Genetics of Tobacco and Alcohol Related Cancers

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DESCRIPTION (provided by applicant): Even though lung and upper aero-digestive tract (UADT) cancers are predominantly caused by tobacco and alcohol, only a minority of heavy smokers and heavy drinkers will develop lung or UADT cancer. A possible explanation for this is that the metabolisation of carcinogenic products, the level of internal dose and subsequent DNA repair and cell cycle control mechanisms vary widely between individuals because of genetic factors. The broad long-term goal of this project will be to investigate in two separate studies the role of 45 genes which are potentially involved in the susceptibility of lung and UADT cancers. The first is a study of lung and UADT cancers in Central Europe, involving approximately 2300 lung cancer cases, 1200 UADT cancer cases and 2800 controls. The second is a separate study of UADT cancer in South America involving approximately 2100 cases and 1700 controls. Both studies are conducted to an identical protocol involving the collection of high quality detailed information on lifestyle and occupational history, as well as blood collection for DNA extraction. Genotyping of 108 SNPs for the 45 genes will be conducted using automated and pre-validated DNA microarrays. The genes comprise those involved in the metabolism of tobacco products, alcohol and other potential carcinogens (e.g. CYPs, GSTs, ADH2, ADH3, MPO), as well as genes involved in DNA repair (e.g. XRCC1, XRCC3, XPD, XPF), tumour suppression (p53, p16, CCND1 ) and nicotine addiction (dopamine D2 and D4 receptor genes). Using these large sample sizes, we will accurately measure the overall effect of each gene in lung and UADT cancer. Subsequently, the effect of combinations of genes will be measured (gene-gene interaction), as well as the effect of individual genes in specific subgroups identified by alcohol and tobacco consumption and occupational history (gene-environment interaction). Statistical techniques will include haplotype reconstruction, empirical Bayes and semi-Bayes analysis to control for false positive results, and modeling of complex pathways.