < .001), including splicing machinery, transcription factors, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures. Notably, 15% of patients showed molecular/clinical overlap with other myeloid neoplasms. We integrated molecular and clinical information to build the international CMML Prognostic Scoring System (iCPSS), incorporating mutations in nine genes together with hematologic parameters and cytogenetic abnormalities. The iCPSS identified five groups with distinct probability of overall and leukemia-free survival in both training and validation cohorts ( P < .001), outperforming existing prognostic models. Importantly, 55% of patients were reassigned to higher or lower risk groups by the iCPSS. Decision analysis demonstrated that iCPSS could refine the optimal timing of allogeneic transplantation at the individual level; compared with conventional prognostic tools, iCPSS-based decision modeling changed transplantation strategy in 31% of cases, resulting in a significant gain-in-life expectancy for eligible patient population ( P < .001). A federated learning platform was implemented to enable continuous, privacy-preserving model update across multiple centers. CONCLUSION Molecular information improves CMML classification and prognostication, supports more effective clinical decision making, and potentially refines the design of clinical trials.

We sought to develop a survival model in chronic myelomonocytic leukemia (CMML) that is primarily based on clinical variables and examine additional impact from mutations and karyotype. 457 molecularly-annotated patients were considered. Multivariable analysis identified circulating Blasts ≥2% (1 point), Leukocytes ≥13 x 109/L (1 point), and severe (2 points) or moderate (1 point) Anemia as preferred risk variables in developing a clinical risk Stratification Tool for overall survival (OS), acronymized to "BLAST": low-risk (0 points; median 63 months); intermediate-risk (1 point; median 28 months; HR 2.2, 95% CI 1.6-3.0), and high-risk (2-4 points; median 13 months; 5.4, 4.1-7.3); the corresponding 3/5 year OS rates were 68%/53%, 43%/18%, and 12%/1%. BLAST model performance (AUC 0.77/0.85 at 3/5-years) was shown to be comparable to that of the molecular CMML-specific prognostic scoring system (CMML-mol; AUC 0.73/0.75) and the international prognostic scoring system-molecular (IPSS-M; AUC 0.73/0.74). Multivariable analysis of mutations and karyotype identified PHF6MUT and TET2MUT as being "favorable" and DNMT3AMUT, U2AF1MUT, BCORMUT, SETBP1MUT, ASXL1MUT, NRASMUT, PTPN11MUT, RUNX1MUT, TP53MUT, and adverse karyotype, "unfavorable". Molecular information was subsequently encoded in a combined clinical-molecular risk model (BLAST-mol; AUC 0.80/0.86 at 3/5-years) that included the aforementioned BLAST clinical risk variables and a 3-tiered molecular risk score. BLAST and BLAST-mol were subsequently validated by two separate external cohorts. Independent risk factors for blast transformation included DNMT3AMUT, ASXL1MUT, PHF6WT, leukocytes ≥13 x 109/L, and ≥2% circulating or ≥10% bone marrow blasts. The current study proposes an easy to implement, globally applicable, and molecularly adaptive risk model for CMML.

Abstract “Nonclassical” myeloproliferative neoplasms (MPNs) and myelodysplastic/myeloproliferative neoplasms (MDS/MPNs) represent a heterogeneous group of malignancies characterized by a wide range of clinical manifestations. Unlike classical MPNs, there is no standardized management approach for these conditions, particularly concerning the indications for and management of allogeneic hematopoietic cell transplantation. To address this gap, the European Society for Blood and Marrow Transplantation (EBMT) Practice Harmonization and Guidelines (PH&G) Committee and the Chronic Malignancies Working Party (CMWP) have collaborated to develop shared guidelines aimed at optimizing the selection and management of patients with these rare forms of neoplasms. A comprehensive review of the literature from the publication of the revised fourth edition of the (2016) World Health Organization classification onward was conducted. A multidisciplinary group of experts in the field convened to produce this document, which was developed through multiple rounds of draft circulation. Key recommendations include the early identification of potential transplant candidates, particularly in cases of chronic neutrophilic leukemia, chronic eosinophilic leukemia (CEL)/CEL, not otherwise specified (CEL-NOS), myeloid/lymphoid neoplasm with eosinophilia and tyrosine kinase gene fusions with FGFR1, JAK2, ABL1, and FLT3 rearrangements, MDS/MPN with neutrophilia/atypical chronic myeloid leukemia, and MDS/MPN, NOS. For patients with MPN, NOS/MPN unclassifiable, standard recommendations for myelofibrosis should be applied. Similarly, in MDS/MPN with thrombocytosis, transplantation is recommended on the basis of established MDS guidelines. Given the current lack of robust evidence, this document will serve as a valuable resource to guide future research activities, providing a framework for addressing critical unanswered questions and advancing the field.

