Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling

Unraveling the Anticancer Potential of SSRIs in Prostate Cancer by Combining Computational Systems Biology and In Vitro Analyses

Selective serotonin reuptake inhibitors (SSRIs) are known to have anticancer activity against different types of cancer. In this study, an integrative informatics approach was applied to identify compound and genetic perturbations that produce similar effects to SSRIs to formulate systems biology hypotheses and identify biological pathways involved in the putative anticancer effects of SSRIs in prostate cancer. An 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay assessed the antiproliferative effects of SSRIs and drug combinations. Cell death mechanisms were studied using annexin V-FITC/PI staining, and the cell cycle analysis was carried out by counterstaining with propidium iodide. Relative gene expression was assessed using a real-time polymerase chain reaction (PCR). Computational results hypothesized that SSRIs could potentially exert anticancer effects in prostate cancer cell lines by modulating apoptotic and tumorigenesis pathways and significantly inhibiting the growth of prostate cancer cells in a time and concentration-dependent manner. The combination of SSRIs with cisplatin, 5-fluorouracil, and raloxifene resulted in either synergistic or additive effects. SSRIs resulted in a significant increase in the early and late apoptotic activity in PC3 cells. Dapoxetine, paroxetine, and sertraline resulted in cell cycle arrest at the G0/G1 phase. Treatment with either dapoxetine or paroxetine decreases the expression of Bcl-2, CASP8, DR5, and VEGF. At the same time, sertraline decreases the expression of Bcl-2 and VEGF and increases the expression of CASP8 and DR5. Results revealed that SSRIs can potentially act as antiproliferative agents against prostate cancer cells, and their activity is mediated through different signaling pathways.

Decoding the neuroimmune axis in colorectal cancer: From neural circuitry to therapeutic innovation

The nervous and immune systems are two major components that maintain body homeostasis, with their functional roles often overlapping significantly. Both systems are capable of identifying, integrating, and organizing responsive reactions to various external stimuli. The gut, referred to as the "second brain" and the largest immune organ in the body, serves as the most frequent focal site for neuroimmune interactions. Colorectal cancer (CRC), as the predominant solid tumor arising in this neuroimmune-rich microenvironment, remains understudied through the lens of neuroimmune regulatory mechanisms. This review systematically synthesizes current evidence to elucidate the neuroimmune axis in CRC pathogenesis, with particular emphasis on neuroimmune crosstalk-mediated remodeling of tumor immunity. We comprehensively catalog the immunomodulatory effects exerted by principal neuroregulatory mediators, categorized as: (1) neurotransmitters (glutamate, glutamine, γ-aminobutyric acid, epinephrine, norepinephrine, dopamine, serotonin, acetylcholine, and gaseous signaling molecules); (2) neuropeptides (substance P, calcitonin gene-related peptide, vasoactive intestinal peptide); and (3) neurotrophic factors. Furthermore, we critically evaluate the translational prospects and therapeutic challenges of targeting neuroimmune pathways and propose strategic priorities and research focuses for advancing the development of neuroimmune interaction-related therapeutic approaches in CRC.

Machine learning models for pancreatic cancer diagnosis based on microbiome markers from serum extracellular vesicles

Pancreatic cancer (PC) is a fatal disease with an extremely low 5-year survival rate, mainly because of its poor detection rate in early stages. Given emerging evidence of the relationship between microbiota composition and diseases, this study aims to identify microbiome markers linked to the diagnosis of pancreatic cancer. We utilized extracellular vesicles (EVs) data obtained from blood samples of 38 pancreatic cancer patients and 51 health controls. Least absolute shrinkage and selection operator (LASSO) and stepwise method were used to obtain some candidate markers in genus and phylum levels. These markers were used to develop various machine learning models including logistic regression (LR), random forest (RF), support vector machine (SVM), and Deep Neural Network (DNN) methods. In phylum level, DNN performed best with three markers (Verrucomicrobia, Actinobacteria and Proteobacteria) selected by stepwise method with the test AUC 0.959. In genus level, DNN using 11 markers selected by LASSO (Ruminococcaceae UCG-013, Ruminiclostridium, Propionibacterium, Lachnospiraceae NK4A136 group, Corynebacterium.1, Akkermansia, Mucispirillum, Pseudomonas, Diaphorobacter, Clostridium sensu stricto 1 and Turicibacter) outperformed others with 0.961 test AUCs. These results highlight the potential of microbiome markers and prediction models in clinical studies of PC diagnosis.

