Prostate cancer is a highly heritable molecularly and clinically heterogeneous disease. To discover germline events involved in prostate cancer predisposition, we develop a computational approach to nominate heritable facilitators of somatic genomic events in the context of the androgen receptor signaling. Here, we use a ranking score and benign prostate transcriptomes to identify a non-coding polymorphic regulatory element at 7p14.3 that associates with DNA repair and hormone-regulated transcript levels and with an early recurrent prostate cancer-specific somatic mutation in the Speckle-Type POZ protein (SPOP) gene. The locus shows allele-specific activity that is concomitantly modulated by androgen receptor and by CCAAT/enhancer-binding protein (C/EBP) beta (CEBPB). Deletion of this locus via CRISPR-Cas9 leads to deregulation of the genes predicted to interact with the 7p14.3 locus by Hi-C chromosome conformation capture data. This study suggests that a polymorphism at 7p14.3 may predispose to SPOP mutant prostate cancer subclass through a hormone-dependent DNA damage response.

Spinobulbar muscular atrophy (SBMA) is caused by CAG expansions in the androgen receptor gene. Androgen binding to polyQ-expanded androgen receptor triggers SBMA through a combination of toxic gain-of-function and loss-of-function mechanisms. Leveraging cell lines, mice, and patient-derived specimens, we show that androgen receptor co-regulators lysine-specific demethylase 1 (LSD1) and protein arginine methyltransferase 6 (PRMT6) are overexpressed in an androgen-dependent manner specifically in the skeletal muscle of SBMA patients and mice. LSD1 and PRMT6 cooperatively and synergistically transactivate androgen receptor, and their effect is enhanced by expanded polyQ. Pharmacological and genetic silencing of LSD1 and PRMT6 attenuates polyQ-expanded androgen receptor transactivation in SBMA cells and suppresses toxicity in SBMA flies, and a preclinical approach based on miRNA-mediated silencing of LSD1 and PRMT6 attenuates disease manifestations in SBMA mice. These observations suggest that targeting overexpressed co-regulators can attenuate androgen receptor toxic gain-of-function without exacerbating loss-of-function, highlighting a potential therapeutic strategy for patients with SBMA.

Next-generation sequencing (NGS) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Identification and stratification of an individual's ancestry is fundamental for the correct interpretation of genetic and genomic profiling. EthSEQ provides an easy and effective computational workflow to determine the ancestry of individuals, exploiting single nucleotide polymorphism genotypes computed from NGS data of whole-exome and targeted sequencing experiments. Genotypes are determined by EthSEQ from sequencing alignment files (BAM files) or can be provided as input in Variant Call Format (VCF) or CoreArray Genomic Data Structure (GDS) format. Ancestry is determined and assigned to individuals by EthSEQ exploiting a reference model and a standard or multi-step refinement approach based on Principal Component Analysis (PCA). A complete and detailed set of textual and graphical output files are generated by EthSEQ as result. EthSEQ is easy to use and can be integrated into any NGS-based processing pipeline also exploiting multi-core capabilities.

Understanding the interaction between human genome regulatory elements and transcription factors is fundamental to elucidate the structure of gene regulatory networks. Here we present CONREL, a web application that allows for the exploration of functionally annotated transcriptional ‘consensus’ regulatory elements at different levels of abstraction. CONREL provides an extensive collection of consensus promoters, enhancers and active enhancers for 198 cell-lines across 38 tissue types, which are also combined to provide global consensuses. In addition, 1000 Genomes Project genotype data and the ‘total binding affinity’ of thousands of transcription factor binding motifs at genomic regulatory elements is fully combined and exploited to characterize and annotate functional properties of our collection. Comparison with other available resources highlights the strengths and advantages of CONREL. CONREL can be used to explore genomic loci, specific genes or genomic regions of interest across different cell lines and tissue types. The resource is freely available at https://bcglab.cibio.unitn.it/conrel.

In the last years, many studies were able to identify associations between common genetic variants and complex diseases. However, the mechanistic biological links explaining these associations are still mostly unknown. Common variants are usually associated with a relatively small effect size, suggesting that interactions among multiple variants might be a major genetic component of complex diseases. Hence, elucidating the presence of functional relations among variants may be fundamental to identify putative variants’ interactions. To this aim, we developed Polympact, a web-based resource that allows to explore functional relations among human common variants by exploiting variants’ functional element landscape, their impact on transcription factor binding motifs, and their effect on transcript levels of protein-coding genes. Polympact characterizes over 18 million common variants and allows to explore putative relations by combining clustering analysis and innovative similarity and interaction network models. The properties of the network models were studied and the utility of Polympact was demonstrated by analysing the rich sets of Breast Cancer and Alzheimer's GWAS variants. We identified relations among multiple variants, suggesting putative interactions. Polympact is freely available at bcglab.cibio.unitn.it/polympact.