Androgen receptor (AR) signalling is essential in nearly all prostate cancers. Any alterations to AR-mediated transcription can have a profound effect on carcinogenesis and tumor growth. While ...mutations of the AR protein have been extensively studied, little is known about those somatic mutations that occur at the non-coding regions where AR binds DNA. Using clinical whole genome sequencing, we show that AR binding sites have a dramatically increased rate of mutations that is greater than any other transcription factor and specific to only prostate cancer. Demonstrating this may be common to lineage-specific transcription factors, estrogen receptor binding sites were also found to have elevated rate of mutations in breast cancer. We provide evidence that these mutations at AR binding sites, and likely other related transcription factors, are caused by faulty repair of abasic sites. Overall, this work demonstrates that non-coding AR binding sites are frequently mutated in prostate cancer and can impact enhancer activity.
Numerous cancers, including prostate cancer (PCa), are addicted to transcription programs driven by specific genomic regions known as super-enhancers (SEs). The robust transcription of genes at such ...SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator mediator complex subunit 1 (MED1) to SEs in androgen-dependent PCa cells, thereby promoting oncogenic transcriptional programs. In this work, we reveal that full-length AR forms foci with liquid-like properties in different PCa models. We demonstrate that foci formation correlates with AR transcriptional activity, as this activity can be modulated by changing cellular foci content chemically or by silencing MED1. AR ability to phase separate was also validated in vitro by using recombinant full-length AR protein. We also demonstrate that AR antagonists, which suppress transcriptional activity by targeting key regions for homotypic or heterotypic interactions of this receptor, hinder foci formation in PCa cells and phase separation in vitro. Our results suggest that enhanced compartmentalization of AR and coactivators may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa.
Abstract
Motivation
Recent advances in the areas of bioinformatics and chemogenomics are poised to accelerate the discovery of small molecule regulators of cell development. Combining large genomics ...and molecular data sources with powerful deep learning techniques has the potential to revolutionize predictive biology. In this study, we present Deep gene COmpound Profiler (DeepCOP), a deep learning based model that can predict gene regulating effects of low-molecular weight compounds. This model can be used for direct identification of a drug candidate causing a desired gene expression response, without utilizing any information on its interactions with protein target(s).
Results
In this study, we successfully combined molecular fingerprint descriptors and gene descriptors (derived from gene ontology terms) to train deep neural networks that predict differential gene regulation endpoints collected in LINCS database. We achieved 10-fold cross-validation RAUC scores of and above 0.80, as well as enrichment factors of >5. We validated our models using an external RNA-Seq dataset generated in-house that described the effect of three potent antiandrogens (with different modes of action) on gene expression in LNCaP prostate cancer cell line. The results of this pilot study demonstrate that deep learning models can effectively synergize molecular and genomic descriptors and can be used to screen for novel drug candidates with the desired effect on gene expression. We anticipate that such models can find a broad use in developing novel cancer therapeutics and can facilitate precision oncology efforts.
Supplementary information
Supplementary data are available at Bioinformatics online.
The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of ...these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.
Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring
. However, owing to past emphasis on targeted and low-resolution ...profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete
. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.
Epithelial-mesenchymal transition (EMT) programs operate within carcinoma cells, where they generate phenotypes associated with malignant progression. In their various manifestations, EMT programs ...enable epithelial cells to enter into a series of intermediate states arrayed along the E-M phenotypic spectrum. At present, we lack a coherent understanding of how carcinoma cells control their entrance into and continued residence in these various states, and which of these states favour the process of metastasis. Here we characterize a layer of EMT-regulating machinery that governs E-M plasticity (EMP). This machinery consists of two chromatin-modifying complexes, PRC2 and KMT2D-COMPASS, which operate as critical regulators to maintain a stable epithelial state. Interestingly, loss of these two complexes unlocks two distinct EMT trajectories. Dysfunction of PRC2, but not KMT2D-COMPASS, yields a quasi-mesenchymal state that is associated with highly metastatic capabilities and poor survival of patients with breast cancer, suggesting that great caution should be applied when PRC2 inhibitors are evaluated clinically in certain patient cohorts. These observations identify epigenetic factors that regulate EMP, determine specific intermediate EMT states and, as a direct consequence, govern the metastatic ability of carcinoma cells.
