Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation.

Enteroendocrine cells (EECs) secrete serotonin (enterochromaffin [EC] cells) or specific peptide hormones (non-EC cells) that serve vital metabolic functions. The basis for terminal EEC diversity remains obscure. By forcing activity of the transcription factor (TF) NEUROG3 in 2D cultures of human intestinal stem cells, we replicated physiologic EEC differentiation and examined transcriptional and cis-regulatory dynamics that culminate in discrete cell types. Abundant EEC precursors expressed stage-specific genes and TFs. Before expressing pre-terminal NEUROD1, post-mitotic precursors oscillated between transcriptionally distinct ASCL1+ and HES6hi cell states. Loss of either factor accelerated EEC differentiation substantially and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and non-EC cell features. These TFs mainly bind cis-elements that are accessible in undifferentiated stem cells, and they tailor subsequent expression of TF combinations that underlie discrete EEC identities. Thus, early TF oscillations retard EEC maturation to enable accurate diversity within a medically important cell lineage.

Thio-2 inhibits key signaling pathways required for the development and progression of castration resistant prostate cancer.

Therapies that abrogate persistent androgen receptor (AR) signaling in castration resistant prostate cancer (CRPC) remain an unmet clinical need. The N-terminal domain (NTD) of the AR that drives transcriptional activity in CRPC remains a challenging therapeutic target. Herein we demonstrate that BAG-1 mRNA is highly expressed and associates with signaling pathways, including AR signaling, that are implicated in the development and progression of CRPC. In addition, interrogation of geometric and physiochemical properties of the BAG domain of BAG-1 isoforms identifies it to be a tractable but challenging drug target. Furthermore, through BAG-1 isoform mouse knockout studies we confirm that BAG-1 isoforms regulate hormone physiology and that therapies targeting the BAG domain will be associated with limited 'on-target' toxicity. Importantly, the postulated inhibitor of BAG-1 isoforms, Thio-2, suppressed AR signaling and other important pathways implicated in the development and progression of CRPC to reduce the growth of treatment resistant prostate cancer cell lines and patient derived models. However, the mechanism by which Thio-2 elicits the observed phenotype needs further elucidation since the genomic abrogation of BAG-1 isoforms was unable to recapitulate the Thio-2 mediated phenotype. Overall, these data support the interrogation of related compounds with improved drug-like properties as a novel therapeutic approach in CRPC, and further highlight the clinical potential of treatments that block persistent AR signaling which are currently undergoing clinical evaluation in CRPC.

Lineage-specific canonical and non-canonical activity of EZH2 in advanced prostate cancer subtypes.

Enhancer of zeste homolog 2 (EZH2) is a histone methyltransferase and emerging therapeutic target that is overexpressed in most castration-resistant prostate cancers and implicated as a driver of disease progression and resistance to hormonal therapies. Here we define the lineage-specific action and differential activity of EZH2 in both prostate adenocarcinoma (PRAD) and neuroendocrine prostate cancer (NEPC) subtypes of advanced prostate cancer to better understand the role of EZH2 in modulating differentiation, lineage plasticity, and to identify mediators of response and resistance to EZH2 inhibitor therapy. Mechanistically, EZH2 modulates bivalent genes that results in upregulation of NEPC-associated transcriptional drivers (e.g., ASCL1) and neuronal gene programs, and leads to forward differentiation after targeting EZH2 in NEPC. Subtype-specific downstream effects of EZH2 inhibition on cell cycle genes support the potential rationale for co-targeting cyclin/CDK to overcome resistance to EZH2 inhibition.

Transcription factor dynamics, oscillation, and functions in human enteroendocrine cell differentiation.

Enteroendocrine cells (EECs), which secrete serotonin (enterochromaffin cells, EC) or a dominant peptide hormone, serve vital physiologic functions. As with any adult human lineage, the basis for terminal cell diversity remains obscure. We replicated human EEC differentiation in vitro , mapped transcriptional and chromatin dynamics that culminate in discrete cell types, and studied abundant EEC precursors expressing selected transcription factors (TFs) and gene programs. Before expressing the pre-terminal factor NEUROD1, non-replicating precursors oscillated between epigenetically similar but transcriptionally distinct ASCL1 + and HES6 hi cell states. Loss of either factor substantially accelerated EEC differentiation and disrupted EEC individuality; ASCL1 or NEUROD1 deficiency had opposing consequences on EC and hormone-producing cell features. Expressed late in EEC differentiation, the latter TFs mainly bind cis -elements that are accessible in undifferentiated stem cells and tailor the subsequent expression of TF combinations that specify EEC types. Thus, TF oscillations retard EEC maturation to enable accurate EEC diversification.

