Abstract: Purpose: The enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase and key epigenetic regulator involved in transcriptional repression and embryonic development. Loss of EZH2 activity by inactivating mutations is associated with poor prognosis in myeloid malignancies such as MDS. More recently, EZH2 inactivation was shown to induce chemoresistance in acute myeloid leukemia (AML) (Göllner et al., 2017). Data on the frequency and prognostic role of EZH2-mutations in AML are rare and mostly confined to smaller cohorts. To investigate the prevalence and prognostic impact of this alteration in more detail, we analyzed a large cohort of AML patients (n = 1604) for EZH2 mutations. Patients and Methods: All patients analyzed had newly diagnosed AML, were registered in clinical protocols of the Study Alliance Leukemia (SAL) (AML96, AML2003 or AML60+, SORAML) and had available material at diagnosis. Screening for EZH2 mutations and associated alterations was done using Next-Generation Sequencing (NGS) (TruSight Myeloid Sequencing Panel, Illumina) on an Illumina MiSeq-system using bone marrow or peripheral blood. Detection was conducted with a defined cut-off of 5% variant allele frequency (VAF). All samples below the predefined threshold were classified as EZH2 wild type (wt). Patient clinical characteristics and co-mutations were analyzed according to the mutational status. Furthermore, multivariate analysis was used to identify the impact of EZH2 mutations on outcome. Results: EZH2-mutations were found in 63 of 1604 (4%) patients, with a median VAF of 44% (range 6-97%; median coverage 3077x). Mutations were detected within several exons (2-6; 8-12; 14-20) with highest frequencies in exons 17 and 18 (29%). The majority of detected mutations (71% missense and 29% nonsense/frameshift) were single nucleotide variants (SNVs) (87%), followed by small indel mutations. Descriptive statistics of clinical parameters and associated co-mutations revealed significant differences between EZH2-mut and -wt patients. At diagnosis, patients with EZH2 mutations were significantly older (median age 59 yrs) than EZH2-wt patients (median 56 yrs; p=0.044). In addition, significantly fewer EZH2-mut patients (71%) were diagnosed with de novo AML compared to EZH2-wt patients (84%; p=0.036). Accordingly, EZH2-mut patients had a higher rate of secondary acute myeloid leukemia (sAML) (21%), evolving from prior MDS or after prior chemotherapy (tAML) (8%; p=0.036). Also, bone marrow (and blood) blast counts differed between the two groups (EZH2-mut patients had significantly lower BM and PB blast counts; p=0.013). In contrast, no differences were observed for WBC counts, karyotype, ECOG performance status and ELN-2017 risk category compared to EZH2-wt patients. Based on cytogenetics according to the 2017 ELN criteria, 35% of EZH2-mut patients were categorized with favorable risk, 28% had intermediate and 37% adverse risk. No association was seen with -7/7q-. In the group of EZH2-mut AML patients, significantly higher rates of co-mutations were detected in RUNX1 (25%), ASXL1 (22%) and NRAS (25%) compared to EZH2-wt patients (with 10%; 8% and 15%, respectively). Vice versa, concomitant mutations in NPM1 were (non-significantly) more common in EZH2-wt patients (33%) vs EZH2-mut patients (21%). For other frequently mutated genes in AML there was no major difference between EZH2-mut and -wt patients, e.g. FLT3ITD (13%), FLT3TKD (10%) and CEBPA (24%), as well as genes encoding epigenetic modifiers, namely, DNMT3A (21%), IDH1/2 (11/14%), and TET2 (21%). The correlation of EZH2 mutational status with clinical outcomes showed no effect of EZH2 mutations on the rate of complete remission (CR), relapse free survival (RFS) and overall survival (OS) (with a median OS of 18.4 and 17.1 months for EZH2-mut and -wt patients, respectively) in the univariate analyses. Likewise, the multivariate analysis with clinical variable