Background. Study of myeloid neoplasms (MN) has been rapidly transformed by genome characterization. Gene mutations have been reported to influence disease phenotype and risk of disease progression, and the evaluation of mutational status can provide valuable insights to improve the decision-making process. In this international study, we evaluated the clinical impact of mutational screening in patients with CMML, which is characterized by significant clinical and genomic heterogeneity, including a substantial proportion of patients with poor outcomes and unmet needs. Currently, only limited molecular information has been incorporated into CMML classification and prognostication. Methods. We retrospectively collected 3182 CMML patients. We used MOSAIC, an AI-based framework for multimodal analysis in rare cancers (PMID: 38875514) to develop innovative, molecular-based tools for classification and prognostication. An independent validation of the results on 516 prospectively collected patients was performed. Results. Bayesian Networs and Hierarchical Dirichlet Processes were used to identify genomic associations and define a CMML molecular classification. We identified 11 clusters with distinct clinical and genomic features, including splicing machinery, signal transduction and tyrosine kinase pathways aberrations, and high-risk molecular signatures (i.e., AML-like and TP53 mutations). Up to 15% of patients showed clear genomic overlap with other MN. Importantly, extensive multi-color flow cytometry on T lymphocytes, NK and myeloid cells (BD FACSymphony A5 Cell Analyzer) revealed specific immunologic and inflammation signatures associated with molecular subgroups. We then integrated molecular and clinical information to build an international CMML Prognostic Scoring System (iCPSS). We assessed the performance of different statistical and AI-based methods; L1-penalized Cox Model resulted as the best performing method. Selected features included hematological parameters (WBC, Hb, PLT and marrow blasts), cytogenetic abnormalities, and mutations in 10 genes (ASXL1, DNMT3A, EZH2, NRAS, RUNX1, SETBP1, STAG2, TET2, TP53, U2AF1). By Bayesian thresholds optimization, we identified 5 risk classes (i.e., very-low, low, intermediate, high and very-high) with median overall survival (OS) ranging from 99 to 9 months (P&lt;.001), and median leukemia-free survival (LFS) ranging from 210 to 18 months (P&lt;.001). iCPSS provided better patient discrimination across all clinical endpoints compared to currently available prognostic tools, with a Concordance Index (CI) for OS of 0.75 versus 0.62-0.64, respectively. Notably, compared to available scoring systems, up to 40% of patients were reassigned to higher or lower risk classes by the iCPSS. External validation on an independent prospective cohort confirmed that the performance of iCPSS is superior to existing prognostic models (CI for OS 0.71 vs 0.54-0.61, respectively) To demonstrate the clinical utility of iCPSS, we focused on 753 patients who underwent allogeneic stem cell transplantation (HSCT). In multivariable analysis, iCPSS stratified the probability of OS post-HSCT (P&lt;.001) and identified groups of patients with different probabilities of disease relapse, ranging from 9% to 62% (P&lt;.001). We employed a clinical- and genomic-based decision support system (PMID: 38723212) to determine the optimal timing of HSCT. Preliminary analyses indicated that higher-risk patients according to iCPSS (i.e., intermediate, high, and very high) benefit from an immediate HSCT strategy. Lower-risk patients (i.e., very low and low) had life expectancy maximized with delayed HSCT. Modeling decision analysis using iCPSS compared to conventional scores, resulted in a change in transplantation policy for a significant proportion of patients (up to 25%). Conclusion. Molecular information significantly improves the classification of CMML patients, providing a basis for refining diagnostic boundaries with other MN and for a more rational inclusion of patients in clinical trials. The iCPSS demonstrated superior performance over currently available scoring systems and improved HSCT decision making process.

