We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.
- Authors
Shukla, Sudhanshu; Evans, Joseph R.; Malik, Rohit; Feng, Felix Y.; Dhanasekaran, Saravana M.; Xuhong Cao; Guoan Chen; Beer, David G.; Hui Jiang; Chinnaiyan, Arul M.; Cao, Xuhong; Chen, Guoan; Jiang, Hui
- Abstract
<bold>Background: </bold>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, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.<bold>Methods: </bold>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.<bold>Results: </bold>In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10-4, 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 (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.<bold>Conclusions: </bold>Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.
- Subjects
RNA sequencing; PROGNOSTIC tests; CANCER treatment; ADENOCARCINOMA; LUNG cancer treatment; GENOMICS; MICROARRAY technology; LINCRNA; ANTIGENS; CARRIER proteins; COMPARATIVE studies; DATABASES; GLYCOPROTEINS; LUNG tumors; RESEARCH methodology; MEDICAL cooperation; MEMBRANE proteins; GENETIC mutation; NERVE tissue proteins; PROGNOSIS; PROTEINS; RESEARCH; RISK assessment; SURVIVAL; TUMOR classification; EVALUATION research; PROPORTIONAL hazards models; GENE expression profiling; KAPLAN-Meier estimator; SEQUENCE analysis
- Publication
JNCI: Journal of the National Cancer Institute, 2017, Vol 109, Issue 1, p1
- ISSN
0027-8874
- Publication type
journal article
- DOI
10.1093/jnci/djw200