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.
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 individually tailor treatment concepts to disease biology. We used nine machine learning (ML) models 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 and validated our results on an external multicenter cohort. Our ML models 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), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin at initial diagnosis were statistically significant markers predictive of CR, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), dmCEBPA, 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 and 0.74, respectively in our test set and 0.71 – 0.80 and 0.65 – 0.75 in the external validation cohort. We demonstrate the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.
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>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.
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