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Abstract Background Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types–including sequence conservation, nucleosome positioning, and negative examples–can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM). Results We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior. Conclusions We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.

Purpose: To infer the prognostic value of simultaneous androgen receptor ( AR ) and TP53 profiling in liquid biopsies from patients with metastatic castration-resistant prostate cancer (mCRPC) starting a new line of AR signaling inhibitors (ARSi). Experimental Design: Between March 2014 and April 2017, we recruited patients with mCRPC ( n = 168) prior to ARSi in a cohort study encompassing 10 European centers. Blood samples were collected for comprehensive profiling of CellSearch-enriched circulating tumor cells (CTC) and circulating tumor DNA (ctDNA). Targeted CTC RNA sequencing (RNA-seq) allowed the detection of eight AR splice variants (ARV). Low-pass whole-genome and targeted gene-body sequencing of AR and TP53 was applied to identify amplifications, loss of heterozygosity, mutations, and structural rearrangements in ctDNA. Clinical or radiologic progression-free survival (PFS) was estimated by Kaplan-Meier analysis, and independent associations were determined using multivariable Cox regression models., Results: Overall, no single AR perturbation remained associated with adverse prognosis after multivariable analysis. Instead, tumor burden estimates (CTC counts, ctDNA fraction, and visceral metastases) were significantly associated with PFS. TP53 inactivation harbored independent prognostic value [HR 1.88; 95% confidence interval (CI), 1.18-3.00; P = 0.008], and outperformed ARV expression and detection of genomic AR alterations. Using Cox coefficient analysis of clinical parameters and TP53 status, we identified three prognostic groups with differing PFS estimates (median, 14.7 vs. 7.51 vs. 2.62 months; P < 0.0001), which was validated in an independent mCRPC cohort ( n = 202) starting first-line ARSi (median, 14.3 vs. 6.39 vs. 2.23 months; P < 0.0001)., Conclusions: In an all-comer cohort, tumor burden estimates and TP53 outperform any AR perturbation to infer prognosis.See related commentary by Rebello et al., p. 1699., (©2018 American Association for Cancer Research.)

Background: There are multiple existing and emerging therapeutic avenues for metastatic prostate cancer, with a common denominator, which is the need for predictive biomarkers. Circulating tumor DNA (ctDNA) has the potential to cost-efficiently accelerate precision medicine trials to improve clinical efficacy and diminish costs and toxicity. However, comprehensive ctDNA profiling in metastatic prostate cancer to date has been limited., Methods: A combination of targeted and low-pass whole genome sequencing was performed on plasma cell-free DNA and matched white blood cell germline DNA in 364 blood samples from 217 metastatic prostate cancer patients., Results: ctDNA was detected in 85.9% of baseline samples, correlated to line of therapy and was mirrored by circulating tumor cell enumeration of synchronous blood samples. Comprehensive profiling of the androgen receptor (AR) revealed a continuous increase in the fraction of patients with intra-AR structural variation, from 15.4% during first-line metastatic castration-resistant prostate cancer therapy to 45.2% in fourth line, indicating a continuous evolution of AR during the course of the disease. Patients displayed frequent alterations in DNA repair deficiency genes (18.0%). Additionally, the microsatellite instability phenotype was identified in 3.81% of eligible samples (≥ 0.1 ctDNA fraction). Sequencing of non-repetitive intronic and exonic regions of PTEN, RB1, and TP53 detected biallelic inactivation in 47.5%, 20.3%, and 44.1% of samples with ≥ 0.2 ctDNA fraction, respectively. Only one patient carried a clonal high-impact variant without a detectable second hit. Intronic high-impact structural variation was twice as common as exonic mutations in PTEN and RB1. Finally, 14.6% of patients presented false positive variants due to clonal hematopoiesis, commonly ignored in commercially available assays., Conclusions: ctDNA profiles appear to mirror the genomic landscape of metastatic prostate cancer tissue and may cost-efficiently provide somatic information in clinical trials designed to identify predictive biomarkers. However, intronic sequencing of the interrogated tumor suppressors challenges the ubiquitous focus on coding regions and is vital, together with profiling of synchronous white blood cells, to minimize erroneous assignments which in turn may confound results and impede true associations in clinical trials.

