Applying Genomic Dosimeters of UV Damage to Predicting Skin Cancer Risk

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Project Summary Cancer prevention programs can reduce cancer incidence, cancer-related deaths, and healthcare costs. Yet population-level cancer prevention programs are expensive and difficult to implement, and their benefit must be weighed against the risk of overdiagnosis and harms associated with followup care. An emerging view is that prevention efforts ought to be focused on the populations at highest risk. In an era of precision medicine, Precision Prevention would objectively measure a person's past exposure to a risk factor in order to predict that person's risk of cancer or occupational disease. High-risk individuals would then be monitored frequently by a specialist. Skin cancers are an ideal starting point because they are nearly as frequent as all other human cancers combined, the carcinogen is typically ultraviolet light (UV), the carcinogenic DNA adduct is known to be the cyclobutane pyrimidine dimer (CPD), and the tissue is readily accessible. The present project takes advantage of two recent technical advances in order to assess individual risk and answer basic questions about using DNA adductomics for risk prediction. First, the project uses whole-genome genomics to identify "genomic dosimeters", genome regions in skin that are up to 1041 fold more sensitive to UV than expected from the genome-wide average. Second, it uses a nonscarring surfactant-based skin biopsy method (Surfactant-mediated Tissue Acquisition for Molecular Profiling, STAMP) in order to increase recruitment rates for human studies and allow sampling of multiple non- diseased sites from a single subject; non-diseased sites reflect the initial exposure more closely than tumor sites do. The project begins by adapting these methods to small samples of human skin, then determines how CPDs in genomic dosimeters vary with UV exposure to normal skin, and finally determines how the incidence of several types of skin cancer varies with the genomic dosimeter CPD level in sun-exposed normal skin, in order to construct a cancer-probability metric. The results will establish a route to Precision Prevention using adductomics.