Genetic Polymorphism of Enzymes Involved in Xenobiotic Metabolism and the Risk of Lung Cancer

ABSTRACT

Chronic inhalation of cigarette smoke is a major risk factor for the development of lung cancer. It has been suggested that genetic susceptibility may contribute to the risk, because only a small portion of smokers develops the disease. Several polymorphisms that involve the metabolic activation or detoxification of carcinogens derived from cigarette smoke have been found to be associated with lung cancer risk. Many studies have focused on the relation between the distribution of polymorphic variants of different forms of the metabolic enzymes and lung cancer susceptibility. In this respect two groups of genetic polymorphisms of enzymes involved in xenobiotic metabolism, cytochrome P450 (CYP) and glutathione S-transferases (GSTs), have been discussed. CYP multigene superfamily consists of 10 subfamilies (CYP1-CYP10). A positive association between development of lung cancer and the mutant homozygous genotype of CYP1A1 gene has been reported in several Japanese populations but such an association has not been observed in either Caucasians or African-Americans. The relation between CYP2D6 and lung cancer remains conflicting and inconclusive. Several polymorphisms have been identified at the CYP2E1 locus. No definitive link between the polymorphisms of CYP2E1 and the risk of lung cancer has, however, been identified. The role of other CYP2 isoforms in lung carcinogenesis has not been sufficiently investigated. GSTs form a superfamily of genes consisting of five distinct families, named GSTA, GSTM, GSTP, GSTT and GSTS. The role of GSTM, GSTT1 or GSTP1 polymorphism in modifying the lung cancer risk may be more limited than has been so far anticipated.Although some genetic polymorphisms discussed here have not shown significant increases/decreases in risk, individuals with differing genotypes may have different susceptibilities to lung cancer. Hopefully, in future studies it will be possible to screen for lung cancer using specific biomarkers.