Phosphorylcholine (PC) is an epitope on oxidized low-density lipoprotein (oxLDL), apoptotic cells and several pathogens like Streptococcus pneumoniae. Immunoglobulin M against PC (IgM anti-PC) has the ability to inhibit uptake of oxLDL by macrophages and increase clearance of apoptotic cells. From our genome-wide association studies (GWASs) in four European-ancestry cohorts, six single nucleotide polymorphisms (SNPs) in 11q24.1 were discovered (in 3002 individuals) and replicated (in 646 individuals) to be associated with serum level of IgM anti-PC (the leading SNP rs35923643-G, combined β = 0.19, 95% confidence interval 0.13-0.24, P = 4.3 × 10-11). The haplotype tagged by rs35923643-G (or its proxy SNP rs735665-A) is also known as the top risk allele for chronic lymphocytic leukemia (CLL), and a main increasing allele for general IgM. By using summary GWAS results of IgM anti-PC and CLL in the polygenic risk score (PRS) analysis, PRS on the basis of IgM anti-PC risk alleles positively associated with CLL risk (explained 0.6% of CLL variance, P = 1.2 × 10-15). Functional prediction suggested that rs35923643-G might impede the binding of Runt-related transcription factor 3, a tumor suppressor playing a central role in the immune regulation of cancers. Contrary to the expectations from the shared genetics between IgM anti-PC and CLL, an inverse relationship at the phenotypic level was found in a nested case-control study (30 CLL cases with 90 age- and sex-matched controls), potentially reflecting reverse causation. The suggested function of the top variant as well as the phenotypic association between IgM anti-PC and CLL risk needs replication and motivates further studies.

Despite being discovered almost 50 years ago, little is known regarding the genetic profile of ductal adenocarcinoma of the prostate (DAC). In recent years, progress has been made in the understanding of the genetics of acinar adenocarcinomas, and at least 7 genetically different subtypes have been identified. DAC is known to present at an advanced stage with a high rate of extraprostatic extension and seminal vesicle invasion, and a decreased interval to biochemical recurrence and the development of metastatic disease when compared with acinar adenocarcinoma. Our aim was to investigate the genetic profile of DAC to determine whether there is a genomic rationale for the aggressive behavior associated with this tumor type. Frozen tissue from 11 cases of DAC with paired benign tissue was analyzed. After DNA extraction, copy-number alteration analysis was performed, as well as identification of mutations and indels. We compared the fraction of the DAC genome with copy-number alteration to previous results from 74 primary acinar adenocarcinomas of the prostate. The alteration rate in DAC was comparable to that of acinar adenocarcinoma of high Gleason score. DAC harbored somatic changes seen in advanced and/or metastatic castration-resistant acinar adenocarcinoma, which likely accounts for its aggressive biological behavior., (Copyright © 2017. Published by Elsevier Inc.)

Background: Expression of the androgen receptor splice variant 7 (AR-V7) is associated with poor response to second-line endocrine therapy in castration-resistant prostate cancer (CRPC). However, a large fraction of nonresponding patients are AR-V7-negative., Objective: To investigate if a comprehensive liquid biopsy-based AR profile may improve patient stratification in the context of second-line endocrine therapy., Design, Setting, and Participants: Peripheral blood was collected from patients with CRPC (n=30) before initiation of a new line of systemic therapy. We performed profiling of circulating tumour DNA via low-pass whole-genome sequencing and targeted sequencing of the entire AR gene, including introns. Targeted RNA sequencing was performed on enriched circulating tumour cell fractions to assess the expression levels of seven AR splice variants (ARVs)., Outcome Measurements and Statistical Analysis: Somatic AR variations, including copy-number alterations, structural variations, and point mutations, were combined with ARV expression patterns and correlated to clinicopathologic parameters., Results and Limitations: Collectively, any AR perturbation, including ARV, was detected in 25/30 patients. Surprisingly, intra-AR structural variation was present in 15/30 patients, of whom 14 expressed ARVs. The majority of ARV-positive patients expressed multiple ARVs, with AR-V3 the most abundantly expressed. The presence of any ARV was associated with progression-free survival after second-line endocrine treatment (hazard ratio 4.53, 95% confidence interval 1.424-14.41; p=0.0105). Six out of 17 poor responders were AR-V7-negative, but four carried other AR perturbations., Conclusions: Comprehensive AR profiling, which is feasible using liquid biopsies, is necessary to increase our understanding of the mechanisms underpinning resistance to endocrine treatment., Patient Summary: Alterations in the androgen receptor are associated with endocrine treatment outcomes. This study demonstrates that it is possible to identify different types of alterations via simple blood draws. Follow-up studies are needed to determine the effect of such alterations on hormonal therapy., (Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical., Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR)., Results: We observed no significant association between genetic variants and prostate cancer survival., Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study., Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes., (©2015 American Association for Cancer Research.)

