Physical and functional mapping of a novel tumor suppressor locus for prostate cancer within chromosome 10p12.31-q11

Prostate cancer remains the second leading cause of male cancer deaths in the United States, yet the molecular mechanisms underlying this disease remain largely unknown. Cytogenetic and molecular analyses of prostate tumors suggest a consistent association with the loss of chromosome 10. Previously, we have defined a novel tumor suppressor locus PAC-1 within chromosome 10pter-q11. Introduction of the short arm of chromosome 10 into a prostatic adenocarcinoma cell line PC-3H resulted in dramatic tumor suppression and restoration of a programmed cell death pathway. Using a combined approach of comparative genomic hybridization and microsatellite analysis of PC-3H, I have identified a region of hemizygosity within 10p12-p15. This region has been shown to be involved in frequent loss of heterozygosity in gliomas and melanoma. To functionally dissect the region within chromosome 10p containing PAC-1, we developed a strategy of serial microcell fusion, a technique that allows the transfer of defined fragments of chromosome 10p into PC-3H. Serial microcell fusion was used to transfer defined 10p fragments into a mouse A9 fibrosarcoma cell line. Once characterized by FISH and microsatellite analyses, the 10p fragments were subsequently transferred into PC-3H to generate a panel of microcell hybrid clones containing overlapping deletions of chromosome 10p. In vivo and microsatellite analyses of these PC hybrids identified a small chromosome 10p fragment (an estimated 31 Mb in size inclusive of the centromere) that when transferred into the PC-3H background, resulted in significant tumor suppression and limited a region of functional tumor suppressor activity to chromosome 10p12.31-q11. This region coincides with a region of LOH demonstrated in prostate cancer. These studies demonstrate the utility of this approach as a powerful tool to limit regions of functional tumor suppressor activity. Furthermore, these data used in conjunction with data generated by the Human Genome Project lent a focused approach to identify candidate tumor suppressor genes involved in prostate cancer. ^

A Bayesian analysis for spatial processes with application to mapping

In geographical epidemiology, maps of disease rates and disease risk provide a spatial perspective for researching disease etiology. For rare diseases or when the population base is small, the rate and risk estimates may be unstable. Empirical Bayesian (EB) methods have been used to spatially smooth the estimates by permitting an area estimate to "borrow strength" from its neighbors. Such EB methods include the use of a Gamma model, of a James-Stein estimator, and of a conditional autoregressive (CAR) process. A fully Bayesian analysis of the CAR process is proposed. One advantage of this fully Bayesian analysis is that it can be implemented simply by using repeated sampling from the posterior densities. Use of a Markov chain Monte Carlo technique such as Gibbs sampler was not necessary. Direct resampling from the posterior densities provides exact small sample inferences instead of the approximate asymptotic analyses of maximum likelihood methods (Clayton & Kaldor, 1987). Further, the proposed CAR model provides for covariates to be included in the model. A simulation demonstrates the effect of sample size on the fully Bayesian analysis of the CAR process. The methods are applied to lip cancer data from Scotland, and the results are compared. ^