Michiels Stefan, Koscielny S, Boulet Thomas, Hill Catherine
Unité de biostatistiques et d'épidémiologie, Institut Gustave Roussy, 39, rue Camille-Desmoulins, 94807 Villejuif.
Bull Cancer. 2007 Nov;94(11):976-80.
Gene expression profiling is increasingly used in cancer research. For each patient, the expression of thousands of genes in the tumour can be measured simultaneously on a microarray. Microarray studies aim at classifying patients based on two types of classification schemes: unsupervised classification, which uses clustering in order to identify homogeneous subtypes of a disease on the basis of gene expression, or supervised classification, which principally aims at the identification of genes or set of genes differentially expressed between tumours with different characteristics (molecular signature), for instance between a group of patients with bad and good prognosis. The data consists of a small number of patients and a large number of variables, raising serious methodological problems. We will use published results on breast cancer in order both to study the power of the experiments and to illustrate the problems in interpretation and validity of their results. We recommend rigorous evaluation of this new technology.
基因表达谱分析在癌症研究中的应用越来越广泛。对于每一位患者,肿瘤中数千个基因的表达情况可在微阵列上同时进行测量。微阵列研究旨在基于两种分类方案对患者进行分类:无监督分类,即利用聚类方法根据基因表达来识别疾病的同质亚型;或监督分类,其主要目的是识别在具有不同特征(分子特征)的肿瘤之间差异表达的基因或基因集,例如在一组预后不良和预后良好的患者之间。该数据包含少量患者和大量变量,引发了严重的方法学问题。我们将使用已发表的乳腺癌研究结果,以研究实验的效能,并说明其结果在解释和有效性方面存在的问题。我们建议对这项新技术进行严格评估。