Robust Gene Signature from Formalin Fixed Paraffin Embedded (FFPE) Samples
Refines Prognosis of Lung Cancer
Dr. Yang Xie, Simmons Comprehensive Cancer Center,
Clinical Sciences Department, UT Southwestern Medical School
Lung Cancer is the leading cause of death from cancer for both men and women in the United States and in most parts of the world, with 5-year survival rate of 15%. Genome-wide expression profiles have been used to identify gene signatures to classify lung cancer patients with different survival outcomes. However, the requirement of frozen tissues for microarray experiments limits the clinical usage of these gene signatures.
The goal of this study is to test the feasibility of developing lung cancer prognosis gene signatures 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. We randomly selected 100 Non-Small-Cell lung cancer (NSCLC) FFPE samples with annotated clinical information from the UT-Lung SPORE Tissue Bank. We micro dissected tumor area from FFPE specimen, 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 data sets were used to validate the prognosis model.
This study demonstrated that the robust gene signature derived from genome-wide expression profiling of FFPE samples is strongly associated with lung cancer clinical outcomes, 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 59-gene lung cancer prognosis signature. 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.