Abstract
Cells are continuously exposed to large numbers of alkylating agents, from both exogenous and endogenous sources, which result in the formation of DNA adducts. DNA alkylation can cause stalled replication, mispairing, and DNA strand breaks, ultimately leading to mutations or cell death. While these modes of DNA damage are known to be prominent key events in carcinogenesis, they have also been exploited in anticancer therapy for a long time. The properties of some alkylating agents (e.g., MMS and methylnitrosourea) that react directly with DNA and trigger highly mutagenic effects make them a popular model in laboratories for low-dose risk assessment and other mutagenicity studies. Efficient DNA repair processes may restore damaged DNA. Unrepaired DNA adducts, however, can initiate the neoplastic process by inducing mutations and altering the expression of critical genes. Similar mechanisms are responsible for the progression from benign to malignant to metastatic neoplasia. In many cases, the formation and persistence of DNA adducts correlate with carcinogenic potency. However, different DNA adducts vary in their potential for initiating carcinogenesis, and their toxicological relevance must also be considered. The significance of individual adducts can be determined by molecular biology techniques, such as site-directed mutagenesis. Information on relevance, together with that of DNA adduct formation and DNA repair, is useful in estimating risk from exposure to exogenous alkylating agents. Recent evidence indicates that significant DNA alkylation also arises from endogenous sources, and may be important in aging and spontaneous carcinogenesis. The use of highly sensitive LC–MS/MS and isotope-labeled compounds provides a tool to accurately identify and differentiate the origins of exogenous and endogenous DNA adducts and understand which sources of exposure drive low dose biology that results in mutations and disease. This kind of information is needed to make science-based regulations, rather than relying on default assumptions for quantitative risk assessment.
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