Valk Peter J M, Verhaak Roel G W, Beijen M Antoinette, Erpelinck Claudia A J, Barjesteh van Waalwijk van Doorn-Khosrovani Sahar, Boer Judith M, Beverloo H Berna, Moorhouse Michael J, van der Spek Peter J, Löwenberg Bob, Delwel Ruud
Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands.
N Engl J Med. 2004 Apr 15;350(16):1617-28. doi: 10.1056/NEJMoa040465.
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.
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.
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.
Gene-expression profiling allows a comprehensive classification of AML that includes previously identified genetically defined subgroups and a novel cluster with an adverse prognosis.
对于急性髓系白血病（AML）患者，必须综合多种方法来对疾病进行分类、做出治疗决策并确定预后。然而，这种综合方法仅在50%的病例中能提供正确的治疗和预后信息。
我们使用包含约13000个独特基因或表达特征标签的Affymetrix U133A基因芯片，测定了285例AML患者外周血或骨髓样本中的基因表达谱。数据分析使用Omniviz、微阵列显著性分析和微阵列预测分析软件进行。进行统计分析以确定具有特定分子特征的AML病例的预后意义。
无监督聚类分析基于分子特征将AML患者分为16组。我们确定了定义这些聚类的基因，并确定了以高度准确性识别具有预后重要性的聚类所需的最少基因数量。聚类由染色体病变（如t(8;21)、t(15;17)和inv(16)）、特定基因突变（CEBPA）和癌基因异常表达（EVI1）驱动。我们识别出了几个新的聚类，其中一些由核型正常的标本组成。一个具有独特基因表达特征的独特聚类包括治疗结果较差的AML病例。
基因表达谱分析能够对AML进行全面分类，包括先前确定的基因定义亚组和一个预后不良的新聚类。