Aurora kinase B inhibitor AZD1152: repurposing for treatment of lupus nephritis driven by the results of clinical trials

BACKGROUND

Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus (SLE). Multitarget therapy (MT) achieves a 20% higher complete remission (CR) rate compared to conventional therapy in LN management. Intrigued by its excellent clinical efficacy, we aimed to develop a single-agent therapy with comparable efficacy to MT, offering a simplified treatment regimen.

METHODS

AZD1152, an Aurora kinase B (Aurkb) inhibitor, was identified through transcriptomic analyses and the L1000 CMap drug repurposing database. The therapeutic efficacy of AZD1152 was evaluated in MRL/lpr mice. Transcriptome sequencing and functional assays were performed to elucidate its mechanisms of action. Aurkb expression and its clinical relevance were assessed in lupus-prone mice and patients with LN.

FINDINGS

AZD1152 significantly attenuated systemic immune activation and renal injury in MRL/lpr mice, demonstrating efficacy comparable to MT regimens in animal studies. AZD1152 treatment modulated immune-inflammatory pathways in the kidney. Aurkb expression was upregulated in T cells infiltrating the renal interstitium in LN. Additionally, Aurkb expression levels positively correlated with the activity index (AI) and serum creatinine (Scr) in patients with LN. Mechanistic studies revealed that AZD1152 exerts therapeutic effects primarily by inhibiting T-cell proliferation.

INTERPRETATION

This study presents a drug development strategy that integrates clinically validated LN therapies with drug repurposing approaches. This strategy could accelerate drug development and clinical translation processes for LN.

FUNDING

A full list of funding sources can be found in the acknowledgements section.

Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery

Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of disease-compound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.

A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs

Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach and reducing the risk of failure in these efforts. Recognizing the potential for drug repurposing, we employed our Iterative Stochastic Elimination (ISE) algorithm to screen known drugs from the DrugBank database. Repurposing in our hands is based on computer models of the actions of ligands: the ISE algorithm is a machine learning tool that creates ligand-based models by distinguishing between the physicochemical properties of known drugs and those of decoys. The models are large sets of "filters" made out, each, of molecular properties. We screen and score external sets of molecules (in our case- the DrugBank molecules) by our agonism and antagonism models based on published data (i.e., IC50, Ki, or EC50) and pick the top-scoring molecules as candidates for experiments. Such agonist and antagonist models for six G-protein coupled receptors (GPCRs) families facilitated the identification of repurposing opportunities. Our screening revealed 5982 new potential molecular actions (agonists, antagonists), which suggest repurposing candidates for the cannabinoid 2 (CB2), histamine (H1, H3, and H4), and dopamine 3 (D3) receptors, which may be useful to treat conditions such as neuroinflammation, obesity, allergic dermatitis, and drug abuse. These sets of best candidates should now be examined by experimentalists: based on previous such experiments, there is a very high chance of discovering novel highly bioactive molecules.

Integrated edge information and pathway topology for drug-disease associations

Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.

Serotonin signalling in cancer: Emerging mechanisms and therapeutic opportunities

BACKGROUND

Serotonin (5-hydroxytryptamine) is a multifunctional bioamine serving as a neurotransmitter, peripheral hormone and mitogen in the vertebrate system. It has pleiotropic activities in central nervous system and gastrointestinal function via an orchestrated action of serotonergic elements, particularly serotonin receptor-mediated signalling cascades. The mitogenic properties of serotonin have garnered recognition for years and have been exploited for repurposing serotonergic-targeted drugs in cancer therapy. However, emerging conflicting findings necessitate a more comprehensive elucidation of serotonin's role in cancer pathogenesis.

MAIN BODY AND CONCLUSION

Here, we provide an overview of the biosynthesis, metabolism and action modes of serotonin. We summarise our current knowledge regarding the effects of the peripheral serotonergic system on tumourigenesis, with a specific emphasis on its immunomodulatory activities in human cancers. We also discuss the dual roles of serotonin in tumour pathogenesis and elucidate the potential of serotonergic drugs, some of which display favourable safety profiles and impressive efficacy in clinical trials, as a promising avenue in cancer treatment.

KEY POINTS

Primary synthesis and metabolic routes of peripheral 5-hydroxytryptamine in the gastrointestinal tract. Advanced research has established a strong association between the serotonergic components and carcinogenic mechanisms. The interplay between serotonergic signalling and the immune system within the tumour microenvironment orchestrates antitumour immune responses. Serotonergic-targeted drugs offer valuable clinical options for cancer therapy.

