While mutations affecting protein-coding regions have been examined across many cancers, structural variants at the genome-wide level are still poorly defined. Through integrative deep whole-genome and -transcriptome analysis of 101 castration-resistant prostate cancer metastases (109X tumor/38X normal coverage), we identified structural variants altering critical regulators of tumorigenesis and progression not detectable by exome approaches. Notably, we observed amplification of an intergenic enhancer region 624 kb upstream of the androgen receptor (AR) in 81% of patients, correlating with increased AR expression. Tandem duplication hotspots also occur near MYC, in lncRNAs associated with post-translational MYC regulation. Classes of structural variations were linked to distinct DNA repair deficiencies, suggesting their etiology, including associations of CDK12 mutation with tandem duplications, TP53 inactivation with inverted rearrangements and chromothripsis, and BRCA2 inactivation with deletions. Together, these observations provide a comprehensive view of how structural variations affect critical regulators in metastatic prostate cancer.

The mammalian genome depends on patterns of methylated cytosines for normal function, but the relationship between genomic methylation patterns and the underlying sequence is unclear. We have characterized the methylation landscape of the human genome by global analysis of patterns of CpG depletion and by direct sequencing of 3073 unmethylated domains and 2565 methylated domains from human brain DNA. The genome was found to consist of short (<4 kb) unmethylated domains embedded in a matrix of long methylated domains. Unmethylated domains were enriched in promoters, CpG islands, and first exons, while methylated domains comprised interspersed and tandem-repeated sequences, exons other than first exons, and non-annotated single-copy sequences that are depleted in the CpG dinucleotide. The enrichment of regulatory sequences in the relatively small unmethylated compartment suggests that cytosine methylation constrains the effective size of the genome through the selective exposure of regulatory sequences. This buffers regulatory networks against changes in total genome size and provides an explanation for the C value paradox, which concerns the wide variations in genome size that scale independently of gene number. This suggestion is compatible with the finding that cytosine methylation is universal among large-genome eukaryotes, while many eukaryotes with genome sizes <5 x 10(8) bp do not methylate their DNA.

Approximately 20% of metastatic prostate cancers harbor mutations in genes required for DNA repair by homologous recombination (HRR) such as BRCA2. HRR defects confer synthetic lethality to PARP inhibitors (PARPi) such as olaparib and talazoparib. In ovarian or breast cancers, olaparib resistance has been associated with HRR restoration, including by BRCA2 mutation reversion. Whether similar mechanisms operate in prostate cancer, and could be detected in liquid biopsies, is unclear. Here, we identify BRCA2 reversion mutations associated with olaparib and talazoparib resistance in prostate cancer patients. Analysis of circulating cell-free DNA reveals reversion mutation heterogeneity not discernable from a single solid tumor biopsy and potentially allows monitoring for the emergence of PARPi resistance.

No abstract

Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called HDFINDER) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using HDFINDER, we have depicted the entire genomic methylation patterns for all 22 human autosomes.DNA methylation ͉ epigenomics ͉ methylation prediction ͉ CpG islands A lthough progress recently has been made toward wholegenome DNA methylation profiling by using molecular techniques, computational epigenomics is still in its infancy (1). Global analyses of DNA methylation have been focused mainly on two themes: the discovery of methylated CpG islands (CGI) and allele-specific cytosine methylation. Computational prediction of CGIs was introduced in 1987 by . They defined CGIs as regions of Ͼ200 bp with GϩC content of Ͼ0.5 and the observed͞expected CpG ratio Ͼ0.6. Takai and Jones (3) later proposed a more stringent definition that requires CGIs to be Ͼ500 bp long, CG content Ͼ55%, and the CpG ratio Ͼ0.65. This latter method is successful in excluding Alu repeats, many of which were annotated as CGIs when the former criteria were used. Matsuo et al. (4) have provided statistical evidence for erosion of mouse CGIs as compared with human ones. They suggested that an accumulation of TpGs and CpAs observed in mouse, presumably due to the higher rate of deamination of the methylated CpGs, results in a lower CpG ratio in mouse. Antequerra and Bird (5) performed comparative analysis on human and mouse and came to a similar conclusion. Yang et al.(6) proposed a computational method to identify genes with significant differences in gene expression between two parental alleles by searching the UniGene database for the presence of monoallelically expressed (or imprinted) genes in the human genome. Wang et al. (7) compared human and mouse sequences for all known imprinted genes and found 15 motifs that are significantly enriched in the imprinted genes. However, currently there is no algorithm that can predict DNA methylation patterns based on the genomic sequence alone. Because almost nothing is known of the mechanisms that target specific sequences for de novo methylation, a key question that arises is whether there are DNA sequences that are more prone or resistant to methylation.To answer this question, we use data that was generated by enzymatic fractionation of 30 Mb of human brain DNA...

No abstract

It has come to our attention that we inadvertently swapped the headings on the two columns of Table S4. From left to right, the headings should read ''No AR peak amplification'' and then ''AR peak amplification''. Only the headings were swapped. The manuscript reports the correct result, and the statistical tests we performed on the values (two-by-two contingency table tests) are unchanged. The error has been corrected online, and we apologize for any confusion it may have caused.

MicroRNA-375 (miR-375) is frequently elevated in prostate tumors and cell-free fractions of patient blood, but its role in genesis and progression of prostate cancer is poorly understood. In this study, we demonstrated that miR-375 is inversely correlated with epithelial-mesenchymal transition signatures (EMT) in clinical samples and can drive mesenchymal-epithelial transition (MET) in model systems. Indeed, miR-375 potently inhibited invasion and migration of multiple prostate cancer lines. The transcription factor YAP1 was found to be a direct target of miR-375 in prostate cancer. Knockdown of YAP1 phenocopied miR-375 overexpression, and overexpression of YAP1 rescued anti-invasive effects mediated by miR-375. Furthermore, transcription of the miR-375 gene was shown to be directly repressed by the EMT transcription factor, ZEB1. Analysis of multiple patient cohorts provided evidence for this ZEB1-miR-375-YAP1 regulatory circuit in clinical samples. Despite its anti-invasive and anti-EMT capacities, plasma miR-375 was found to be correlated with circulating tumor cells in men with metastatic disease. Collectively, this study provides new insight into the function of miR-375 in prostate cancer, and more broadly identifies a novel pathway controlling epithelial plasticity and tumor cell invasion in this disease.