Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Computational Methods for Drug Repurposing

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

A survey of optimal strategy for signature-based drug repositioning and an application to liver cancer

Pharmacologic perturbation projects, such as Connectivity Map (CMap) and Library of Integrated Network-based Cellular Signatures (LINCS), have produced many perturbed expression data, providing enormous opportunities for computational therapeutic discovery. However, there is no consensus on which methodologies and parameters are the most optimal to conduct such analysis. Aiming to fill this gap, new benchmarking standards were developed to quantitatively evaluate drug retrieval performance. Investigations of potential factors influencing drug retrieval were conducted based on these standards. As a result, we determined an optimal approach for LINCS data-based therapeutic discovery. With this approach, homoharringtonine (HHT) was identified to be a candidate agent with potential therapeutic and preventive effects on liver cancer. The antitumor and antifibrotic activity of HHT was validated experimentally using subcutaneous xenograft tumor model and carbon tetrachloride (CCL₄)-induced liver fibrosis model, demonstrating the reliability of the prediction results. In summary, our findings will not only impact the future applications of LINCS data but also offer new opportunities for therapeutic intervention of liver cancer.

Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination

Background

LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches.

Results

Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD.

Conclusions

The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04530-9.

Integrative analysis of CRISPR screening data uncovers new opportunities for optimizing cancer immunotherapy

Background

In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them.

Methods

In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses.

Results

We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined.

Conclusion

Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01462-z.

Ibrutinib Blocks YAP1 Activation and Reverses BRAF Inhibitor Resistance in Melanoma Cells

Most B-Raf proto-oncogene (BRAF)-mutant melanoma tumors respond initially to BRAF inhibitor (BRAFi)/mitogen-activated protein kinase kinase 1 inhibitor (MEKi) therapy, although few patients have durable long-term responses to these agents. The goal of this study was to use an unbiased computational approach to identify inhibitors that reverse an experimentally derived BRAFi resistance gene expression signature. Using this approach, we found that ibrutinib effectively reverses this signature, and we demonstrate experimentally that ibrutinib resensitizes a subset of BRAFi-resistant melanoma cells to vemurafenib. Ibrutinib is used clinically as an inhibitor of the Src family kinase Bruton tyrosine kinase (BTK); however, neither BTK deletion nor treatment with acalabrutinib, another BTK inhibitor with reduced off-target activity, resensitized cells to vemurafenib. These data suggest that ibrutinib acts through a BTK-independent mechanism in vemurafenib resensitization. To better understand this mechanism, we analyzed the transcriptional profile of ibrutinib-treated BRAFi-resistant melanoma cells and found that the transcriptional profile of ibrutinib was highly similar to that of multiple Src proto-oncogene kinase inhibitors. Since ibrutinib, but not acalabrutinib, has appreciable off-target activity against multiple Src family kinases, it suggests that ibrutinib may be acting through this mechanism. Furthermore, genes that are differentially expressed in ibrutinib-treated cells are enriched in Yes1-associated transcriptional regulator (YAP1) target genes, and we showed that ibrutinib, but not acalabrutinib, reduces YAP1 activity in BRAFi-resistant melanoma cells. Taken together, these data suggest that ibrutinib, or other Src family kinase inhibitors, may be useful for treating some BRAFi/MEKi-refractory melanoma tumors. SIGNIFICANCE STATEMENT: MAPK-targeted therapies provide dramatic initial responses, but resistance develops rapidly; a subset of these tumors may be rendered sensitive again by treatment with an approved Src family kinase inhibitor-ibrutinub-potentially providing improved clinical outcomes.

Phyto-targeting the CEMIP Expression as a Strategy to Prevent Pancreatic Cancer Metastasis

INTRODUCTION

Metastasis of primary pancreatic cancer (PC) to adjacent or distant organs is responsible for the poor survival rate of affected individuals. Chemotherapy, radiotherapy, and immunotherapy are currently being prescribed to treat PC in addition to surgical resection. Surgical resection is the preferred treatment for PC that leads to 20% of 5-year survival, but only less than 20% of patients are eligible for surgical resection because of the poor prognosis. To improve the prognosis and clinical outcome, early diagnostic markers need to be identified, and targeting them would be of immense benefit to increase the efficiency of the treatment. Cell migration-inducing hyaluronan-binding protein (CEMIP) is identified as an important risk factor for the metastasis of various cancers, including PC. Emerging studies have pointed out the crucial role of CEMIP in the regulation of various signaling mechanisms, leading to enhanced migration and metastasis of PC.

METHODS

The published findings on PC metastasis, phytoconstituents, and CEMIP were retrieved from Pubmed, ScienceDirect, and Cochrane Library. Computational tools, such as gene expression profiling interactive analysis (GEPIA) and Kaplan-Meier (KM) plotter, were used to study the relationship between CEMIP expression and survival of PC individuals.

RESULTS

Gene expression analysis using the GEPIA database identified a stupendous increase in the CEMIP transcript in PC compared to adjacent normal tissues. KM plotter analysis revealed the impact of CEMIP on the overall survival (OS) and disease-free survival (DFS) among PC patients. Subsequently, several risk factors associated with PC development were screened, and their ability to regulate CEMIP gene expression was analyzed using computational tools.

CONCLUSION

The current review is focused on gathering information regarding the regulatory role of phytocomponents in PC migration and exploring their possible impact on the CEMIP expression.

A Multiparametric Pharmacogenomic Strategy for Drug Repositioning predicts Therapeutic Efficacy for Glioblastoma Cell Lines

Background

Poor prognosis of glioblastoma patients and the extensive heterogeneity of glioblastoma at both the molecular and cellular level necessitates developing novel individualized treatment modalities via genomics-driven approaches.

