Abstract Details
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
|
Abstract:
|
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).
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.