such as age, cytogenetics and WBC using Cox proportional hazard regression, revealed that EZH2 mutations were not an independent risk factor for OS or RFS. Conclusion EZH mutations are recurrent alterations in patients with AML. The association with certain clinical factors and typical mutations such as RUNX1 and ASXL1 points to the fact that these mutations are associated with secondary AML. Our data do not indicate that EZH2 mutations represent an independent prognostic factor. Disclosures Middeke: Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees; Roche: Membership on an entity's Board of Directors or advisory committees. Rollig:Bayer: Research Funding; Janssen: Research Funding. Scholl:Jazz Pharma: Membership on an entity's Board of Directors or advisory committees; Abbivie: Other: Travel support; Alexion: Other: Travel support; MDS: Other: Travel support; Novartis: Other: Travel support; Deutsche Krebshilfe: Research Funding; Carreras Foundation: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees. Hochhaus:Pfizer: Research Funding; Incyte: Research Funding; Novartis: Research Funding; Bristol-Myers Squibb: Research Funding; Takeda: Research Funding. Brümmendorf:Janssen: Consultancy; Takeda: Consultancy; Novartis: Consultancy, Research Funding; Merck: Consultancy; Pfizer: Consultancy, Research Funding. Burchert:AOP Orphan: Honoraria, Research Funding; Bayer: Research Funding; Pfizer: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; Novartis: Research Funding. Krause:Novartis: Research Funding. Hänel:Amgen: Honoraria; Roche: Honoraria; Takeda: Honoraria; Novartis: Honoraria. Platzbecker:Celgene: Research Funding. Mayer:Eisai: Research Funding; Novartis: Research Funding; Roche: Research Funding; Johnson & Johnson: Research Funding; Affimed: Research Funding. Serve:Bayer: Research Funding. Ehninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; Bayer: Research Funding; GEMoaB Monoclonals GmbH: Employment, Equity Ownership. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding.

Abstract: Biallelic mutations of the CEBPA gene (CEBPAbi) define a distinct entity associated with favorable prognosis; however, the role of monoallelic mutations (CEBPAsm) is poorly understood. We retrospectively analyzed 4708 adults with acute myeloid leukemia (AML) who had been recruited into the Study Alliance Leukemia trials, to investigate the prognostic impact of CEBPAsm. CEBPA mutations were identified in 240 patients (5.1%): 131 CEBPAbi and 109 CEBPAsm (60 affecting the N-terminal transactivation domains [CEBPAsmTAD] and 49 the C-terminal DNA-binding or basic leucine zipper region [CEBPAsmbZIP] ). Interestingly, patients carrying CEBPAbi or CEBPAsmbZIP shared several clinical factors: they were significantly younger (median, 46 and 50 years, respectively) and had higher white blood cell (WBC) counts at diagnosis (median, 23.7 × 109/L and 35.7 × 109/L) than patients with CEBPAsmTAD (median age, 63 years, median WBC 13.1 × 109/L; P & lt; .001). Co-mutations were similar in both groups: GATA2 mutations (35.1% CEBPAbi; 36.7% CEBPAsmbZIP vs 6.7% CEBPAsmTAD; P & lt; .001) or NPM1 mutations (3.1% CEBPAbi; 8.2% CEBPAsmbZIP vs 38.3% CEBPAsmTAD; P & lt; .001). CEBPAbi and CEBPAsmbZIP, but not CEBPAsmTAD were associated with significantly improved overall (OS; median 103 and 63 vs 13 months) and event-free survival (EFS; median, 20.7 and 17.1 months vs 5.7 months), in univariate and multivariable analyses. Additional analyses revealed that the clinical and molecular features as well as the favorable survival were confined to patients with in-frame mutations in bZIP (CEBPAbZIP-inf). When patients were classified according to CEBPAbZIP-inf and CEBPAother (including CEBPAsmTAD and non-CEBPAbZIP-inf), only patients bearing CEBPAbZIP-inf showed superior complete remission rates and the longest median OS and EFS, arguing for a previously undefined prognostic role of this type of mutation.

