Lung cancer is the leading cause of cancer death in the United States and many other countries. Genetic factors play an important role in the etiology of lung cancer. Recent genome-wide association studies (GWAS) have identified multiple genetic susceptibility loci for lung cancer. However, these newly-identified genetic factors explain only a small fraction of the heritability for lung cancer. Moreover, these studies were conducted primarily among smokers, raising the possibility that the identified associations could be related to tobacco use behavior, lung carcinogenesis, or both. Genetic studies conducted among never-smokers provide exceptional opportunities to discover genetic variants that confer risk for lung cancer independent of smoking. Recently evidence has emerged to strongly suggest that most of the heritable risk for cancer and other complex diseases may be due to a large number of low-frequency, moderate-penetrance genes. Herein, we propose a whole-exome sequencing study to systematically search the entire coding region in the human genome to detect lung cancer susceptibility genes and variants that cannot be identified through conventional GWAS or family-based linkage analyses. The proposed study will be built on the resources established in the Shanghai Women's Health Study, Guangzhou Lung Cancer Study, and the Female Lung Cancer Consortium in Asia, all of which are conducted among East-Asian women.
The specific aims of the study are as follows;all cases and controls are female never-smokers.
Aim 1 is to sequence the whole exome of 600 NSCLC cases and 600 controls (Stage 1).
Aim 2 is to validate variants in approximately 350 promising genes identified in Aim 1 in 2,500 NSCLC cases and 2,500 controls (Stage 2).
Aim 3 is to validate approximately 15 genes from Stage 2 in an additional 2,500 NSCLC cases and 2,500 controls (Stage 3). To our knowledge, this is the first large association study of lung cancer conducted among never-smokers using whole-exome sequencing. With its strong methodology and use of novel technologies and study design, the proposed study will significantly improve our understanding of lung cancer genetics and biology through the identification of novel genes and pathways. Newly-identified genes and pathways could serve as targets for novel cancer treatments, and genetic variants from these genes could be used for cancer screening and risk assessment aimed at identifying high-risk individuals for targeted lung cancer prevention.

Public Health Relevance

We propose to conduct a whole-exome sequencing study to systematically search the entire coding region in the human genome for lung cancer susceptibility genes and variants among never-smokers. Genetic studies conducted among never-smokers provide exceptional opportunities to discover genetic variants that confer risk for lung cancer independent of smoking. The proposed study is not only highly innovative and extremely cost-efficient, but also has great potential to identify novel genes and pathways that will significantly improve our understanding of lung cancer genetics and biology.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA161045-03
Application #
8681192
Study Section
Special Emphasis Panel (ZRG1-PSE-G (02))
Program Officer
Mechanic, Leah E
Project Start
2012-06-01
Project End
2017-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
3
Fiscal Year
2014
Total Cost
$575,134
Indirect Cost
$185,583
Name
Vanderbilt University Medical Center
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004413456
City
Nashville
State
TN
Country
United States
Zip Code
37212
Guo, Yan; Wu, Jie; Zhao, Shilin et al. (2016) RNA Sequencing of Formalin-Fixed, Paraffin-Embedded Specimens for Gene Expression Quantification and Data Mining. Int J Genomics 2016:9837310
Wu, Haijian; Liu, Yan; Shu, Xiao Ou et al. (2016) MiR-374a suppresses lung adenocarcinoma cell proliferation and invasion by targeting TGFA gene expression. Carcinogenesis 37:567-75
Zhang, Yanfeng; Li, Bingshan; Li, Chun et al. (2014) Improved variant calling accuracy by merging replicates in whole-exome sequencing studies. Biomed Res Int 2014:319534
Guo, Yan; Cai, Qiuyin; Li, Chun et al. (2013) An evaluation of allele frequency estimation accuracy using pooled sequencing data. Int J Comput Biol Drug Des 6:279-93