BACKGROUND: In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. METHODS: We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. RESULTS: Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. CONCLUSIONS: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis. Copyright 2004 Massachusetts Medical Society
BACKGROUND: In patients with acute myeloid leukemia (AML) a combination of methods must be used to classify the disease, make therapeutic decisions, and determine the prognosis. However, this combined approach provides correct therapeutic and prognostic information in only 50 percent of cases. METHODS: We determined the gene-expression profiles in samples of peripheral blood or bone marrow from 285 patients with AML using Affymetrix U133A GeneChips containing approximately 13,000 unique genes or expression-signature tags. Data analyses were carried out with Omniviz, significance analysis of microarrays, and prediction analysis of microarrays software. Statistical analyses were performed to determine the prognostic significance of cases of AML with specific molecular signatures. RESULTS: Unsupervised cluster analyses identified 16 groups of patients with AML on the basis of molecular signatures. We identified the genes that defined these clusters and determined the minimal numbers of genes needed to identify prognostically important clusters with a high degree of accuracy. The clustering was driven by the presence of chromosomal lesions (e.g., t(8;21), t(15;17), and inv(16)), particular genetic mutations (CEBPA), and abnormal oncogene expression (EVI1). We identified several novel clusters, some consisting of specimens with normal karyotypes. A unique cluster with a distinctive gene-expression signature included cases of AML with a poor treatment outcome. CONCLUSIONS: Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis. Copyright 2004 Massachusetts Medical Society
Authors: Ying Wu; Peggy de Kievit; Lars Vahlkamp; Dirk Pijnenburg; Maarten Smit; Martijn Dankers; Diana Melchers; Martijn Stax; Piet J Boender; Colin Ingham; Niek Bastiaensen; Rik de Wijn; Dirk van Alewijk; Henk van Damme; Anton K Raap; Alan B Chan; Rinie van Beuningen Journal: Nucleic Acids Res Date: 2004-08-27 Impact factor: 16.971
Authors: Bas J Wouters; Bob Löwenberg; Claudia A J Erpelinck-Verschueren; Wim L J van Putten; Peter J M Valk; Ruud Delwel Journal: Blood Date: 2009-01-26 Impact factor: 22.113
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 Journal: J Clin Oncol Date: 2013-02-04 Impact factor: 44.544