Single-cell transcriptional signature-based drug repurposing and in vitro evaluation in colorectal cancer

BACKGROUND

The need for intelligent and effective treatment of diseases and the increase in drug design costs have raised drug repurposing as one of the effective strategies in biomedicine. There are various computational methods for drug repurposing, one of which is using transcription signatures, especially single-cell RNA sequencing (scRNA-seq) data, which show us a clear and comprehensive view of the inside of the cell to compare the state of disease and health.

METHODS

In this study, we used 91,103 scRNA-seq samples from 29 patients with colorectal cancer (GSE144735 and GSE132465). First, differential gene expression (DGE) analysis was done using the ASAP website. Then we reached a list of drugs that can reverse the gene signature pattern from cancer to normal using the iLINCS website. Further, by searching various databases and articles, we found 12 drugs that have FDA approval, and so far, no one has reported them as a drug in the treatment of any cancer. Then, to evaluate the cytotoxicity and performance of these drugs, the MTT assay and real-time PCR were performed on two colorectal cancer cell lines (HT29 and HCT116).

RESULTS

According to our approach, 12 drugs were suggested for the treatment of colorectal cancer. Four drugs were selected for biological evaluation. The results of the cytotoxicity analysis of these drugs are as follows: tezacaftor (IC10 = 19 µM for HCT-116 and IC10 = 2 µM for HT-29), fenticonazole (IC10 = 17 µM for HCT-116 and IC10 = 7 µM for HT-29), bempedoic acid (IC10 = 78 µM for HCT-116 and IC10 = 65 µM for HT-29), and famciclovir (IC10 = 422 µM for HCT-116 and IC10 = 959 µM for HT-29).

CONCLUSIONS

Cost, time, and effectiveness are the main challenges in finding new drugs for diseases. Computational approaches such as transcriptional signature-based drug repurposing methods open new horizons to solve these challenges. In this study, tezacaftor, fenticonazole, and bempedoic acid can be introduced as promising drug candidates for the treatment of colorectal cancer. These drugs were evaluated in silico and in vitro, but it is necessary to evaluate them in vivo.

Small-molecule drugs of colorectal cancer: Current status and future directions

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the world's fourth most deadly cancer. CRC, as a genetic susceptible disease, faces significant challenges in optimizing prognosis through optimal drug treatment modalities. In recent decades, the development of innovative small-molecule drugs is expected to provide targeted interventions that accurately address the different molecular characteristics of CRC. Although the clinical application of single-target drugs is limited by the heterogeneity and high metastasis of CRC, novel small-molecule drug treatment strategies such as dual/multiple-target drugs, drug repurposing, and combination therapies can help overcome these challenges and provide new insights for improving CRC treatment. In this review, we focus on the current status of a range of small molecule drugs that are being considered for CRC therapy, including single-target drugs, dual/multiple-target drugs, drug repurposing and combination strategies, which will pave the way for targeting CRC vulnerabilities with small-molecule drugs in future personalized treatment.

Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

De novo drug design based on patient gene expression profiles via deep learning

Computational de novo drug design is a challenging issue in medicine, and it is desirable to consider all of the relevant information of the biological systems in a disease state. Here, we propose a novel computational method to generate drug candidate molecular structures from patient gene expression profiles via deep learning, which we call DRAGONET. Our model can generate new molecules that are likely to counteract disease-specific gene expression patterns in patients, which is made possible by exploring the latent space constructed by a transformer-based variational autoencoder and integrating the substructures of disease-correlated molecules. We applied DRAGONET to generate drug candidate molecules for gastric cancer, atopic dermatitis, and Alzheimer's disease, and demonstrated that the newly generated molecules were chemically similar to registered drugs for each disease. This approach is applicable to diseases with unknown therapeutic target proteins and will make a significant contribution to the field of precision medicine.

An Innovative Drug Repurposing Approach to Restrain Endometrial Cancer Metastatization

BACKGROUND

Endometrial cancer (EC) is the most common gynecologic tumor and the world's fourth most common cancer in women. Most patients respond to first-line treatments and have a low risk of recurrence, but refractory patients, and those with metastatic cancer at diagnosis, remain with no treatment options. Drug repurposing aims to discover new clinical indications for existing drugs with known safety profiles. It provides ready-to-use new therapeutic options for highly aggressive tumors for which standard protocols are ineffective, such as high-risk EC.

METHODS

Here, we aimed at defining new therapeutic opportunities for high-risk EC using an innovative and integrated computational drug repurposing approach.

