The ANTsX Ecosystem for Mapping the Mouse Brain.

Precision mapping techniques coupled with high resolution image acquisition of the mouse brain permit the study of the spatial organization of gene expression and their mutual interaction for a comprehensive view of salient structural/functional relationships. Such research is facilitated by standardized anatomical coordinate systems, such as the well-known Allen Common Coordinate Framework (AllenCCFv3), and the ability to spatially map to such standardized spaces. The Advanced Normalization Tools Ecosystem is a comprehensive open-source software toolkit for generalized quantitative imaging with applicability to multiple organ systems, modalities, and animal species. Herein, we illustrate the utility of ANTsX for generating precision spatial mappings of the mouse brain and potential subsequent quantitation. We describe ANTsX-based workflows for mapping domain-specific image data to AllenCCFv3 accounting for common artefacts and other confounds. Novel contributions include ANTsX functionality for velocity flow-based mapping spanning the spatiotemporal domain of a longitudinal trajectory which we apply to the Developmental Common Coordinate Framework. Additionally, we present an automated structural morphological pipeline for determining volumetric and cortical thickness measurements analogous to the well-utilized ANTsX pipeline for human neuroanatomical structural morphology which illustrates a general open-source framework for tailored brain parcellations.

Estrogen receptor 1 chromatin profiling in human breast tumors reveals high inter-patient heterogeneity with enrichment of risk SNPs and enhancer activity at most-conserved regions.

Estrogen Receptor 1 (ESR1; also known as ERα, encoded by ESR1 gene) is the main driver and prime drug target in luminal breast cancer. ESR1 chromatin binding is extensively studied in cell lines and a limited number of human tumors, using consensi of peaks shared among samples. However, little is known about inter-tumor heterogeneity of ESR1 chromatin action, along with its biological implications. Here, we use a large set of ESR1 ChIP-seq data from 70 ESR1+ breast cancers to explore inter-patient heterogeneity in ESR1 DNA binding to reveal a striking inter-tumor heterogeneity of ESR1 action. Of note, commonly shared ESR1 sites show the highest estrogen-driven enhancer activity and are most engaged in long-range chromatin interactions. In addition, the most commonly shared ESR1-occupied enhancers are enriched for breast cancer risk SNP loci. We experimentally confirm SNVs to impact chromatin binding potential for ESR1 and its pioneer factor FOXA1. Finally, in the TCGA breast cancer cohort, we can confirm these variations to associate with differences in expression for the target gene. Cumulatively, we reveal a natural hierarchy of ESR1-chromatin interactions in breast cancers within a highly heterogeneous inter-tumor ESR1 landscape, with the most common shared regions being most active and affected by germline functional risk SNPs for breast cancer development.

Validation of Diagnostic Thresholds for Compensated Advanced Chronic Liver Disease Using Supersonic Shear Imaging.

Background The Society of Radiologists in Ultrasound (SRU) has proposed thresholds for acoustic radiation force impulse techniques to diagnose compensated advanced chronic liver disease (cACLD). However, the diagnostic performance of these thresholds has not been extensively validated. Purpose To validate the SRU thresholds in patients with chronic liver disease who underwent supersonic shear imaging and, if suboptimal diagnostic performance is observed, to identify optimal values for diagnosing cACLD. Materials and Methods This retrospective single-center study included high-risk patients with chronic liver disease who had liver stiffness (LS) measurements and had undergone endoscopy or liver biopsy between January 2018 and December 2021. Patients were randomly allocated to test and validation sets. cACLD was defined as varices at endoscopy and/or severe fibrosis or cirrhosis at liver biopsy. The diagnostic performance of the SRU guidelines was evaluated, and optimal threshold values were identified using receiver operating characteristic (ROC) curve analysis. Results A total of 1180 patients (median age, 57 years [IQR, 50-64 years]; 761 men), of whom 544 (46%) had cACLD, were included. With the SRU recommended thresholds of less than 9 kPa and greater than 13 kPa in the test set (n = 786), the sensitivity and specificity for ruling out and ruling in cACLD were 81% (303 of 374 patients; 95% CI: 77, 85) and 92% (380 of 412 patients; 95% CI: 89, 94), respectively. In ROC curve analysis, the identified optimal threshold values were less than 7 kPa and greater than 12 kPa, showing 91% sensitivity (340 of 374 patients; 95% CI: 88, 93) for ruling out cACLD and 91% specificity (373 of 412 patients; 95% CI: 87, 93) for ruling in cACLD, respectively. In the validation set (n = 394), the optimal thresholds showed 91% sensitivity (155 of 170 patients; 95% CI: 86, 95) and 92% specificity (206 of 224 patients; 95% CI: 88, 95). Conclusion Compared with the SRU guidelines, the dual LS threshold values of less than 7 kPa and greater than 12 kPa were better for diagnosing cACLD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Barr in this issue.