Abstract: Purpose Mutations of the isocitrate dehydrogenase-1 (IDH1) and IDH2 genes are one of the most frequent alterations in acute myeloid leukemia (AML) and can be found in ~20% of patients at diagnosis. Several IDH inhibitors are currently in late stage clinical development with Enasidenib, an IDH2 inhibitor, being recently approved by the FDA. Previous analyses have reported differential impact on response to chemotherapy and outcome, depending on the IDH-mutation type, co-occurring mutations and cytogenetic abnormalities, as well as the variant allele frequency (VAF) of IDH mutations. In order to better understand its prognostic role, we analyzed newly diagnosed AML patients enrolled in prospective trials of the Study Alliance Leukemia (SAL) to investigate the impact of IDH1/2 mutations on outcome. Patients and Methods All AML patients consecutively enrolled into intensive AML treatment protocols of the SAL or into the SAL registry were included in this analysis. Next-generation sequencing (NGS) on an Illumina MiSeq-system was performed to detect IDH1/2 mutations using pre-treatment samples. Overall survival (OS) and response to therapy were analyzed for all patients with intensive treatment and according to the mutational status. Results Overall, samples of 3898 patients were analyzed. The median follow-up was 91 months (95% CI 87.2 - 93.9). Patients' characteristics are shown in Tbl.1. Three-hundred twenty-nine patients (8.4%) had IDH1 mutations and 423 (11%) had IDH2 mutations; both mutations were found in 12 pts, so the overall mutation rate in IDH1 and 2 was 19% (740/3898 patients). Of the IDH1 variants, the most common ones were the R132C found in 143 patients (43%) and R132H in 137 patients (42%). For IDH2, 324 patients had the R140Q (77%) and 80 patients the R172K (19%) variant. According to the two main variants of the more common IDH2 mutations, as reported before, the IDH2 R172K was mutually exclusive with NPM1 and/or FLT3-ITD mutations. Overall, there was a trend for increased OS for patients with IDH2 R172K (26 vs. 15 months) as compared to those with R140Q. Considering only patients with a normal karyotype and no NPM1/FLT3-ITD mutation, these patients (n=27) had a highly significant better OS than patients with IDH2 R140Q (46.3 vs. 13.1 months, p=.012), supporting the findings published by Papaemmanuil et al. (NEJM 2016). In IDH1-mutated patients, we observed statistically significant differences in baseline characteristics between the two most common mutation types, IDH1 R132C and R132H. Patients carrying the R132C mutation were older (62 vs. 55 years, p=.001), had lower WBC (3.6 vs. 21 Gpt/L, p≤.001) and were less likely to have a normal karyotype (43% vs. 66%, p=.002), NPM1 (23% vs. 66%, p= 〈 .001), and FLT3-ITD mutations (8% vs. 27%, p 〈 .001) than those with the R132H variant. In univariate testing, the CR rate was also statistically significant lower in patients with IDH1 R132C (53% vs. 72%, p≤.001), with a median OS of 12.9 months compared to 17.4 months for patients with R132H variant (p=.08). In multivariate analysis including age, WBC, NPM1 and FLT3 status, and ELN risk, the CR rate was significantly lower in patients with the IDH1 R132C variant (p=.038). The median IDH VAF was 38% (range, 0.1 - 58) with no difference according to the different types of mutation. Patients with a VAF 〉 30% had a significantly higher BM blast count (73% vs 40% for VAF≤5%) and WBC (21.2 Gpt/L vs. 3.7 Gpt/L) at baseline, but there was no clear impact on CR rate or OS found in multivariate analysis. Conclusion In this large cohort of AML patients with IDH1/2 mutations, we found significant and so far not reported differences for one of the two most prominent mutations types of IDH1. The R132C variant was associated with increased age, lower WBC, and lower NPM1 and/or FLT3 co-mutation rate. Further, these patients had lower CR rates and a trend for shorter OS. For IDH2 we were able to reproduce findings on co-mutations and showed a favorable outcome for intensively treated patients