Increased chromatin accessibility drives transition to androgen receptor splice variant dependence in castration-resistant prostate cancer.

Androgen receptor (AR) splice variants, of which ARv7 is the most common, are increased in prostate cancer (PC) that develops resistance to androgen signaling inhibitor drugs, but the extent to which these variants drive AR activity, and whether they have novel functions or dependencies, remain to be determined. We generated a subline of VCaP PC cells (VCaP16) that is resistant to the AR inhibitor enzalutamide (ENZ) and found that AR activity was independent of the full-length AR (ARfl), despite its continued high-level expression, and was instead driven by ARv7. The ARv7 cistrome and transcriptome in VCaP16 cells mirrored that of the ARfl in VCaP cells, although ARv7 chromatin binding was weaker, and strong ARv7 binding sites correlated with higher affinity ARfl binding sites across multiple models and clinical samples. Notably, although ARv7 expression in VCaP cells increased rapidly in response to ENZ, there was a long lag before it gained chromatin binding and transcriptional activity. This lag was associated with an increase in chromatin accessibility, with the AR and nuclear factor I (NFI) motifs being most enriched at these more accessible sites. Moreover, the transcriptional effects of combined NFIB and NFIX knockdown versus ARv7 knockdown were highly correlated. These findings indicate that ARv7 can drive the AR program, but that its activity is dependent on adaptations that increase chromatin accessibility to enhance its intrinsically weak chromatin binding.

scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data.

Recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key analysis task is to determine cell type identity based on the epigenetic data. We introduce scATAnno, a python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates the reference atlases from publicly available datasets enabling accurate cell type annotation by integrating query data with reference atlases, without the use of scRNA-seq data. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compare and benchmark scATAnno against 7 other published approaches for cell annotation and show superior performance in multiple data sets and metrics. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), Triple Negative Breast Cancer (TNBC), and basal cell carcinoma (BCC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a useful tool for scATAC-seq reference building and cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

Cistrome Data Browser: integrated search, analysis and visualization of chromatin data.

The Cistrome Data Browser is a resource of ChIP-seq, ATAC-seq and DNase-seq data from humans and mice. It provides maps of the genome-wide locations of transcription factors, cofactors, chromatin remodelers, histone post-translational modifications and regions of chromatin accessible to endonuclease activity. Cistrome DB v3.0 contains approximately 45 000 human and 44 000 mouse samples with about 32 000 newly collected datasets compared to the previous release. The Cistrome DB v3.0 user interface is implemented as a single page application that unifies menu driven and data driven search functions and provides an embedded genome browser, which allows users to find and visualize data more effectively. Users can find informative chromatin profiles through keyword, menu, and data-driven search tools. Browser search functions can predict the regulators of query genes as well as the cell type and factor dependent functionality of potential cis-regulatory elements. Cistrome DB v3.0 expands the display of quality control statistics, incorporates sequence logos into motif enrichment displays and includes more expansive sample metadata. Cistrome DB v3.0 is available at http://db3.cistrome.org/browser.

Heterogeneity and transcriptional drivers of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a heterogeneous disease with limited treatment options. To characterize TNBC heterogeneity, we defined transcriptional, epigenetic, and metabolic subtypes and subtype-driving super-enhancers and transcription factors by combining functional and molecular profiling with computational analyses. Single-cell RNA sequencing revealed relative homogeneity of the major transcriptional subtypes (luminal, basal, and mesenchymal) within samples. We found that mesenchymal TNBCs share features with mesenchymal neuroblastoma and rhabdoid tumors and that the PRRX1 transcription factor is a key driver of these tumors. PRRX1 is sufficient for inducing mesenchymal features in basal but not in luminal TNBC cells via reprogramming super-enhancer landscapes, but it is not required for mesenchymal state maintenance or for cellular viability. Our comprehensive, large-scale, multiplatform, multiomics study of both experimental and clinical TNBC is an important resource for the scientific and clinical research communities and opens venues for future investigation.

TP63 fusions drive multicomplex enhancer rewiring, lymphomagenesis, and EZH2 dependence.

