In 2004, 98,400 people in the U.S. were diagnosed with bladder cancer, 12,710 died of the disease. Within the US, Northern New England (including New Hampshire) has among the highest bladder cancer mortality rates. Epidemiologic studies that examined single SNPs and cancer susceptibility indicate modifying effects of certain genetic polymorphisms (e.g., NAT2). However, results for a number of polymorphisms are inconsistent. As yet no large-scale studies of bladder cancer have incorporated haplotype, gene-gene and gene-environment interactions in a comprehensive evaluation of variations in critical cancer control process genes. The completed parent population-based case-control study of bladder cancer incidence in New Hampshire has collected detailed exposure history and biologic specimens. Using a recently designed, cost-effective, genotyping panel for cancer, we propose to test approximately 542 cases and 585 controls for 1536 coding and haplotype tagging single nucleotide polymorphisms (SNPs) in approximately 250 cancer susceptibility genes. We will use both traditional logistic regression methods and newly developed methods for evaluating gene-gene and gene-environment interactions, i.e., Multifactor Dimensionality Reduction (MDR) and the Interaction Graph function in Orange Canvas. We will focus our initial analysis efforts on 48 genes in four important cancer regulatory pathways (the detoxification, cell cycle checkpoint, apoptosis, and inflammatory pathways). Analysis will be first be performed on inferred haplotypes and coding SNPs. We will then evaluate gene-gene and gene-environment interactions. Identifying the genetic and exposure factors that influence the risk of bladder cancer will provide critical information to reduce bladder cancer incidence and will facilitate chemoprevention efforts and the use of molecular diagnostic tools to design individualized treatment regimens. Relevance Of The Proposed Research To Public Health: This research project will use newly developed analysis tools to efficiently identify genetic risk factors for bladder cancer. We will also investigate the genetic basis for the inter-individual variation in risk following carcinogenic exposures including smoking. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
1R03CA121382-01
Application #
7102924
Study Section
Special Emphasis Panel (ZCA1-SRRB-Q (J1))
Program Officer
Kagan, Jacob
Project Start
2006-04-01
Project End
2008-03-31
Budget Start
2006-04-01
Budget End
2007-03-31
Support Year
1
Fiscal Year
2006
Total Cost
$79,950
Indirect Cost
Name
Dartmouth College
Department
Family Medicine
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755
Andrew, Angeline S; Marsit, Carmen J; Schned, Alan R et al. (2015) Expression of tumor suppressive microRNA-34a is associated with a reduced risk of bladder cancer recurrence. Int J Cancer 137:1158-66
Andrew, Angeline S; Gui, Jiang; Hu, Ting et al. (2015) Genetic polymorphisms modify bladder cancer recurrence and survival in a USA population-based prognostic study. BJU Int 115:238-47
Pan, Qinxin; Hu, Ting; Malley, James D et al. (2014) A system-level pathway-phenotype association analysis using synthetic feature random forest. Genet Epidemiol 38:209-19
Wyszynski, Asaf; Tanyos, Sam A; Rees, Judy R et al. (2014) Body mass and smoking are modifiable risk factors for recurrent bladder cancer. Cancer 120:408-14
Hu, Ting; Pan, Qinxin; Andrew, Angeline S et al. (2014) Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility. BioData Min 7:5
Su, Chengwei; Andrew, Angeline; Karagas, Margaret R et al. (2013) Using Bayesian networks to discover relations between genes, environment, and disease. BioData Min 6:6
Andrew, Angeline S; Hu, Ting; Gu, Jian et al. (2012) HSD3B and gene-gene interactions in a pathway-based analysis of genetic susceptibility to bladder cancer. PLoS One 7:e51301
Lesseur, Corina; Gilbert-Diamond, Diane; Andrew, Angeline S et al. (2012) A case-control study of polymorphisms in xenobiotic and arsenic metabolism genes and arsenic-related bladder cancer in New Hampshire. Toxicol Lett 210:100-6
Hu, Ting; Sinnott-Armstrong, Nicholas A; Kiralis, Jeff W et al. (2011) Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinformatics 12:364
Gui, Jiang; Moore, Jason H; Kelsey, Karl T et al. (2011) A novel survival multifactor dimensionality reduction method for detecting gene-gene interactions with application to bladder cancer prognosis. Hum Genet 129:101-10

Showing the most recent 10 out of 16 publications