PRMT inhibition induces a viral mimicry response in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with the worst prognosis and few effective therapies. Here we identified MS023, an inhibitor of type I protein arginine methyltransferases (PRMTs), which has antitumor growth activity in TNBC. Pathway analysis of TNBC cell lines indicates that the activation of interferon responses before and after MS023 treatment is a functional biomarker and determinant of response, and these observations extend to a panel of human-derived organoids. Inhibition of type I PRMT triggers an interferon response through the antiviral defense pathway with the induction of double-stranded RNA, which is derived, at least in part, from inverted repeat Alu elements. Together, our results represent a shift in understanding the antitumor mechanism of type I PRMT inhibitors and provide a rationale and biomarker approach for the clinical development of type I PRMT inhibitors.

Evaluation of statistical approaches for association testing in noisy drug screening data.

BACKGROUND: Identifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. RESULTS: To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. CONCLUSIONS: We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies.

Recent cancer pharmacogenomic studies profiled large panels of cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbations, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging these valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest cancer pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated cancer pharmacogenomic datasets that are otherwise disparate and difficult to integrate.

HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models.

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

Effects of cytosine methylation on transcription factor binding sites.

BACKGROUND: DNA methylation in promoters is closely linked to downstream gene repression. However, whether DNA methylation is a cause or a consequence of gene repression remains an open question. If it is a cause, then DNA methylation may affect the affinity of transcription factors (TFs) for their binding sites (TFBSs). If it is a consequence, then gene repression caused by chromatin modification may be stabilized by DNA methylation. Until now, these two possibilities have been supported only by non-systematic evidence and they have not been tested on a wide range of TFs. An average promoter methylation is usually used in studies, whereas recent results suggested that methylation of individual cytosines can also be important. RESULTS: We found that the methylation profiles of 16.6% of cytosines and the expression profiles of neighboring transcriptional start sites (TSSs) were significantly negatively correlated. We called the CpGs corresponding to such cytosines "traffic lights". We observed a strong selection against CpG "traffic lights" within TFBSs. The negative selection was stronger for transcriptional repressors as compared with transcriptional activators or multifunctional TFs as well as for core TFBS positions as compared with flanking TFBS positions. CONCLUSIONS: Our results indicate that direct and selective methylation of certain TFBS that prevents TF binding is restricted to special cases and cannot be considered as a general regulatory mechanism of transcription.

Bimodal Gene Expression in Patients with Cancer Provides Interpretable Biomarkers for Drug Sensitivity.

Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic datasets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation support a better translation of gene expression biomarkers between model systems using bimodal gene expression. SIGNIFICANCE: A new machine learning pipeline exploits the bimodality of gene expression to provide a reliable set of candidate predictive biomarkers with a high potential for clinical translatability.

Biomimetic hydrogel supports initiation and growth of patient-derived breast tumor organoids.

Patient-derived tumor organoids (PDOs) are a highly promising preclinical model that recapitulates the histology, gene expression, and drug response of the donor patient tumor. Currently, PDO culture relies on basement-membrane extract (BME), which suffers from batch-to-batch variability, the presence of xenogeneic compounds and residual growth factors, and poor control of mechanical properties. Additionally, for the development of new organoid lines from patient-derived xenografts, contamination of murine host cells poses a problem. We propose a nanofibrillar hydrogel (EKGel) for the initiation and growth of breast cancer PDOs. PDOs grown in EKGel have histopathologic features, gene expression, and drug response that are similar to those of their parental tumors and PDOs in BME. In addition, EKGel offers reduced batch-to-batch variability, a range of mechanical properties, and suppressed contamination from murine cells. These results show that EKGel is an improved alternative to BME matrices for the initiation, growth, and maintenance of breast cancer PDOs.

Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins.

Statins, a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the mevalonate metabolic pathway, have demonstrated anticancer activity. Evidence shows that dipyridamole potentiates statin-induced cancer cell death by blocking a restorative feedback loop triggered by statin treatment. Leveraging this knowledge, we develop an integrative pharmacogenomics pipeline to identify compounds similar to dipyridamole at the level of drug structure, cell sensitivity and molecular perturbation. To overcome the complex polypharmacology of dipyridamole, we focus our pharmacogenomics pipeline on mevalonate pathway genes, which we name mevalonate drug-network fusion (MVA-DNF). We validate top-ranked compounds, nelfinavir and honokiol, and identify that low expression of the canonical epithelial cell marker, E-cadherin, is associated with statin-compound synergy. Analysis of remaining prioritized hits led to the validation of additional compounds, clotrimazole and vemurafenib. Thus, our computational pharmacogenomic approach identifies actionable compounds with pathway-specific activities.