Distributed X chromosome inactivation in brain circuitry is associated with X-linked disease penetrance of behavior.

The precise anatomical degree of brain X chromosome inactivation (XCI) that is sufficient to alter X-linked disorders in females is unclear. Here, we quantify whole-brain XCI at single-cell resolution to discover a prevalent activation ratio of maternal to paternal X at 60:40 across all divisions of the adult brain. This modest, non-random XCI influences X-linked disease penetrance: maternal transmission of the fragile X mental retardation 1 (Fmr1)-knockout (KO) allele confers 55% of total brain cells with mutant X-active, which is sufficient for behavioral penetrance, while 40% produced from paternal transmission is tolerated. Local XCI mosaicism within affected maternal Fmr1-KO mice further specifies sensorimotor versus social anxiety phenotypes depending on which distinct brain circuitry is most affected, with only a 50%-55% mutant X-active threshold determining penetrance. Thus, our results define a model of X-linked disease penetrance in females whereby distributed XCI among single cells populating brain circuitries can regulate the behavioral penetrance of an X-linked mutation.

Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections.

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.

Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Impaired drainage through capillary-venous networks contributes to age-related white matter loss.

The gradual loss of cerebral white matter contributes to cognitive decline during aging. However, microvascular networks that support the metabolic demands of white matter remain poorly defined. We used in vivo deep multi-photon imaging to characterize microvascular networks that perfuse cortical layer 6 and corpus callosum, a highly studied region of white matter in the mouse brain. We show that these deep tissues are exclusively drained by sparse and wide-reaching venules, termed principal cortical venules, which mirror vascular architecture at the human cortical-U fiber interface. During aging, capillary networks draining into deep branches of principal cortical venules are selectively constricted, reduced in density, and diminished in pericyte numbers. This causes hypo-perfusion in deep tissues, and correlates with gliosis and demyelination, whereas superficial tissues become relatively hyper-perfused. Thus, age-related impairment of capillary-venular drainage is a key vascular deficit that contributes to the unique vulnerability of cerebral white matter during brain aging.

Spatiotemporal Mapping and Molecular Basis of Whole-brain Circuit Maturation.

Brain development is highly dynamic and asynchronous, marked by the sequential maturation of functional circuits across the brain. The timing and mechanisms driving circuit maturation remain elusive due to an inability to identify and map maturing neuronal populations. Here we create DevATLAS (Developmental Activation Timing-based Longitudinal Acquisition System) to overcome this obstacle. We develop whole-brain mapping methods to construct the first longitudinal, spatiotemporal map of circuit maturation in early postnatal mouse brains. Moreover, we uncover dramatic impairments within the deep cortical layers in a neurodevelopmental disorders (NDDs) model, demonstrating the utility of this resource to pinpoint when and where circuit maturation is disrupted. Using DevATLAS, we reveal that early experiences accelerate the development of hippocampus-dependent learning by increasing the synaptically mature granule cell population in the dentate gyrus. Finally, DevATLAS enables the discovery of molecular mechanisms driving activity-dependent circuit maturation.

Assessment and comparison of viability assays for cellular products.

