Modern conception of carcinogenesis creates opportunities to advance cancer risk assessment

Abstract

Tumor initiation can be viewed as abnormal tissue development. During carcinogenesis, co-localized clones of cells carrying genetic and epigenetic lesions may interact to overcome normal cellular homeostasis, setting the stage for tumor progression. This interpretation and available data support the use of some cancer driver mutations (CDMs) as reporters of carcinogenesis. Cells carrying a subset of CDMs, often referred to as hotspot CDMs, are remarkably prevalent in normal tissues, as well as in tumors, where they are detected frequently as subpopulations. Studies with model carcinogens established that geometric mean CDM mutant fractions (MFs) can serve as biomarkers of carcinogenic potential following acute or sub-chronic exposures. For some studies, the CDMs measured in tissues of treated rodents (as log10 MF geomean or log10 MF standard deviation) can be correlated with tumor responses produced by chronic exposures to the same carcinogens. Variation in cancer driver MF across rodents within a treatment group (log10 MF standard deviation), for a panel of CDMs, may more accurately capture the stochastic nature of cancer driver mutational impact on tumor initiation, clonal expansion and tumor response than a measure of treatment group central tendency. CDMs have important advantages as biomarkers of cancer risk that warrant broader, systematic investigation; they are sensitive in vivo endpoints and carcinogenic effects detected using evolutionarily conserved rodent CDMs are likely relevant to human. Progress in this area has been limited by the inability to analyze more than one CDM at a time, but error-corrected next-generation sequencing (EC-NGS) approaches now provide an opportunity to examine multiple hotpot CDMs at once. Thus, EC-NGS should be utilized to test the hypothesis that within-treatment group variation for a battery of CDMs can be used as a metric to predict future cancer risk and provide a biologically-based foundation for rodent to human extrapolation.