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Activity Number: 589
Type: Topic Contributed
Date/Time: Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract - #310364
Title: Integrative Analysis and Systems Biology Approaches for Cancer Predictive Signatures
Author(s): Yang Xie*+ and Hao Tang and Guanghua Xiao and John Minna and Ignacio Wistuba
Companies: The University of Texas Southwestern Medical Center and UT Southwestern Medical Center and UT Southwestern Medical Center and UT Southwestern Medical Center and UT MD Anderson Cancer Center
Keywords: integrative analysis ; network analysis ; predictive signature ; lung cancer

Prospectively identifying who will benefit from adjuvant chemotherapy (ACT) would improve clinical decisions for non-small-cell lung cancer (NSCLC) patients. In this study, we aim to develop and validate a functional gene set that predicts the clinical benefits of ACT in NSCLC. Using a cohort of 442 Stage I-III NSCLC patients who underwent surgical resection, we identified an 18-hub-gene set which robustly predicted the prognosis of patients with adenocarcinoma in all six validation datasets across four microarray platforms. The hub genes, identified through a purely data-driven approach, have significant biological implications in tumor pathogenesis. The 18-hub-gene set was then integrated with genome-wide functional (RNAi) data and genetic aberration data to derive a 12-gene predictive signature for ACT benefits in NSCLC. The 12-gene predictive signature was successfully validated in two independent datasets (N=90 and N=176).

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