BACKGROUND AIMS: Accurate assessment of cell viability is crucial in cellular product manufacturing, yet selecting the appropriate viability assay presents challenges due to various factors. This study compares and evaluates different viability assays on fresh and cryopreserved cellular products, including peripheral blood stem cell (PBSC) and peripheral blood mononuclear cell (PBMC) apheresis products, purified PBMCs and cultured chimeric antigen receptor and T-cell receptor-engineered T-cell products. METHODS: Viability assays, including manual Trypan Blue exclusion, flow cytometry-based assays using 7-aminoactinomycin D (7-AAD) or propidium iodide (PI) direct staining or cell surface marker staining in conjunction with 7-AAD, Cellometer (Nexcelom Bioscience LLC, Lawrence, MA, USA) Acridine Orange/PI staining and Vi-CELL BLU Cell Viability Analyzer (Beckman Coulter, Inc, Brea, CA, USA), were evaluated. A viability standard was established using live and dead cell mixtures to assess the accuracy of these assays. Furthermore, precision assessment was conducted to determine the reproducibility of the viability assays. Additionally, the viability of individual cell populations from cryopreserved PBSC and PBMC apheresis products was examined. RESULTS: All methods provided accurate viability measurements and generated consistent and reproducible viability data. The assessed viability assays were demonstrated to be reliable alternatives when evaluating the viability of fresh cellular products. However, cryopreserved products exhibited variability among the tested assays. Additionally, analyzing the viability of each subset of the cryopreserved PBSC and PBMC apheresis products revealed that T cells and granulocytes were more susceptible to the freeze-thaw process, showing decreased viability. CONCLUSIONS: The study demonstrates the importance of careful assay selection, validation and standardization, particularly for assessing the viability of cryopreserved products. Given the complexity of cellular products, choosing a fit-for-purpose viability assay is essential.

epDevAtlas: Mapping GABAergic cells and microglia in postnatal mouse brains.

During development, brain regions follow encoded growth trajectories. Compared to classical brain growth charts, high-definition growth charts could quantify regional volumetric growth and constituent cell types, improving our understanding of typical and pathological brain development. Here, we create high-resolution 3D atlases of the early postnatal mouse brain, using Allen CCFv3 anatomical labels, at postnatal days (P) 4, 6, 8, 10, 12, and 14, and determine the volumetric growth of different brain regions. We utilize 11 different cell type-specific transgenic animals to validate and refine anatomical labels. Moreover, we reveal region-specific density changes in γ-aminobutyric acid-producing (GABAergic), cortical layer-specific cell types, and microglia as key players in shaping early postnatal brain development. We find contrasting changes in GABAergic neuronal densities between cortical and striatal areas, stabilizing at P12. Moreover, somatostatin-expressing cortical interneurons undergo regionally distinct density reductions, while vasoactive intestinal peptide-expressing interneurons show no significant changes. Remarkably, microglia transition from high density in white matter tracks to gray matter at P10, and show selective density increases in sensory processing areas that correlate with the emergence of individual sensory modalities. Lastly, we create an open-access web-visualization (https://kimlab.io/brain-map/epDevAtlas) for cell-type growth charts and developmental atlases for all postnatal time points.

Breast cancer risk SNPs converge on estrogen receptor binding sites commonly shared between breast tumors to locally alter estrogen signalling output.

Estrogen Receptor alpha (ERα) is the main driver and prime drug target in luminal breast. ERα chromatin binding is extensively studied in cell lines and a limited number of human tumors, using consensi of peaks shared among samples. However, little is known about inter-tumor heterogeneity of ERα chromatin action, along with its biological implications. Here, we use a large set of ERα ChIP-seq data from 70 ERα+ breast cancers to explore inter-patient heterogeneity in ERα DNA binding, to reveal a striking inter-tumor heterogeneity of ERα action. Interestingly, commonly-shared ERα sites showed the highest estrogen-driven enhancer activity and were most-engaged in long-range chromatin interactions. In addition, the most-commonly shared ERα-occupied enhancers were enriched for breast cancer risk SNP loci. We experimentally confirm SNVs to impact chromatin binding potential for ERα and its pioneer factor FOXA1. Finally, in the TCGA breast cancer cohort, we could confirm these variations to associate with differences in expression for the target gene. Cumulatively, we reveal a natural hierarchy of ERα-chromatin interactions in breast cancers within a highly heterogeneous inter-tumor ERα landscape, with the most-common shared regions being most active and affected by germline functional risk SNPs for breast cancer development.

