I will discuss the statistical analysis of certain binary data arising in molecular studies of cancer. In allelic-loss experiments, tumor cell genomes are analyzed at informative molecular marker loci to identify deleted chromosomal regions. The resulting binary data are used to infer the locations of putative suppressor genes, genes whose function maintains the normal cell cycle characteristics. Various factors can complicate this inference, including background loss of heterozygosity, spatial (i.e., within chromosome) dependence of the binary responses, noninformativeness of markers, covariates such as protein levels or tumor histology, heterogeneity of cells within tumors, and measurement error. I will focus on the first three factors, discussing methods for statistical inference that separate background loss from significant loss. The extension to other inferences will be outlined, such as comparison questions and the relationship to covariates. Using characteristic features of tumorigenesis, I will present a mathematical framework for the stochastic modeling of allelic-loss data, and will build models within this framework; in particular, I study a simple model having chromosome breaks at locations of a Poisson process, and preferential selection of cells with inactivated suppressor genes. These methods are demonstrated on allelic-loss data from induced rat mammary tumors and human cancers.

This paper is available electronically at
http://www.stat.wisc.edu/~newton/papers/abstracts/btr102a.html

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