Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, University of Michigan researchers undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.
Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.
In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00×10-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort and a University of Michigan institutional cohort, and worse metastasis-free survival in the TCGA validation cohort. The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage, EGFR wild-type, and EGFR mutant. The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.
Identification of prognostic gene signature
A) RNA-seq prognostic analysis and signature generation pipeline. B) Oncomine lung adenocarcinoma signature concept analysis of top 96 prognostic genes.