The spindle assembly checkpoint is a therapeutic vulnerability of CDK4/6 inhibitor-resistant ER+ breast cancer with mitotic aberrations.

Inhibitors of cyclin-dependent kinases 4 and 6 (CDK4/6i) are standard first-line treatments for metastatic ER+ breast cancer. However, acquired resistance to CDK4/6i invariably develops, and the molecular phenotypes and exploitable vulnerabilities associated with resistance are not yet fully characterized. We developed a panel of CDK4/6i-resistant breast cancer cell lines and patient-derived organoids and demonstrate that a subset of resistant models accumulates mitotic segregation errors and micronuclei, displaying increased sensitivity to inhibitors of mitotic checkpoint regulators TTK and Aurora kinase A/B. RB1 loss, a well-recognized mechanism of CDK4/6i resistance, causes such mitotic defects and confers enhanced sensitivity to TTK inhibition. In these models, inhibition of TTK with CFI-402257 induces premature chromosome segregation, leading to excessive mitotic segregation errors, DNA damage, and cell death. These findings nominate the TTK inhibitor CFI-402257 as a therapeutic strategy for a defined subset of ER+ breast cancer patients who develop resistance to CDK4/6i.

PRMT5 inhibition disrupts splicing and stemness in glioblastoma.

Glioblastoma (GBM) is a deadly cancer in which cancer stem cells (CSCs) sustain tumor growth and contribute to therapeutic resistance. Protein arginine methyltransferase 5 (PRMT5) has recently emerged as a promising target in GBM. Using two orthogonal-acting inhibitors of PRMT5 (GSK591 or LLY-283), we show that pharmacological inhibition of PRMT5 suppresses the growth of a cohort of 46 patient-derived GBM stem cell cultures, with the proneural subtype showing greater sensitivity. We show that PRMT5 inhibition causes widespread disruption of splicing across the transcriptome, particularly affecting cell cycle gene products. We identify a GBM splicing signature that correlates with the degree of response to PRMT5 inhibition. Importantly, we demonstrate that LLY-283 is brain-penetrant and significantly prolongs the survival of mice with orthotopic patient-derived xenografts. Collectively, our findings provide a rationale for the clinical development of brain penetrant PRMT5 inhibitors as treatment for GBM.

GLUT1 inhibition blocks growth of RB1-positive triple negative breast cancer.

Triple negative breast cancer (TNBC) is a deadly form of breast cancer due to the development of resistance to chemotherapy affecting over 30% of patients. New therapeutics and companion biomarkers are urgently needed. Recognizing the elevated expression of glucose transporter 1 (GLUT1, encoded by SLC2A1) and associated metabolic dependencies in TNBC, we investigated the vulnerability of TNBC cell lines and patient-derived samples to GLUT1 inhibition. We report that genetic or pharmacological inhibition of GLUT1 with BAY-876 impairs the growth of a subset of TNBC cells displaying high glycolytic and lower oxidative phosphorylation (OXPHOS) rates. Pathway enrichment analysis of gene expression data suggests that the functionality of the E2F pathway may reflect to some extent OXPHOS activity. Furthermore, the protein levels of retinoblastoma tumor suppressor (RB1) strongly correlate with the degree of sensitivity to GLUT1 inhibition in TNBC, where RB1-negative cells are insensitive to GLUT1 inhibition. Collectively, our results highlight a strong and targetable RB1-GLUT1 metabolic axis in TNBC and warrant clinical evaluation of GLUT1 inhibition in TNBC patients stratified according to RB1 protein expression levels.

Epigenetic Switch-Induced Viral Mimicry Evasion in Chemotherapy-Resistant Breast Cancer.

Tumor progression upon treatment arises from preexisting resistant cancer cells and/or adaptation of persister cancer cells committing to an expansion phase. Here, we show that evasion from viral mimicry response allows the growth of taxane-resistant triple-negative breast cancer (TNBC). This is enabled by an epigenetic state adapted to taxane-induced metabolic stress, where DNA hypomethylation over loci enriched in transposable elements (TE) is compensated by large chromatin domains of H3K27me3 to warrant TE repression. This epigenetic state creates a vulnerability to epigenetic therapy against EZH2, the H3K27me3 methyltransferase, which alleviates TE repression in taxane-resistant TNBC, leading to double-stranded RNA production and growth inhibition through viral mimicry response. Collectively, our results illustrate how epigenetic states over TEs promote cancer progression under treatment and can inform about vulnerabilities to epigenetic therapy. SIGNIFICANCE: Drug-resistant cancer cells represent a major barrier to remission for patients with cancer. Here we show that drug-induced metabolic perturbation and epigenetic states enable evasion from the viral mimicry response induced by chemotherapy in TNBC. These epigenetic states define a vulnerability to epigenetic therapy using EZH2 inhibitors in taxane-resistant TNBC.See related commentary by Janin and Esteller, p. 1258.This article is highlighted in the In This Issue feature, p. 1241.