Developmental Mouse Brain Common Coordinate Framework.

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

Measured sodium excretion is associated with cardiovascular outcomes in non-dialysis CKD patients: results from the KNOW-CKD study.

Background: There are insufficient studies on the effect of dietary salt intake on cardiovascular (CV) outcomes in chronic kidney disease (CKD) patients, and there is no consensus on the sodium (Na) intake level that increases the risk of CV disease in CKD patients. Therefore, we investigated the association between dietary salt intake and CV outcomes in CKD patients. Methods: In the Korean cohort study for Outcome in patients with CKD (KNOW-CKD), 1,937 patients were eligible for the study, and their dietary Na intake was estimated using measured 24h urinary Na excretion. The primary outcome was a composite of CV events and/or all-cause death. The secondary outcome was a major adverse cardiac event (MACE). Results: Among 1,937 subjects, there were 205 (10.5%) events for the composite outcome and 110 (5.6%) events for MACE. Compared to the reference group (urinary Na excretion< 2.0g/day), the group with the highest measured 24h urinary Na excretion (urinary Na excretion ≥ 8.0g/day) was associated with increased risk of both the composite outcome (hazard ratio 3.29 [95% confidence interval 1.00-10.81]; P = 0.049) and MACE (hazard ratio 6.28 [95% confidence interval 1.45-27.20]; P = 0.013) in a cause-specific hazard model. Subgroup analysis also showed a pronounced association between dietary salt intake and the composite outcome in subgroups of patients with abdominal obesity, female, lower estimated glomerular filtration rate (< 60 ml/min per 1.73m2), no overt proteinuria, or a lower urinary potassium-to-creatinine ratio (< 46 mmol/g). Conclusion: A high-salt diet is associated with CV outcomes in non-dialysis CKD patients.

Multi-scale spatial modeling of immune cell distributions enables survival prediction in primary central nervous system lymphoma.

To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.

Aging drives cerebrovascular network remodeling and functional changes in the mouse brain.

Aging is the largest risk factor for neurodegenerative disorders, and commonly associated with compromised cerebrovasculature and pericytes. However, we do not know how normal aging differentially impacts the vascular structure and function in different brain areas. Here we utilize mesoscale microscopy methods (serial two-photon tomography and light sheet microscopy) and in vivo imaging (wide field optical spectroscopy and two-photon imaging) to determine detailed changes in aged cerebrovascular networks. Whole-brain vascular tracing showed an overall ~10% decrease in vascular length and branching density, and light sheet imaging with 3D immunolabeling revealed increased arteriole tortuosity in aged brains. Vasculature and pericyte densities showed significant reductions in the deep cortical layers, hippocampal network, and basal forebrain areas. Moreover, in vivo imaging in awake mice identified delays in neurovascular coupling and disrupted blood oxygenation. Collectively, we uncover regional vulnerabilities of cerebrovascular network and physiological changes that can mediate cognitive decline in normal aging.

A guide to the BRAIN Initiative Cell Census Network data ecosystem.

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Prognostic Value of T-Cell Density in the Tumor Center and Outer Margins in Gastric Cancer.

