New drug candidates for treatment of atypical meningiomas: An integrated approach using gene expression signatures for drug repurposing

Drug Repurposing Using Molecular Network Analysis Identifies Jak as Targetable Driver in Necrobiosis Lipoidica

Drug repurposing is an attractive strategy for therapy development, particularly in rare diseases where traditional drug development approaches may be challenging owing to high cost and small numbers of patients. In this study, we used a drug identification and repurposing pipeline to identify candidate targetable drivers of disease and corresponding therapies through application of causal reasoning using a combination of open-access resources and transcriptomics data. We optimized our approach on psoriasis as a disease model, demonstrating the ability to identify known and, to date, unrecognized molecular drivers of psoriasis and link them to current and emerging therapies. Application of our approach to a cohort of tissue samples of necrobiosis lipoidica (an unrelated; rare; and, to date, molecularly poorly characterized cutaneous inflammatory disorder) identified a unique set of upstream regulators, particularly highlighting the role of IFNG and the Jak-signal transducer and activator of transcription pathway as a likely driver of disease pathogenesis and linked it to Jak inhibitors as potential therapy. Analysis of an independent cohort of necrobiosis lipoidica samples validated these findings, with the overall agreement of drug-matched upstream regulators above 96%. These data highlight the utility of our approach in rare diseases and offer an opportunity for drug discovery in other rare diseases in dermatology and beyond.

Use of Drug Sensitisers to Improve Therapeutic Index in Cancer

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug-drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit-risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field.

Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers

Introduction

Despite the many benefits immunotherapy has brought to patients with different cancers, its clinical applications and improvements are still hindered by drug resistance. Fostering a reliable approach to identifying sufferers who are sensitive to certain immunotherapeutic agents is of great clinical relevance.

Methods

We propose an ELISE (Ensemble Learning for Immunotherapeutic Response Evaluation) pipeline to generate a robust and highly accurate approach to predicting individual responses to immunotherapies. ELISE employed iterative univariable logistic regression to select genetic features of patients, using Monte Carlo Tree Search (MCTS) to tune hyperparameters. In each trial, ELISE selected multiple models for integration based on add or concatenate stacking strategies, including deep neural network, automatic feature interaction learning via self-attentive neural networks, deep factorization machine, compressed interaction network, and linear neural network, then adopted the best trial to generate a final approach. SHapley Additive exPlanations (SHAP) algorithm was applied to interpret ELISE, which was then validated in an independent test set.

Result

Regarding prediction of responses to atezolizumab within esophageal adenocarcinoma (EAC) patients, ELISE demonstrated a superior accuracy (Area Under Curve [AUC] = 100.00%). AC005786.3 (Mean [|SHAP value|] = 0.0097) was distinguished as the most valuable contributor to ELISE output, followed by SNORD3D (0.0092), RN7SKP72 (0.0081), EREG (0.0069), IGHV4-80 (0.0063), and MIR4526 (0.0063). Mechanistically, immunoglobulin complex, immunoglobulin production, adaptive immune response, antigen binding and others, were downregulated in ELISE-neg EAC subtypes and resulted in unfavorable responses. More encouragingly, ELISE could be extended to accurately estimate the responsiveness of various immunotherapeutic agents against other cancers, including PD1/PD-L1 suppressor against metastatic urothelial cancer (AUC = 88.86%), and MAGE-A3 immunotherapy against metastatic melanoma (AUC = 100.00%).

Discussion

This study presented deep insights into integrating ensemble deep learning with self-attention as a mechanism for predicting immunotherapy responses to human cancers, highlighting ELISE as a potential tool to generate reliable approaches to individualized treatment.

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of >530 PKs have been targeted by FDA-approved medications, and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to investigate the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on word and concept neighborhoods in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. Our results with historical data show that associations between PKs and specific cancers can be predicted years in advance with good accuracy. Our tool can be used to predict the relevance of inhibiting PKs for specific cancers and to support the design of well-focused clinical trials to discover novel PKIs for cancer therapy.

