title: Identification of a 24-gene prognostic signature that improves the European LeukemiaNet risk classification of acute myeloid leukemia: an international collaborative study.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: University of Chicago, Chicago, IL 60637, USA.
authors: Zejuan
authors: Z
authors: Li
authors: Tobias
authors: T
authors: Herold
authors: Chunjiang
authors: C
authors: He
authors: Peter J M
authors: PJ
authors: Valk
authors: Ping
authors: P
authors: Chen
authors: Vindi
authors: V
authors: Jurinovic
authors: Ulrich
authors: U
authors: Mansmann
authors: Michael D
authors: MD
authors: Radmacher
authors: Kati S
authors: KS
authors: Maharry
authors: Miao
authors: M
authors: Sun
authors: Xinan
authors: X
authors: Yang
authors: Hao
authors: H
authors: Huang
authors: Xi
authors: X
authors: Jiang
authors: Maria-Cristina
authors: MC
authors: Sauerland
authors: Thomas
authors: T
authors: Büchner
authors: Wolfgang
authors: W
authors: Hiddemann
authors: Abdel
authors: A
authors: Elkahloun
authors: Mary Beth
authors: MB
authors: Neilly
authors: Yanming
authors: Y
authors: Zhang
authors: Richard A
authors: RA
authors: Larson
authors: Michelle M
authors: MM
authors: Le Beau
authors: Michael A
authors: MA
authors: Caligiuri
authors: Konstanze
authors: K
authors: Döhner
authors: Lars
authors: L
authors: Bullinger
authors: Paul P
authors: PP
authors: Liu
authors: Ruud
authors: R
authors: Delwel
authors: Guido
authors: G
authors: Marcucci
authors: Bob
authors: B
authors: Lowenberg
authors: Clara D
authors: CD
authors: Bloomfield
authors: Janet D
authors: JD
authors: Rowley
authors: Stefan K
authors: SK
authors: Bohlander
authors: Jianjun
authors: J
authors: Chen
mesh_terms: D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D007391:International Cooperation; D053208:Kaplan-Meier Estimate; D015470:Leukemia, Myeloid, Acute / Q000235:genetics* / Q000401:mortality*; D046228:Microarray Analysis; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models
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