Prospects for applying genotypic selection of somatic oncomutation to chemical risk assessment

Genotypic selection methods detect rare sequence changes in populations of DNA molecules. These methods have been used to investigate the chemical induction of mutation and for the detection and diagnosis of cancer. The possible use of genotypic selection for improving current risk assessment practices is based on the premise that the frequency of somatic mutation is of critical importance in understanding and modeling carcinogenesis. If genotypic selection can measure the induction of specific mutations that disrupt normal cell/tissue homeostasis, then it could provide key mechanistic information for cancer risk assessment. For example, genotypic selection data might support a particular low-dose extrapolation method or characterize the relationship between rodent and human cancer risk. Strategies for evaluating the use of genotypic selection in cancer risk assessment include the concept of developing a battery of targets that detect a range of agent-specific effects. Ideal targets to examine by genotypic selection are the oncogene and tumor suppressor gene mutations frequently detected in human tumors because these are thought to represent tumor-initiating events. The most commonly occurring basepair (bp) substitutions within the ras and p53 genes are identified. Also, the battery of genotypic selection methods is defined in terms of the most important mutational specificities to include. In theory, the major basepair substitution mutations induced by 29 of 31 chemical carcinogens could be detected by analyzing three different mutations: G:C-->T:A, G:C-->A:T, and A:T-->T:A. Genotypic selection will have the greatest impact on risk assessment if measurement of spontaneous mutation is possible. Data from phenotypic selection assays suggest this corresponds to detection of mutant fractions of approximately 10(-7), and this would necessitate examining DNA samples containing >10(7) target molecules. Despite its apparent potential, considerable development and validation is needed before genotypic selection data can be applied to cancer risk assessment.