Sudhanshu Shukla1,2, Joseph R Evans3, Rohit Malik1,2, Felix Y Feng2,3,4, Saravana M Dhanasekaran1,2, Xuhong Cao1,2, Guoan Chen5, David G Beer5, Hui Jiang6, Arul M Chinnaiyan1,2,6,7. 1. Department of Pathology, University of Michigan, Ann Arbor, MI. 2. Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI. 3. Department of Radiation Oncology, University of Michigan, Ann Arbor, MI. 4. Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI. 5. Department of Surgery, Section of Thoracic Surgery, University of Michigan, Ann Arbor, MI. 6. Department of Biostatistics, University of Michigan, Ann Arbor, MI. 7. Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI.
Abstract
Background: 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. Methods: 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. Results: 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. Conclusions: 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.
Background: 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. Methods: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. Results: 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. Conclusions: 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.
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