The androgen receptor (AR) is the best studied drug target for the treatment of prostate cancer. While there are a number of drugs that target the AR, they all work through the same mechanism of ...action and are prone to the development of drug resistance. There is a large unmet need for novel AR inhibitors which work through alternative mechanism(s). Recent studies have identified a novel site on the AR called binding function 3 (BF3) that is involved into AR transcriptional activity. In order to identify inhibitors that target the BF3 site, we have conducted a large-scale in silico screen followed by experimental evaluation. A number of compounds were identified that effectively inhibited the AR transcriptional activity with no obvious cytotoxicity. The mechanism of action of these compounds was validated by biochemical assays and X-ray crystallography. These findings lay a foundation for the development of alternative or supplementary therapies capable of combating prostate cancer even in its antiandrogen resistant forms.
Androgen deprivation therapy for prostate cancer (PCa) benefits patients with early disease, but becomes ineffective as PCa progresses to a castration-resistant state (CRPC). Initially CRPC remains ...dependent on androgen receptor (AR) signaling, often through increased expression of full-length AR (ARfl) or expression of dominantly active splice variants such as ARv7. We show in ARv7-dependent CRPC models that ARv7 binds together with ARfl to repress transcription of a set of growth-suppressive genes. Expression of the ARv7-repressed targets and ARv7 protein expression are negatively correlated and predicts for outcome in PCa patients. Our results provide insights into the role of ARv7 in CRPC and define a set of potential biomarkers for tumors dependent on ARv7.
Display omitted
•ARfl and ARv7 genomic binding is interdependent and colocalized•ARv7, unlike ARfl, preferentially represses transcription•Expression of ARv7-repressed genes negatively correlates with recurrence•Re-expression of ARv7-repressed genes may serve as a biomarker of ARv7 inhibition
Cato et al. utilize cistrome and transcriptome analyses in castration-resistant prostate cancer (CRPC) to reveal that the androgen receptor (AR) splice variant ARv7 functions as a transcriptional repressor and heterodimerizes with full-length AR at a subset of growth-suppressive genes to support CRPC growth.
Androgen receptor (AR)-mediated transcription is critical in almost all stages of prostate cancer (PCa) growth and differentiation. This process involves a complex interplay of coregulatory proteins, ...chromatin remodeling complexes, and other transcription factors that work with AR at
-regulatory enhancer regions to induce the spatiotemporal transcription of target genes. This enhancer-driven mechanism is remarkably dynamic and undergoes significant alterations during PCa progression. In this review, we discuss the AR mechanism of action in PCa with a focus on how
-regulatory elements modulate gene expression. We explore emerging evidence of genetic variants that can impact AR regulatory regions and alter gene transcription in PCa. Finally, we highlight several outstanding questions and discuss potential mechanisms of this critical transcription factor.
Androgen receptor (AR) is critical to the initiation, growth, and progression of prostate cancer. Once activated, the AR binds to cis-regulatory enhancer elements on DNA that drive gene expression. ...Yet, there are 10-100× more binding sites than differentially expressed genes. It is unclear how or if these excess binding sites impact gene transcription.
To characterize the regulatory logic of AR-mediated transcription, we generated a locus-specific map of enhancer activity by functionally testing all common clinical AR binding sites with Self-Transcribing Active Regulatory Regions sequencing (STARRseq). Only 7% of AR binding sites displayed androgen-dependent enhancer activity. Instead, the vast majority of AR binding sites were either inactive or constitutively active enhancers. These annotations strongly correlated with enhancer-associated features of both in vitro cell lines and clinical prostate cancer samples. Evaluating the effect of each enhancer class on transcription, we found that AR-regulated enhancers frequently interact with promoters and form central chromosomal loops that are required for transcription. Somatic mutations of these critical AR-regulated enhancers often impact enhancer activity.
Using a functional map of AR enhancer activity, we demonstrated that AR-regulated enhancers act as a regulatory hub that increases interactions with other AR binding sites and gene promoters.
PDF
