The myImageAnalysis project: a web-based application for high-content screening

A novel ERβ high throughput microscopy platform for testing endocrine disrupting chemicals

In this study we present an inducible biosensor model for the Estrogen Receptor Beta (ERβ), GFP-ERβ:PRL-HeLa, a single-cell-based high throughput (HT) in vitro assay that allows direct visualization and measurement of GFP-tagged ERβ binding to ER-specific DNA response elements (EREs), ERβ-induced chromatin remodeling, and monitor transcriptional alterations via mRNA fluorescence in situ hybridization for a prolactin (PRL)-dsRED2 reporter gene. The model was used to accurately (Z' = 0.58-0.8) differentiate ERβ-selective ligands from ERα ligands when treated with a panel of selective agonists and antagonists. Next, we tested an Environmental Protection Agency (EPA)-provided set of 45 estrogenic reference chemicals with known ERα in vivo activity and identified several that activated ERβ as well, with varying sensitivity, including a subset that is completely novel. We then used an orthogonal ERE-containing transgenic zebrafish (ZF) model to cross validate ERβ and ERα selective activities at the organism level. Using this environmentally relevant ZF assay, some compounds were confirmed to have ERβ activity, validating the GFP-ERβ:PRL-HeLa assay as a screening tool for potential ERβ active endocrine disruptors (EDCs). These data demonstrate the value of sensitive multiplex mechanistic data gathered by the GFP-ERβ:PRL-HeLa assay coupled with an orthogonal zebrafish model to rapidly identify environmentally relevant ERβ EDCs and improve upon currently available screening tools for this understudied nuclear receptor.

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression

BACKGROUND

Diverse toxicants and mixtures that affect hormone responsive cells [endocrine disrupting chemicals (EDCs)] are highly pervasive in the environment and are directly linked to human disease. They often target the nuclear receptor family of transcription factors modulating their levels and activity. Many high-throughput assays have been developed to query such toxicants; however, single-cell analysis of EDC effects on endogenous receptors has been missing, in part due to the lack of quality control metrics to reproducibly measure cell-to-cell variability in responses.

OBJECTIVE

We began by developing single-cell imaging and informatic workflows to query whether the single cell distribution of the estrogen receptor-α (ER), used as a model system, can be used to measure effects of EDCs in a sensitive and reproducible manner.

METHODS

We used high-throughput microscopy, coupled with image analytics to measure changes in single cell ER nuclear levels on treatment with ∼100 toxicants, over a large number of biological and technical replicates.

RESULTS

We developed a two-tiered quality control pipeline for single cell analysis and tested it against a large set of biological replicates, and toxicants from the EPA and Agency for Toxic Substances and Disease Registry lists. We also identified a subset of potentially novel EDCs that were active only on the endogenous ER level and activity as measured by single molecule RNA fluorescence in situ hybridization (RNA FISH).

DISCUSSION

We demonstrated that the distribution of ER levels per cell, and the changes upon chemical challenges were remarkably stable features; and importantly, these features could be used for quality control and identification of endocrine disruptor toxicants with high sensitivity. When coupled with orthogonal assays, ER single cell distribution is a valuable resource for high-throughput screening of environmental toxicants. https://doi.org/10.1289/EHP9297.

Predicting the Estrogen Receptor Activity of Environmental Chemicals by Single-Cell Image Analysis and Data-driven Modeling

A comprehensive evaluation of toxic chemicals and understanding their potential harm to human physiology is vital in mitigating their adverse effects following exposure from environmental emergencies. In this work, we develop data-driven classification models to facilitate rapid decision making in such catastrophic events and predict the estrogenic activity of environmental toxicants as estrogen receptor-α (ERα) agonists or antagonists. By combining high-content analysis, big-data analytics, and machine learning algorithms, we demonstrate that highly accurate classifiers can be constructed for evaluating the estrogenic potential of many chemicals. We follow a rigorous, high throughput microscopy-based high-content analysis pipeline to measure the single cell-level response of benchmark compounds with known in vivo effects on the ERα pathway. The resulting high-dimensional dataset is then pre-processed by fitting a non-central gamma probability distribution function to each feature, compound, and concentration. The characteristic parameters of the distribution, which represent the mean and the shape of the distribution, are used as features for the classification analysis via Random Forest (RF) and Support Vector Machine (SVM) algorithms. The results show that the SVM classifier can predict the estrogenic potential of benchmark chemicals with higher accuracy than the RF algorithm, which misclassifies two antagonist compounds.

