There is accumulating evidence that chronic injury and inflammation in the respiratory tract, such as that caused by cigarette smoking, predispose to lung cancer. We therefore propose to conduct an in depth pathway-based analysis of gene variants in the inflammatory pathway using test and validation sets of cases and controls. This proposal builds on a well-annotated specimen repository of lung cancer cases and controls enrolled in an ongoing risk factor study (CA55769, Spitz, PI). Cases are frequency-matched to controls on age, gender, ethnicity and smoking status recruited from a multi-specialty physician practice. Data collected include smoking history, dietary intake, cancer family history, specific occupational exposures (e.g., asbestos, dust), and previous medical history including chronic obstructive airway disease, asthma and hay fever. Genomic DNA and rich candidate genotype and phenotype data are available.
Aim 1 : To identify novel genetic variants influencing lung cancer risk in a test set of 1500 cases with non-small cell lung cancer and 1500 matched controls (all Caucasian), using the Illumina iSelect Infinium chip with 8.5 to 9K SNP's.
Aim 2 : In a replication set of an additional 1000 cases and 1000 controls, using a GoldenGate assay, we will evaluate the top 1500 SNPs identified from Aim1 as meeting the P<0.1 criterion, or selected by a rational prioritizing approach that incorporates published results, type of SNP, evolutionary biology, physico-chemical properties and haplotype tagging SNPs.
Aim 3 : To perform fine mapping in the flanking regions of 50 SNPs selected by the same approach as in Aim 2, combining prior information with in silico approaches for predicting functionality. For each of these 50 SNPs, we will select an average of 10 additional SNPs per gene region to regenotype in all 2500 cases and 2500 controls.
Aim 4. To extend our epidemiologic risk prediction model by incorporating established epidemiologic risk factor and gene variant data. We will apply machine-learning tools to identify gene-environment and gene-gene interactions. Covariates will include prior emphysema, asthma, hay fever, dust and asbestos exposure, smoking characteristics, family history of cancer, and anti-inflammatory drug use. The International Lung Cancer Consortium will perform external validation in a proposal to be developed. Our approach to comprehensively evaluate variants in a candidate pathway in a large well- powered study will be applicable to a variety of other cancer sites where inflammation plays an important etiologic role, as well as in non-neoplastic diseases with a strong inflammatory component such as emphysema. The public health potential of a useful risk prediction modes for lung cancer is substantial.

Public Health Relevance

Cigarette smoking results in inflammation in the respiratory tract and there is growing evidence that chronic inflammatory processes predispose to lung cancer. However, the molecular mechanisms underlying the causal nature of this association are unclear. We propose to conduct an in depth analysis of gene variants in the inflammatory pathway as susceptibility factors for lung cancer. This proposal builds upon an existing well annotated specimen repository. We will evaluate gene variants in a test set of lung cancer cases and matched controls and validate the findings in an independent dataset. Finally we will incorporate these findings into an extended risk prediction model for lung cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA127219-07
Application #
8506981
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Carrick, Danielle M
Project Start
2008-01-01
Project End
2014-06-30
Budget Start
2013-07-03
Budget End
2014-06-30
Support Year
7
Fiscal Year
2013
Total Cost
$192,450
Indirect Cost
$30,606
Name
Baylor College of Medicine
Department
Anatomy/Cell Biology
Type
Schools of Medicine
DUNS #
051113330
City
Houston
State
TX
Country
United States
Zip Code
77030
Pan, Yongchu; Liu, Hongliang; Wang, Yanru et al. (2017) Associations between genetic variants in mRNA splicing-related genes and risk of lung cancer: a pathway-based analysis from published GWASs. Sci Rep 7:44634
Yin, Jieyun; Liu, Hongliang; Liu, Zhensheng et al. (2017) Pathway-analysis of published genome-wide association studies of lung cancer: A potential role for the CYP4F3 locus. Mol Carcinog 56:1663-1672
Feng, Yun; Wang, Yanru; Liu, Hongliang et al. (2017) Genetic variants of PTPN2 are associated with lung cancer risk: a re-analysis of eight GWASs in the TRICL-ILCCO consortium. Sci Rep 7:825
Zhou, Fei; Wang, Yanru; Liu, Hongliang et al. (2017) Susceptibility loci of CNOT6 in the general mRNA degradation pathway and lung cancer risk-A re-analysis of eight GWASs. Mol Carcinog 56:1227-1238
Liu, Yanhong; Kheradmand, Farrah; Davis, Caleb F et al. (2016) Focused Analysis of Exome Sequencing Data for Rare Germline Mutations in Familial and Sporadic Lung Cancer. J Thorac Oncol 11:52-61
Fehringer, Gordon; Kraft, Peter; Pharoah, Paul D et al. (2016) Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations. Cancer Res 76:5103-14
Yuan, Hua; Liu, Hongliang; Liu, Zhensheng et al. (2016) A Novel Genetic Variant in Long Non-coding RNA Gene NEXN-AS1 is Associated with Risk of Lung Cancer. Sci Rep 6:34234
Chen, Li-Shiun; Baker, Timothy; Hung, Rayjean J et al. (2016) Genetic Risk Can Be Decreased: Quitting Smoking Decreases and Delays Lung Cancer for Smokers With High and Low CHRNA5 Risk Genotypes - A Meta-Analysis. EBioMedicine 11:219-226
David, Sean P; Wang, Ange; Kapphahn, Kristopher et al. (2016) Gene by Environment Investigation of Incident Lung Cancer Risk in African-Americans. EBioMedicine 4:153-61
Patel, Yesha M; Park, Sunghim L; Han, Younghun et al. (2016) Novel Association of Genetic Markers Affecting CYP2A6 Activity and Lung Cancer Risk. Cancer Res 76:5768-5776

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