Genomic DNA Methylation-Derived Algorithm Enables Accurate Detection of Malignant Prostate Tissues

DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array.

During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.

Perineural invasion is the only interaction between cancer cells and nerves studied to date. It is a symbiotic relationship between cancer and nerves that results in growth advantage for both. In this article, we present data on a novel biological phenomenon, cancer-related axonogenesis and neurogenesis. We identify spatial and temporal associations between increased nerve density and preneoplastic and neoplastic lesions of the human prostate. Nerve density was increased in cancer areas as well as in preneoplastic lesions compared with controls. Two- and three-dimensional reconstructions of entire prostates confirmed axonogenesis in human tumors. Furthermore, patients with prostate cancer had increased numbers of neurons in their prostatic ganglia compared with controls, corroborating neurogenesis. Finally, two in vitro models confirmed that cancer cells, particularly when interacting with nerves in perineural invasion, induce neurite outgrowth in prostate cancer. Neurogenesis is correlated with features of aggressive prostate cancer and with recurrence in prostate cancer. We also present a putative regulatory mechanism based on semaphorin 4F (S4F). S4F is overexpressed in cancers cells in the perineural in vitro model. Overexpression of S4F in prostate cancer cells induces neurogenesis in the N1E-115 neurogenesis assay and S4F inhibition by small interfering RNA blocks this effect. This is the first description of cancer-related neurogenesis and its putative regulatory mechanism.