PRSS50 is a testis protease responsible for proper sperm tail formation and function

Multiple morphological abnormalities of the sperm flagella (MMAF) are a major cause of asthenoteratozoospermia. We have identified protease serine 50 (PRSS50) as having a crucial role in sperm development, because Prss50-null mice presented with impaired fertility and sperm tail abnormalities. PRSS50 could also be involved in centrosome function because these mice showed a threefold increase in acephalic sperm (head-tail junction defect), sperm with multiple heads (spermatid division defect) and sperm with multiple tails, including novel two conjoined sperm (complete or partial parts of several flagellum on the same plasma membrane). Our data support that, in the testis, as in tumorigenesis, PRSS50 activates NFκB target genes, such as the centromere protein leucine-rich repeats and WD repeat domain-containing protein 1 (LRWD1), which is required for heterochromatin maintenance. Prss50-null testes have increased IκκB, and reduced LRWD1 and histone expression. Low levels of de-repressed histone markers, such as H3K9me3, in the Prss50-null mouse testis may cause increases in post-meiosis proteins, such as AKAP4, affecting sperm formation. We provide important insights into the complex mechanisms of sperm development, the importance of testis proteases in fertility and a novel mechanism for MMAF.

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.

Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists.

Acquisition of Cisplatin Resistance Shifts Head and Neck Squamous Cell Carcinoma Metabolism toward Neutralization of Oxidative Stress

Background: Cisplatin (CDDP) is commonly utilized in the treatment of advanced solid tumors including head and neck squamous cell carcinoma (HNSCC). Cisplatin response remains highly variable among individual tumors and development of cisplatin resistance is common. We hypothesized that development of cisplatin resistance is partially driven by metabolic reprogramming. Methods: Using a pre-clinical HNSCC model and an integrated approach to steady state metabolomics, metabolic flux and gene expression data we characterized the interaction between cisplatin resistance and metabolic reprogramming. Results: Cisplatin toxicity in HNSCC was driven by generation of intra-cellular oxidative stress. This was validated by demonstrating that acquisition of cisplatin resistance generates cross-resistance to ferroptosis agonists despite the fact that cisplatin itself does not trigger ferroptosis. Acquisition of cisplatin resistance dysregulated the expression of genes involved in amino acid, fatty acid metabolism and central carbon catabolic pathways, enhanced glucose catabolism and serine synthesis. Acute cisplatin exposure increased intra-tumoral levels of S-methyl-5-thiadenosine (MTA) precursors and metabotoxins indicative of generalized oxidative stress. Conclusions: Acquisition of cisplatin resistance is linked to metabolic recovery from oxidative stress. Although this portends poor effectiveness for directed metabolic targeting, it supports the potential for biomarker development of cisplatin effectiveness using an integrated approach.

Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals

Cell-to-cell variation of protein expression in genetically homogeneous populations is a common biological trait often neglected during analysis of high-throughput (HT) screens and is rarely used as a metric to characterize chemicals. We have captured single-cell distributions of androgen receptor (AR) nuclear levels after perturbations as a means to evaluate assay reproducibility and characterize a small subset of chemicals. AR, a member of the nuclear receptor family of transcription factors, is the central regulator of male reproduction and is involved in many pathophysiological processes. AR protein levels and nuclear localization often increase following ligand binding, with dihydrotestosterone (DHT) being the natural agonist. HT AR immunofluorescence imaging was used in multiple cell lines to define single-cell nuclear values extracted from thousands of cells per condition treated with DHT or DMSO (control). Analysis of numerous biological replicates led to a quality control metric that takes into account the distribution of single-cell data, and how it changes upon treatments. Dose-response experiments across several cell lines showed a large range of sensitivity to DHT, prompting us to treat selected cell lines with 45 Environmental Protection Agency (EPA)-provided chemicals that include many endocrine-disrupting chemicals (EDCs); data from six of the compounds were then integrated with orthogonal assays. Our comprehensive results indicate that quantitative single-cell distribution analysis of AR protein levels is a valid method to detect the potential androgenic and antiandrogenic actions of environmentally relevant chemicals in a sensitive and reproducible manner.