Methods

This study leverages numerous pharmacogenomic and tissue databases to examine drug repositioning for glioblastoma. RNAseq of glioblastoma tumor samples from The Cancer Genome Atlas (TCGA, n=117) were compared to “normal” frontal lobe samples from Genotype-Tissue Expression Portal (GTEX, n=120) to find differentially expressed genes (DEGs). Using compound-gene expression data and drug activity data from the Library of Integrated Network-Based Cellular Signatures (LINCS, n=66,512 compounds) CCLE (71 glioma cell lines), and Chemical European Molecular Biology Laboratory (ChEMBL) platforms, we employed a summarized reversal gene expression metric (sRGES) to “reverse” the resultant disease signature for GBM and its subtypes. A multi-parametric strategy was employed to stratify compounds capable of blood brain barrier penetrance with a favorable pharmacokinetic profile (CNS-MPO).

Results

Significant correlations were identified between sRGES and drug efficacy in GBM cell lines in both ChEMBL(r=0.37,p<.001) and Cancer Therapeutic Response Portal (CTRP) databases (r=0.35, p<0.001). Our multiparametric algorithm identified two classes of drugs with highest sRGES and CNS-MPO: HDAC inhibitors (vorinostat and entinostat) and topoisomerase inhibitors suitable for drug repurposing.

Conclusions

Our studies suggest that reversal of glioblastoma disease signature correlates with drug potency for various GBM subtypes. This multiparametric approach may set the foundation for an early-phase personalized -omics clinical trial for glioblastoma by effectively identifying drugs that are capable of reversing the disease signature and have favorable pharmacokinetic and safety profiles.

Deciphering the Role of Pyrvinium Pamoate in the Generation of Integrated Stress Response and Modulation of Mitochondrial Function in Myeloid Leukemia Cells through Transcriptome Analysis

Pyrvinium pamoate, a widely-used anthelmintic agent, reportedly exhibits significant anti-tumor effects in several cancers. However, the efficacy and mechanisms of pyrvinium against myeloid leukemia remain unclear. The growth inhibitory effects of pyrvinium were tested in human AML cell lines. Transcriptome analysis of Molm13 myeloid leukemia cells suggested that pyrvinium pamoate could trigger an unfolded protein response (UPR)-like pathway, including responses to extracellular stimulus [p-value = 2.78 × 10^-6] and to endoplasmic reticulum stress [p-value = 8.67 × 10^-7], as well as elicit metabolic reprogramming, including sulfur compound catabolic processes [p-value = 2.58 × 10^-8], and responses to a redox state [p-value = 5.80 × 10^-5]; on the other hand, it could elicit a pyrvinium blunted protein folding function, including protein folding [p-value = 2.10 × 10^-8] and an ATP metabolic process [p-value = 3.95 × 10^-4]. Subsequently, pyrvinium was verified to induce an integrated stress response (ISR), demonstrated by activation of the eIF2α-ATF4 pathway and inhibition of mTORC1 signaling, in a dose- and time-dependent manner. Additionally, pyrvinium could co-localize with mitochondria and then decrease the mitochondrial basal oxidative consumption rate, ultimately dysregulating the mitochondrial function. Similar effects were observed in cabozantinib-resistant Molm13-XR cell lines. Furthermore, pyrvinium treatment retarded Molm13 and Molm13-XR xenograft tumor growth. Thus, we concluded that pyrvinium exerts anti-tumor activity, at least, via the modulation of the mitochondrial function and by triggering ISR.

Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

Connecting chemistry and biology through molecular descriptors

Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.

Drug repositioning by merging active subnetworks validated in cancer and COVID-19

Computational drug repositioning aims at ranking and selecting existing drugs for novel diseases or novel use in old diseases. In silico drug screening has the potential for speeding up considerably the shortlisting of promising candidates in response to outbreaks of diseases such as COVID-19 for which no satisfactory cure has yet been found. We describe DrugMerge as a methodology for preclinical computational drug repositioning based on merging multiple drug rankings obtained with an ensemble of disease active subnetworks. DrugMerge uses differential transcriptomic data on drugs and diseases in the context of a large gene co-expression network. Experiments with four benchmark diseases demonstrate that our method detects in first position drugs in clinical use for the specified disease, in all four cases. Application of DrugMerge to COVID-19 found rankings with many drugs currently in clinical trials for COVID-19 in top positions, thus showing that DrugMerge can mimic human expert judgment.

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease

The evident genetic, pathological, and clinical heterogeneity of Alzheimer's disease (AD) poses challenges for traditional drug development. We conducted a computational drug repurposing screen for drugs to treat apolipoprotein (apo) E4-related AD. We first established apoE-genotype-dependent transcriptomic signatures of AD by analyzing publicly-available human brain database. We then queried these signatures against the Connectivity Map database containing transcriptomic perturbations of >1300 drugs to identify those that best reverse apoE-genotype-specific AD signatures. Bumetanide was identified as a top drug for apoE4 AD. Bumetanide treatment of apoE4 mice without or with Aβ accumulation rescued electrophysiological, pathological, or cognitive deficits. Single-nucleus RNA-sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed in apoE4-iPSC-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 in two electronic health record databases, suggesting effectiveness of bumetanide in preventing AD.