Gene fusions involving tumor protein p63 gene (TP63) occur in multiple T and B cell lymphomas and portend a dismal prognosis for patients. The function and mechanisms of TP63 fusions remain unclear, and there is no target therapy for patients with lymphoma harboring TP63 fusions. Here, we show that TP63 fusions act as bona fide oncogenes and are essential for fusion-positive lymphomas. Transgenic mice expressing TBL1XR1::TP63, the most common TP63 fusion, develop diverse lymphomas that recapitulate multiple human T and B cell lymphomas. Here, we identify that TP63 fusions coordinate the recruitment of two epigenetic modifying complexes, the nuclear receptor corepressor (NCoR)-histone deacetylase 3 (HDAC3) by the N-terminal TP63 fusion partner and the lysine methyltransferase 2D (KMT2D) by the C-terminal TP63 component, which are both required for fusion-dependent survival. TBL1XR1::TP63 localization at enhancers drives a unique cell state that involves up-regulation of MYC and the polycomb repressor complex 2 (PRC2) components EED and EZH2. Inhibiting EZH2 with the therapeutic agent valemetostat is highly effective at treating transgenic lymphoma murine models, xenografts, and patient-derived xenografts harboring TP63 fusions. One patient with TP63-rearranged lymphoma showed a rapid response to valemetostat treatment. In summary, TP63 fusions link partner components that, together, coordinate multiple epigenetic complexes, resulting in therapeutic vulnerability to EZH2 inhibition.

Multi-batch single-cell comparative atlas construction by deep learning disentanglement.

Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL's capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Analysis of evolutionary dynamics and clonal architecture in prostate cancer.

The extent to which clinical and genomic characteristics associate with prostate cancer clonal architecture, tumor evolution, and therapeutic response remains unclear. Here, we reconstructed the clonal architecture and evolutionary trajectories of 845 prostate cancer tumors with harmonized clinical and molecular data. We observed that tumors from patients who self-reported as Black had more linear and monoclonal architectures, despite these men having higher rates of biochemical recurrence. This finding contrasts with prior observations relating polyclonal architecture to adverse clinical outcomes. Additionally, we utilized a novel approach to mutational signature analysis that leverages clonal architecture to uncover additional cases of homologous recombination and mismatch repair deficiency in primary and metastatic tumors and link the origin of mutational signatures to specific subclones. Broadly, prostate cancer clonal architecture analysis reveals novel biological insights that may be immediately clinically actionable and provide multiple opportunities for subsequent investigation. Statement of significance: Tumors from patients who self-reported as Black demonstrate linear and monoclonal evolutionary trajectories yet experience higher rates of biochemical recurrence. In addition, analysis of clonal and subclonal mutational signatures identifies additional tumors with potentially actionable alterations such as deficiencies in mismatch repair and homologous recombination.

Cistrome Explorer: an interactive visual analysis tool for large-scale epigenomic data.

SUMMARY: The regulation of genes by cis-regulatory elements (CREs) is complex and differs between cell types. Visual analysis of large collections of chromatin profiles across diverse cell types, integrated with computational methods, can reveal meaningful biological insights. We developed Cistrome Explorer, a web-based interactive visual analytics tool for exploring thousands of chromatin profiles in diverse cell types. Integrated with the Cistrome Data Browser database which contains thousands of ChIP-seq, DNase-seq and ATAC-seq samples, Cistrome Explorer enables the discovery of patterns of CREs across cell types and the identification of transcription factor binding underlying these patterns. AVAILABILITY AND IMPLEMENTATION: Cistrome Explorer and its source code are available at http://cisvis.gehlenborglab.org/ and released under the MIT License. Documentation can be accessed via http://cisvis.gehlenborglab.org/docs/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression.

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Finally, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of preimmunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.

Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response.

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

MIRA: joint regulatory modeling of multimodal expression and chromatin accessibility in single cells.

Rigorously comparing gene expression and chromatin accessibility in the same single cells could illuminate the logic of how coupling or decoupling of these mechanisms regulates fate commitment. Here we present MIRA, probabilistic multimodal models for integrated regulatory analysis, a comprehensive methodology that systematically contrasts transcription and accessibility to infer the regulatory circuitry driving cells along cell state trajectories. MIRA leverages topic modeling of cell states and regulatory potential modeling of individual gene loci. MIRA thereby represents cell states in an efficient and interpretable latent space, infers high-fidelity cell state trees, determines key regulators of fate decisions at branch points and exposes the variable influence of local accessibility on transcription at distinct loci. Applied to epidermal differentiation and embryonic brain development from two different multimodal platforms, MIRA revealed that early developmental genes were tightly regulated by local chromatin landscape whereas terminal fate genes were titrated without requiring extensive chromatin remodeling.

