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

Project Narrative. 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 #
5R01CA152301-02
Application #
8132363
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
2011-03-01
Budget End
2012-02-29
Support Year
2
Fiscal Year
2011
Total Cost
$332,666
Indirect Cost
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
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Lee, Sungyul; Kopp, Florian; Chang, Tsung-Cheng et al. (2016) Noncoding RNA NORAD Regulates Genomic Stability by Sequestering PUMILIO Proteins. Cell 164:69-80
Kim, Jiwoong; Kim, Min Soo; Koh, Andrew Y et al. (2016) FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics 17:420
Laccetti, Andrew L; Chen, Beibei; Cai, Jennifer et al. (2016) Increase in Cancer Center Staff Effort Related to Electronic Patient Portal Use. J Oncol Pract 12:e981-e990

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