with a normal karyotype and no NPM1/FLT3-ITD mutation and the IDH2 R172K variant, providing additional evidence for classification as a separate AML entity. Disclosures Middeke: Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Membership on an entity's Board of Directors or advisory committees. Rollig:Bayer: Research Funding; Janssen: Research Funding. Kramer:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; Daiichi Sankyo: Consultancy. Scholl:Alexion: Other: Travel support; Abbivie: Other: Travel support; Novartis: Other: Travel support; Deutsche Krebshilfe: Research Funding; Carreras Foundation: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; MDS: Other: Travel support; Jazz Pharma: Membership on an entity's Board of Directors or advisory committees. Hochhaus:Incyte: Research Funding; Pfizer: Research Funding; Takeda: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis: Research Funding. Brümmendorf:Takeda: Consultancy; Pfizer: Consultancy, Research Funding; Janssen: Consultancy; Merck: Consultancy; Novartis: Consultancy, Research Funding. Burchert:Novartis: Research Funding; Pfizer: Honoraria; Bristol Myers Squibb: Honoraria, Research Funding; AOP Orphan: Honoraria, Research Funding; Bayer: Research Funding. Krause:Novartis: Research Funding. Hänel:Amgen: Honoraria; Novartis: Honoraria; Roche: Honoraria; Takeda: Honoraria. Platzbecker:Celgene: Research Funding. Mayer:Johnson & Johnson: Research Funding; Roche: Research Funding; Eisai: Research Funding; Affimed: Research Funding; Novartis: Research Funding. Serve:Bayer: Research Funding. Ehninger:Cellex Gesellschaft fuer Zellgewinnung mbH: Employment, Equity Ownership; Bayer: Research Funding; GEMoaB Monoclonals GmbH: Employment, Equity Ownership. Schetelig:Gilead: Consultancy, Honoraria, Research Funding; Abbvie: Honoraria; Janssen: Consultancy, Honoraria; Roche: Honoraria; Sanofi: Consultancy, Research Funding; Novartis: Consultancy, Honoraria, Research Funding. Thiede:AgenDix: Other: Ownership; Novartis: Honoraria, Research Funding. Stoelzel:Neovii: Speakers Bureau.

Abstract: Purpose: We conducted a pilot study to assess the feasibility and the potential implications of detecting TERT promoter (TERTp)–mutant cell-free tumor-derived DNA (tDNA) in the cerebrospinal fluid (CSF) and plasma of glioblastoma patients. Experimental Design: Matched CSF and plasma samples were collected in 60 patients with glial tumors. The CSF collection was obtained during surgery, before any surgical manipulation of the tumor. The extracted tDNA and corresponding tumor DNA samples were analyzed for TERTp and isocitrate dehydrogenase (IDH) hotspot mutations. In addition, the variant allele frequency (VAF) of TERTp mutation in the CSF-tDNA was correlated with tumor features and patients’ outcome. Results: Thirty-eight patients had TERTp-mutant/IDH wild-type glioblastomas. The matched TERTp mutation in the CSF-tDNA was successfully detected with 100% specificity (95% CI, 87.6–100%) and 92.1% sensitivity (95% CI, 78.6–98.3%) (n = 35/38). In contrast, the sensitivity in the plasma-tDNA was far lower [n = 3/38, 7.9% (95% CI, 1.6–21.4%)]. We concordantly observed a longer overall survival of patients with low VAF in the CSF-tDNA when compared with patients with high VAF, irrespective of using the lower quartile VAF [11.45%; 14.0 mo. (95% confidence interval, CI, 10.3–17.6) vs. 8.6 mo. (95% CI, 4.1–13.2), P = 0.035] , the lower third VAF [13%; 15.4 mo. (95% CI, 11.6–19.2) vs. 8.3 mo. (95% CI, 2.3–14.4), P = 0.008], or the median VAF [20.3%; 14.0 mo. (95% CI, 9.2–18.7) vs. 8.6 mo. (95% CI, 7.5–9.8), P = 0.062] to dichotomize the patients. Conclusions: This pilot study highlights the value of CSF-tDNA for an accurate and reliable detection of TERTp mutations. Furthermore, our findings suggest that high TERTp mutation VAF levels in the CSF-tDNA may represent a suitable predictor of poor survival in glioblastoma patients. Further studies are needed to complement the findings of our exploratory analysis. Clin Cancer Res; 24(21); 5282–91. ©2018 AACR.

