The PENGUIN approach to reconstruct protein interactions at enhancer-promoter regions and its application to prostate cancer.

We introduce Promoter-Enhancer-Guided Interaction Networks (PENGUIN), a method for studying protein-protein interaction (PPI) networks within enhancer-promoter interactions. PENGUIN integrates H3K27ac-HiChIP data with tissue-specific PPIs to define enhancer-promoter PPI networks (EPINs). We validated PENGUIN using cancer (LNCaP) and benign (LHSAR) prostate cell lines. Our analysis detected EPIN clusters enriched with the architectural protein CTCF, a regulator of enhancer-promoter interactions. CTCF presence was coupled with the prevalence of prostate cancer (PrCa) single nucleotide polymorphisms (SNPs) within the same EPIN clusters, suggesting functional implications in PrCa. Within the EPINs displaying enrichments in both CTCF and PrCa SNPs, we also show enrichment in oncogenes. We substantiated our identified SNPs through CRISPR/Cas9 knockout and RNAi screens experiments. Here we show that PENGUIN provides insights into the intricate interplay between enhancer-promoter interactions and PPI networks, which are crucial for identifying key genes and potential intervention targets. A dedicated server is available at https://penguin.life.bsc.es/ .

Liquid biopsy epigenomic profiling for cancer subtyping.

Although circulating tumor DNA (ctDNA) assays are increasingly used to inform clinical decisions in cancer care, they have limited ability to identify the transcriptional programs that govern cancer phenotypes and their dynamic changes during the course of disease. To address these limitations, we developed a method for comprehensive epigenomic profiling of cancer from 1 ml of patient plasma. Using an immunoprecipitation-based approach targeting histone modifications and DNA methylation, we measured 1,268 epigenomic profiles in plasma from 433 individuals with one of 15 cancers. Our assay provided a robust proxy for transcriptional activity, allowing us to infer the expression levels of diagnostic markers and drug targets, measure the activity of therapeutically targetable transcription factors and detect epigenetic mechanisms of resistance. This proof-of-concept study in advanced cancers shows how plasma epigenomic profiling has the potential to unlock clinically actionable information that is currently accessible only via direct tissue sampling.

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma.

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Utilizing Electronic Health Records (EHR) and Tumor Panel Sequencing to Demystify Prognosis of Cancer of Unknown Primary (CUP) patients.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its original site and accounts for 3-5% of all cancers. It does not have established targeted therapies, leading to poor outcomes. We developed OncoNPC, a machine learning classifier trained on targeted next-generation sequencing data from 34,567 tumors from three institutions. OncoNPC achieved a weighted F1 score of 0.94 for high confidence predictions on known cancer types (65% of held-out samples). When applied to 971 CUP tumors from patients treated at the Dana-Farber Cancer Institute, OncoNPC identified actionable molecular alterations in 23% of the tumors. Furthermore, OncoNPC identified CUP subtypes with significantly higher polygenic germline risk for the predicted cancer type and significantly different survival outcomes, supporting its validity. Importantly, CUP patients who received first palliative intent treatments concordant with their OncoNPC-predicted cancer sites had significantly better outcomes (H.R. 0.348, 95% C.I. 0.210 - 0.570, p-value 2.32 × 10-5). OncoNPC thus provides evidence of distinct CUP subtypes and offers the potential for clinical decision support for managing patients with CUP.

Nucleosome Patterns in Circulating Tumor DNA Reveal Transcriptional Regulation of Advanced Prostate Cancer Phenotypes.

Advanced prostate cancers comprise distinct phenotypes, but tumor classification remains clinically challenging. Here, we harnessed circulating tumor DNA (ctDNA) to study tumor phenotypes by ascertaining nucleosome positioning patterns associated with transcription regulation. We sequenced plasma ctDNA whole genomes from patient-derived xenografts representing a spectrum of androgen receptor active (ARPC) and neuroendocrine (NEPC) prostate cancers. Nucleosome patterns associated with transcriptional activity were reflected in ctDNA at regions of genes, promoters, histone modifications, transcription factor binding, and accessible chromatin. We identified the activity of key phenotype-defining transcriptional regulators from ctDNA, including AR, ASCL1, HOXB13, HNF4G, and GATA2. To distinguish NEPC and ARPC in patient plasma samples, we developed prediction models that achieved accuracies of 97% for dominant phenotypes and 87% for mixed clinical phenotypes. Although phenotype classification is typically assessed by IHC or transcriptome profiling from tumor biopsies, we demonstrate that ctDNA provides comparable results with diagnostic advantages for precision oncology. SIGNIFICANCE: This study provides insights into the dynamics of nucleosome positioning and gene regulation associated with cancer phenotypes that can be ascertained from ctDNA. New methods for classification in phenotype mixtures extend the utility of ctDNA beyond assessments of somatic DNA alterations with important implications for molecular classification and precision oncology. This article is highlighted in the In This Issue feature, p. 517.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

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.

