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Cancer Control Research

Abstract

DESCRIPTION (provided by applicant): Carcinogen exposure from tobacco smoking is the major cause of human lung cancer, yet heavy smokers only have about a 10% life-time risk of developing lung cancer but only limited prediction of cancer risk is currently possible. We will assess somatic mutational load in mutagen-exposed tissues by sensitive detection of mutations in a select set of hypermutable microsatellites (Analysis of Infrequent Microsatellite Mutations, AIMM). This novel technology is based on small-pool PCR (SP-PCR) to detect rare mutations in hypermutable microsatellite DNAs in exposed target tissue (buccal cells). Preliminary data indicate that somatic mutational load measured by AIMM increase with age but it is currently unclear if high levels reflect increased cancer risk. We hypothesize that somatic mutational load by AIMM will more accurately reflect cancer risk than the traditional measures such as age and tobacco exposure. The aim of the study is to determine levels of microsatellite mutations in target cells using a panel of three hypermutable microsatellite markers and correlate these levels to cancer status in a case-control study for lung cancer. We expect that the combined effects of carcinogen exposure and DNA repair lead to detectable levels of microsatellite mutations in biological specimens. Furthermore, clonal selection and field cancerization are expected to result in expansion of cells with molecular abnormalities potentially increasing the proportion of cells carrying microsatellite mutations. We will then compare mutation levels in clinical specimens in relation to cancer status. The ultimate goal is to develop an assay based on hypermutable microsatellite markers that accurately reflects individual risk of developing lung and/or head and neck cancer. We hypothesize that measurement of somatic mutational load as mutations in microsatellites will reflect not only age and carcinogen exposure, but also the genetic background of the exposed individual and field cancerization and therefore will more accurately reflect cancer risk than the more traditional risk factors for tobacco-associated cancers.