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 as a factor in predicting that individual'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 known to usually be ultraviolet light
(UV), the carcinogenic DNA photoproduct is known to be the cyclobutane pyrimidine dimer (CPD), the CPD
leaves telltale UV signature mutations, and normal sun-exposed tissue is readily accessible. The present
project takes advantage of three recent technical advances in order to assess individual risk and answer basic
questions about using UV-induced mutations for risk prediction.
First, the project uses a nonscarring surfactant-based skin biopsy method (Surfactant-mediated Tissue
Acquisition for Molecular Profiling, STAMP) in order to sample multiple non-diseased sites from a single
subject and to facilitate recruitment. Non-diseased sites reflect the initial UV exposure more closely than tumor
sites. Second, mutation detection sensitivity is enhanced by adapting cutting-edge techniques developed for
liquid biopsies, including multiplexed genome targets and error-correction techniques that bring the detection
limit down to 1 mutation per million bases. Third, the project takes advantage of recently-identified "genomic
dosimeters" that are ~100 fold more sensitive to UV than a typical CPD target in the genome.
The project begins by adapting these methods to small samples of human skin, then determines how
mutations 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 mutation level in sun-exposed
normal skin, in order to construct a cancer-probability metric. The results will establish a route to Precision
Prevention using UV signature mutations.
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