Less than 5% of all cancers result from the action of a single tumor suppressor gene or dominantly acting oncogenes. Instead, for all of the common cancers, it is the cumulative effect of many 'low penetrance' genetic mutations which confer the majority of the risk. Identifying these low penetrance genes has been a challenge which has been greatly assisted by the development of mouse cancer models between resistant and susceptible strains. In these inter-specific crosses the resistant phenotype is dominant and backcrossing to the sensitive strain has been used to perform linkage analyses to identify the chromosomal location of the tumor susceptibility genes. Over the past several years we have used a skin carcinogenesis model to identify 13 susceptibility loci and, in one case, have identified a gene, AURORA2, which confers increased risk in mice and human populations. At present the remaining 12 susceptibility loci are too large for positional cloning strategies. In this proposal, therefore, we will use a combination of strategies to define the positions of the susceptibility genes more precisely as a prelude to identifying the critical gene. In the first aim we use heavy ion beam irradiation to create microdeletions in the testes of resistant mice, which if they affect susceptibility genes, will make the F1 animals susceptible and simultaneously define the critical locus. In the second aim we will use inbred and outbred backcrosses together with linkage disequilibrium haplotype analysis to define the susceptibility loci.
The third aim will exploit our previous observation that tumor-specific genetic changes (deletions and amplifications), when they occur in regions known to contain susceptibility genes, can be used to define their location more accurately. These genetic changes will be identified with high-resolution custom BAC arrays available at RPCI. It is anticipated that this combination of approaches will identify candidate suppressor genes which can then be analyzed in more detail for their involvement in cancer risk in mice and human populations. In the long term, understanding the genetic load which leads to cancer susceptibility in the human population will provide rational alternatives to cancer prevention, screening and risk assessment. ? ?
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