Histone deacetylase 6 acts upstream of DNA damage response activation to support the survival of glioblastoma cells

DNA repair promotes the progression and recurrence of glioblastoma (GBM). However, there remain no effective therapies for targeting the DNA damage response and repair (DDR) pathway in the clinical setting. Thus, we aimed to conduct a comprehensive analysis of DDR genes in GBM specimens to understand the molecular mechanisms underlying treatment resistance. Herein, transcriptomic analysis of 177 well-defined DDR genes was performed with normal and GBM specimens (n = 137) from The Cancer Genome Atlas and further integrated with the expression profiling of histone deacetylase 6 (HDAC6) inhibition in temozolomide (TMZ)-resistant GBM cells and patient-derived tumor cells. The effects of HDAC6 inhibition on DDR signaling were examined both in vitro and intracranial mouse models. We found that the expression of DDR genes, involved in repair pathways for DNA double-strand breaks, was upregulated in highly malignant primary and recurrent brain tumors, and their expression was related to abnormal clinical features. However, a potent HDAC6 inhibitor, MPT0B291, attenuated the expression of these genes, including RAD51 and CHEK1, and was more effective in blocking homologous recombination repair in GBM cells. Interestingly, it resulted in lower cytotoxicity in primary glial cells than other HDAC6 inhibitors. MPT0B291 reduced the growth of both TMZ-sensitive and TMZ-resistant tumor cells and prolonged survival in mouse models of GBM. We verified that HDAC6 regulated DDR genes by affecting Sp1 expression, which abolished MPT0B291-induced DNA damage. Our findings uncover a regulatory network among HDAC6, Sp1, and DDR genes for drug resistance and survival of GBM cells. Furthermore, MPT0B291 may serve as a potential lead compound for GBM therapy.

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals

The global efforts in the past year have led to the discovery of nearly 200 drug repurposing candidates for COVID-19. Gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. Following bioinformatics analyses revealed that the expression of these genes is associated with COVID-19 patient severity and has predictive power on anti-SARS-CoV-2 efficacy in vitro. Monensin, a top new compound that regulates these genes, was further confirmed as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. Interestingly, drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics could be centered around combinations of targeting these processes and viral proteins.

Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.

Methods

A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.

Results

We identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.

Conclusion

This study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated 16 of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Microbial and genetic-based framework identifies drug targets in inflammatory bowel disease

Rationale: With increasing incidence and prevalence of inflammatory bowel disease (IBD), it has become one of the major public health threats, and there is an urgent need to develop new therapeutic agents. Although the pathogenesis of IBD is still unclear, previous research has provided evidence for complex interplays between genetic, immune, microbial, and environmental factors. Here, we constructed a gene-microbiota interaction-based framework to discover IBD biomarkers and therapeutics. Methods: We identified candidate biomarkers for IBD by analyzing the publicly available transcriptomic and microbiome data from IBD cohorts. Animal models of IBD and diarrhea were established. The inflammation-correlated microbial and genetic variants in gene knockout mice were identified by 16S rRNA sequences and PCR array. We performed bioinformatic analysis of microbiome functional prediction and drug repurposing. Our validation experiments with cells and animals confirmed anti-inflammatory properties of a drug candidate. Results: We identified the DNA-sensing enzyme cyclic GMP-AMP synthase (cGAS) as a potential biomarker for IBD in both patients and murine models. cGAS knockout mice were less susceptible to DSS-induced colitis. cGAS-associated gut microbiota and host genetic factors relating to IBD pathogenesis were also identified. Using a computational drug repurposing approach, we predicted 43 candidate drugs with high potency to reverse colitis-associated gene expression and validated that brefeldin-a mitigates inflammatory response in colitis mouse model and colon cancer cell lines. Conclusions: By integrating computational screening, microbiota interference, gene knockout techniques, and in vitro and in vivo validation, we built a framework for predicting biomarkers and host-microbe interaction targets and identifying repurposing drugs for IBD, which may be tested further for clinical application. This approach may also be a tool for repurposing drugs for treating other diseases.

Reconciling multiple connectivity scores for drug repurposing

The basis of several recent methods for drug repurposing is the key principle that an efficacious drug will reverse the disease molecular ‘signature’ with minimal side effects. This principle was defined and popularized by the influential ‘connectivity map’ study in 2006 regarding reversal relationships between disease- and drug-induced gene expression profiles, quantified by a disease-drug ‘connectivity score.’ Over the past 15 years, several studies have proposed variations in calculating connectivity scores toward improving accuracy and robustness in light of massive growth in reference drug profiles. However, these variations have been formulated inconsistently using various notations and terminologies even though they are based on a common set of conceptual and statistical ideas. Therefore, we present a systematic reconciliation of multiple disease-drug similarity metrics (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$ES$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$css$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Sum$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Cosine$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$XSum$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$XCor$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$XSpe$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$XCos$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$EWCos$\end{document}) and connectivity scores (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CS$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$RGES$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$NCS$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$WCS$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$Tau$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$CSS$\end{document}, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$EMUDRA$\end{document}) by defining them using consistent notation and terminology. In addition to providing clarity and deeper insights, this coherent definition of connectivity scores and their relationships provides a unified scheme that newer methods can adopt, enabling the computational drug-development community to compare and investigate different approaches easily. To facilitate the continuous and transparent integration of newer methods, this article will be available as a live document (https://jravilab.github.io/connectivity_scores) coupled with a GitHub repository (https://github.com/jravilab/connectivity_scores) that any researcher can build on and push changes to.

