Significance Analysis of Prognostic Signatures

A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that \"random\" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random ...

转化癌症研究（translational cancer research）的核心目标之一，是识别驱动癌症进展与转移的生物学特征（biological signatures）。基因组学研究中常用的数据分析方法为：基于候选预后基因集的基因表达数据对患者进行聚类，若所得聚类结果呈现出具有统计学显著性的预后分层效应，则将该基因集与预后相关联，以此提示其生物学与临床价值。近期有研究对该方法的有效性提出了质疑：通过多项乳腺癌数据集分析显示，“随机”生成的基因集往往也能将患者聚类为预后差异显著的亚组。该研究表明，亟需开发严谨的新型统计方法，以识别具备生物学信息价值的预后基因集。为解决这一问题，我们开发了预后特征显著性分析（Significance Analysis of Prognostic Signatures, SAPS）工具，该方法将标准预后检验与一种基于将患者分层至预后亚型的新型预后显著性检验相结合，其中随机……