Global approach to the diagnosis of leukemia using gene expression profiling
Global approach to the diagnosis of leukemia using gene expression profiling
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DOI：
10.1182/blood-2004-12-4938
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发表时间：
2005-08-15
期刊：
影响因子：
20.3
通讯作者：
Schoch, C
中科院分区：
文献类型：
--
作者：
Haferlach, T;Kohlmann, A;Schoch, C
白血病的准确诊断和分类是妥善处理患者的基础。本方法的诊断准确性和效率可通过使用基因表达谱微阵列来提高。我们用U133A和U133B基因芯片阵列分析了892例所有临床相关白血病亚型患者和45例非白血病对照的937例骨髓和外周血标本的基因表达谱。对于每个亚组，计算差异表达基因。使用支持向量机进行类别预测。通过10倍交叉验证估计预测精度，并使用由三分之一样本组成的随机选择的测试集，在100倍重采样方法中评估预测精度。应用每个亚组的前100个基因，总体预测准确率为95.1%，重新采样证实了这一点(中位数93.8%，95%可信区间91.4%-95.8%)。尤其是t(15；17)的急性髓系白血病(AML)、t(8；21)的AML、inv(16)的AML、慢性淋巴细胞性白血病(CLL)和t(11q23)的前B细胞急性淋巴细胞白血病(PRO-B-ALL)的敏感性和特异性均为100%。相应地，聚类分析完全分离了所分析的所有13个亚组。基因表达谱可预测所有临床相关的白血病亚实体，准确度高。
Accurate diagnosis and classification of leukemias are the bases for the appropriate management of patients. The diagnostic accuracy and efficiency of present methods may be improved by the use of microarrays for gene expression profiling. We analyzed gene expression profiles in 937 bone marrow and peripheral blood samples from 892 patients with all clinically relevant leukemia subtypes and from 45 nonleukemic controls by U133A and U133B GeneChip arrays. For each subgroup, differentially expressed genes were calculated. Class prediction was performed using support vector machines. Prediction accuracy was estimated by 10-fold cross-validation and was assessed for robustness in a 100-fold resampling approach using randomly chosen test sets consisting of one third of the samples. Applying the top 100 genes of each subgroup, an overall prediction accuracy of 95.1% was achieved that was confirmed by resampling (median, 93.8%; 95% confidence interval, 91.4%-95.8%). In particular, acute myeloid leukemia (AML) with t(15;17), AML with t(8;21), AML with inv(16), chronic lymphatic leukemia (CLL), and pro-B-cell acute lymphoblastic leukemia (pro-B-ALL) with t(11q23) were classified with 100% sensitivity and 100% specificity. Accordingly, cluster analysis completely separated all 13 subgroups analyzed. Gene expression profiling can predict all clinically relevant subentities of leukemia with high accuracy.