Extensive androgen receptor enhancer heterogeneity in primary prostate cancers underlies transcriptional diversity and metastatic potential.

Androgen receptor (AR) drives prostate cancer (PCa) development and progression. AR chromatin binding profiles are highly plastic and form recurrent programmatic changes that differentiate disease stages, subtypes and patient outcomes. While prior studies focused on concordance between patient subgroups, inter-tumor heterogeneity of AR enhancer selectivity remains unexplored. Here we report high levels of AR chromatin binding heterogeneity in human primary prostate tumors, that overlap with heterogeneity observed in healthy prostate epithelium. Such heterogeneity has functional consequences, as somatic mutations converge on commonly-shared AR sites in primary over metastatic tissues. In contrast, less-frequently shared AR sites associate strongly with AR-driven gene expression, while such heterogeneous AR enhancer usage also distinguishes patients' outcome. These findings indicate that epigenetic heterogeneity in primary disease is directly informative for risk of biochemical relapse. Cumulatively, our results illustrate a high level of AR enhancer heterogeneity in primary PCa driving differential expression and clinical impact.

Integrative molecular analyses define correlates of high B7-H3 expression in metastatic castrate-resistant prostate cancer.

B7-H3 (CD276) is an immune checkpoint overexpressed in prostate cancer with minimal expression in normal tissues and associated with poor prognosis, making it an excellent therapy target. We interrogated B7-H3 expression and its regulation in metastatic castration-resistant prostate cancer (mCRPC). We found greater expression of B7-H3 transcript relative to other immunotherapy targets (CTLA-4, PD-L1/2), including in tumors that lacked expression of prostate-specific membrane antigen (PSMA). Enzalutamide-resistant mCRPC cells demonstrated increased amounts of B7-H3, and this was associated with resistance signaling pathways. Using a machine-learning algorithm, the gene network of B7-H3 was strongly correlated with androgen receptor (AR) and AR co-factor (HOXB13, FOXA1) networks. In mCRPC samples, the B7-H3 promoter and distal enhancer regions exhibited enhanced transcriptional activity and were directly bound by AR and its co-factors. Altogether, our study characterizes molecular profiles and epigenetic regulation of B7-H3-expressing mCRPC tumors, which informs optimal precision-oncology approaches for mCRPC patients.

Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation.

Many genetic variants affect disease risk by altering context-dependent gene regulation. Such variants are difficult to study mechanistically using current methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs). To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for identifying genotypic and allele-specific effects on chromatin that are also associated with disease. In prostate cancer, CWAS identified regulatory elements and androgen receptor-binding sites that explained the association at 52 of 98 known prostate cancer risk loci and discovered 17 additional risk loci. CWAS implicated key developmental transcription factors in prostate cancer risk that are overlooked by eQTL-based approaches due to context-dependent gene regulation. We experimentally validated associations and demonstrated the extensibility of CWAS to additional epigenomic datasets and phenotypes, including response to prostate cancer treatment. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting transcriptional regulation.

Drug-Induced Epigenomic Plasticity Reprograms Circadian Rhythm Regulation to Drive Prostate Cancer toward Androgen Independence.

In prostate cancer, androgen receptor (AR)-targeting agents are very effective in various disease stages. However, therapy resistance inevitably occurs, and little is known about how tumor cells adapt to bypass AR suppression. Here, we performed integrative multiomics analyses on tissues isolated before and after 3 months of AR-targeting enzalutamide monotherapy from patients with high-risk prostate cancer enrolled in a neoadjuvant clinical trial. Transcriptomic analyses demonstrated that AR inhibition drove tumors toward a neuroendocrine-like disease state. Additionally, epigenomic profiling revealed massive enzalutamide-induced reprogramming of pioneer factor FOXA1 from inactive chromatin sites toward active cis-regulatory elements that dictate prosurvival signals. Notably, treatment-induced FOXA1 sites were enriched for the circadian clock component ARNTL. Posttreatment ARNTL levels were associated with patients' clinical outcomes, and ARNTL knockout strongly decreased prostate cancer cell growth. Our data highlight a remarkable cistromic plasticity of FOXA1 following AR-targeted therapy and revealed an acquired dependency on the circadian regulator ARNTL, a novel candidate therapeutic target. SIGNIFICANCE: Understanding how prostate cancers adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenomic plasticity toward prosurvival signaling and uncovered the circadian regulator ARNTL as an acquired vulnerability after AR inhibition, presenting a novel lead for therapeutic development. See related commentary by Zhang et al., p. 2017. This article is highlighted in the In This Issue feature, p. 2007.

The Prostate Cancer Androgen Receptor Cistrome in African American Men Associates with Upregulation of Lipid Metabolism and Immune Response.

