Replication of prostate cancer risk loci on 8q24, 11q13, 17q12, 19q33, and Xp11 in African Americans

Molecular genetics of prostate cancer: new prospects for old challenges

Despite much recent progress, prostate cancer continues to represent a major cause of cancer-related mortality and morbidity in men. Since early studies on the role of the androgen receptor that led to the advent of androgen deprivation therapy in the 1940s, there has long been intensive interest in the basic mechanisms underlying prostate cancer initiation and progression, as well as the potential to target these processes for therapeutic intervention. Here, we present an overview of major themes in prostate cancer research, focusing on current knowledge of principal events in cancer initiation and progression. We discuss recent advances, including new insights into the mechanisms of castration resistance, identification of stem cells and tumor-initiating cells, and development of mouse models for preclinical evaluation of novel therapuetics. Overall, we highlight the tremendous research progress made in recent years, and underscore the challenges that lie ahead.

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression

Prediction of cancer progression after radical prostatectomy is one of the most challenging problems in the management of prostate cancer. Gene-expression profiling is widely used to identify genes associated with such progression. Usually candidate genes are identified according to a gene-by-gene comparison of expression. Recent reports suggested that relative expression of a gene pair more efficiently predicts cancer progression than single-gene analysis does. The top-scoring pair (TSP) algorithm classifies phenotypes according to the relative expression of a pair of genes. We applied the TSP approach to predict, which patients would experience systemic tumor progression after radical prostatectomy. Relative expression of TPD52L2/SQLE and CEACAM1/BRCA1 gene pairs identified those patients with more than 99% specificity but relatively low sensitivity (approximately 10%). These two gene pairs were validated in three independent data sets. In addition, combining two pairs of genes improved sensitivity without compromising specificity. Functional annotation of the TSP genes showed that they cluster by a limited number of biological functions and pathways, suggesting that relatively lower expression of genes from specific pathways can predict cancer progression. In conclusion, comparative analysis of the expression of two genes may be a simple and effective classifier for prediction of prostate cancer progression. In summary, the TSP approach can be used to identify patients whose prostate cancer will progress after they undergo radical prostatectomy. Two gene pairs can predict which men would experience progression to the metastatic form of the disease. However, because our analysis was based on a relatively small number of genes, a larger study will be needed to identify the best predictors of disease outcome overall.