Creating reproducible pharmacogenomic analysis pipelines.

The field of pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts.

Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. SIGNIFICANCE: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX.See related commentary by Meehan, p. 4324.

ONECUT2 is a driver of neuroendocrine prostate cancer.

Neuroendocrine prostate cancer (NEPC), a lethal form of the disease, is characterized by loss of androgen receptor (AR) signaling during neuroendocrine transdifferentiation, which results in resistance to AR-targeted therapy. Clinically, genomically and epigenetically, NEPC resembles other types of poorly differentiated neuroendocrine tumors (NETs). Through pan-NET analyses, we identified ONECUT2 as a candidate master transcriptional regulator of poorly differentiated NETs. ONECUT2 ectopic expression in prostate adenocarcinoma synergizes with hypoxia to suppress androgen signaling and induce neuroendocrine plasticity. ONEUCT2 drives tumor aggressiveness in NEPC, partially through regulating hypoxia signaling and tumor hypoxia. Specifically, ONECUT2 activates SMAD3, which regulates hypoxia signaling through modulating HIF1α chromatin-binding, leading NEPC to exhibit higher degrees of hypoxia compared to prostate adenocarcinomas. Treatment with hypoxia-activated prodrug TH-302 potently reduces NEPC tumor growth. Collectively, these results highlight the synergy between ONECUT2 and hypoxia in driving NEPC, and emphasize the potential of hypoxia-directed therapy for NEPC patients.

DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.

High-throughput screening (HTS) performs the experimental testing of a large number of chemical compounds aiming to identify those active in the considered assay. Alternatively, faster and cheaper methods of large-scale virtual screening are performed computationally through quantitative structure-activity relationship (QSAR) models. However, the vast amount of available HTS heterogeneous data and the imbalanced ratio of active to inactive compounds in an assay make this a challenging problem. Although different QSAR models have been proposed, they have certain limitations, e.g., high false positive rates, complicated user interface, and limited utilization options. Therefore, we developed DPubChem, a novel web tool for deriving QSAR models that implement the state-of-the-art machine-learning techniques to enhance the precision of the models and enable efficient analyses of experiments from PubChem BioAssay database. DPubChem also has a simple interface that provides various options to users. DPubChem predicted active compounds for 300 datasets with an average geometric mean and F1 score of 76.68% and 76.53%, respectively. Furthermore, DPubChem builds interaction networks that highlight novel predicted links between chemical compounds and biological assays. Using such a network, DPubChem successfully suggested a novel drug for the Niemann-Pick type C disease. DPubChem is freely available at www.cbrc.kaust.edu.sa/dpubchem .

Statin-Induced Cancer Cell Death Can Be Mechanistically Uncoupled from Prenylation of RAS Family Proteins.

The statin family of drugs preferentially triggers tumor cell apoptosis by depleting mevalonate pathway metabolites farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), which are used for protein prenylation, including the oncoproteins of the RAS superfamily. However, accumulating data indicate that activation of the RAS superfamily are poor biomarkers of statin sensitivity, and the mechanism of statin-induced tumor-specific apoptosis remains unclear. Here we demonstrate that cancer cell death triggered by statins can be uncoupled from prenylation of the RAS superfamily of oncoproteins. Ectopic expression of different members of the RAS superfamily did not uniformly sensitize cells to fluvastatin, indicating that increased cellular demand for protein prenylation cannot explain increased statin sensitivity. Although ectopic expression of HRAS increased statin sensitivity, expression of myristoylated HRAS did not rescue this effect. HRAS-induced epithelial-to-mesenchymal transition (EMT) through activation of zinc finger E-box binding homeobox 1 (ZEB1) sensitized tumor cells to the antiproliferative activity of statins, and induction of EMT by ZEB1 was sufficient to phenocopy the increase in fluvastatin sensitivity; knocking out ZEB1 reversed this effect. Publicly available gene expression and statin sensitivity data indicated that enrichment of EMT features was associated with increased sensitivity to statins in a large panel of cancer cell lines across multiple cancer types. These results indicate that the anticancer effect of statins is independent from prenylation of RAS family proteins and is associated with a cancer cell EMT phenotype.Significance: The use of statins to target cancer cell EMT may be useful as a therapy to block cancer progression. Cancer Res; 78(5); 1347-57. ©2017 AACR.