A Mechanistic High-Content Analysis Assay Using a Chimeric Androgen Receptor That Rapidly Characterizes Androgenic Chemicals

Human health is at risk from environmental exposures to a wide range of chemical toxicants and endocrine-disrupting chemicals (EDCs). As part of understanding this risk, the U.S. Environmental Protection Agency (EPA) has been pursuing new high-throughput in vitro assays and computational models to characterize EDCs. EPA models have incorporated our high-content analysis-based green fluorescent protein estrogen receptor (GFP-ER): PRL-HeLa assay, which allows direct visualization of ER binding to DNA regulatory elements. Here, we characterize a modified functional assay based on the stable expression of a chimeric androgen receptor (ARER), wherein a region containing the native AR DNA-binding domain (DBD) was replaced with the ERα DBD (amino acids 183-254). We demonstrate that the AR agonist dihydrotestosterone induces GFP-ARER nuclear translocation, PRL promoter binding, and transcriptional activity at physiologically relevant concentrations (<1 nM). In contrast, the AR antagonist bicalutamide induces only nuclear translocation of the GFP-ARER receptor (at μM concentrations). Estradiol also fails to induce visible chromatin binding, indicating androgen specificity. In a screen of reference chemicals from the EPA and the Agency for Toxic Substances and Disease Registry, the GFP-ARER cell model identified and mechanistically grouped activity by known (anti-)androgens based on the ability to induce nuclear translocation and/or chromatin binding. Finally, the cell model was used to identify potential (anti-)androgens in environmental samples in collaboration with the Houston Ship Channel/Galveston Bay Texas A&M University EPA Superfund Research Program. Based on these data, the chromatin-binding, in vitro assay-based GFP-ARER model represents a selective tool for rapidly identifying androgenic activity associated with drugs, chemicals, and environmental samples.

Estrogen-induced transcription at individual alleles is independent of receptor level and active conformation but can be modulated by coactivators activity

Steroid hormones are pivotal modulators of pathophysiological processes in many organs, where they interact with nuclear receptors to regulate gene transcription. However, our understanding of hormone action at the single cell level remains incomplete. Here, we focused on estrogen stimulation of the well-characterized GREB1 and MYC target genes that revealed large differences in cell-by-cell responses, and, more interestingly, between alleles within the same cell, both over time and hormone concentration. We specifically analyzed the role of receptor level and activity state during allele-by-allele regulation and found that neither receptor level nor activation status are the determinant of maximal hormonal response, indicating that additional pathways are potentially in place to modulate cell- and allele-specific responses. Interestingly, we found that a small molecule inhibitor of the arginine methyltransferases CARM1 and PRMT6 was able to increase, in a gene specific manner, the number of active alleles/cell before and after hormonal stimulation, suggesting that mechanisms do indeed exist to modulate hormone receptor responses at the single cell and allele level.

