Identification of a 24-Gene Prognostic Signature That Improves the European LeukemiaNet Risk Classification of Acute Myeloid Leukemia: An International Collaborative Study
Year: 2013
Type: article
Abstract: PURPOSE: 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. PATIENTS AND 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. CONCLUSION: 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.
Source: Journal of Clinical Oncology
Authors: Zejuan Li, Tobias Herold, Chunjiang He, Peter J.M. Valk, Ping Chen, Vindi Jurinovic, Ulrich Mansmann, Michael D. Radmacher, Kati S. 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 P. Liu, Ruud Delwel, Guido Marcucci, Bob Lowenberg, Clara D. Bloomfield, Janet D. Rowley, Stefan K. Bohlander, Jianjun Chen
Institutions: Northwestern University, University of Chicago
Language: English
Subfield: Hematology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W2164594706