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The association between the cytochrome P-450 1B1 (CYP1B1) Val432Leu polymorphism and breast cancer was assessed through a meta-analysis of all published case-control studies and a pooled analysis of both published and unpublished case-control studies from the Genetic Susceptibility to Environmental Carcinogens (GSEC) database ( www.upci.upmc.edu/research/ccps/ccontrol/g_intro.html ). GSEC is a collaborative project that gathers information on studies of metabolic gene polymorphisms and cancer. Thirteen articles were included in the meta-analysis (14,331 subjects; 7,514 cases, 6,817 controls); nine data sets were included in the pooled analysis (6,842 subjects; 3,391 cases, 3,451 controls). A summary meta- or pooled estimate of the association between the CYP1B1 Val432Leu polymorphism and breast cancer could not be calculated because of statistically significant heterogeneity in the point estimates among studies. No association between the CYP1B1 Val432Leu polymorphism and breast cancer was observed in Asians (for Val/Val and Val/Leu combined, odds ratio (OR) = 1.0, 95% confidence interval (CI): 0.8, 1.2). An inverse association was observed in populations of mixed/African origin (OR = 0.8, 95% CI: 0.7, 0.9). The pooled analysis suggested a possible association in Caucasians (for Val/Val and Val/Leu combined, OR = 1.5, 95% CI: 1.1, 2.1), with effect modification across age categories. The observed effect of age on the association in Caucasians indicates that further studies are needed on the role of CYP1B1 Val432Leu in estrogen metabolism according to age, ethnicity, and menopausal status. [ABSTRACT FROM AUTHOR]

Given the promise of rich biological information in microarray data we will expect an increasing demand for a robust, practical and well-tested methodology to provide patient prognosis based on gene expression data. In standard settings, with few clinical predictors, such a methodology has been provided by the Cox proportional hazard model, but no corresponding methodology is available to deal with the full set of genes in microarray data. Furthermore, we want the procedure to be able to deal with the general survival data that include censored information. Conceptually such a procedure can be constructed quite easily, but its implementation will never be straightforward due to computational problems. We have developed an approach that relies on an extension of the Cox proportional likelihood that allows random effects parameters. In this approach, we use the full set of genes in the analysis and deal with survival data in the most general way. We describe the development of the model and the steps in the implementation, including a fast computational formula based on a subsampling of the risk set and the singular value decomposition. Finally, we illustrate the methodology using a data set obtained from a cohort of breast cancer patients. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It has become increasingly clear that cancer can be considered neither purely genetic nor purely environmental. A relatively new area of cancer research has focused on the interaction between genes and environment in the same causal mechanism. Primary candidates for gene-environment interaction studies have been genes that encode enzymes involved in the metabolism of established cancer risk factors. There are common variant forms of these genes (polymorphisms), which may alter metabolism and increase or decrease exposure to carcinogens, thus impacting the risk of cancer. We present an overview of enzymes involved in carcinogen metabolism, present epidemiological tools to evaluate gene-environment interactions, and provide examples from cancers of the breast, lung and large bowel. [ABSTRACT FROM AUTHOR]