African-American (AA) men are more likely to be diagnosed with and die from prostate cancer than European American (EA) men. Despite the central role of the androgen receptor (AR) transcription factor in prostate cancer, little is known about the contribution of epigenetics to observed racial disparities. We performed AR chromatin immunoprecipitation sequencing on primary prostate tumors from AA and EA men, finding that sites with greater AR binding intensity in AA relative to EA prostate cancer are enriched for lipid metabolism and immune response genes. Integration with transcriptomic and metabolomic data demonstrated coinciding upregulation of lipid metabolism gene expression and increased lipid levels in AA prostate cancer. In a metastatic prostate cancer cohort, upregulated lipid metabolism associated with poor prognosis. These findings offer the first insights into ancestry-specific differences in the prostate cancer AR cistrome. The data suggest a model whereby increased androgen signaling may contribute to higher levels of lipid metabolism, immune response, and cytokine signaling in AA prostate tumors. Given the association of upregulated lipogenesis with prostate cancer progression, our study provides a plausible biological explanation for the higher incidence and aggressiveness of prostate cancer observed in AA men. SIGNIFICANCE: With immunotherapies and inhibitors of metabolic enzymes in clinical development, the altered lipid metabolism and immune response in African-American men provides potential therapeutic opportunities to attenuate racial disparities in prostate cancer.

CREB5 reprograms FOXA1 nuclear interactions to promote resistance to androgen receptor-targeting therapies.

Metastatic castration-resistant prostate cancers (mCRPCs) are treated with therapies that antagonize the androgen receptor (AR). Nearly all patients develop resistance to AR-targeted therapies (ARTs). Our previous work identified CREB5 as an upregulated target gene in human mCRPC that promoted resistance to all clinically approved ART. The mechanisms by which CREB5 promotes progression of mCRPC or other cancers remains elusive. Integrating ChIP-seq and rapid immunoprecipitation and mass spectroscopy of endogenous proteins, we report that cells overexpressing CREB5 demonstrate extensive reprogramming of nuclear protein-protein interactions in response to the ART agent enzalutamide. Specifically, CREB5 physically interacts with AR, the pioneering actor FOXA1, and other known co-factors of AR and FOXA1 at transcription regulatory elements recently found to be active in mCRPC patients. We identified a subset of CREB5/FOXA1 co-interacting nuclear factors that have critical functions for AR transcription (GRHL2, HOXB13) while others (TBX3, NFIC) regulated cell viability and ART resistance and were amplified or overexpressed in mCRPC. Upon examining the nuclear protein interactions and the impact of CREB5 expression on the mCRPC patient transcriptome, we found that CREB5 was associated with Wnt signaling and epithelial to mesenchymal transitions, implicating these pathways in CREB5/FOXA1-mediated ART resistance. Overall, these observations define the molecular interactions among CREB5, FOXA1, and pathways that promote ART resistance.

HOXB13 suppresses de novo lipogenesis through HDAC3-mediated epigenetic reprogramming in prostate cancer.

HOXB13, a homeodomain transcription factor, critically regulates androgen receptor (AR) activities and androgen-dependent prostate cancer (PCa) growth. However, its functions in AR-independent contexts remain elusive. Here we report HOXB13 interaction with histone deacetylase HDAC3, which is disrupted by the HOXB13 G84E mutation that has been associated with early-onset PCa. Independently of AR, HOXB13 recruits HDAC3 to lipogenic enhancers to catalyze histone deacetylation and suppress lipogenic regulators such as fatty acid synthase. Analysis of human tissues reveals that the HOXB13 gene is hypermethylated and downregulated in approximately 30% of metastatic castration-resistant PCa. HOXB13 loss or G84E mutation leads to lipid accumulation in PCa cells, thereby promoting cell motility and xenograft tumor metastasis, which is mitigated by pharmaceutical inhibition of fatty acid synthase. In summary, we present evidence that HOXB13 recruits HDAC3 to suppress de novo lipogenesis and inhibit tumor metastasis and that lipogenic pathway inhibitors may be useful to treat HOXB13-low PCa.

ASCL1 activates neuronal stem cell-like lineage programming through remodeling of the chromatin landscape in prostate cancer.

Treatment with androgen receptor pathway inhibitors (ARPIs) in prostate cancer leads to the emergence of resistant tumors characterized by lineage plasticity and differentiation toward neuroendocrine lineage. Here, we find that ARPIs induce a rapid epigenetic alteration mediated by large-scale chromatin remodeling to support activation of stem/neuronal transcriptional programs. We identify the proneuronal transcription factor ASCL1 motif to be enriched in hyper-accessible regions. ASCL1 acts as a driver of the lineage plastic, neuronal transcriptional program to support treatment resistance and neuroendocrine phenotype. Targeting ASCL1 switches the neuroendocrine lineage back to the luminal epithelial state. This effect is modulated by disruption of the polycomb repressive complex-2 through UHRF1/AMPK axis and change the chromatin architecture in favor of luminal phenotype. Our study provides insights into the epigenetic alterations induced by ARPIs, governed by ASCL1, provides a proof of principle of targeting ASCL1 to reverse neuroendocrine phenotype, support luminal conversion and re-addiction to ARPIs.

