Toll-like receptor 4 (TLR4) is one of the best known TLR members expressed on the surface of several leukocytes and tissue cells and has a key function in detecting pathogen and danger-associated molecular patterns. The role of TLR4 in the pathophysiology of several age-related diseases is also well recognized, such as prostate cancer (PCa). TLR4 polymorphisms have been related to PCa risk, but the relationship between TLR4 genotypes and aggressive PCa risk has not been evaluated by any systematic reviews.

The prevalence of specific infections in UK prostate cancer patients was investigated. Serum from 84 patients and 62 controls was tested for neutralisation of xenotropic murine leukaemia virus-related virus (XMRV) Envelope. No reactivity was found in the patient samples. In addition, a further 100 prostate DNA samples were tested for XMRV, BK virus, Trichomonas vaginalis and human papilloma viruses by nucleic acid detection techniques. Despite demonstrating DNA integrity and assay sensitivity, we failed to detect the presence of any of these agents in DNA samples, bar one sample that was weakly positive for HPV16. Therefore we conclude that these infections are absent in this typical cohort of men with prostate cancer.

Kallikrein 15 (KLK15)/Prostinogen is a plausible candidate for prostate cancer susceptibility. Elevated KLK15 expression has been reported in prostate cancer and it has been described as an unfavorable prognostic marker for the disease.

Prostate-specific antigen (PSA) testing is a widely accepted screening method for prostate cancer, but with low specificity at thresholds giving good sensitivity. Previous research identified four single nucleotide polymorphisms (SNPs) principally associated with circulating PSA levels rather than with prostate cancer risk (TERT rs2736098, FGFR2 rs10788160, TBX3 rs11067228, KLK3 rs17632542). Removing the genetic contribution to PSA levels may improve the ability of the remaining biologically-determined variation in PSA to discriminate between high and low risk of progression within men with identified prostate cancer. We investigate whether incorporating information on the PSA-SNPs improves the discrimination achieved by a single PSA threshold in men with raised PSA levels.

Genome-wide association studies have found type 2 diabetes-associated variants in the HNF1B gene to exhibit reciprocal associations with prostate cancer risk. We aimed to identify whether these variants may have an effect on cancer risk in general versus a specific effect on prostate cancer only.

Sample tracking errors have been and always will be a part of the practical implementation of large experiments. It has recently been proposed that expression quantitative trait loci (eQTLs) and their associated effects could be used to identify sample mix-ups and this approach has been applied to a number of large population genomics studies to illustrate the prevalence of the problem. We had adopted a similar approach, termed 'BADGER', in the METABRIC project. METABRIC is a large breast cancer study that may have been the first in which eQTL-based detection of mismatches was used during the study, rather than after the event, to aid quality assurance. We report here on the particular issues associated with large cancer studies performed using historical samples, which complicate the interpretation of such approaches. In particular we identify the complications of using tumour samples, of considering cellularity and RNA quality, of distinct subgroups existing in the study population (including family structures), and of choosing eQTLs to use. We also present some results regarding the design of experiments given consideration of these matters. The eQTL-based approach to identifying sample tracking errors is seen to be of value to these studies, but requiring care in its implementation.

Timing common and specific modulators of disease progression is crucial for treatment, but the understanding of the underlying complex system of interactions is limited. While attempts at elucidating this experimentally have produced enormous amounts of phenotypic data, tools that are able to visualize and analyze them are scarce and the insight obtained from the data is often unsatisfactory. Linking and visualizing processes from genes to phenotypes and back, in a temporal context, remains a challenge in systems biology. We introduce PhenoTimer, a 2D/3D visualization tool for the mapping of time-resolved phenotypic links in a genetic context. It uses a novel visualization approach for relations between morphological defects, pathways or diseases, to enable fast pattern discovery and hypothesis generation. We illustrate its capabilities of tracing dynamic motifs on cell cycle datasets that explore the phenotypic order of events upon perturbations of the system, transcriptional activity programs and their connection to disease. By using this tool we are able to fine-grain regulatory programs for individual time points of the cell cycle and better understand which patterns arise when these programs fail. We also illustrate a way to identify common mechanisms of misregulation in diseases and drug abuse.