Journal Article

Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

Dobbin, Kevin K., Yeatman, Timothy J., Rusch, Valerie, Ding, Kuyue, Szoke, Janos, Enkemann, Steve A., Chang, Andrew C., Beer, David G., Kris, Mark, Gerald, William L., Hanash, Samir, Naoki, Katsuhiko, Motoi, Noriko, Verhaak, Roel, Travis, William, Gruidl, Mike, Kuick, Rork, Strumpf, Dan, Taylor, Jeremy M.G., Eschrich, Steve, Jurisica, Igor, Ladd-Acosta, Christine, Weir, Barbara, Misek, David E., Venkatraman, Seshan E., Viale, Agnes, Golub, Todd, Meyerson, Matthew, Shepherd, Francis A., Jacobson, James W., Pennell, Nathan, Sharma, Anupama, Zhu, Chang Qi, Tsao, Ming S., Giordano, Thomas J., Seymour, Lesley, Zakowski, Maureen, Lively, Tracy, Shedden, Kerby

2008

Overview

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) can 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.