Abstract: Achievement of complete remission (CR) signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. Machine Learning (ML) is a branch of computer science that can process large data sets for a plethora of purposes. The underlying mechanism does not necessarily begin with a manually drafted hypothesis model. Rather the ML algorithms can detect patterns in pre-processed data and derive abstract information. We used ML to predict CR and 2-year overall survival (OS) in a large multi-center cohort of 1383 AML patients who received intensive induction therapy using clinical, laboratory, cytogenetic and molecular genetic data. To enable a customizable and reusable technological approach and achieve optimal results, we designed a data-driven platform with an embedded, automated ML pipeline integrating state-of-the-art software technology for data management and ML models. The platform consists of five scalable modules for data import and modelling, data transformation, model refinement, machine learning algorithms, feature support and performance feedback that are executed in an iterative manner to approach step-wisely the optimal configuration. To reduce dimensionality and the the risk of overfitting, dynamic feature selection was used, i.e. features were selected according to their support by feature selection algorithms. To be included in an ML model, a feature had to pass a pre-determined threshold of overall predictive power determined by summing the normalized scores of the feature selection algorithms. Features below the threshold were automatically excluded from the ML models for the respective iteration. In that way, features of high redundancy or low entropy were automatically filtered out. Our classification algorithms were completely agnostic of pre-existing risk classifications and autonomously selected predictive features both including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated (dm) CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, U2AF1, t(8;21), inv(16)/t(16;16), del5/del5q, del17, normal or complex karyotypes, age and hemoglobin at initial diagnosis were statistically significant markers predictive of CR while t(8;21), del5/del5q, inv(16)/t(16;16), del17, dm CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD , DNMT3A, SF3B1, U2AF1, TP53, age, white blood cell count, peripheral blast count, serum LDH and Hb at initial diagnosis as well as extramedullary manifestations were predictive for 2-year OS. For prediction of CR and 2-year OS, AUROCs ranged between 0.77 - 0.86 and 0.63 - 0.74, respectively. We provide a method to automatically select predictive features from different data types, cope with gaps and redundancies, apply and optimize different ML models, and evaluate optimal configurations in a scalable and reusable ML platform. In a proof-of-concept manner, our algorithms utilize both established markers of favorable or adverse risk and also provide further evidence for the roles of U2AF1, IKZF1, SF3B1, DNMT3A and bZIP mutations of CEBPA in AML risk prediction. Our study serves as a fundament for prospective validation and data-driven ML-guided risk assessment in AML at initial diagnosis for the individual patient. Image caption: Patient features were automatically selected by machine learning to predict complete remission (CR) and 2-year overall survival (OS) after intensive induction therapy. Based on a continuous feature support metric with a predefined cut-off of 0.5 (determined by optimal classification performance), 27 and 25 features were automatically selected for prediction of CR (A) and 2-year OS (C), respectively. For each of these