Prediction of cancer outcome with microarrays: a multiple random validation strategy
Prediction of cancer outcome with microarrays: a multiple random validation strategy
复制标题
DOI：
10.1016/s0140-6736(05)17866-0
复制
发表时间：
2005-02-05
期刊：
影响因子：
168.9
通讯作者：
Hill, C
中科院分区：
文献类型：
--
作者：
Michiels, S;Koscielny, S;Hill, C
背景微阵列基因表达谱的一般性研究已被用于预测癌症的预后。对这种基因表达谱或分子特征的了解应该通过允许针对疾病的严重程度进行治疗来改善对患者的治疗。我们重新分析了7项最大的已发表研究的数据，这些研究试图在DNA微阵列分析的基础上预测癌症患者的预后。（即，在具有不同结果的患者中最差异表达的基因子集）在患者的训练集中，并估计在独立的患者验证集上具有该签名的错误分类的比例。我们扩大了这一战略（基于独特的训练和验证集），通过使用多个随机集，研究的稳定性的分子签名和比例的misclassification.Findings-作为预后的预测基因的列表是高度不稳定的分子签名强烈依赖于选择的患者在训练集中。除了一项研究外，所有研究的误分类比例都随着训练集中患者数量的增加而下降。由于验证不充分，我们选择的研究发表的结果与我们自己的分析相比过于乐观。七项研究中有五项没有对患者进行分类，这比chances.Interpretation发表的微阵列结果在癌症研究中的预后价值应谨慎考虑。我们提倡通过重复随机抽样进行验证。
Background General studies of microarray gene-expression profiling have been under-taken to predict cancer outcome. Knowledge of this gene-expression profile or molecular signature should improve treatment of patients by allowing treatment to be tailored to the severity of the disease. We reanalysed data from the seven largest published studies that have attempted to predict prognosis of cancer patients on the basis of DNA microarray analysis.Methods The standard strategy is to identify a molecular signature (ie, the subset of genes most differentially expressed in patients with different outcomes) in a training set of patients and to estimate the proportion of misclassifications with this signature on an independent validation set of patients. We expanded this strategy (based on unique training and validation sets) by using multiple random sets, to study the stability of the molecular signature and the proportion of misclassifications.Findings The list of genes identified as predictors of prognosis was highly unstable; molecular signatures strongly depended on the selection of patients in the training sets. For all but one study, the proportion misclassified decreased as the number of patients in the training set increased. Because of inadequate validation, our chosen studies published overoptimistic results compared with those from our own analyses. Five of the seven studies did not classify patients better than chance.Interpretation The prognostic value of published microarray results in cancer studies should be considered with caution. We advocate the use of validation by repeated random sampling.