Leveraging Image-Derived Phenotypic Measurements for Drug-Target Interaction Predictions

In recent years, protein kinases have become some of the most significant drug targets in cancer patients. Kinases are known to regulate the activity of many human proteins, and consequently their inhibition has been used to control cancer proliferation. A significant challenge in drug discovery is the rapid and efficient identification of new small molecules. In this study, we propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). A high-throughput imaging dataset was generated from an siRNA human kinome screen on engineered cells that allow direct visualization of effects on estrogen receptor-α or a chimeric progesterone receptor B binding to specific DNA. Two types of kinase descriptors are extracted from these imaging data: first, a population-median-based descriptor and second a bag-of-words (BoW) descriptor that can capture heterogeneity information in the imaging data. Using these descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. The best performing model in target-based drug discovery gives an area under the receiver operating characteristic curve (AUC) of 0.86, whereas the best model in ligand-based discovery gives an AUC of 0.79. These promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient.

Cisplatin generates oxidative stress which is accompanied by rapid shifts in central carbon metabolism

Cisplatin is commonly utilized in the treatment of solid tumors. Its mechanism of action is complex and multiple mechanisms of resistance have been described. We sought to determine the impact of cisplatin-generated oxidative stress on head and neck squamous cell carcinoma (HNSCC) proliferation, survival and metabolic activity in order to identify a potential metabolic signature associated with cisplatin response. DNA-bound cisplatin represents a small fraction of total intra-cellular cisplatin but generates a robust oxidative stress response. Neutralization of oxidative stress reverses cisplatin toxicity independent of the mechanism of cell death and TP53 mutational status. Cisplatin-induced oxidative stress triggers rapid shifts in carbon flux in 3 commonly utilized catabolic pathways: glycolysis, pentose phosphate pathway and citric acid cycle. Among these metabolic shifts, decreased flux from pyruvate into lactate is the only metabolic effect consistently observed across multiple HNSCC cell lines of varying genomic backgrounds and may reflect differential cisplatin sensitivity. Oxidative stress is a critical component of cisplatin cytotoxicity in HNSCC and is reflected in acute changes in carbon flux from pyruvate into lactate. This suggests that lactate may contribute to a metabolic signature of acute cisplatin toxicity, and could prove useful in optimizing cisplatin-based treatment regimens in HNSCC.

High throughput microscopy identifies bisphenol AP, a bisphenol A analog, as a novel AR down-regulator

Prostate cancer remains a deadly disease especially when patients become resistant to drugs that target the Androgen Receptor (AR) ligand binding domain. At this stage, patients develop recurring castrate-resistant prostate cancers (CRPCs). Interestingly, CRPC tumors maintain dependency on AR for growth; moreover, in CRPCs, constitutively active AR splice variants (e.g., AR-V7) begin to be expressed at higher levels. These splice variants lack the ligand binding domain and are rendered insensitive to current endocrine therapies. Thus, it is of paramount importance to understand what regulates the expression of AR and its splice variants to identify new therapeutic strategies in CRPCs. Here, we used high throughput microscopy and quantitative image analysis to evaluate effects of selected endocrine disruptors on AR levels in multiple breast and prostate cancer cell lines. Bisphenol AP (BPAP), which is used in chemical and medical industries, was identified as a down-regulator of both full length AR and the AR-V7 splice variant. We validated its activity by performing time-course, dose-response, Western blot and qPCR analyses. BPAP also reduced the percent of cells in S phase, which was accompanied by a ~60% loss in cell numbers and colony formation in anchorage-independent growth assays. Moreover, it affected mitochondria size and cell metabolism. In conclusion, our high content analysis-based screening platform was used to classify the effect of compounds on endogenous ARs, and identified BPAP as being capable of causing AR (both full-length and variants) down-regulation, cell cycle arrest and metabolic alterations in CRPC cell lines.