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

Transcriptomics-based drug repositioning pipeline identifies therapeutic candidates for COVID-19

The novel SARS-CoV-2 virus emerged in December 2019 and has few effective treatments. We applied a computational drug repositioning pipeline to SARS-CoV-2 differential gene expression signatures derived from publicly available data. We utilized three independent published studies to acquire or generate lists of differentially expressed genes between control and SARS-CoV-2-infected samples. Using a rank-based pattern matching strategy based on the Kolmogorov-Smirnov Statistic, the signatures were queried against drug profiles from Connectivity Map (CMap). We validated sixteen of our top predicted hits in live SARS-CoV-2 antiviral assays in either Calu-3 or 293T-ACE2 cells. Validation experiments in human cell lines showed that 11 of the 16 compounds tested to date (including clofazimine, haloperidol and others) had measurable antiviral activity against SARS-CoV-2. These initial results are encouraging as we continue to work towards a further analysis of these predicted drugs as potential therapeutics for the treatment of COVID-19.

Published Anti-SARS-CoV-2 In Vitro Hits Share Common Mechanisms of Action that Synergize with Antivirals

The global efforts in the past few months have led to the discovery of around 200 drug repurposing candidates for COVID-19. Although most of them only exhibited moderate anti- SARS-CoV-2 activity, gaining more insights into their mechanisms of action could facilitate a better understanding of infection and the development of therapeutics. Leveraging large-scale drug-induced gene expression profiles, we found 36% of the active compounds regulate genes related to cholesterol homeostasis and microtubule cytoskeleton organization. The expression change upon drug treatment was further experimentally confirmed in human lung primary small airway. Following bioinformatics analysis on COVID-19 patient data revealed that these genes are associated with COVID-19 patient severity. The expression level of these genes also has predicted power on anti-SARS-CoV-2 efficacy in vitro, which led to the discovery of monensin as an inhibitor of SARS-CoV-2 replication in Vero-E6 cells. The final survey of recent drug- combination data indicated that drugs co-targeting cholesterol homeostasis and microtubule cytoskeleton organization processes more likely present a synergistic effect with antivirals. Therefore, potential therapeutics should be centered around combinations of targeting these processes and viral proteins.

A Transcriptome-Based Drug Discovery Paradigm for Neurodevelopmental Disorders

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin-associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach originally developed for cancer called "transcriptome reversal" for these neurodevelopmental disorders. This approach attempts to identify compounds that reverse gene-expression signatures associated with disease states. ANN NEUROL 2021;89:199-211.

Computational Drug Repositioning Identifies Statins as Modifiers of Prognostic Genetic Expression Signatures and Metastatic Behavior in Melanoma

Despite advances in melanoma treatment, more than 70% of patients with distant metastasis die within 5 years. Proactive treatment of early melanoma to prevent metastasis could save lives and reduce overall healthcare costs. Currently, there are no treatments specifically designed to prevent early melanoma from progressing to metastasis. We used the Connectivity Map to conduct an in silico drug screen and identified 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) as a drug class that might prevent melanoma metastasis. To confirm the in vitro effect of statins, RNA sequencing was completed on A375 cells after treatment with fluvastatin to describe changes in the melanoma transcriptome. Statins induced differential expression in genes associated with metastasis and are used in commercially available prognostic tests for melanoma metastasis. Finally, we completed a chart review of 475 patients with melanoma. Patients taking statins were less likely to have metastasis at the time of melanoma diagnosis in both univariate and multivariate analyses (24.7% taking statins vs. 37.6% not taking statins, absolute risk reduction = 12.9%, P = 0.038). These findings suggest that statins might be useful as a treatment to prevent melanoma metastasis. Prospective trials are required to verify our findings and to determine the mechanism of metastasis prevention.

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

As the field of precision medicine progresses, treatments for patients with cancer are starting to be tailored to their molecular as well as their clinical features. The emerging cancer subtypes defined by these molecular features require that dedicated resources be used to assist the discovery of drug candidates for preclinical evaluation. Voluminous gene expression profiles of patients with cancer have been accumulated in public databases, enabling the creation of cancer-specific expression signatures. Meanwhile, large-scale gene expression profiles of cellular responses to chemical compounds have also recently became available. By matching the cancer-specific expression signature to compound-induced gene expression profiles from large drug libraries, researchers can prioritize small molecules that present high potency to reverse expression of signature genes for further experimental testing of their efficacy. This approach has proven to be an efficient and cost-effective way to identify efficacious drug candidates. However, the success of this approach requires multiscale procedures, imposing considerable challenges to many labs. To address this, we developed Open Cancer TherApeutic Discovery (OCTAD; http://octad.org ): an open workspace for virtually screening compounds targeting precise groups of patients with cancer using gene expression features. Its database includes 19,127 patient tissue samples covering more than 50 cancer types and expression profiles for 12,442 distinct compounds. The program is used to perform deep-learning-based reference tissue selection, disease gene expression signature creation, drug reversal potency scoring and in silico validation. OCTAD is available as a web portal and a standalone R package to allow experimental and computational scientists to easily navigate the tool.

Increased activation of HDAC1/2/6 and Sp1 underlies therapeutic resistance and tumor growth in glioblastoma

BACKGROUND

Glioblastoma is associated with poor prognosis and high mortality. Although the use of first-line temozolomide can reduce tumor growth, therapy-induced stress drives stem cells out of quiescence, leading to chemoresistance and glioblastoma recurrence. The specificity protein 1 (Sp1) transcription factor is known to protect glioblastoma cells against temozolomide; however, how tumor cells hijack this factor to gain resistance to therapy is not known.