RESULTS

We compared gene-expression profiles, from publicly available databases, of metastatic and non-metastatic EC patients being metastatization the most severe feature of EC aggressiveness. A comprehensive analysis of transcriptomic data through a two-arm approach was applied to obtain a robust prediction of drug candidates.

CONCLUSIONS

Some of the identified therapeutic agents are already successfully used in clinical practice to treat other types of tumors. This highlights the potential to repurpose them for EC and, therefore, the reliability of the proposed approach.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis

Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

Validation of transcriptome signature reversion for drug repurposing in oncology

Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.

Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors

BACKGROUND

Vast amounts of rapidly accumulating biological data related to cancer and a remarkable progress in the field of artificial intelligence (AI) have paved the way for precision oncology. Our recent contribution to this area of research is CancerOmicsNet, an AI-based system to predict the therapeutic effects of multitargeted kinase inhibitors across various cancers. This approach was previously demonstrated to outperform other deep learning methods, graph kernel models, molecular docking, and drug binding pocket matching.

METHODS

CancerOmicsNet integrates multiple heterogeneous data by utilizing a deep graph learning model with sophisticated attention propagation mechanisms to extract highly predictive features from cancer-specific networks. The AI-based system was devised to provide more accurate and robust predictions than data-driven therapeutic discovery using gene signature reversion.

RESULTS

Selected CancerOmicsNet predictions obtained for "unseen" data are positively validated against the biomedical literature and by live-cell time course inhibition assays performed against breast, pancreatic, and prostate cancer cell lines. Encouragingly, six molecules exhibited dose-dependent antiproliferative activities, with pan-CDK inhibitor JNJ-7706621 and Src inhibitor PP1 being the most potent against the pancreatic cancer cell line Panc 04.03.

CONCLUSIONS

CancerOmicsNet is a promising AI-based platform to help guide the development of new approaches in precision oncology involving a variety of tumor types and therapeutics.

Regulome-based characterization of drug activity across the human diseasome

Drugs are expected to recover the cell system away from the impaired state to normalcy through disease treatment. However, the understanding of gene regulatory machinery underlying drug activity or disease pathogenesis is far from complete. Here, we perform large-scale regulome analysis for various diseases in terms of gene regulatory machinery. Transcriptome signatures were converted into regulome signatures of transcription factors by integrating publicly available ChIP-seq data. Regulome-based correlations between diseases and their approved drugs were much clearer than the transcriptome-based correlations. For example, an inverse correlation was observed for cancers, whereas a positive correlation was observed for immune system diseases. After demonstrating the usefulness of the regulome-based drug discovery method in terms of accuracy and applicability, we predicted new drugs for nonsmall cell lung cancer and validated the anticancer activity in vitro. The proposed method is useful for understanding disease–disease relationships and drug discovery.

Targeted delivery of 5-fluorouracil, miR-532-3p, and si-KRAS to the colorectal tumor using layer-by-layer liposomes

Co-delivery of siRNA or miRNA with chemotherapeutic drugs into tumor sites is an attractive synergetic strategy for treating colorectal cancer (CRC) due to their complementary mechanisms. In the current work, a liposome nanoparticle (Huang et al., Cancer Metastasis Rev., 2018, 37, 173-187) coated by cationic chitosan (CS) using a controlled layer-by-layer (LbL) process was designed to deliver simultaneous si-KRAS, miRNA-532-3p, and 5-Fluorouracil (5-FU) into CRC cells. The LbL NPs exhibited a spherical structure with an average size of 165.9 nm and effectively protected si-KRAS and miRNA-532-3p against degradation by serum and nucleases. Interestingly, the LbL NPs were successfully entered into cells and efficiently promoted cytotoxicity and suppressed cancer cell migration and invasion. In vivo, the LbL NPs reduced tumor growth in SW480-tumor-bearing mice models. In conclusion, these results suggested that the LbL NPs co-loaded with 5-FU and miR-532-3p/si-KRAS might provide a promising potential strategy for inhibiting the malignant phenotypes of CRC cells.

From drug repositioning to target repositioning: prediction of therapeutic targets using genetically perturbed transcriptomic signatures

Motivation

A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.

Results

In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target–disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.

Availability and implementation

Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].

Supplementary information

Supplementary data are available at Bioinformatics online.