Characterizing properties of non-estrogenic substituted bisphenol analogs using high throughput microscopy and image analysis

Animal studies have linked the estrogenic properties of bisphenol A (BPA) to adverse effects on the endocrine system. Because of concerns for similar effects in humans, there is a desire to replace BPA in consumer products, and a search for BPA replacements that lack endocrine-disrupting bioactivity is ongoing. We used multiple cell-based models, including an established multi-parametric, high throughput microscopy-based platform that incorporates engineered HeLa cell lines with visible ERα- or ERβ-regulated transcription loci, to discriminate the estrogen-like and androgen-like properties of previously uncharacterized substituted bisphenol derivatives and hydroquinone. As expected, BPA induced 70–80% of the estrogen-like activity via ERα and ERβ compared to E2 in the HeLa prolactin array cell line. 2,2’ BPA, Bisguaiacol F, CHDM 4-hydroxybuyl acrylate, hydroquinone, and TM modified variants of BPF showed very limited estrogen-like or androgen-like activity (< 10% of that observed with the control compounds). Interestingly, TM-BFP and CHDM 4-hydroxybuyl acrylate, but not their derivatives, demonstrated evidence of anti-estrogenic and anti-androgenic activity. Our findings indicate that Bisguaiacol F, TM-BFP-ER and TM-BPF-DGE demonstrate low potential for affecting estrogenic or androgenic endocrine activity. This suggest that the tested compounds could be suitable commercially viable alternatives to BPA.

High-Content Screening Identifies Src Family Kinases as Potential Regulators of AR-V7 Expression and Androgen-Independent Cell Growth

BACKGROUND

AR-V7 is an androgen receptor (AR) splice variant that lacks the ligand-binding domain and is isolated from prostate cancer cell lines. Increased expression of AR-V7 is associated with the transition from hormone-sensitive prostate cancer to more advanced castration-resistant prostate cancer (CRPC). Due to the loss of the ligand-binding domain, AR-V7 is not responsive to traditional AR-targeted therapies, and the mechanisms that regulate AR-V7 are still incompletely understood. Therefore, we aimed to explore existing classes of small molecules that may regulate AR-V7 expression and intracellular localization and their potential therapeutic role in CRPC.

METHODS

We used AR high-content analysis (AR-HCA) to characterize the effects of a focused library of well-characterized clinical compounds on AR-V7 expression at the single-cell level in PC3 prostate cancer cells stably expressing green fluorescent protein (GFP)-AR-V7 (GFP-AR-V7:PC3). In parallel, an orthogonal AR-HCA screen of a small interfering (si)RNA library targeting 635 protein kinases was performed in GFP-AR-V7:PC3. The effect of the Src-Abl inhibitor PD 180970 was further characterized using cell-proliferation assays, quantitative PCR, and western blot analysis in multiple hormone-sensitive and CRPC cell lines.

RESULTS

Compounds that tended to target Akt, Abl, and Src family kinases (SFKs) decreased overall AR-V7 expression, nuclear translocation, absolute nuclear level, and/or altered nuclear distribution. We identified 20 protein kinases that, when knocked down, either decreased nuclear GFP-AR-V7 levels or altered AR-V7 nuclear distribution, a set that included the SFKs Src and Fyn. The Src-Abl dual kinase inhibitor PD180970 decreased expression of AR-V7 by greater than 46% and decreased ligand-independent transcription of AR target genes in the 22RV1 human prostate carcinoma cell line. Further, PD180970 inhibited androgen-independent cell proliferation in endogenous-AR-V7-expressing prostate cancer cell lines and also overcame bicalutamide resistance observed in the 22RV1 cell line.

CONCLUSIONS

SFKs, especially Src and Fyn, may be important upstream regulators of AR-V7 expression and represent promising targets in a subset of CRPCs expressing high levels of AR-V7. Prostate 77:82-93, 2017. © 2016 Wiley Periodicals, Inc.

Effects of Circular DNA Length on Transfection Efficiency by Electroporation into HeLa Cells

The ability to produce extremely small and circular supercoiled vectors has opened new territory for improving non-viral gene therapy vectors. In this work, we compared transfection of supercoiled DNA vectors ranging from 383 to 4,548 bp, each encoding shRNA against GFP under control of the H1 promoter. We assessed knockdown of GFP by electroporation into HeLa cells. All of our vectors entered cells in comparable numbers when electroporated with equal moles of DNA. Despite similar cell entry, we found length-dependent differences in how efficiently the vectors knocked down GFP. As vector length increased up to 1,869 bp, GFP knockdown efficiency per mole of transfected DNA increased. From 1,869 to 4,257 bp, GFP knockdown efficiency per mole was steady, then decreased with increasing vector length. In comparing GFP knockdown with equal masses of vectors, we found that the shorter vectors transfect more efficiently per nanogram of DNA transfected. Our results rule out cell entry and DNA mass as determining factors for gene knockdown efficiency via electroporation. The length-dependent effects we have uncovered are likely explained by differences in nuclear translocation or transcription. These data add an important step towards clinical applications of non-viral vector delivery.

Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer

The precise molecular alterations driving castration-resistant prostate cancer (CRPC) are not clearly understood. Using a novel network-based integrative approach, here, we show distinct alterations in the hexosamine biosynthetic pathway (HBP) to be critical for CRPC. Expression of HBP enzyme glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1) is found to be significantly decreased in CRPC compared with localized prostate cancer (PCa). Genetic loss-of-function of GNPNAT1 in CRPC-like cells increases proliferation and aggressiveness, in vitro and in vivo. This is mediated by either activation of the PI3K-AKT pathway in cells expressing full-length androgen receptor (AR) or by specific protein 1 (SP1)-regulated expression of carbohydrate response element-binding protein (ChREBP) in cells containing AR-V7 variant. Strikingly, addition of the HBP metabolite UDP-N-acetylglucosamine (UDP-GlcNAc) to CRPC-like cells significantly decreases cell proliferation, both in-vitro and in animal studies, while also demonstrates additive efficacy when combined with enzalutamide in-vitro. These observations demonstrate the therapeutic value of targeting HBP in CRPC.

The molecular alterations driving anti-androgen resistance in prostate cancer are unclear. Here, the authors show, using a network-based approach, that inhibition of the hexosamine biosynthetic pathway is necessary to develop resistance and that increasing the activity of the pathway enhances the anti-androgen response.

CUDC-101, a Novel Inhibitor of Full-Length Androgen Receptor (flAR) and Androgen Receptor Variant 7 (AR-V7) Activity: Mechanism of Action and In Vivo Efficacy

Castration-resistant prostate cancer (CRPC) is an androgen receptor (AR)-dependent disease expected to cause the death of more than 27,000 Americans in 2015. There are only a few available treatments for CRPC, making the discovery of new drugs an urgent need. We report that CUDC-101 (an inhibitor od HER2/NEU, EGFR and HDAC) inhibits both the full length AR (flAR) and the AR variant AR-V7. This observation prompted experiments to discover which of the known activities of CUDC-101 is responsible for the inhibition of flAR/AR-V7 signaling. We used pharmacologic and genetic approaches, and found that the effect of CUDC-101 on flAR and AR-V7 was duplicated only by other HDAC inhibitors, or by silencing the HDAC isoforms HDAC5 and HDAC10. We observed that CUDC-101 treatment or AR-V7 silencing by RNAi equally reduced transcription of the AR-V7 target gene, PSA, without affecting viability of 22Rv1 cells. However, when cellular proliferation was used as an end point, CUDC-101 was more effective than AR-V7 silencing, raising the prospect that CUDC-101 has additional targets beside AR-V7. In support of this, we found that CUDC-101 increased the expression of the cyclin-dependent kinase inhibitor p21, and decreased that of the oncogene HER2/NEU. To determine if CUDC-101 reduces growth in a xenograft model of prostate cancer, this drug was given for 14 days to castrated male SCID mice inoculated with 22Rv1 cells. Compared to vehicle, CUDC-101 reduced xenograft growth in a statistically significant way, and without macroscopic side effects. These studies demonstrate that CUDC-101 inhibits wtAR and AR-V7 activity and growth of 22Rv1 cells in vitro and in vivo. These effects result from the ability of CUDC-101 to target not only HDAC signaling, which was associated with decreased flAR and AR-V7 activity, but multiple additional oncogenic pathways. These observations raise the possibility that treatment of CRPC may be achieved by using similarly multi-targeted approaches.