Background: Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction., Methods: We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls., Results: The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk., Conclusions: Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction., (© 2015 Wiley Periodicals, Inc.)

Genome-wide association studies have identified thousands of SNPs associated with predisposition to various diseases, including prostate cancer. However, the mechanistic roles of these SNPs remain poorly defined, particularly for noncoding polymorphisms. Here we find that the prostate cancer risk-associated SNP rs339331 at 6q22 lies within a functional HOXB13-binding site. The risk-associated T allele at rs339331 increases binding of HOXB13 to a transcriptional enhancer, conferring allele-specific upregulation of the rs339331-associated gene RFX6. Suppression of RFX6 diminishes prostate cancer cell proliferation, migration and invasion. Clinical data indicate that RFX6 upregulation in human prostate cancers correlates with tumor progression, metastasis and risk of biochemical relapse. Finally, we observe a significant association between the risk-associated T allele at rs339331 and increased RFX6 mRNA levels in human prostate tumors. Together, our results suggest that rs339331 affects prostate cancer risk by altering RFX6 expression through a functional interaction with the prostate cancer susceptibility gene HOXB13.

During cell division, transcription factors (TFs) are removed from chromatin twice, during DNA synthesis and during condensation of chromosomes. How TFs can efficiently find their sites following these stages has been unclear. Here, we have analyzed the binding pattern of expressed TFs in human colorectal cancer cells. We find that binding of TFs is highly clustered and that the clusters are enriched in binding motifs for several major TF classes. Strikingly, almost all clusters are formed around cohesin, and loss of cohesin decreases both DNA accessibility and binding of TFs to clusters. We show that cohesin remains bound in S phase, holding the nascent sister chromatids together at the TF cluster sites. Furthermore, cohesin remains bound to the cluster sites when TFs are evicted in early M phase. These results suggest that cohesin-binding functions as a cellular memory that promotes re-establishment of TF clusters after DNA replication and chromatin condensation., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Most cancer-associated single-nucleotide polymorphisms (SNP) identified using genome-wide association studies are located outside of protein-coding regions, and their significance and mode of action have been a source of continuing debate. One proposed mechanism of action of the SNPs is that they would affect the activity of enhancer elements regulating critical target genes. In this review, we summarize recent results that substantiate this model. These studies have identified a cancer-specific enhancer element at the 8q24 gene desert that controls the expression of the MYC oncogene. We further discuss implications of the observed difference between normal growth control and cancer for drug development, and the inherent features of genome-wide association studies that may specifically lead to identification of disease-specific regulatory elements., (©2013 AACR.)

Motivation: Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored., Results: We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consistently outperforms a simple binary filter based on the same data. We also show that our approach performs competitively with a more complex method, CENTIPEDE, and suggest that the relative simplicity of the log-posterior odds scoring method makes it an appealing and very general method for identifying functional TFBSs on the basis of DNA and epigenetic evidence., Availability and Implementation: FIMO, part of the MEME Suite software toolkit, now supports log-posterior odds scoring using position-specific priors for motif search. A web server and source code are available at http://meme.nbcr.net. Utilities for creating priors are at http://research.imb.uq.edu.au/t.bailey/SD/Cuellar2011., Contact: t.bailey@uq.edu.au, Supplementary Information: Supplementary data are available at Bioinformatics online.

