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Identification of a 24-Gene Prognostic Signature That Improves the European LeukemiaNet Risk Classification of Acute Myeloid Leukemia: An International Collaborative Study

2013; Lippincott Williams & Wilkins; Volume: 31; Issue: 9 Linguagem: Inglês

10.1200/jco.2012.44.3184

1527-7755

Zejuan Li, Tobias Herold, Chunjiang He, Peter J.M. Valk, Ping Chen, Vindi Jurinović, Ulrich Mansmann, Michael D. Radmacher, Kati Maharry, Miao Sun, Xinan Yang, Hao Huang, Xi Jiang, Maria-Cristina Sauerland, Thomas Büchner, Wolfgang Hiddemann, Abdel Elkahloun, Mary Beth Neilly, Yanming Zhang, Richard A. Larson, Michelle M. Le Beau, Michael A. Caligiuri, Konstanze Döhner, Lars Bullinger, Paul Liu, Ruud Delwel, Guido Marcucci, Bob Löwenberg, Clara D. Bloomfield, Janet D. Rowley, Stefan K. Bohlander, Jianjun Chen,

Retinoids in leukemia and cellular processes

To identify a robust prognostic gene expression signature as an independent predictor of survival of patients with acute myeloid leukemia (AML) and use it to improve established risk classification.Four independent sets totaling 499 patients with AML carrying various cytogenetic and molecular abnormalities were used as training sets. Two independent patient sets composed of 825 patients were used as validation sets. Notably, patients from different sets were treated with different protocols, and their gene expression profiles were derived using different microarray platforms. Cox regression and Kaplan-Meier methods were used for survival analyses.A prognostic signature composed of 24 genes was derived from a meta-analysis of Cox regression values of each gene across the four training sets. In multivariable models, a higher sum value of the 24-gene signature was an independent predictor of shorter overall (OS) and event-free survival (EFS) in both training and validation sets (P < .01). Moreover, this signature could substantially improve the European LeukemiaNet (ELN) risk classification of AML, and patients in three new risk groups classified by the integrated risk classification showed significantly (P < .001) distinct OS and EFS.Despite different treatment protocols applied to patients and use of different microarray platforms for expression profiling, a common prognostic gene signature was identified as an independent predictor of survival of patients with AML. The integrated risk classification incorporating this gene signature provides a better framework for risk stratification and outcome prediction than the ELN classification.