Tumor-infiltrating lymphocytes are associated with the survival of gastric cancer patients. T-cell densities in the tumor and its periphery were previously identified as prognostic T-cell markers for resectable gastric cancer. Immunohistochemistry for 5 T-cell markers, CD3, CD45RO, CD8, FOXP3, and granzyme B was performed on serial sections of N = 251 surgical resection specimens of patients treated with surgery only in the D1/D2 trial. Positive T cells were digitally quantified into tiles of 0.25 mm2 across 3 regions: the tumor center (TC), the inner invasive margin, and the outer invasive margin (OIM). A classification and regression tree model was employed to identify the optimal combination of median T-cell densities per region with cancer-specific survival (CSS) as the outcome. All statistical tests were 2-sided. CD8OIM was identified as the most dominant prognostic factor, followed by FOXP3TC, resulting in a decision tree containing 3 prognostically distinct subgroups with high (Hi) or low (Lo) density of the markers: CD8OIMHi, CD8OIMLo/FOXP3TCHi, and CD8OIMLo/FOXP3TCLo. In a multivariable Cox regression analysis, which included pathological T and N stages, Lauren histologic types, EBV status, microsatellite instability, and type of surgery, the immune subgroups were independent predictors for CSS. CSS was lower for CD8OIMLo/FOXP3TCHi (HR: 5.02; 95% CI: 2.03-12.42) and for CD8OIMLo/FOXP3TCLo (HR: 7.99; 95% CI: 3.22-19.86), compared with CD8OIMHi (P < .0001). The location and density of both CD8+ and FOXP3+ T cells in resectable gastric cancer are independently associated with survival. The combination of CD8OIM and FOXP3TC T-cell densities is a promising stratification factor that should be validated in independent studies.

A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming.

This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.

The association between vitamin D deficiency and risk of renal event: Results from the Korean cohort study for outcomes in patients with chronic kidney disease (KNOW-CKD).

Backgrounds: Some observational studies have suggested a possible association between vitamin D deficiency and CKD. However, in most studies, the causality between low levels of vitamin D and risk of renal events could not be explained. We investigated the relationship between vitamin D deficiency and risk of severe CKD stage and renal event in a large-scale prospective cohort study. Methods: We used data from a prospective cohort of 2,144 patients with available information on serum 25-hydroxyvitamin D (25(OH)D) levels at baseline from KNOW-CKD, 2011-2015 were included. Vitamin D deficiency was defined as serum 25(OH)D levels < 15 ng/mL. We performed a cross-sectional analysis to elucidate the relationship between 25(OH)D and CKD stage using baseline CKD patient data. We further examined a cohort analysis to clarify the association between 25(OH)D and risk of renal event. Renal event was a composite of the first occurrence of a 50% decline in eGFR from the baseline value or the onset of CKD stage 5 (initiation of dialysis or kidney transplantation) across the follow-up period. We also investigated the associations of vitamin D deficiency with risk of renal event according to diabetes and overweight status. Results: Vitamin D deficiency were significantly associated with an increased risk of severe CKD stage - 1.30-fold (95% CI: 1.10-1.69) for 25(OH)D. Deficiency of 25(OH)D with 1.64-fold (95% CI: 1.32-2.65) was related to renal event compared with the reference. Furthermore, vitamin D deficiency patients with presence of DM and overweight status also displayed higher risk than non-deficient patients for risk of renal event. Conclusion: Vitamin D deficiency is associated with significantly increased risk of severe CKD stage and renal event.

Enhancer profiling identifies epigenetic markers of endocrine resistance and reveals therapeutic options for metastatic castration-resistant prostate cancer patients.

Androgen Receptor (AR) signaling inhibitors, including enzalutamide, are treatment options for patients with metastatic castration-resistant prostate cancer (mCRPC), but resistance inevitably develops. Using metastatic samples from a prospective phase II clinical trial, we epigenetically profiled enhancer/promoter activities with H3K27ac chromatin immunoprecipitation followed by sequencing, before and after AR-targeted therapy. We identified a distinct subset of H3K27ac-differentially marked regions that associated with treatment responsiveness. These data were successfully validated in mCRPC patient-derived xenograft models (PDX). In silico analyses revealed HDAC3 as a critical factor that can drive resistance to hormonal interventions, which we validated in vitro . Using cell lines and mCRPC PDX tumors in vitro , we identified drug-drug synergy between enzalutamide and the pan-HDAC inhibitor vorinostat, providing therapeutic proof-of-concept. These findings demonstrate rationale for new therapeutic strategies using a combination of AR and HDAC inhibitors to improve patient outcome in advanced stages of mCRPC.