Prognostically useful gene-expression profiles in acute myeloid leukemia.

阅读量:

235

摘要:

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

展开

DOI:

10.1056/NEJMoa040465

被引量:

2977

年份:

2004

通过文献互助平台发起求助,成功后即可免费获取论文全文。

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献

参考文献

引证文献

引用走势

2010
被引量:273

辅助模式

0

引用

文献可以批量引用啦~
欢迎点我试用!

关于我们

百度学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们保持学习的态度,不忘初心,砥砺前行。
了解更多>>

友情链接

百度云百度翻译

联系我们

合作与服务

期刊合作 图书馆合作 下载产品手册

©2025 Baidu 百度学术声明 使用百度前必读

引用