Abstract To determine the survival benefit of allogeneic hematopoietic cell transplantation (allo-HCT) in chronic myelomonocytic leukemias (CMML), we assembled a retrospective cohort of CMML patients 18-70 years old diagnosed between 2000 and 2014 from an international CMML dataset (n = 730) and the EBMT registry (n = 384). The prognostic impact of allo-HCT was analyzed through univariable and multivariable time-dependent models and with a multistate model, accounting for age, sex, CMML prognostic scoring system (low or intermediate-1 grouped as lower-risk, intermediate-2 or high as higher-risk) at diagnosis, and AML transformation. In univariable analysis, lower-risk CMMLs had a 5-year overall survival (OS) of 20% with allo-HCT vs 42% without allo-HCT (P &lt; .001). In higher-risk patients, 5-year OS was 27% with allo-HCT vs 15% without allo-HCT (P = .13). With multistate models, performing allo-HCT before AML transformation reduced OS in patients with lower-risk CMML, and a survival benefit was predicted for men with higher-risk CMML. In a multivariable analysis of lower-risk patients, performing allo-HCT before transformation to AML significantly increased the risk of death within 2 years of transplantation (hazard ratio [HR], 3.19; P &lt; .001), with no significant change in long-term survival beyond this time point (HR, 0.98; P = .92). In higher-risk patients, allo-HCT significantly increased the risk of death in the first 2 years after transplant (HR 1.46; P = .01) but not beyond (HR, 0.60; P = .09). Performing allo-HCT before AML transformation decreases life expectancy in lower-risk patients but may be considered in higher-risk patients.

Background: CMML is a heterogeneous disease with overlapping features of MDS and MPN. Several risk scores have been proposed in CMML, including CPSS (Such Blood 2013) that identifies 4 risk groups (low, intermediate-1 [int-1], int-2 and high risk) with median overall survival (OS) ranging from 12 months to > 5 years. HSCT is the only curative treatment in CMML, with an expected 20-40% post-transplantation OS. Current guidelines, common to MDS and CMML recommend rapidly transplanting higher-risk CMML and delaying transplant in lower-risk disease (de Witte Blood 2017). Retrospective transplant studies have shown better outcome in lower-risk CMML (Liu et al BBMT 2017). Whether this translates into a survival benefit compared to a delayed transplant strategy requires prospective studies. Multistate models based on retrospective data from transplant and non-transplant patients (pts) have addressed similar issues in MDS (Koreth JCO 2013, Della Porta Leukemia 2017). Through a large collaborative study, we address the question of optimal timing for HSCT in CMML patients through a similar large international collaborative study. Method: We retrospectively selected pts from 2 registries: International MDS/MPN Working Group (IWG cohort, Padron Blood Cancer J 2015) and EBMT with the following criteria: WHO-defined CMML, age ≤ 70y, ECOG 0-2, diagnosis after 2000, available CPSS at the time of diagnosis. Different states were considered in higher- (CPSS int-2 and high) and lower- risk (CPSS low/int-1) pts: diagnosis, AML transformation, transplantation and death. IWG data was used to estimate transition probabilities from diagnosis to AML, transplantation and death and from AML to transplantation and death. EBMT data was used for transplantation to death transitions (Figure 1). Results: 719 and 403 pts were identified in the IWG and EMBT registry, respectively (resp). Median age was 64 (range 16-70) in IWG and 58 (19-70) years in EBMT cohort. Patients were male in 69% and 67% of IWG and EBMT cohorts, respectively (resp). CPSS was low in 22% and 13%, int-1 in 31% and 31%, int-2 in 40% and 45% and high in 8% and 11% of the IWG and EBMT