METHODS

Sp1 acetylation in temozolomide-resistant cells and stemlike tumorspheres was analyzed by immunoprecipitation and immunoblotting experiments. Effects of the histone deacetylase (HDAC)/Sp1 axis on malignant growth were examined using cell proliferation-related assays and in vivo experiments. Furthermore, integrative analysis of gene expression with chromatin immunoprecipitation sequencing and the recurrent glioblastoma omics data were also used to further determine the target genes of the HDAC/Sp1 axis.

RESULTS

We identified Sp1 as a novel substrate of HDAC6, and observed that the HDAC1/2/6/Sp1 pathway promotes self-renewal of malignancy by upregulating B cell-specific Mo-MLV integration site 1 (BMI1) and human telomerase reverse transcriptase (hTERT), as well as by regulating G2/M progression and DNA repair via alteration of the transcription of various genes. Importantly, HDAC1/2/6/Sp1 activation is associated with poor clinical outcome in both glioblastoma and low-grade gliomas. However, treatment with azaindolyl sulfonamide, a potent HDAC6 inhibitor with partial efficacy against HDAC1/2, induced G2/M arrest and senescence in both temozolomide-resistant cells and stemlike tumorspheres.

CONCLUSION

Our study uncovers a previously unknown regulatory mechanism in which the HDAC6/Sp1 axis induces cell division and maintains the stem cell population to fuel tumor growth and therapeutic resistance.

Combining kinase inhibitors for optimally co-targeting cancer and drug escape by exploitation of drug target promiscuities

Cancers resist targeted therapeutics by drug-escape signaling. Multitarget drugs co-targeting cancer and drug-escape mediators (DEMs) are clinically advantageous. DEM coverage may be expanded by drug combinations. This work evaluated to what extent the kinase DEMs (KDEMs) can be optimally co-targeted by drug combinations based on target promiscuities of individual drugs. We focused on 41 approved and 28 clinical trial small molecule kinase inhibitor drugs with available experimental kinome and clinical pharmacokinetic data. From the kinome inhibitory profiles of these drugs, drug combinations were assembled for optimally co-targeting an established cancer target (EGFR, HER2, ABL1, or MEK1) and 9-16 target-associated KDEMs at comparable potency levels as that against the cancer target. Each set of two-, three-, and four-drug combinations co-target 36-71%, 44-89%, 50-88%, and 27-55% KDEMs of EGFR, HER2, ABL1, and MEK1, respectively, compared with the 36, 33, 38, and 18% KDEMs maximally co-targeted by an existing drug or drug combination approved or clinically tested for the respective cancer. Some co-targeted KDEMs are not covered by any existing drug or drug combination. Our work suggested that novel drug combinations may be constructed for optimally co-targeting cancer and drug escape by the exploitation of drug target promiscuities.

Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma

In recent years, personalized cancer immunotherapy, especially stratification-driven precision treatments have gained significant traction. However, due to the heterogeneity in clinical cohorts, the uncombined analysis of stratification/therapeutics may lead to confusion in determining ideal therapeutic options. We report that the coupled immune stratification and drug repurposing could facilitate identification of therapeutic candidates in patients with lung adenocarcinoma (LUAD). First, we categorized the patients into four groups based on immune gene profiling, associated with distinct molecular characteristics and clinical outcomes. Then, the weighted gene co-expression network analysis (WGCNA) algorithm was used to identify co-expression modules of each groups. We focused on C3 group which is characterized by low immune infiltration (cold tumor) and wild-type EGFR, posing a significant challenge for treatment of LUAD. Five drug candidates against the C3 status were identified which have potential dual functions to correct aberrant immune microenvironment and also halt tumorigenesis. Furthermore, their steady binding affinity against the targets was verified through molecular docking analysis. In sum, our findings suggest that such coupled analysis could be a promising methodology for identification and exploration of therapeutic candidates in the practice of personalized immunotherapy.

A guide for bioinformaticians: 'omics-based drug discovery for precision oncology

Bioinformatics-centric drug development is inevitable in the era of precision medicine. Clinical 'omics information, including genomics, epigenomics, transcriptomics, and proteomics, provides the most comprehensive molecular landscape in which each patient's pathological history is delineated. Hence, the capability of bioinformaticians to manage integrative 'omics data is crucial to current drug development. Bioinformatics can accelerate drug development from initial time-consuming discoveries to the clinical stage by providing information-guided solutions. However, many bioinformaticians do not have opportunities to participate in drug discovery programs. As a starting point for bioinformaticians with no prior drug development experience, here we discuss bioinformatics applications during drug development with a focus on working-level omics-based methodologies.

Tales of 1,008 small molecules: phenomic profiling through live-cell imaging in a panel of reporter cell lines

Phenomic profiles are high-dimensional sets of readouts that can comprehensively capture the biological impact of chemical and genetic perturbations in cellular assay systems. Phenomic profiling of compound libraries can be used for compound target identification or mechanism of action (MoA) prediction and other applications in drug discovery. To devise an economical set of phenomic profiling assays, we assembled a library of 1,008 approved drugs and well-characterized tool compounds manually annotated to 218 unique MoAs, and we profiled each compound at four concentrations in live-cell, high-content imaging screens against a panel of 15 reporter cell lines, which expressed a diverse set of fluorescent organelle and pathway markers in three distinct cell lineages. For 41 of 83 testable MoAs, phenomic profiles accurately ranked the reference compounds (AUC-ROC ≥ 0.9). MoAs could be better resolved by screening compounds at multiple concentrations than by including replicates at a single concentration. Screening additional cell lineages and fluorescent markers increased the number of distinguishable MoAs but this effect quickly plateaued. There remains a substantial number of MoAs that were hard to distinguish from others under the current study's conditions. We discuss ways to close this gap, which will inform the design of future phenomic profiling efforts.