HTR1A Inhibits the Progression of Triple-Negative Breast Cancer via TGF-β Canonical and Noncanonical Pathways

Triple-negative breast cancer is the most aggressive subtype of breast cancer and the incidence of depression in breast cancer patients is high, which leading to worse survival and increased risk of recurrence. The effect of antidepressants on breast cancer patients remains contradictory, which might be due to variations in antidepression targets. Therefore, there is significant value to explore the antitumor potential of antidepressants and discover new therapeutic targets for breast patients. The authors screen antidepressant-related oncogenes or suppressors by using siRNAs. After combining functional experiments with online database analysis, 5-hydroxytryptamine receptor 1A (HTR1A is selected with antitumor potential in breast cancer cells in vivo and in vitro. RNA-seq analysis and coimmunoprecipitation assays indicate that HTR1A interacts with TRIM21 and PSMD7 to inhibit the degradation of TβRII through the ubiquitin-proteasome pathway, thereby inhibiting the transforming growth factor-β (TGF-β) canonical and noncanonical pathway. In addition, HTR1A is an independent predictive factor for breast cancer patients. The combined treatment of HTR1A agonists with demethylation drugs may significantly improve patient survival. It is of great significance to clarify the function and mechanism of the depression-related gene HTR1A in breast cancer, which might provide a new approach for triple-negative breast cancer patients.

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.

Repurposing non-oncology small-molecule drugs to improve cancer therapy: Current situation and future directions

Drug repurposing or repositioning has been well-known to refer to the therapeutic applications of a drug for another indication other than it was originally approved for. Repurposing non-oncology small-molecule drugs has been increasingly becoming an attractive approach to improve cancer therapy, with potentially lower overall costs and shorter timelines. Several non-oncology drugs approved by FDA have been recently reported to treat different types of human cancers, with the aid of some new emerging technologies, such as omics sequencing and artificial intelligence to overcome the bottleneck of drug repurposing. Therefore, in this review, we focus on summarizing the therapeutic potential of non-oncology drugs, including cardiovascular drugs, microbiological drugs, small-molecule antibiotics, anti-viral drugs, anti-inflammatory drugs, anti-neurodegenerative drugs, antipsychotic drugs, antidepressants, and other drugs in human cancers. We also discuss their novel potential targets and relevant signaling pathways of these old non-oncology drugs in cancer therapies. Taken together, these inspiring findings will shed new light on repurposing more non-oncology small-molecule drugs with their intricate molecular mechanisms for future cancer drug discovery.

Epigenetic landscape of drug responses revealed through large-scale ChIP-seq data analyses

Background

Elucidating the modes of action (MoAs) of drugs and drug candidate compounds is critical for guiding translation from drug discovery to clinical application. Despite the development of several data-driven approaches for predicting chemical–disease associations, the molecular cues that organize the epigenetic landscape of drug responses remain poorly understood.

Results

With the use of a computational method, we attempted to elucidate the epigenetic landscape of drug responses, in terms of transcription factors (TFs), through large-scale ChIP-seq data analyses. In the algorithm, we systematically identified TFs that regulate the expression of chemically induced genes by integrating transcriptome data from chemical induction experiments and almost all publicly available ChIP-seq data (consisting of 13,558 experiments). By relating the resultant chemical–TF associations to a repository of associated proteins for a wide range of diseases, we made a comprehensive prediction of chemical–TF–disease associations, which could then be used to account for drug MoAs. Using this approach, we predicted that: (1) cisplatin promotes the anti-tumor activity of TP53 family members but suppresses the cancer-inducing function of MYCs; (2) inhibition of RELA and E2F1 is pivotal for leflunomide to exhibit antiproliferative activity; and (3) CHD8 mediates valproic acid-induced autism.

Conclusions

Our proposed approach has the potential to elucidate the MoAs for both approved drugs and candidate compounds from an epigenetic perspective, thereby revealing new therapeutic targets, and to guide the discovery of unexpected therapeutic effects, side effects, and novel targets and actions.

Drug repositioning: Using psychotropic drugs for the treatment of glioma

Psychotropic drugs can penetrate the blood-brain barrier and regulate the levels of neurotransmitters and neuromodulators such as γ-aminobutyric acid, glutamate, serotonin, dopamine, and norepinephrine in the brain, and thus influence neuronal activity. Neuronal activity in the tumor microenvironment can promote the growth and expansion of glioma. There is increasing evidence that in addition to their use in the treatment of mental disorders, antipsychotic, antidepressant, and mood-stabilizing drugs have clinical potential for cancer therapy. These drugs have been shown to inhibit the malignant progression of glioma by targeting signaling pathways related to cell proliferation, apoptosis, or invasion/migration or by increasing the sensitivity of glioma cells to conventional chemotherapy or radiotherapy. In this review, we summarize findings from preclinical and clinical studies investigating the use of antipsychotics, antidepressants, and mood stabilizers in the treatment of various types of cancer, with a focus on glioma; and discuss their presumed antitumor mechanisms. The existing evidence indicates that psychotropic drugs with established pharmacologic and safety profiles can be repurposed as anticancer agents, thus providing new options for the treatment of glioma.

