Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
Clin Cancer Res. 2010 Jan 15;16(2):629-36. doi: 10.1158/1078-0432.CCR-09-1815. Epub 2010 Jan 12.
The reliability of microarray-based cancer prognosis is questioned by Michiels et al. They reanalyzed seven studies published in the prominent journals as successful stories of microarray-based cancer prognosis and concluded that the originally reported assessments are over optimistic. We set to investigate the reality of microarrays for predicting cancer prognosis by using the same data sets with commonly accepted data analysis approaches.
Michiels et al.'s analysis protocol used a correlation-based feature selection method, split sample validation, and a nearest-centroid rule classifier. We examined their results through systematically replacing their analysis approaches with other commonly used methods as a parameter study. In addition, we applied a widely accepted permutation test in conjunction with 5-fold cross-validation to verify Michiels et al.'s findings.
The stability of signature genes is likely obtained when a fold change-based feature selection method is applied. When cross-validation procedures are used to replace Michiels et al.'s split sample validation, only one of the seven studies yielded uninformative classifiers. The permutation test reveals that the confidence interval based on the split sample used in the Michiels et al.'s review is not a rigorous and robust approach to assess the validity of a classifier.
We concluded that the use of DNA microarrays for cancer prognosis can be demonstrated. We also stressed that caution should be exercised when a general conclusion is withdrawn based on a single statistical practice without alternative validation, which can leave a false impression and pessimistic perspective for emerging biomarker methodologies to advance cancer research.
Michiels 等人质疑基于微阵列的癌症预后的可靠性。他们重新分析了在知名期刊上发表的七项被认为是基于微阵列的癌症预后成功案例的研究，并得出结论，最初报道的评估过于乐观。我们通过使用相同的数据集和普遍接受的数据分析方法，来研究微阵列在预测癌症预后中的实际情况。
Michiels 等人的分析方案使用了基于相关性的特征选择方法、样本拆分验证和最近中心规则分类器。我们通过系统地用其他常用方法替代他们的分析方法进行参数研究，来检验他们的结果。此外，我们还应用了广泛接受的置换检验与 5 倍交叉验证相结合的方法来验证 Michiels 等人的发现。
当使用基于倍数变化的特征选择方法时，签名基因的稳定性很可能得到。当使用交叉验证程序替代 Michiels 等人的样本拆分验证时，只有七项研究中的一项产生了无信息的分类器。置换检验表明，Michiels 等人综述中使用的基于拆分样本的置信区间不是评估分类器有效性的严格和稳健方法。
我们得出的结论是，DNA 微阵列可用于癌症预后。我们还强调，在没有替代验证的情况下，基于单一统计实践得出一般性结论时应谨慎，这可能会给新兴生物标志物方法在推进癌症研究方面留下错误的印象和悲观的观点。