features predicted by machine learning, odds ratios and 95% confidence intervals (CI) were calculated for CR (B) and 2 year OS (D). BMB: bone marrow blast count; FLT3h/low: FLT3-ITD ratio, h=high & gt;0.5; Hb: hemoglobin; karyotype, c: complex aberrant karyotype (≥ 3 aberrations); karyotype, n: normal karyotype (no aberrations); LDH: lactate dehydrogenase; PBB: peripheral blood blast count; PLT: platelet count; WBC: white blood cell count. Figure 1 Figure 1. Disclosures Schetelig: Roche: Honoraria, Other: lecture fees; Novartis: Honoraria, Other: lecture fees; BMS: Honoraria, Other: lecture fees; Abbvie: Honoraria, Other: lecture fees; AstraZeneca: Honoraria, Other: lecture fees; Gilead: Honoraria, Other: lecture fees; Janssen: Honoraria, Other: lecture fees . Platzbecker: Janssen: Honoraria; Celgene/BMS: Honoraria; AbbVie: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Geron: Honoraria. Müller-Tidow: Pfizer: Research Funding; Janssen: Consultancy, Research Funding; Bioline: Research Funding. Baldus: Celgene/BMS: Honoraria; Amgen: Honoraria; Novartis: Honoraria; Jazz: Honoraria. Krause: Siemens: Research Funding; Takeda: Honoraria; Pfizer: Honoraria; art-tempi: Honoraria; Kosmas: Honoraria; Gilead: Other: travel support; Abbvie: Other: travel support. Haenel: Bayer Vital: Honoraria; Jazz: Consultancy, Honoraria; GSK: Consultancy; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Amgen: Consultancy; Celgene: Consultancy, Honoraria. Schliemann: Philogen S.p.A.: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Other: travel grants; Astellas: Consultancy; AstraZeneca: Consultancy; Boehringer-Ingelheim: Research Funding; BMS: Consultancy, Other: travel grants; Jazz Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy; Roche: Consultancy; Pfizer: Consultancy. Middeke: Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Jazz: Consultancy; Astellas: Consultancy, Honoraria; Sanofi: Honoraria, Research Funding; Novartis: Consultancy; Gilead: Consultancy; Glycostem: Consultancy; UCB: Honoraria.

Abstract: NPM 1-mutated acute myeloid leukemia (AML) shows unique features. However, the characteristics of “therapy-related” NPM1-mutated AML (t-NPM1 AML) are poorly understood. We compared the genetics, transcriptional profile, and clinical outcomes of t-NPM1 AML, de novo NPM1-mutated AML (dn-NPM1 AML), and therapy-related AML (t-AML) with wild-type NPM1 (t-AML). Normal karyotype was more frequent in t-NPM1 AML (n = 78/96, 88%) and dn-NPM1 (n = 1986/2394, 88%) than in t-AML (n = 103/390, 28%; P  & lt; .001). DNMT3A and TET2 were mutated in 43% and 40% of t-NPM1 AML (n = 107), similar to dn-NPM1 (n = 88, 48% and 30%; P & gt; 0.1), but more frequently than t-AML (n = 162; 14% and 10%; P  & lt; 0.001). Often mutated in t-AML, TP53 and PPM1D were wild-type in 97% and 96% of t-NPM1 AML, respectively. t-NPM1 and dn-NPM1 AML were transcriptionally similar, (including HOX genes upregulation). At 62 months of median follow-up, the 3-year overall survival (OS) for t-NPM1 AML (n = 96), dn-NPM1 AML (n = 2394), and t-AML (n = 390) were 54%, 60%, and 31%, respectively. In multivariable analysis, OS was similar for the NPM1-mutated groups (hazard ratio [HR] 0.9; 95% confidence interval [CI] , 0.65-1.25; P = .45), but better in t-NPM1 AML than in t-AML (HR, 1.86; 95% CI, 1.30-2.68; P  & lt; .001). Relapse-free survival was similar between t-NPM1 and dn-NPM1 AML (HR, 1.02; 95% CI, 0.72-1.467; P = .90), but significantly higher in t-NPM1 AML versus t-AML (HR, 1.77; 95% CI, 1.19-2.64; P = .0045). t-NPM1 and dn-NPM1 AML have overlapping features, suggesting that they should be classified as a single disease entity.