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.

Differential Interactome Based Drug Repositioning Unraveled Abacavir, Exemestane, Nortriptyline Hydrochloride, and Tolcapone as Potential Therapeutics for Colorectal Cancers

There is a critical requirement for alternative strategies to provide the better treatment in colorectal cancer (CRC). Hence, our goal was to propose novel biomarkers as well as drug candidates for its treatment through differential interactome based drug repositioning. Differentially interacting proteins and their modules were identified, and their prognostic power were estimated through survival analyses. Drug repositioning was carried out for significant target proteins, and candidate drugs were analyzed via in silico molecular docking prior to in vitro cell viability assays in CRC cell lines. Six modules (mAPEX1, mCCT7, mHSD17B10, mMYC, mPSMB5, mRAN) were highlighted considering their prognostic performance. Drug repositioning resulted in eight drugs (abacavir, ribociclib, exemestane, voriconazole, nortriptyline hydrochloride, theophylline, bromocriptine mesylate, and tolcapone). Moreover, significant in vitro inhibition profiles were obtained in abacavir, nortriptyline hydrochloride, exemestane, tolcapone, and theophylline (positive control). Our findings may provide new and complementary strategies for the treatment of CRC.

The role of 5-HT metabolism in cancer

Serotonin (5-hydroxytryptamine, 5-HT) metabolism has long been linked to tumorigenesis and tumor progression. Numerous studies have shown the functions of 5-HT and its metabolites in the regulation of tumor biological processes like cell proliferation, invasion, metastasis, tumor angiogenesis and immunomodulatory through multi-step complex mechanisms. Reprogramming of 5-HT metabolism has been revealed in various tumors paving way for development of drugs that target enzymes, metabolites or receptors involved in 5-HT metabolic pathway. However, information on the role of 5-HT metabolism in cancer is scanty. This review briefly describes the main metabolic routes of 5-HT, the role of 5-HT metabolism in cancer and systematically summarizes the most recent advances in 5-HT metabolism-targeted cancer therapy.

Emerging hiPSC Models for Drug Discovery in Neurodegenerative Diseases

Neurodegenerative diseases affect millions of people worldwide and are characterized by the chronic and progressive deterioration of neural function. Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), represent a huge social and economic burden due to increasing prevalence in our aging society, severity of symptoms, and lack of effective disease-modifying therapies. This lack of effective treatments is partly due to a lack of reliable models. Modeling neurodegenerative diseases is difficult because of poor access to human samples (restricted in general to postmortem tissue) and limited knowledge of disease mechanisms in a human context. Animal models play an instrumental role in understanding these diseases but fail to comprehensively represent the full extent of disease due to critical differences between humans and other mammals. The advent of human-induced pluripotent stem cell (hiPSC) technology presents an advantageous system that complements animal models of neurodegenerative diseases. Coupled with advances in gene-editing technologies, hiPSC-derived neural cells from patients and healthy donors now allow disease modeling using human samples that can be used for drug discovery.

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.

Prognostic Biomarkers on a Competitive Endogenous RNA Network Reveals Overall Survival in Triple-Negative Breast Cancer

The objective of this study was to construct a competitive endogenous RNA (ceRNA) regulatory network using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with triple-negative breast cancer (TNBC) and to construct a prognostic model for predicting overall survival (OS) in patients with TNBC. Differentially expressed lncRNAs, miRNAs, and mRNAs in TNBC patients from the TCGA and Metabric databases were examined. A prognostic model based on prognostic scores (PSs) was established for predicting OS in TNBC patients, and the performance of the model was assessed by a recipient that operated on a distinctive curve. A total of 874 differentially expressed RNAs (DERs) were screened, among which 6 lncRNAs, 295 miRNAs and 573 mRNAs were utilized to construct targeted and coexpression ceRNA regulatory networks. Eight differentially expressed genes (DEGs) associated with survival prognosis, DBX2, MYH7, TARDBP, POU4F1, ABCB11, LHFPL5, TRHDE and TIMP4, were identified by multivariate Cox regression and then used to establish a prognostic model. Our study shows that the ceRNA network has a critical role in maintaining the aggressiveness of TNBC and provides comprehensive molecular-level insight for predicting individual mortality hazards for TNBC patients. Our data suggest that these prognostic mRNAs from the ceRNA network are promising therapeutic targets for clinical intervention.