Identification of Novel Drug Candidate for Epithelial Ovarian Cancer via In Silico Investigation and In Vitro Validation

Epithelial ovarian cancer (EOC) has a poor prognosis and high mortality rate; patients are easy to relapse with standard therapies. So, there is an urgent need to develop novel drugs. In this study, differentially expressed genes (DEGs) of EOC were identified in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Enrichment and protein–protein interaction (PPI) analyses were performed. The drug candidate which has the possibility to treat EOC was predicted by Connectivity Map (CMAP) databases. Moreover, molecular docking was selected to calculate the binding affinity between drug candidate and hub genes. The cytotoxicity of drug candidates was assessed by MTT and colony formation analysis, the proteins coded by hub genes were detected by Western blots, and apoptosis analysis was evaluated by flow cytometry. Finally, 296 overlapping DEGs (|log 2 fold change|>1; q-value <0.05), which were principally involved in the cell cycle (p < 0.05), and cyclin-dependent kinase 1 (CDK1) were screened as the significant hub gene from the PPI network. Furthermore, the 21 drugs were extracted from CMAPs; among them, piperlongumine (PL) showed a lower CMAP score (-0.80, -62.92) and was regarded as the drug candidate. Furthermore, molecular docking results between PL and CDK1 with a docking score of –8.121 kcal/mol were close to the known CDK1 inhibitor (–8.24 kcal/mol). Additionally, in vitro experiments showed that PL inhibited proliferation and induced apoptosis via targeting CDK1 in EOC SKOV3 cells. Our results reveal that PL may be a novel drug candidate for EOC by inhibiting cell cycle.

Evaluation of connectivity map shows limited reproducibility in drug repositioning

The Connectivity Map (CMap) is a popular resource designed for data-driven drug repositioning using a large transcriptomic compendium. However, evaluations of its performance are limited. We used two iterations of CMap (CMap 1 and 2) to assess their comparability and reliability. We queried CMap 2 with CMap 1-derived signatures, expecting CMap 2 would highly prioritize the queried compounds; the success rate was 17%. Analysis of previously published prioritizations yielded similar results. Low recall is caused by low differential expression (DE) reproducibility both between CMaps and within each CMap. DE strength was predictive of reproducibility, and is influenced by compound concentration and cell-line responsiveness. Reproducibility of CMap 2 sample expression levels was also lower than expected. We attempted to identify the "better" CMap by comparison with a third dataset, but they were mutually discordant. Our findings have implications for CMap usage and we suggest steps for investigators to limit false positives.

Protective effect of grape seed extract and orlistat co-treatment against stroke: Effect on oxidative stress and energy failure

Ischemic stroke is a major health concern and a leading cause of mortality worldwide. Oxidative stress is an early event in the course of stroke inducing neuro-inflammation and cell death. Grape seed extract (GSE) is a natural phytochemical mixture exhibiting antioxidant, anti-inflammatory and neuroprotective properties. Orlistat (ORL) is an anti-obesity agent and a gastro-intestinal lipase inhibitor which showed recently beneficial effects on brain lipotoxicity. Recent studies reported the increase of lipase activity upon stroke which led us to investigate the neuroprotective effect of ORL on rat brain I/R injury as well as the putative synergism with GSE. I/R insult infarcted the brain parenchyma as assessed by TTC staining, induced an oxidative stress as revealed by increased lipoperoxidation along with alteration of antioxidant enzymes activities which was corrected using the cotreatment of ORL + GSE. I/R also disturbed the main metabolic pathways involved in brain fueling as glycolysis, neoglucogenesis, glycogenolysis, TCA cycle and electron transfer chain (ETC) complexes. These disturbances were also corrected with the cotreatment ORL + GSE which maintained energetic activities near to the control level. I/R also disrupted transition metals distribution, along with associated enzymes as tyrosinase, LDH or glutamine synthetase activities and induced hippocampal inflammation as revealed by glycogen depletion from dentate gyrus area along with depressed anti-inflammatory IL1β cytokine and increased pro-inflammatory CD68 antigen. Interestingly almost all I/R-induced disturbances were corrected either partially upon ORL and GSE on their own and the best neuroprotection was obtained in the presence of both drugs (ORL + GSE) enabling robust neuroprotection of the sub granular zone within hippocampal dentate gyrus area.

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases.

Integrating Differential Gene Expression Analysis with Perturbagen-Response Signatures May Identify Novel Therapies for Thyroid-Associated Orbitopathy

Purpose

To evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify small molecules that revert pathologic gene signature and alter disease phenotype in orbital adipose stem cells (OASCs) derived from patients with thyroid-associated orbitopathy (TAO).