MYC Interacts with the G9a Histone Methyltransferase to Drive Transcriptional Repression and Tumorigenesis.

MYC is an oncogenic driver that regulates transcriptional activation and repression. Surprisingly, mechanisms by which MYC promotes malignant transformation remain unclear. We demonstrate that MYC interacts with the G9a H3K9-methyltransferase complex to control transcriptional repression. Inhibiting G9a hinders MYC chromatin binding at MYC-repressed genes and de-represses gene expression. By identifying the MYC box II region as essential for MYC-G9a interaction, a long-standing missing link between MYC transformation and gene repression is unveiled. Across breast cancer cell lines, the anti-proliferative response to G9a pharmacological inhibition correlates with MYC sensitivity and gene signatures. Consistently, genetically depleting G9a in vivo suppresses MYC-dependent tumor growth. These findings unveil G9a as an epigenetic regulator of MYC transcriptional repression and a therapeutic vulnerability in MYC-driven cancers.

DASPfind: new efficient method to predict drug-target interactions.

BACKGROUND: Identification of novel drug-target interactions (DTIs) is important for drug discovery. Experimental determination of such DTIs is costly and time consuming, hence it necessitates the development of efficient computational methods for the accurate prediction of potential DTIs. To-date, many computational methods have been proposed for this purpose, but they suffer the drawback of a high rate of false positive predictions. RESULTS: Here, we developed a novel computational DTI prediction method, DASPfind. DASPfind uses simple paths of particular lengths inferred from a graph that describes DTIs, similarities between drugs, and similarities between the protein targets of drugs. We show that on average, over the four gold standard DTI datasets, DASPfind significantly outperforms other existing methods when the single top-ranked predictions are considered, resulting in 46.17 % of these predictions being correct, and it achieves 49.22 % correct single top ranked predictions when the set of all DTIs for a single drug is tested. Furthermore, we demonstrate that our method is best suited for predicting DTIs in cases of drugs with no known targets or with few known targets. We also show the practical use of DASPfind by generating novel predictions for the Ion Channel dataset and validating them manually. CONCLUSIONS: DASPfind is a computational method for finding reliable new interactions between drugs and proteins. We show over six different DTI datasets that DASPfind outperforms other state-of-the-art methods when the single top-ranked predictions are considered, or when a drug with no known targets or with few known targets is considered. We illustrate the usefulness and practicality of DASPfind by predicting novel DTIs for the Ion Channel dataset. The validated predictions suggest that DASPfind can be used as an efficient method to identify correct DTIs, thus reducing the cost of necessary experimental verifications in the process of drug discovery. DASPfind can be accessed online at: http://www.cbrc.kaust.edu.sa/daspfind.Graphical abstractThe conceptual workflow for predicting drug-target interactions using DASPfind.

DRABAL: novel method to mine large high-throughput screening assays using Bayesian active learning.

BACKGROUND: Mining high-throughput screening (HTS) assays is key for enhancing decisions in the area of drug repositioning and drug discovery. However, many challenges are encountered in the process of developing suitable and accurate methods for extracting useful information from these assays. Virtual screening and a wide variety of databases, methods and solutions proposed to-date, did not completely overcome these challenges. This study is based on a multi-label classification (MLC) technique for modeling correlations between several HTS assays, meaning that a single prediction represents a subset of assigned correlated labels instead of one label. Thus, the devised method provides an increased probability for more accurate predictions of compounds that were not tested in particular assays. RESULTS: Here we present DRABAL, a novel MLC solution that incorporates structure learning of a Bayesian network as a step to model dependency between the HTS assays. In this study, DRABAL was used to process more than 1.4 million interactions of over 400,000 compounds and analyze the existing relationships between five large HTS assays from the PubChem BioAssay Database. Compared to different MLC methods, DRABAL significantly improves the F1Score by about 22%, on average. We further illustrated usefulness and utility of DRABAL through screening FDA approved drugs and reported ones that have a high probability to interact with several targets, thus enabling drug-multi-target repositioning. Specifically DRABAL suggests the Thiabendazole drug as a common activator of the NCP1 and Rab-9A proteins, both of which are designed to identify treatment modalities for the Niemann-Pick type C disease. CONCLUSION: We developed a novel MLC solution based on a Bayesian active learning framework to overcome the challenge of lacking fully labeled training data and exploit actual dependencies between the HTS assays. The solution is motivated by the need to model dependencies between existing experimental confirmatory HTS assays and improve prediction performance. We have pursued extensive experiments over several HTS assays and have shown the advantages of DRABAL. The datasets and programs can be downloaded from https://figshare.com/articles/DRABAL/3309562.Graphical abstract.