Response to supraphysiological testosterone is predicted by a distinct androgen receptor cistrome.

The androgen receptor (AR) is a master transcription factor that regulates prostate cancer (PC) development and progression. Inhibition of AR signaling by androgen deprivation is the first-line therapy with initial efficacy for advanced and recurrent PC. Paradoxically, supraphysiological levels of testosterone (SPT) also inhibit PC progression. However, as with any therapy, not all patients show a therapeutic benefit, and responses differ widely in magnitude and duration. In this study, we evaluated whether differences in the AR cistrome before treatment can distinguish between SPT-responding (R) and -nonresponding (NR) tumors. We provide the first preclinical evidence to our knowledge that SPT-R tumors exhibit a distinct AR cistrome when compared with SPT-NR tumors, indicating a differential biological role of the AR. We applied an integrated analysis of ChIP-Seq and RNA-Seq to the pretreatment tumors and identified an SPT-R signature that distinguishes R and NR tumors. Because transcriptomes of SPT-treated clinical specimens are not available, we interrogated available castration-resistant PC (CRPC) transcriptomes and showed that the SPT-R signature is associated with improved survival and has the potential to identify patients who would respond to SPT. These findings provide an opportunity to identify the subset of patients with CRPC who would benefit from SPT therapy.

MYC drives aggressive prostate cancer by disrupting transcriptional pause release at androgen receptor targets.

c-MYC (MYC) is a major driver of prostate cancer tumorigenesis and progression. Although MYC is overexpressed in both early and metastatic disease and associated with poor survival, its impact on prostate transcriptional reprogramming remains elusive. We demonstrate that MYC overexpression significantly diminishes the androgen receptor (AR) transcriptional program (the set of genes directly targeted by the AR protein) in luminal prostate cells without altering AR expression. Analyses of clinical specimens reveal that concurrent low AR and high MYC transcriptional programs accelerate prostate cancer progression toward a metastatic, castration-resistant disease. Data integration of single-cell transcriptomics together with ChIP-seq uncover an increase in RNA polymerase II (Pol II) promoter-proximal pausing at AR-dependent genes following MYC overexpression without an accompanying deactivation of AR-bound enhancers. Altogether, our findings suggest that MYC overexpression antagonizes the canonical AR transcriptional program and contributes to prostate tumor initiation and progression by disrupting transcriptional pause release at AR-regulated genes.

A chemical probe for BAG1 targets androgen receptor-positive prostate cancer through oxidative stress signaling pathway.

BAG1 is a family of polypeptides with a conserved C-terminal BAG domain that functions as a nucleotide exchange factor for the molecular chaperone HSP70. BAG1 proteins also control several signaling processes including proteostasis, apoptosis, and transcription. The largest isoform, BAG1L, controls the activity of the androgen receptor (AR) and is upregulated in prostate cancer. Here, we show that BAG1L regulates AR dynamics in the nucleus and its ablation attenuates AR target gene expression especially those involved in oxidative stress and metabolism. We show that a small molecule, A4B17, that targets the BAG domain downregulates AR target genes similar to a complete BAG1L knockout and upregulates the expression of oxidative stress-induced genes involved in cell death. Furthermore, A4B17 outperformed the clinically approved antagonist enzalutamide in inhibiting cell proliferation and prostate tumor development in a mouse xenograft model. BAG1 inhibitors therefore offer unique opportunities for antagonizing AR action and prostate cancer growth.

p16INK4A-deficiency predicts response to combined HER2 and CDK4/6 inhibition in HER2+ breast cancer brain metastases.

Approximately 50% of patients with metastatic HER2-positive (HER2+) breast cancer develop brain metastases (BCBMs). We report that the tumor suppressor p16INK4A is deficient in the majority of HER2+ BCBMs. p16INK4A-deficiency as measured by protein immunohistochemistry predicted response to combined tucatinib and abemaciclib in orthotopic patient-derived xenografts (PDXs) of HER2 + BCBMs. Our findings establish the rationale for a biomarker-driven clinical trial of combined CDK4/6- and HER2-targeted agents for patients with HER2 + BCBM.