title: Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma.
abstract: 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.
  
  
  
  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 1.00x10-4, 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.
  
  
  
  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.
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.
authors.forename: Sudhanshu
authors.initials: S
authors.lastname: Shukla
authors.affiliation: Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.
authors.forename: Joseph R
authors.initials: JR
authors.lastname: Evans
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.
authors.forename: Rohit
authors.initials: R
authors.lastname: Malik
authors.affiliation: Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.|Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.|Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI.
authors.forename: Felix Y
authors.initials: FY
authors.lastname: Feng
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.
authors.forename: Saravana M
authors.initials: SM
authors.lastname: Dhanasekaran
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.
authors.forename: Xuhong
authors.initials: X
authors.lastname: Cao
authors.affiliation: Department of Surgery, Section of Thoracic Surgery, University of Michigan, Ann Arbor, MI.
authors.forename: Guoan
authors.initials: G
authors.lastname: Chen
authors.affiliation: Department of Surgery, Section of Thoracic Surgery, University of Michigan, Ann Arbor, MI.
authors.forename: David G
authors.initials: DG
authors.lastname: Beer
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI.
authors.forename: Hui
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Department of Pathology, University of Michigan, Ann Arbor, MI.|Michigan Center for Translational Pathology, University of Michigan, Ann Arbor, MI.|Department of Biostatistics, University of Michigan, Ann Arbor, MI.|Howard Hughes Medical Institute, University of Michigan, Ann Arbor, MI.
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics* / Q000556:secondary; D000368:Aged; D015703:Antigens, CD / Q000235:genetics; D030541:Databases, Genetic; D005260:Female; D058851:GPI-Linked Proteins / Q000235:genetics; D019204:GTP-Binding Proteins / Q000235:genetics; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000235:genetics* / Q000473:pathology; D008297:Male; D008565:Membrane Proteins / Q000235:genetics; D008875:Middle Aged; D009154:Mutation; D009363:Neoplasm Proteins / Q000235:genetics; D009367:Neoplasm Staging; D009419:Nerve Tissue Proteins / Q000235:genetics; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment; D017423:Sequence Analysis, RNA; D015996:Survival Rate; D059467:Transcriptome
chemical_list: D015703:Antigens, CD; C120523:CD109 protein, human; C403268:FRRS1L protein, human; D058851:GPI-Linked Proteins; D008565:Membrane Proteins; D009363:Neoplasm Proteins; D009419:Nerve Tissue Proteins; C465202:RHOV protein, human; D019204:GTP-Binding Proteins