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

89 Scopus citations

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

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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