Methods

Differentially expressed genes identified via RNA sequencing were inputted into LINCS L1000 Characteristic Direction Signature Search Engine (L1000CDS²) to predict candidate small molecules to reverse pathologic gene expression. TAO OASC cell lines were treated in vitro with six identified small molecules (Torin-2, PX12, withaferin A, isoliquiritigenin, mitoxantrone, and MLN8054), and expression of key adipogenic and differentially expressed genes was measured with quantitative polymerase chain reaction after 7 days of treatment. OASCs were differentiated into adipocytes, treated for 15 days, and stained with Oil Red O (OD 490 nm) to evaluate adipogenic changes.

Results

The expression of key differentially expressed genes (IRX1, HOXB2, S100B, and KCNA4) and adipogenic genes (peroxisome proliferator activated receptor-γ, FABP4) was significantly decreased in TAO OASCs after treatment (P < .05). In treated TAO adipocytes (n = 3), all six tested small molecules yielded significant decrease (P < .05) in Oil Red O staining. In treated non-TAO adipocytes (n = 3), only three of the drugs yielded a significant decrease in Oil Red O staining.

Conclusions

Combining disease expression signatures with LINCS small molecule prediction software can identify promising preclinical drug candidates for TAO.

Translational Relevance

These findings may offer insight into future potential therapeutic options for TAO and demonstrate a streamlined model to predict drug candidates for other diseases.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

Cellular Stress-Modulating Drugs Can Potentially Be Identified by in Silico Screening with Connectivity Map (CMap)

Accompanied by increased life span, aging-associated diseases, such as metabolic diseases and cancers, have become serious health threats. Recent studies have documented that aging-associated diseases are caused by prolonged cellular stresses such as endoplasmic reticulum (ER) stress, mitochondrial stress, and oxidative stress. Thus, ameliorating cellular stresses could be an effective approach to treat aging-associated diseases and, more importantly, to prevent such diseases from happening. However, cellular stresses and their molecular responses within the cell are typically mediated by a variety of factors encompassing different signaling pathways. Therefore, a target-based drug discovery method currently being used widely (reverse pharmacology) may not be adequate to uncover novel drugs targeting cellular stresses and related diseases. The connectivity map (CMap) is an online pharmacogenomic database cataloging gene expression data from cultured cells treated individually with various chemicals, including a variety of phytochemicals. Moreover, by querying through CMap, researchers may screen registered chemicals in silico and obtain the likelihood of drugs showing a similar gene expression profile with desired and chemopreventive conditions. Thus, CMap is an effective genome-based tool to discover novel chemopreventive drugs.

Drug repurposing: a promising tool to accelerate the drug discovery process

Traditional drug discovery and development involves several stages for the discovery of a new drug and to obtain marketing approval. It is necessary to discover new strategies for reducing the drug discovery time frame. Today, drug repurposing has gained importance in identifying new therapeutic uses for already-available drugs. Typically, repurposing can be achieved serendipitously (unintentional fortunate observations) or through systematic approaches. Numerous strategies to discover new indications for FDA-approved drugs are discussed in this article. Drug repurposing has therefore become a productive approach for drug discovery because it provides a novel way to explore old drugs for new use but encounters several challenges. Some examples of different approaches are reviewed here.

Further investigation of the potential anti-neoplastic, anti-inflammatory and immunomodulatory actions of phenoxybenzamine using the Broad Institute CLUE platform.. [DOI: 10.1101/767392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Previous clinical studies with the FDA-approved alpha-adrenergic antagonist, phenoxybenzamine, showed apparent efficacy to reverse the symptoms and disabilities of the neuropathic condition, Complex Regional Pain Syndrome; also, the anatomic spread and intensity of this syndrome has a proliferative character and it was proposed that phenoxybenzamine may have an anti-inflammatory, immunomodulatory mode of action. A previous study gave evidence that phenoxybenzamine had anti-proliferative activity in suppression of growth in several human tumor cell cultures. The same report demonstrated that the drug possessed significant histone deacetylase inhibitory activity. Utilizing the Harvard/Massachusetts Institute of Technology Broad Institute genomic database, CLUE, the present study suggests that the gene expression signature of phenoxybenzamine in malignant cell lines is consistent with anti-inflammatory/immunomodulatory activity and suppression of tumor expansion by several possible mechanisms of action. Of particular note, phenoxybenzamine demonstrated signatures that were highly similar to those with glucocorticoid agonist activity. Also, gene expression signatures of phenoxbenzamine were consistent with several agents in each case that were known to suppress tumor proliferation, notably, protein kinase C inhibitors, Heat Shock Protein inhibitors, epidermal growth factor receptor inhibitors, and glycogen synthase kinase inhibitors. Searches in CLUE also confirmed the earlier observations of strong similarities between gene expression signatures of phenoxybenzamine and several histone deacetylase inhibitors.

