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Robust gene expression signature from formalin-fixed paraffin-embedded samples predicts prognosis of non-small-cell lung cancer patients

  • Yang Xie
  • , Guanghua Xiao
  • , Kevin R. Coombes
  • , Carmen Behrens
  • , Luisa M. Solis
  • , Gabriela Raso
  • , Luc Girard
  • , Heidi S. Erickson
  • , Jack Roth
  • , John V. Heymach
  • , Cesar Moran
  • , Kathy Danenberg
  • , John D. Minna
  • , Ignacio I. Wistuba

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The requirement of frozen tissues for microarray experiments limits the clinical usage of genome-wide expression profiling by using microarray technology. The goal of this study is to test the feasibility of developing lung cancer prognosis gene signatures by using genome-wide expression profiling of formalin-fixed paraffin-embedded (FFPE) samples, which are widely available and provide a valuable rich source for studying the association of molecular changes in cancer and associated clinical outcomes. Experimental Design: We randomly selected 100 Non-Small-Cell lung cancer (NSCLC) FFPE samples with annotated clinical information from the UT-Lung SPORE Tissue Bank. We microdissected tumor area from FFPE specimens and used Affymetrix U133 plus 2.0 arrays to attain gene expression data. After strict quality control and analysis procedures, a supervised principal component analysis was used to develop a robust prognosis signature for NSCLC. Three independent published microarray datasets were used to validate the prognosis model. Results: This study showed that the robust gene signature derived from genome-wide expression profiling of FFPE samples is strongly associated with lung cancer clinical outcomes and can be used to refine the prognosis for stage I lung cancer patients, and the prognostic signature is independent of clinical variables. This signature was validated in several independent studies and was refined to a 59-gene lung cancer prognosis signature. Conclusions: We conclude that genome-wide profiling of FFPE lung cancer samples can identify a set of genes whose expression level provides prognostic information across different platforms and studies, which will allow its application in clinical settings.

Original languageEnglish (US)
Pages (from-to)5705-5714
Number of pages10
JournalClinical Cancer Research
Volume17
Issue number17
DOIs
StatePublished - Sep 1 2011
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • General Medicine

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