Abstract: Mutations of the key myeloid transcription factor CCAAT/enhancer binding protein alpha (C/EBPa) are found in 5-10% of patients with acute myeloid leukemia (AML). Two mutational clusters exist, in the aminoterminal transcription activation domains (TAD1 or 2) and in the basic leucine zipper domain (bZIP) located at the carboxyterminal-part of the protein. Biallelic mutations (biCEBPA) have been found to be associated with improved outcome and are now included as an independent entity in the WHO-classification. In contrast, monoallelic CEBPA-mutations (moCEBPA) do not appear to provide prognostic information. We characterized a large cohort of AML patients for CEBPA mutations and further analyzed the mutational spectrum of mono- and biallelic CEBPA-mutant AML patients to better understand potential differences in the biology of these groups. Patients and Methods: Patients (including all age groups) analyzed had a newly diagnosed AML and were registered in clinical protocols of the Study Alliance Leukemia (SAL)(AML96, AML2003 or AML60+, SORAML) or the SAL-register. Screening for CEBPA mutations was done using PCR and capillary electrophoresis. All identified CEBPA mutations were confirmed using conventional Sanger sequencing and the samples were further analyzed using next generation sequencing (Trusight Myeloid Panel, Illumina) for the presence of associated alterations. Results: In the 4578 patients analyzed, 228 (5%) with CEBPA-mutations were identified. An initial analysis revealed substantial clinical differences between the different mutation subtypes. Patients with biCEBPA (n=111) were significantly younger (median age 46 yrs) than wt-CEBPA patients (median 57 yrs; p 〈 .001). Interestingly, single bZIP mutant patients (n=64) had a similar median age (50 yrs.) as biCEBPA, whereas single TAD mutant patients (n=53) were significantly older (median 63 yrs.). In addition, WBC counts, CD34 positivity as well as the history of prior MDS differed between the subgroups (single TAD mutant had significantly lower WBC counts, lower rate of CD34 positivity and had a higher rate of prior MDS than biCEBPA and single bZIP mutant patients). Along with this, the distribution of co-mutations differed significantly between the subgroups, especially GATA2 mutations were more common in biCEBPA and single bZIP mutant patients (37% and 34%, respectively) compared to only 3% (single TAD)(p=.001). A similar pattern was seen for mutations in DNMT3A (8% biCEBPA, 20% single bZIP vs. 36% single TAD; p=.001), and NPM1 (3% biCEBPA, 8% single bZIP, 32% single TAD; p 〈 .001). In 2897 patients, the different CEBPA mutations were correlated with outcome. This analysis indicated a differential effect of the individual mutations on outcome, with an improved rate of complete remission (CR), overall survival (OS) and event free survival (EFS) for biCEBPA and single bZIP mutations in univariate and multivariate analyses (shown for OS in Figure 1a). Given the similarity of single bZIP and biCEBPA mutations, it appears reasonable to speculate on a common mechanistical background, since most of the biCEBPA mutants include a bZIP alteration. Recent experimental evidence generated by several groups indeed supports a specific role of these bZIP missense mutations. To address this in the clinical context, we regrouped patients with mutant CEBPA into patients with (n=157) or without bZIP mutations (n=71), irrespective of the biallelic status. As illustrated in Figure 1b, the bZIP mutant group had a significantly better OS, similar results were obtained for EFS and CR. In multivariate analysis, the presence of a bZIP mutation was the strongest indicator for achievement of CR (HR 7.5, 95% CI: 3-19; p 〈 .001), and together with favorable cytogenetics the factor associated with best OS (HR: .48; 95% CI .36-.64; p 〈 .001). In conclusion, our results obtained in one of the largest cohorts of AML patients analyzed for CEBPA mutations indicate that especially the presence of a missense bZIP mutation is associated with a favorable outcome in AML patients. These data point to substantial differences in prognostic implications of individual CEBPA mutations and support the major functional divergence of these alterations. If confirmed, these results might necessitate further refinement of their use in AML-classification. Disclosures Middeke: Sanofi: Honoraria. Platzbecker:Janssen-Cilag: Honoraria, Research Funding; Celgene Corporation: Honoraria, Research Funding; TEVA Pharmaceutical Industries: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Thiede:AgenDix: Employment, Other: Ownership.