There is an urgent need for biomarkers capable of identifying patients at risk during early phases neoplasia in pancreatic, breast, and colon tumors. Having access to surrogate samples for the analysis of these tissues provides an opportunity for the development of novel biomarkers whose status can be assessed through non-invasive of minimally invasive procedures, using currently available technologies. We propose that quantitating the proportion of mutated cancer alleles (cells) in a population of somatic cells, and measuring (with sufficient statistical power) the degree of diversity at specific gene loci, will accurately reflect the risk of cancer and is likely to emerge as a biomarker that can be validated prospectively and applied widely. We refer to this analysis as Mutational Load Distribution Analysis (MLDA). Surrogate tissue samples containing a sufficiently small number of cells, will enable us to perform MLDA analysis during the preneoplastic stages of tumor development. We will use technologies that lend themselves well to quantitative analysis. A logical extension of a simple and powerful PCR-based mutation detection technology, employing molecular beacon microarrays, will permit the simultaneous screening of 400 mutant loci with a discrimination of one mutant allele in a background of 60 wild type alleles. In parallel, alterations involving loss or gain loci will be assessed using state-of-the-art array comparative genomic hybridization at 500 different loci. After somatic genotyping, the tissue samples will be further analyzed by a newly-developed technology (in situ RCA-CACHET), which has the power to detect and quantify point mutations at the cytological level. This highly informative analysis will serve to corroborate at the cellular level the presence of specific mutations previously detected by PCR- molecular beacon assays, and, more importantly, will permit the co-localization of specific mutations with immunohistochemisty- based biomarkers developed by other members of the Early Detection Research Network. This capability will be particularly useful for future validation efforts involving combined biomarkers. We expext that a mutational load biomarker, based on a scalable dataset of considerable statistical power, will be of value not only in early detection efforts, but also in parallel cancer prevention efforts comprising nutritional, chemoprevention, and environmental quality components.
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