Influence of batch effect correction methods on drug induced differential gene expression profiles

Background

Batch effects were not accounted for in most of the studies of computational drug repositioning based on gene expression signatures. It is unknown how batch effect removal methods impact the results of signature-based drug repositioning. Herein, we conducted differential analyses on the Connectivity Map (CMAP) database using several batch effect correction methods to evaluate the influence of batch effect correction methods on computational drug repositioning using microarray data and compare several batch effect correction methods.

Results

Differences in average signature size were observed with different methods applied. The gene signatures identified by the Latent Effect Adjustment after Primary Projection (LEAPP) method and the methods fitted with Linear Models for Microarray Data (limma) software demonstrated little agreement. The external validity of the gene signatures was evaluated by connectivity mapping between the CMAP database and the Library of Integrated Network-based Cellular Signatures (LINCS) database. The results of connectivity mapping indicate that the genes identified were not reliable for drugs with total sample size (drug + control samples) smaller than 40, irrespective of the batch effect correction method applied. With total sample size larger than 40, the methods correcting for batch effects produced significantly better results than the method with no batch effect correction. In a simulation study, the power was generally low for simulated data with sample size smaller than 40. We observed best performance when using the limma method correcting for two principal components.

Conclusion

Batch effect correction methods strongly impact differential gene expression analysis when the sample size is large enough to contain sufficient information and thus the downstream drug repositioning. We recommend including two or three principal components as covariates in fitting models with limma when sample size is sufficient (larger than 40 drug and controls combined).

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3028-6) contains supplementary material, which is available to authorized users.

Microenvironment of ruptured cerebral aneurysms discovered using data driven analysis of gene expression

BACKGROUND

It is well known that ruptured intracranial aneurysms are associated with substantial morbidity and mortality, yet our understanding of the genetic mechanisms of rupture remains poor. We hypothesize that applying novel techniques to the genetic analysis of aneurysmal tissue will yield key rupture-associated mechanisms and novel drug candidates for the prevention of rupture.

METHODS

We applied weighted gene co-expression networks (WGCNA) and population-specific gene expression analysis (PSEA) to transcriptomic data from 33 ruptured and unruptured aneurysm domes. Mechanisms were annotated using Gene Ontology, and gene network/population-specific expression levels correlated with rupture state. We then used computational drug repurposing to identify plausible drug candidates for the prevention of aneurysm rupture.

RESULTS

Network analysis of bulk tissue identified multiple immune mechanisms to be associated with aneurysm rupture. Targeting these processes with computational drug repurposing revealed multiple candidates for preventing rupture including Btk inhibitors and modulators of hypoxia inducible factor. In the macrophage-specific analysis, we identify rupture-associated mechanisms MHCII antigen processing, cholesterol efflux, and keratan sulfate catabolism. These processes map well onto several of highly ranked drug candidates, providing further validation.

CONCLUSIONS

Our results are the first to demonstrate population-specific expression levels and intracranial aneurysm rupture, and propose novel drug candidates based on network-based transcriptomics.

Pharmacotherapeutic options for atypical meningiomas

Introduction: Atypical meningiomas are aggressive tumors associated with high rates of recurrence and mortality. Current therapy is surgical resection followed by radiotherapy which has reasonable success rates. However, there are cases where surgical resection is not possible, and radiotherapy is not advisable. Areas covered: In this short review, the authors have searched the current literature for explorations of adjuvant treatments such as chemotherapy and pharmaceutical agents. Most current chemotherapeutic agents have been unsuccessful in producing radiographic reduction or disease stabilization, although drugs like somatostatin analogs and plant-derived chemotherapeutics have shown some promise. The authors note that most of the studies in this field have been case series with a few randomized trials present. This makes it hard to ascertain the effectiveness of the drugs and so further research is required in the field. Expert opinion: Finding pharmacotherapies to combat atypical meningiomas needs Big data genomic analysis. This will assist in generating drug candidates and a multidrug approach to therapy that will exploit several of the pathological pathways of atypical meningiomas. Using multidrug therapy that affects several pathways also addresses the issue of meningioma heterogeneity and adaptability.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.