Molecular classification and therapeutic targets in extrahepatic cholangiocarcinoma

BACKGROUND & AIMS

Cholangiocarcinoma (CCA), a deadly malignancy of the bile ducts, can be classified based on its anatomical location into either intrahepatic (iCCA) or extrahepatic (eCCA), each with different pathogenesis and clinical management. There is limited understanding of the molecular landscape of eCCA and no targeted therapy with clinical efficacy has been approved. We aimed to provide a molecular classification of eCCA and identify potential targets for molecular therapies.

METHODS

An integrative genomic analysis of an international multicenter cohort of 189 eCCA cases was conducted. Genomic analysis included whole-genome expression, targeted DNA-sequencing and immunohistochemistry. Molecular findings were validated in an external set of 181 biliary tract tumors from the ICGC.

RESULTS

KRAS (36.7%), TP53 (34.7%), ARID1A (14%) and SMAD4 (10.7%) were the most prevalent mutations, with ∼25% of tumors having a putative actionable genomic alteration according to OncoKB. Transcriptome-based unsupervised clustering helped us define 4 molecular classes of eCCA. Tumors classified within the Metabolic class (19%) showed a hepatocyte-like phenotype with activation of the transcription factor HNF4A and enrichment in gene signatures related to bile acid metabolism. The Proliferation class (23%), more common in patients with distal CCA, was characterized by enrichment of MYC targets, ERBB2 mutations/amplifications and activation of mTOR signaling. The Mesenchymal class (47%) was defined by signatures of epithelial-mesenchymal transition, aberrant TGFβ signaling and poor overall survival. Finally, tumors in the Immune class (11%) had a higher lymphocyte infiltration, overexpression of PD-1/PD-L1 and molecular features associated with a better response to immune checkpoint inhibitors.

CONCLUSION

An integrative molecular characterization identified distinct subclasses of eCCA. Genomic traits of each class provide the rationale for exploring patient stratification and novel therapeutic approaches.

LAY SUMMARY

Targeted therapies have not been approved for the treatment of extrahepatic cholangiocarcinoma. We performed a multi-platform molecular characterization of this tumor in a cohort of 189 patients. These analyses revealed 4 novel transcriptome-based molecular classes of extrahepatic cholangiocarcinoma and identified ∼25% of tumors with actionable genomic alterations, which has potential prognostic and therapeutic implications.

Transcriptome Signature Reversion as a Method to Reposition Drugs Against Cancer for Precision Oncology

Transcriptome signature reversion (TSR) has been hypothesized as a promising method for discovery and use of existing noncancer drugs as potential drugs in the treatment of cancer (i.e., drug repositioning, drug repurposing). The TSR assumes that drugs with the ability to revert the gene expression associated with a diseased state back to its healthy state are potentially therapeutic candidates for that disease. This article reviews methodology of TSR and critically discusses key TSR studies. In addition, potential conceptual and computational improvements of this novel methodology are discussed as well as its current and possible future application in precision oncology trials.

Understanding Drug Repurposing From the Perspective of Biomedical Entities and Their Evolution: Bibliographic Research Using Aspirin

BACKGROUND

Drug development is still a costly and time-consuming process with a low rate of success. Drug repurposing (DR) has attracted significant attention because of its significant advantages over traditional approaches in terms of development time, cost, and safety. Entitymetrics, defined as bibliometric indicators based on biomedical entities (eg, diseases, drugs, and genes) studied in the biomedical literature, make it possible for researchers to measure knowledge evolution and the transfer of drug research.

OBJECTIVE

The purpose of this study was to understand DR from the perspective of biomedical entities (diseases, drugs, and genes) and their evolution.

METHODS

In the work reported in this paper, we extended the bibliometric indicators of biomedical entities mentioned in PubMed to detect potential patterns of biomedical entities in various phases of drug research and investigate the factors driving DR. We used aspirin (acetylsalicylic acid) as the subject of the study since it can be repurposed for many applications. We propose 4 easy, transparent measures based on entitymetrics to investigate DR for aspirin: Popularity Index (P₁), Promising Index (P₂), Prestige Index (P₃), and Collaboration Index (CI).

RESULTS

We found that the maxima of P₁, P₃, and CI are closely associated with the different repurposing phases of aspirin. These metrics enabled us to observe the way in which biomedical entities interacted with the drug during the various phases of DR and to analyze the potential driving factors for DR at the entity level. P₁ and CI were indicative of the dynamic trends of a specific biomedical entity over a long time period, while P₂ was more sensitive to immediate changes. P₃ reflected the early signs of the practical value of biomedical entities and could be valuable for tracking the research frontiers of a drug.

CONCLUSIONS

In-depth studies of side effects and mechanisms, fierce market competition, and advanced life science technologies are driving factors for DR. This study showcases the way in which researchers can examine the evolution of DR using entitymetrics, an approach that can be valuable for enhancing decision making in the field of drug discovery and development.

Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing

PURPOSE

Drug development is becoming increasingly expensive and time consuming. Drug repurposing is one potential solution to accelerate drug discovery. However, limited research exists on the use of electronic health record (EHR) data for drug repurposing, and most published studies have been conducted in a hypothesis-driven manner that requires a predefined hypothesis about drugs and new indications. Whether EHRs can be used to detect drug repurposing signals is not clear. We want to demonstrate the feasibility of mining large, longitudinal EHRs for drug repurposing by detecting candidate noncancer drugs that can potentially be used for the treatment of cancer.

