Lung Cancer is the leading cause of death from cancer in the United States. Adjuvant chemotherapy is increasingly used as the standard of care for patients with resected Non-Small-Cell Lung Cancer (NSCLC). However, such treatment is also associated with serious adverse effects. A large amount of drug sensitivity data, as well as clinical, epidemiology and genome-wide molecular profiling data have been collected by The University of Texas Specialized Program in Research Excellence (UT SPORE) in Lung Cancer to develop personalized cancer treatments. However, the integration and translation of these massive data to scientific knowledge and clinical usage has become a bottleneck of current cancer research. This study aims at tackling this problem and building a comprehensive prediction model of response to adjuvant chemotherapy in lung cancer. We will use the existing preclinical, clinical and epidemiology data to develop a comprehensive prediction model. We will collaborate with UT SPORE in Lung Cancer to collect new data on an independent patient cohort to validate the model.
The specific aims of this study are: (1) To develop and compare predictive signatures from individual molecular profiling datasets including mRNA expression, protein expression, copy number variation and germline polymorphism data. (2) To build a comprehensive prediction model of response to adjuvant chemotherapy by integrating predictive molecular signatures and clinical information. (3) To validate and characterize the comprehensive prediction model using an independent patient cohort. This project assembles an outstanding research team with complementary expertise in quantitative research, clinical research, translational research, pathology and genetic epidemiology, and is dedicated to improving lung cancer treatments. If implemented successfully, this project will have substantial impact on lung cancer clinical practice and translational cancer research.

Public Health Relevance

. The goal of this proposal is to develop a comprehensive prediction model for adjuvant chemotherapy response to assist in the clinical decision of whether an individual lung cancer patient should receive adjuvant chemotherapy or not.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA152301-05
Application #
8617729
Study Section
Special Emphasis Panel (ZRG1-CBSS-J (08))
Program Officer
Kim, Kelly Y
Project Start
2010-09-01
Project End
2015-02-28
Budget Start
2014-03-01
Budget End
2015-02-28
Support Year
5
Fiscal Year
2014
Total Cost
$300,604
Indirect Cost
$93,190
Name
University of Texas Sw Medical Center Dallas
Department
Other Clinical Sciences
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390
Zhong, Rui; Allen, Jeffrey D; Xiao, Guanghua et al. (2014) Ensemble-based network aggregation improves the accuracy of gene network reconstruction. PLoS One 9:e106319
Wang, Tao; Chen, Beibei; Kim, MinSoo et al. (2014) A model-based approach to identify binding sites in CLIP-Seq data. PLoS One 9:e93248
Yang, Jichen; Wang, Xinlei; Kim, Minsoo et al. (2014) Detection of candidate tumor driver genes using a fully integrated Bayesian approach. Stat Med 33:1784-800
Zhong, Rui; Kim, Jimi; Kim, Hyun Seok et al. (2014) Computational detection and suppression of sequence-specific off-target phenotypes from whole genome RNAi screens. Nucleic Acids Res 42:8214-22
Xiao, Guanghua; Ma, Shuangge; Minna, John et al. (2014) Adaptive prediction model in prospective molecular signature-based clinical studies. Clin Cancer Res 20:531-9
Shi, Xingjie; Liu, Jin; Huang, Jian et al. (2014) A penalized robust method for identifying gene-environment interactions. Genet Epidemiol 38:220-30
Liu, Jin; Huang, Jian; Zhang, Yawei et al. (2014) Integrative analysis of prognosis data on multiple cancer subtypes. Biometrics 70:480-8
Yun, Jonghyun; Wang, Tao; Xiao, Guanghua (2014) Bayesian hidden Markov models to identify RNA-protein interaction sites in PAR-CLIP. Biometrics 70:430-40
Tang, Hao; Xiao, Guanghua; Behrens, Carmen et al. (2013) A 12-gene set predicts survival benefits from adjuvant chemotherapy in non-small cell lung cancer patients. Clin Cancer Res 19:1577-86
Liu, Jin; Huang, Jian; Ma, Shuangge (2013) Incorporating network structure in integrative analysis of cancer prognosis data. Genet Epidemiol 37:173-83

Showing the most recent 10 out of 21 publications