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) allows researchers to determine the genome-wide binding locations of individual transcription factors (TFs) at high resolution. This information can be interrogated to study various aspects of TF behaviour, including the mechanisms that control TF binding. Physical interaction between TFs comprises one important aspect of TF binding in eukaryotes, mediating tissue-specific gene expression. We have developed an algorithm, spaced motif analysis (SpaMo), which is able to infer physical interactions between the given TF and TFs bound at neighbouring sites at the DNA interface. The algorithm predicts TF interactions in half of the ChIP-seq data sets we test, with the majority of these predictions supported by direct evidence from the literature or evidence of homodimerization. High resolution motif spacing information obtained by this method can facilitate an improved understanding of individual TF complex structures. SpaMo can assist researchers in extracting maximum information relating to binding mechanisms from their TF ChIP-seq data. SpaMo is available for download and interactive use as part of the MEME Suite (http://meme.nbcr.net).

Cell differentiation during development is controlled by extracellular morphogens, which induce responding cells to differentiate into distinct cell fates based on the dose of morphogen they receive. Genes that specify the distinct cell fates are differentially responsive to morphogens, and the extracellular morphogen gradient is converted in responding cells to graded activity of transcription factors. In the case of Hedgehog, the gradient is converted to opposing gradients of transcriptional activator and repressor forms of the transcription factor Cubitus interruptus (Ci). It has been generally assumed that the balance between activator and repressor determines target gene responses within this gradient. However, new evidence shows that enhancers can respond selectively to the activator and repressor forms of Ci, and that this selectivity is determined by the affinity of Ci sites within the enhancers.

KLF1 regulates a diverse suite of genes to direct erythroid cell differentiation from bipotent progenitors. To determine the local cis-regulatory contexts and transcription factor networks in which KLF1 operates, we performed KLF1 ChIP-seq in the mouse. We found at least 945 sites in the genome of E14.5 fetal liver erythroid cells which are occupied by endogenous KLF1. Many of these recovered sites reside in erythroid gene promoters such as Hbb-b1, but the majority are distant to any known gene. Our data suggests KLF1 directly regulates most aspects of terminal erythroid differentiation including production of alpha- and beta-globin protein chains, heme biosynthesis, coordination of proliferation and anti-apoptotic pathways, and construction of the red cell membrane and cytoskeleton by functioning primarily as a transcriptional activator. Additionally, we suggest new mechanisms for KLF1 cooperation with other transcription factors, in particular the erythroid transcription factor GATA1, to maintain homeostasis in the erythroid compartment.

Background: Position-specific priors have been shown to be a flexible and elegant way to extend the power of Gibbs sampler-based motif discovery algorithms. Information of many types-including sequence conservation, nucleosome positioning, and negative examples-can be converted into a prior over the location of motif sites, which then guides the sequence motif discovery algorithm. This approach has been shown to confer many of the benefits of conservation-based and discriminative motif discovery approaches on Gibbs sampler-based motif discovery methods, but has not previously been studied with methods based on expectation maximization (EM)., Results: We extend the popular EM-based MEME algorithm to utilize position-specific priors and demonstrate their effectiveness for discovering transcription factor (TF) motifs in yeast and mouse DNA sequences. Utilizing a discriminative, conservation-based prior dramatically improves MEME's ability to discover motifs in 156 yeast TF ChIP-chip datasets, more than doubling the number of datasets where it finds the correct motif. On these datasets, MEME using the prior has a higher success rate than eight other conservation-based motif discovery approaches. We also show that the same type of prior improves the accuracy of motifs discovered by MEME in mouse TF ChIP-seq data, and that the motifs tend to be of slightly higher quality those found by a Gibbs sampling algorithm using the same prior., Conclusions: We conclude that using position-specific priors can substantially increase the power of EM-based motif discovery algorithms such as MEME algorithm.

In silico prediction of transcription factor binding sites (TFBSs) is central to the task of gene regulatory network elucidation. Genomic DNA sequence information provides a basis for these predictions, due to the sequence specificity of TF-binding events. However, DNA sequence alone is an impoverished source of information for the task of TFBS prediction in eukaryotes, as additional factors, such as chromatin structure regulate binding events. We show that incorporating high-throughput chromatin modification estimates can greatly improve the accuracy of in silico prediction of in vivo binding for a wide range of TFs in human and mouse. This improvement is superior to the improvement gained by equivalent use of either transcription start site proximity or phylogenetic conservation information. Importantly, predictions made with the use of chromatin structure information are tissue specific. This result supports the biological hypothesis that chromatin modulates TF binding to produce tissue-specific binding profiles in higher eukaryotes, and suggests that the use of chromatin modification information can lead to accurate tissue-specific transcriptional regulatory network elucidation.