A large study of androgen receptor germline variants and their relation to sex hormone levels and prostate cancer risk. Results from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium

Background: Androgens are key regulators of prostate gland maintenance and prostate cancer growth, and androgen deprivation therapy has been the mainstay of treatment for advanced prostate cancer for many years. A long-standing hypothesis has been that inherited variation in the androgen receptor (AR) gene plays a role in prostate cancer initiation. However, studies to date have been inconclusive and often suffered from small sample sizes. Objective and Methods: We investigated the association of AR sequence variants with circulating sex hormone levels and prostate cancer risk in 6058 prostate cancer cases and 6725 controls of Caucasian origin within the Breast and Prostate Cancer Cohort Consortium. We genotyped a highly polymorphic CAG microsatellite in exon 1 and six haplotype tagging single nucleotide polymorphisms and tested each genetic variant for association with prostate cancer risk and with sex steroid levels. Results: We observed no association between AR genetic variants and prostate cancer risk. However, there was a strong association between longer CAG repeats and higher levels of testosterone (P = 4.73 × 10−5) and estradiol (P = 0.0002), although the amount of variance explained was small (0.4 and 0.7%, respectively). Conclusions: This study is the largest to date investigating AR sequence variants, sex steroid levels, and prostate cancer risk. Although we observed no association between AR sequence variants and prostate cancer risk, our results support earlier findings of a relation between the number of CAG repeats and circulating levels of testosterone and estradiol.

Cancer genes hypermethylated in human embryonic stem cells.

Developmental genes are silenced in embryonic stem cells by a bivalent histone-based chromatin mark. It has been proposed that this mark also confers a predisposition to aberrant DNA promoter hypermethylation of tumor suppressor genes (TSGs) in cancer. We report here that silencing of a significant proportion of these TSGs in human embryonic and adult stem cells is associated with promoter DNA hypermethylation. Our results indicate a role for DNA methylation in the control of gene expression in human stem cells and suggest that, for genes repressed by promoter hypermethylation in stem cells in vivo, the aberrant process in cancer could be understood as a defect in establishing an unmethylated promoter during differentiation, rather than as an anomalous process of de novo hypermethylation.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).