Journal
NATURE MEDICINE
Volume 14, Issue 8, Pages 822-827Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/nm.1790
Keywords
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Funding
- NCI NIH HHS [U19 CA084953, U01 CA084999-05, U01 CA085052-05, CA46592, R01 CA098522-05, CA84995, R01 CA154365, U01 CA085052, P30 CA046592, U19 CA084953-050003, U01 CA084995-05, U01 CA084999, R01 CA098522, U01 CA084995, CA84999, CA85052, CA84953] Funding Source: Medline
- NIDDK NIH HHS [R01 DK046952, R01 DK046952-14] Funding Source: Medline
- PHS HHS [263-MQ-319735, 263-MQ-510430, 263-MQ-319740, 263-MQ-319746] Funding Source: Medline
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0821196] Funding Source: National Science Foundation
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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.
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