Shedden Kerby, Taylor Jeremy M G, Enkemann Steven A, Tsao Ming-Sound, Yeatman Timothy J, Gerald William L, Eschrich Steven, Jurisica Igor, Giordano Thomas J, Misek David E, Chang Andrew C, Zhu Chang Qi, Strumpf Daniel, Hanash Samir, Shepherd Frances A, Ding Keyue, Seymour Lesley, Naoki Katsuhiko, Pennell Nathan, Weir Barbara, Verhaak Roel, Ladd-Acosta Christine, Golub Todd, Gruidl Michael, Sharma Anupama, Szoke Janos, Zakowski Maureen, Rusch Valerie, Kris Mark, Viale Agnes, Motoi Noriko, Travis William, Conley Barbara, Seshan Venkatraman E, Meyerson Matthew, Kuick Rork, Dobbin Kevin K, Lively Tracy, Jacobson James W, Beer David G
Department of Statistics, 1085 South University, University of Michigan, Ann Arbor, Michigan 48109, USA.
Nat Med. 2008 Aug;14(8):822-7. doi: 10.1038/nm.1790. Epub 2008 Jul 20.
Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
尽管已经提出了早期肺癌生存的预后基因表达特征,但对于临床应用而言,在不同的受试者群体和不同实验室中确定它们的性能至关重要。在此,我们报告一项大型的、训练-测试、多中心、盲法验证研究,以表征基于442例肺腺癌基因表达的几种预后模型的性能。所提出的假设检验了基因表达的微阵列测量单独或与基本临床协变量(分期、年龄、性别)相结合是否可用于预测肺癌受试者的总生存期。所检验的几种模型产生的风险评分与实际受试者结局显著相关。大多数方法结合临床数据时表现更佳,这支持在构建早期肺癌预后模型时联合使用临床和分子信息。本研究还提供了最大的一组具有广泛病理和临床注释的肺腺癌微阵列数据。