PATIENTS AND METHODS

By linking cancer registry data to EHRs, we identified 43,310 patients with cancer treated at Vanderbilt University Medical Center (VUMC) and 98,366 treated at the Mayo Clinic. We assessed the effect of 146 noncancer drugs on cancer survival using VUMC EHR data and sought to replicate significant associations (false discovery rate < .1) using the identical approach with Mayo Clinic EHR data. To evaluate replicated signals further, we reviewed the biomedical literature and clinical trials on cancers for corroborating evidence.

RESULTS

We identified 22 drugs from six drug classes (statins, proton pump inhibitors, angiotensin-converting enzyme inhibitors, β-blockers, nonsteroidal anti-inflammatory drugs, and α-1 blockers) associated with improved overall cancer survival (false discovery rate < .1) from VUMC; nine of the 22 drug associations were replicated at the Mayo Clinic. Literature and cancer clinical trial evaluations also showed very strong evidence to support the repurposing signals from EHRs.

CONCLUSION

Mining of EHRs for drug exposure–mediated survival signals is feasible and identifies potential candidates for antineoplastic repurposing. This study sets up a new model of mining EHRs for drug repurposing signals.

Analysis of Infected Host Gene Expression Reveals Repurposed Drug Candidates and Time-Dependent Host Response Dynamics for COVID-19

The repurposing of existing drugs offers the potential to expedite therapeutic discovery against the current COVID-19 pandemic caused by the SARS-CoV-2 virus. We have developed an integrative approach to predict repurposed drug candidates that can reverse SARS-CoV-2-induced gene expression in host cells, and evaluate their efficacy against SARS-CoV-2 infection in vitro. We found that 13 virus-induced gene expression signatures computed from various viral preclinical models could be reversed by compounds previously identified to be effective against SARS- or MERS-CoV, as well as drug candidates recently reported to be efficacious against SARS-CoV-2. Based on the ability of candidate drugs to reverse these 13 infection signatures, as well as other clinical criteria, we identified 10 novel candidates. The four drugs bortezomib, dactolisib, alvocidib, and methotrexate inhibited SARS-CoV-2 infection-induced cytopathic effect in Vero E6 cells at < 1 µM, but only methotrexate did not exhibit unfavorable cytotoxicity. Although further improvement of cytotoxicity prediction and bench testing is required, our computational approach has the potential to rapidly and rationally identify repurposed drug candidates against SARS-CoV-2. The analysis of signature genes induced by SARS-CoV-2 also revealed interesting time-dependent host response dynamics and critical pathways for therapeutic interventions (e.g. Rho GTPase activation and cytokine signaling suppression).

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic paclitaxel-induced peripheral neuropathy

Background

The major dose-limiting toxicity of paclitaxel, one of the most commonly used drugs to treat breast cancer, is peripheral neuropathy (paclitaxel-induced peripheral neuropathy). Paclitaxel-induced peripheral neuropathy, which persists into survivorship, has a negative impact on patient’s mood, functional status, and quality of life. Currently, no interventions are available to treat paclitaxel-induced peripheral neuropathy. A critical barrier to the development of efficacious interventions is the lack of understanding of the mechanisms that underlie paclitaxel-induced peripheral neuropathy. While data from preclinical studies suggest that disrupting cytoskeleton- and axon morphology-related processes are a potential mechanism for paclitaxel-induced peripheral neuropathy, clinical evidence is limited. The purpose of this study in breast cancer survivors was to evaluate whether differential gene expression and co-expression patterns in these pathways are associated with paclitaxel-induced peripheral neuropathy.

Methods

Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology were identified between survivors who received paclitaxel and did (n = 25) or did not (n = 25) develop paclitaxel-induced peripheral neuropathy.

Results

Pathway impact analysis identified four significantly perturbed cytoskeleton- and axon morphology-related signaling pathways. Weighted gene co-expression network analysis identified three co-expression modules. One module was associated with paclitaxel-induced peripheral neuropathy group membership. Functional analysis found that this module was associated with four signaling pathways and two ontology annotations related to cytoskeleton and axon morphology.

Conclusions

This study, which is the first to apply systems biology approaches using circulating whole blood RNA-seq data in a sample of breast cancer survivors with and without chronic paclitaxel-induced peripheral neuropathy, provides molecular evidence that cytoskeleton- and axon morphology-related mechanisms identified in preclinical models of various types of neuropathic pain including chemotherapy-induced peripheral neuropathy are found in breast cancer survivors and suggests pathways and a module of genes for validation and as potential therapeutic targets.

Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

Connectivity map-based drug repositioning of bortezomib to reverse the metastatic effect of GALNT14 in lung cancer

Despite the continual discovery of promising new cancer targets, drug discovery is often hampered by the poor druggability of these targets. As such, repurposing FDA-approved drugs based on cancer signatures is a useful alternative to cancer precision medicine. Here, we adopted an in silico approach based on large-scale gene expression signatures to identify drug candidates for lung cancer metastasis. Our clinicogenomic analysis identified GALNT14 as a putative driver of lung cancer metastasis, leading to poor survival. To overcome the poor druggability of GALNT14 in the control of metastasis, we utilized the Connectivity Map and identified bortezomib (BTZ) as a potent metastatic inhibitor, bypassing the direct inhibition of the enzymatic activity of GALNT14. The antimetastatic effect of BTZ was verified both in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes. These results demonstrate that our in silico approach is a viable strategy for the use of undruggable targets in cancer therapies and for revealing the underlying mechanisms of these targets.

Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted therapies regorafenib, lenvatinib and cabozantinib, the anti-angiogenic antibody ramucirumab, and the immune checkpoint inhibitors nivolumab and pembrolizumab. Although these agents offer new promise to patients with HCC, the optimal choice and sequence of therapies remains unknown and without established biomarkers, and many patients do not respond to treatment. The advances and the decreasing costs of molecular measurement technologies enable profiling of HCC molecular features (such as genome, transcriptome, proteome and metabolome) at different levels, including bulk tissues, animal models and single cells. The release of such data sets to the public enhances the ability to search for information from these legacy studies and provides the opportunity to leverage them to understand HCC mechanisms, rationally develop new therapeutics and identify candidate biomarkers of treatment response. Here, we provide a comprehensive review of public data sets related to HCC and discuss how emerging artificial intelligence methods can be applied to identify new targets and drugs as well as to guide therapeutic choices for improved HCC treatment.

Multiparametric Assays for Accelerating Early Drug Discovery

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Computational discovery of therapeutic candidates for preventing preterm birth

Few therapeutic methods exist for preventing preterm birth (PTB), or delivery before completing 37 weeks of gestation. In the US, progesterone (P4) supplementation is the only FDA-approved drug for use in preventing recurrent spontaneous PTB. However, P4 has limited effectiveness, working in only approximately one-third of cases. Computational drug repositioning leverages data on existing drugs to discover novel therapeutic uses. We used a rank-based pattern-matching strategy to compare the differential gene expression signature for PTB to differential gene expression drug profiles in the Connectivity Map database and assigned a reversal score to each PTB-drug pair. Eighty-three drugs, including P4, had significantly reversed differential gene expression compared with that found for PTB. Many of these compounds have been evaluated in the context of pregnancy, with 13 belonging to pregnancy category A or B - indicating no known risk in human pregnancy. We focused our validation efforts on lansoprazole, a proton-pump inhibitor, which has a strong reversal score and a good safety profile. We tested lansoprazole in an animal inflammation model using LPS, which showed a significant increase in fetal viability compared with LPS treatment alone. These promising results demonstrate the effectiveness of the computational drug repositioning pipeline to identify compounds that could be effective in preventing PTB.

A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease

Numerous clinical trials of drug candidates for Alzheimer’s disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug‐gene‐adverse drug reaction (ADR) interaction network. For the first time, the drug safety was assessed in a broad landscape of 1,156 distinct ADRs. We also designed a parameter ToxicityScore to quantify the overall drug safety. Moreover, we determined association strength for every 3,807,631 gene‐ADR interactions, which clues mechanistic exploration of ADRs. For convenience, we deployed the model as a web service ADRAlert‐gene at http://www.bio-add.org/ADRAlert/. In summary, this study offers insights into prioritizing safe drug therapy. It helps reduce the attrition rate of new drug discovery by providing a reliable ADR profile in the early preclinical stage.

A Pathway-Based Genomic Approach to Identify Medications: Application to Alcohol Use Disorder

Chronic, excessive alcohol use alters brain gene expression patterns, which could be important for initiating, maintaining, or progressing the addicted state. It has been proposed that pharmaceuticals with opposing effects on gene expression could treat alcohol use disorder (AUD). Computational strategies comparing gene expression signatures of disease to those of pharmaceuticals show promise for nominating novel treatments. We reasoned that it may be sufficient for a treatment to target the biological pathway rather than lists of individual genes perturbed by AUD. We analyzed published and unpublished transcriptomic data using gene set enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify biological pathways disrupted in AUD brain and by compounds in the Library of Network-based Cellular Signatures (LINCS L1000) and Connectivity Map (CMap) databases. Several pathways were consistently disrupted in AUD brain, including an up-regulation of genes within the Complement and Coagulation Cascade, Focal Adhesion, Systemic Lupus Erythematosus, and MAPK signaling, and a down-regulation of genes within the Oxidative Phosphorylation pathway, strengthening evidence for their importance in AUD. Over 200 compounds targeted genes within those pathways in an opposing manner, more than twenty of which have already been shown to affect alcohol consumption, providing confidence in our approach. We created a user-friendly web-interface that researchers can use to identify drugs that target pathways of interest or nominate mechanism of action for drugs. This study demonstrates a unique systems pharmacology approach that can nominate pharmaceuticals that target pathways disrupted in disease states such as AUD and identify compounds that could be repurposed for AUD if sufficient evidence is attained in preclinical studies.

Landscape of cancer diagnostic biomarkers from specifically expressed genes

Although there has been great progress in cancer treatment, cancer remains a serious health threat to humans because of the lack of biomarkers for diagnosis, especially for early-stage diagnosis. In this study, we comprehensively surveyed the specifically expressed genes (SEGs) using the SEGtool based on the big data of gene expression from the The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects. In 15 solid tumors, we identified 233 cancer-specific SEGs (cSEGs), which were specifically expressed in only one cancer and showed great potential to be diagnostic biomarkers. Among them, three cSEGs (OGDH, MUDENG and ACO2) had a sample frequency >80% in kidney cancer, suggesting their high sensitivity. Furthermore, we identified 254 cSEGs as early-stage diagnostic biomarkers across 17 cancers. A two-gene combination strategy was applied to improve the sensitivity of diagnostic biomarkers, and hundreds of two-gene combinations were identified with high frequency. We also observed that 13 SEGs were targets of various drugs and nearly half of these drugs may be repurposed to treat cancers with SEGs as their targets. Several SEGs were regulated by specific transcription factors in the corresponding cancer, and 39 cSEGs were prognosis-related genes in 7 cancers. This work provides a survey of cancer biomarkers for diagnosis and early diagnosis and new insights to drug repurposing. These biomarkers may have great potential in cancer research and application.