Prostate biopsy in selecting candidates for hemiablative focal therapy.

Focal therapy (FT) for the management of clinically localized prostate cancer (PCa) is growing from a concept to reality because of increased interest of both patients and physicians. Selection protocols, however, are yet to be established. We discuss the role of prostate biopsy in candidate selection for FT and highlight the different strategies and technical aspects of the use of prostate biopsy in this setting. In our opinion, prostate biopsy plays a major role in the selection process and tailoring appropriate treatment strategy to the patient. FT necessitates dedicated biopsy schemes that would reliably predict the extent, nature, and location of PCa in selected patients. Currently, there is insufficient scientific evidence to propose a specific biopsy scheme that could fit every candidate, providing accurate characterization of the disease in the individual patient. Further research is necessary to establish solid selection protocols that would reliably identify appropriate candidates for FT of PCa.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Genetic variants in the TEP1 gene are associated with prostate cancer risk and recurrence.

BACKGROUND: Telomere-related genes play an important role in carcinogenesis and progression of prostate cancer (PCa). It is not fully understood whether genetic variations in telomere-related genes are associated with development and progression in PCa patients. METHODS: Six potentially functional single-nucleotide polymorphisms (SNPs) of three key telomere-related genes were evaluated in 1015 PCa cases and 1052 cancer-free controls, to test their associations with risk of PCa. Among 426 PCa patients who underwent radical prostatectomy (RP), the prognostic significance of the studied SNPs on biochemical recurrence (BCR) was also assessed using the Kaplan-Meier analysis and Cox proportional hazards regression model. The relative telomere lengths (RTLs) were measured in peripheral blood leukocytes using real-time PCR in the RP patients. RESULTS: TEP1 rs1760904 AG/AA genotypes were significantly associated with a decreased risk of PCa (odds ratio (OR): 0.77, 95% confidence interval (CI): 0.64-0.93, P=0.005) compared with the GG genotype. By using median RTL as a cutoff level, RP patients with TEP1 rs1760904 AG/AA genotypes tended to have a longer RTL than those with the GG genotype (OR: 1.55, 95% CI: 1.04-2.30, P=0.031). A significant interaction between TEP1 rs1713418 and age in modifying PCa risk was observed (P=0.005). After adjustment for clinicopathologic risk factors, the presence of heterozygotes or rare homozygotes of TEP1 rs1760904 and TNKS2 rs1539042 were associated with BCR in the RP cohorts (hazard ratio: 0.53, 95% CI: 0.36-0.79, P=0.002 and hazard ratio: 1.67, 95% CI: 1.07-2.48, P=0.017, respectively). CONCLUSIONS: These data suggest that genetic variations in the TEP1 gene may be biomarkers for risk of PCa and BCR after RP.