Quantitation of Molecular Endpoints for the Dose-Response Component of Cancer Risk Assessment

Abstract

Cancer risk assessment involves the steps of hazard identification, dose-response assessment, exposure assessment, and risk characterization. The rapid advances in the use of molecular biology approaches has had an impact on all 4 components, but the greatest overall current and future impact will be on the dose-response assessment because this requires an understanding of the mechanisms of carcinogenesis, both background and induced by environmental agents. In this regard, hazard identification is a qualitative assessment and dose-response is a quantitative estimate. Thus, the latter will ultimately require a quantitative assessment of molecular endpoints that are used to describe the dose-response for cancer. It has been possible for many years to quantitate alterations at the level of the single gene. For example, analysis of mutation frequency by phenotypic selection, analysis of transcription (mRNA) by Northern blot, analysis of translation (proteins) by Western blot, and analysis of kinetics of metabolism from metabolite levels. However, it is becoming clear that it is necessary when considering risk for adverse health outcomes to develop quantitative approaches for whole cell phenotypes or organ effects. For example, cancer is a whole tissue phenotype, not a feature of single gene mutations, in spite of the multistep (multimutation) mode of formation of a tumor. Thus, there is the need to quantitate the circuitry of a cell: the metabolic/biochemical pathways, genetic regulation pathways, and signaling pathway s in normal and stressed conditions. The hypothesis presented by Hanahan and Weinberg of the requirement for 6 acquired characteristics for tumor development, independent of tissue type and species or inducer, seems to provide a viable approach. This hypothesi s can be addressed through whole cell molecular assessment using microarray s and quantitative PCR together with the emerging proteomic approaches. This is the world of the new computational cell biology.