Discovery of Prognostic and Predictive Gene Signatures in Non-Small-Cell Lung Cancer from an Integrative Systems Biology Approach

• Activity Number: 558
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Hao Tang*+ and Guanghua Xiao and Jeffrey Allen and Michael White and Ignacio Ivan Wistuba and John Minna and Yang Xie
• Companies: The University of Texas Southwestern Medical Center and The University of Texas Southwestern Medical Center at Dallas and The University of Texas Southwestern Medical Center and The University of Texas Southwestern Medical Center and MD Anderson Cancer Center and The University of Texas Southwestern Medical Center and The University of Texas Southwestern Medical Center
• Keywords: prognostic and predictive ; non-small-cell lung cancer ; gene signature ; network analysis

Abstract

The use of biomarkers that capture aberrant gene expression and activity profiles is already playing a critical role in tumor diagnosis and classification, assisting decision-making in treating lung cancers. In this study, we developed a supervised network approach to select 18 "features" (genes) using a microarray dataset of over 400 lung cancer patients. 67% of the 18 genes were previously observed as targets in paclitaxel-dependent synthetic lethal screen, having known genetic alterations in tumor samples, or both. A predictive model was developed on the basis of the expression levels of these 18 genes, and the prognosis value of the model was validated in multiple datasets including 5 most widely used microarray platforms. Furthermore, we demonstrated that the 12 functionally important genes are predictive to treatment-sensitivity for adjuvant chemotherapy (ACT) when applied to a proprietary clinical study and the JBR.10 clinical trial. This function-centric approach can be easily extended to and is potentially helpful to studies on other types of diseases.