Antidepressants and Antipsychotic Agents as Repurposable Oncological Drug Candidates

Drug repurposing, also known as drug repositioning/reprofiling, is a relatively new strategy for the identification of alternative uses of well-known therapeutics that are outside the scope of their original medical indications. Such an approach might entail a number of advantages compared to standard de novo drug development, including less time needed to introduce the drug to the market, and lower costs. The group of compounds that could be considered as promising candidates for repurposing in oncology include the central nervous system drugs, especially selected antidepressant and antipsychotic agents. In this article, we provide an overview of some antidepressants (citalopram, fluoxetine, paroxetine, sertraline) and antipsychotics (chlorpromazine, pimozide, thioridazine, trifluoperazine) that have the potential to be repurposed as novel chemotherapeutics in cancer treatment, as they have been found to exhibit preventive and/or therapeutic action in cancer patients. Nevertheless, although drug repurposing seems to be an attractive strategy to search for oncological drugs, we would like to clearly indicate that it should not replace the search for new lead structures, but only complement de novo drug development.

Differential Effects of Trp53 Alterations in Murine Colorectal Cancer

BACKGROUND

Colorectal cancer (CRC) development is a multi-step process resulting in the accumulation of genetic alterations. Despite its high incidence, there are currently no mouse models that accurately recapitulate this process and mimic sporadic CRC. We aimed to develop and characterize a genetically engineered mouse model (GEMM) of Apc/Kras/Trp53 mutant CRC, the most frequent genetic subtype of CRC.

METHODS

Tumors were induced in mice with conditional mutations or knockouts in Apc, Kras, and Trp53 by a segmental adeno-cre viral infection, monitored via colonoscopy and characterized on multiple levels via immunohistochemistry and next-generation sequencing.

RESULTS

The model accurately recapitulates human colorectal carcinogenesis clinically, histologically and genetically. The Trp53 R172H hotspot mutation leads to significantly increased metastatic capacity. The effects of Trp53 alterations, as well as the response to treatment of this model, are similar to human CRC. Exome sequencing revealed spontaneous protein-modifying alterations in multiple CRC-related genes and oncogenic pathways, resulting in a genetic landscape resembling human CRC.

CONCLUSIONS

This model realistically mimics human CRC in many aspects, allows new insights into the role of TP53 in CRC, enables highly predictive preclinical studies and demonstrates the value of GEMMs in current translational cancer research and drug development.

Modeling drug response using network-based personalized treatment prediction (NetPTP) with applications to inflammatory bowel disease

For many prevalent complex diseases, treatment regimens are frequently ineffective. For example, despite multiple available immunomodulators and immunosuppressants, inflammatory bowel disease (IBD) remains difficult to treat. Heterogeneity in the disease across patients makes it challenging to select the optimal treatment regimens, and some patients do not respond to any of the existing treatment choices. Drug repurposing strategies for IBD have had limited clinical success and have not typically offered individualized patient-level treatment recommendations. In this work, we present NetPTP, a Network-based Personalized Treatment Prediction framework which models measured drug effects from gene expression data and applies them to patient samples to generate personalized ranked treatment lists. To accomplish this, we combine publicly available network, drug target, and drug effect data to generate treatment rankings using patient data. These ranked lists can then be used to prioritize existing treatments and discover new therapies for individual patients. We demonstrate how NetPTP captures and models drug effects, and we apply our framework to individual IBD samples to provide novel insights into IBD treatment.

Offering personalized treatment results is an important tenant of precision medicine, particularly in complex diseases which have high variability in disease manifestation and treatment response. We have developed a novel framework, NetPTP (Network-based Personalized Treatment Prediction), for making personalized drug ranking lists for patient samples. Our method uses networks to model drug effects from gene expression data and applies these captured effects to individual samples to produce tailored drug treatment rankings. We applied NetPTP to inflammatory bowel disease, yielding insights into the treatment of this particular disease. Our method is modular and generalizable, and thus can be applied to other diseases that could benefit from a personalized treatment approach.