pts, resp. In the IWG cohort, the 1 year cumulative incidence of transformation into AML were 7.3% and 18.2% in lower and higher risk patients, resp. Among the 719 pts from IWG, 102 received HSCT. At the time of diagnosis the expected life time of higher risk (int-2 and high) patients was 25.2 months while it was 44.1 months in lower risk (int-1 and low) patients. Table 1 reports gain or loss in expected life time (until 60 months after diagnosis) for transplantation at different intervals from diagnosis (within 6, 12, 18 and 24 months) for patients who have survived and not transformed to AML until the prediction time point, taking into account CPSS at diagnosis. Expected life time is also given separately for higher risk pts after transformation into AML. There was a modest gain of life expectancy with transplantation in higher risk patients (CPSS int-2 and high) while it was not the case in lower risk patients. The survival gain in higher risk pts with HSCT rose from 1.11 at 6 months to 3.4 months at 24 months. There was also a survival benefit of HSCT in higher risk pts who were transformed to AML during their follow-up which was even higher in terms of gain of months: from 9.89 month if HSCT performed within 6 months to 14.25 months if performed within 18 months. Of note, the loss of survival in lower risk patients who underwent HSCT decreased over time: 11.1 months if HSCT is performed within 6 months and 3.7 months if HSCT is performed within 24 months, probably owing to transition to higher-risk CMML at this stage in some pts, a transition that could not be captured in the present dataset. Conclusion: For the first time, using a multistate model, we could provide evidence that the transplant benefit in CMML is restricted to higher-risk pts, in line with findings in MDS and current guidelines. More analyses will be performed to analyze other subgroups of patients, especially regarding their risk (other classification than CPSS, the role of somatic mutation…). More studies are needed to analyze the role of pre HSCT treatment and its potential impact on post-transplant outcome. Disclosures Robin: Novartis Neovii: Research Funding. Beelen:Medac GmbH Wedel Germany: Consultancy, Honoraria. Fenaux:Celgene Corporation: Honoraria, Research Funding; Astex: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Aprea: Research Funding. Kroeger:Riemser: Research Funding; Novartis: Honoraria, Research Funding; Medac: Honoraria; DKMS: Research Funding; Neovii: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; JAZZ: Honoraria; Sanofi-Aventis: Honoraria. Nazha:Tolero, Karyopharma: Honoraria; MEI: Other: Data monitoring Committee; Novartis: Speakers Bureau; Jazz Pharmacutical: Research Funding; Incyte: Speakers Bureau; Daiichi Sankyo: Consultancy; Abbvie: Consultancy. Rampal:Agios, Apexx, Blueprint Medicines, Celgene, Constellation, and Jazz: Consultancy; Constellation, Incyte, and Stemline Therapeutics: Research Funding. Finke:Riemser: Honoraria, Other: research support, Speakers Bureau; Neovii: Honoraria, Other: research support, Speakers Bureau; Medac: Honoraria, Other: research support, Speakers Bureau. Komrokji:Agios: Consultancy; DSI: Consultancy; pfizer: Consultancy; celgene: Consultancy; JAZZ: Consultancy; Novartis: Speakers Bureau; JAZZ: Speakers Bureau; Incyte: Consultancy. Killick:JAZZ: Honoraria; Celgene: Honoraria; NOVARTIS: Honoraria, Membership on an entity's Board of Directors or advisory committees; ALEXION: Honoraria. Blaise:Molmed: Consultancy, Honoraria; Sanofi: Honoraria; Pierre Fabre medicaments: Honoraria; Jazz Pharmaceuticals: Honoraria. Garcia-Manero:Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Patnaik:Stem Line Pharmaceuticals.: Membership on an entity's Board of Directors or advisory committees.

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.