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Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study

  • Kerby Shedden
  • , Jeremy M.G. Taylor
  • , Steven A. Enkemann
  • , Ming Sound Tsao
  • , Timothy J. Yeatman
  • , William L. Gerald
  • , Steven Eschrich
  • , Igor Jurisica
  • , Thomas J. Giordano
  • , David E. Misek
  • , Andrew C. Chang
  • , Chang Qi Zhu
  • , Daniel Strumpf
  • , Samir Hanash
  • , Frances A. Shepherd
  • , Keyue Ding
  • , Lesley Seymour
  • , Katsuhiko Naoki
  • , Nathan Pennell
  • , Barbara Weir
  • Roel Verhaak, Christine Ladd-Acosta, Todd Golub, Michael Gruidl, Anupama Sharma, Janos Szoke, Maureen Zakowski, Valerie Rusch, Mark Kris, Agnes Viale, Noriko Motoi, William Travis, Barbara Conley, Venkatraman E. Seshan, Matthew Meyerson, Rork Kuick, Kevin K. Dobbin, Tracy Lively, James W. Jacobson, David G. Beer
  • University of Michigan, Ann Arbor
  • Moffitt Cancer Center
  • Centre of Excellence on Aging and Chronic Diseases of McGill Integrated University Health Network
  • Memorial Sloan-Kettering Cancer Center
  • Queen's University Kingston
  • Dana-Farber Cancer Institute
  • Broad Institute
  • Michigan State University
  • Columbia University
  • National Institutes of Health

Research output: Contribution to journalArticlepeer-review

996 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)822-827
Number of pages6
JournalNature Medicine
Volume14
Issue number8
DOIs
StatePublished - Aug 2008

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