A genome-scale CRISPR screen reveals PRMT1 as a critical regulator of androgen receptor signaling in prostate cancer.

Androgen receptor (AR) signaling is the central driver of prostate cancer across disease states. While androgen deprivation therapy (ADT) is effective in the initial treatment of prostate cancer, resistance to ADT or to next-generation androgen pathway inhibitors invariably arises, most commonly through the re-activation of the AR axis. Thus, orthogonal approaches to inhibit AR signaling in advanced prostate cancer are essential. Here, via genome-scale CRISPR-Cas9 screening, we identify protein arginine methyltransferase 1 (PRMT1) as a critical mediator of AR expression and signaling. PRMT1 regulates the recruitment of AR to genomic target sites and the inhibition of PRMT1 impairs AR binding at lineage-specific enhancers, leading to decreased expression of key oncogenes, including AR itself. In addition, AR-driven prostate cancer cells are uniquely susceptible to combined AR and PRMT1 inhibition. Our findings implicate PRMT1 as a key regulator of AR output and provide a preclinical framework for co-targeting of AR and PRMT1 in advanced prostate cancer.

Detecting Neuroendocrine Prostate Cancer Through Tissue-Informed Cell-Free DNA Methylation Analysis.

PURPOSE: Neuroendocrine prostate cancer (NEPC) is a resistance phenotype that emerges in men with metastatic castration-resistant prostate adenocarcinoma (CR-PRAD) and has important clinical implications, but is challenging to detect in practice. Herein, we report a novel tissue-informed epigenetic approach to noninvasively detect NEPC. EXPERIMENTAL DESIGN: We first performed methylated immunoprecipitation and high-throughput sequencing (MeDIP-seq) on a training set of tumors, identified differentially methylated regions between NEPC and CR-PRAD, and built a model to predict the presence of NEPC (termed NEPC Risk Score). We then performed MeDIP-seq on cell-free DNA (cfDNA) from two independent cohorts of men with NEPC or CR-PRAD and assessed the accuracy of the model to predict the presence NEPC. RESULTS: The test cohort comprised cfDNA samples from 48 men, 9 with NEPC and 39 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 4.3 × 10-7) and discriminated between NEPC and CR-PRAD with high accuracy (AUROC 0.96). The optimal NEPC Risk Score cutoff demonstrated 100% sensitivity and 90% specificity for detecting NEPC. The independent, multi-institutional validation cohort included cfDNA from 53 men, including 12 with NEPC and 41 with CR-PRAD. NEPC Risk Scores were significantly higher in men with NEPC than CR-PRAD (P = 7.5×10-12) and perfectly discriminated NEPC from CR-PRAD (AUROC 1.0). Applying the predefined NEPC Risk Score cutoff to the validation cohort resulted in 100% sensitivity and 95% specificity for detecting NEPC. CONCLUSIONS: Tissue-informed cfDNA methylation analysis is a promising approach for noninvasive detection of NEPC in men with advanced prostate cancer.

H3K27ac HiChIP in prostate cell lines identifies risk genes for prostate cancer susceptibility.

Genome-wide association studies (GWASs) have identified more than 200 prostate cancer (PrCa) risk regions, which provide potential insights into causal mechanisms. Multiple lines of evidence show that a significant proportion of PrCa risk can be explained by germline causal variants that dysregulate nearby target genes in prostate-relevant tissues, thus altering disease risk. The traditional approach to explore this hypothesis has been correlating GWAS variants with steady-state transcript levels, referred to as expression quantitative trait loci (eQTLs). In this work, we assess the utility of chromosome conformation capture (3C) coupled with immunoprecipitation (HiChIP) to identify target genes for PrCa GWAS risk loci. We find that interactome data confirm previously reported PrCa target genes identified through GWAS/eQTL overlap (e.g., MLPH). Interestingly, HiChIP identifies links between PrCa GWAS variants and genes well-known to play a role in prostate cancer biology (e.g., AR) that are not detected by eQTL-based methods. HiChIP predicted enhancer elements at the AR and NKX3-1 prostate cancer risk loci, and both were experimentally confirmed to regulate expression of the corresponding genes through CRISPR interference (CRISPRi) perturbation in LNCaP cells. Our results demonstrate that looping data harbor additional information beyond eQTLs and expand the number of PrCa GWAS loci that can be linked to candidate susceptibility genes.