Interaction of citalopram and escitalopram with calf Thymus DNA: A spectrofluorometric, voltammetric, and liquid chromatographic approach

Citalopram (CIT) and its S-enantiomer, escitalopram (ESC), are antidepressants belonging to the class called selective serotonin reuptake inhibitors and have many different pharmacological and biological properties. Understanding the interaction mechanism of small drug molecules with DNA both helps in the development of new DNA-targeted drugs and provides more in-depth knowledge for controlling gene expression. In this study, the interaction of CIT and ESC with double-stranded calf thymus DNA (ct-dsDNA) was investigated for the first time. Spectrofluorometric, liquid chromatographic, and voltammetric response profiles of drugs and ct-dsDNA at different concentrations showed DNA-drug complex formation. Calculated binding constants were greater with all three techniques for ESC compared to CIT and were of the order of 10³-10⁴, which is in accordance with those of well-known groove binders. The results also showed the significant effect of chirality on complex formation. The thermodynamic parameters, including free energy change (ΔG < 0) and enthalpy change (ΔH < 0) obtained at different temperatures, indicated that complex formation was mainly driven by hydrogen bonding and van der Waals forces for both drugs. The results of this study may enhance the understanding of the interaction between CIT or ESC and ct-dsDNA and can be considered as the pioneer for future studies to uncover possible hidden phenotypes of these compounds.

Escitalopram Ameliorates Cardiomyopathy in Type 2 Diabetic Rats via Modulation of Receptor for Advanced Glycation End Products and Its Downstream Signaling Cascades

Type 2 diabetes mellitus (T2DM) has been recognized as a known risk factor for cardiovascular diseases. Additionally, studies have shown the prevalence of depression among people with diabetes. Thus, the current study aimed to investigate the possible beneficial effects of escitalopram, a selective serotonin reuptake inhibitor, on metabolic changes and cardiac complications in type 2 diabetic rats. Diabetes was induced by feeding the rats high fat-high fructose diet (HFFD) for 8 weeks followed by a subdiabetogenic dose of streptozotocin (STZ) (35 mg/kg, i. p.). Treatment with escitalopram (10 mg/kg/day; p. o.) was then initiated for 4 weeks. At the end of the experiment, electrocardiography was performed and blood samples were collected for determination of glycemic and lipid profiles. Animals were then euthanized and heart samples were collected for biochemical and histopathological examinations. Escitalopram alleviated the HFFD/STZ-induced metabolic and cardiac derangements as evident by improvement of oxidative stress, inflammatory, fibrogenic and apoptotic markers in addition to hypertrophy and impaired conduction. These results could be secondary to its beneficial effects on the glycemic control and hence the reduction of receptor for advanced glycation end products content as revealed in the present study. In conclusion, escitalopram could be considered a favorable antidepressant medication in diabetic patients as it seems to positively impact the glycemic control in diabetes in addition to prevention of its associated cardiovascular complications.

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.

Harmine and Piperlongumine Revert TRIB2-Mediated Drug Resistance

Simple Summary

Poor survival and treatment failure of patients with cancer are mainly due to resistance to therapy. Tribbles homologue 2 (TRIB2) has recently been identified as a protein that promotes resistance to several anti-cancer drugs. In this study, RNA sequencing and bioinformatics analysis were used with the aim of characterizing the impact of TRIB2 on the expression of genes and developing pharmacological strategies to revert these TRIB2-mediated changes, thereby overcoming therapy resistance. We show that two naturally occurring alkaloids, harmine and piperlongumine, inverse the gene expression profile produced by TRIB2 and sensitize cancer cells to anti-cancer drugs. Our data suggest that harmine and piperlongumine or similar compounds might have the potential to overcome TRIB2-mediated therapy resistance in cancer patients.

Abstract

Therapy resistance is responsible for most relapses in patients with cancer and is the major challenge to improving the clinical outcome. The pseudokinase Tribbles homologue 2 (TRIB2) has been characterized as an important driver of resistance to several anti-cancer drugs, including the dual ATP-competitive PI3K and mTOR inhibitor dactolisib (BEZ235). TRIB2 promotes AKT activity, leading to the inactivation of FOXO transcription factors, which are known to mediate the cell response to antitumor drugs. To characterize the downstream events of TRIB2 activity, we analyzed the gene expression profiles of isogenic cell lines with different TRIB2 statuses by RNA sequencing. Using a connectivity map-based computational approach, we identified drug-induced gene-expression profiles that invert the TRIB2-associated expression profile. In particular, the natural alkaloids harmine and piperlongumine not only produced inverse gene expression profiles but also synergistically increased BEZ235-induced cell toxicity. Importantly, both agents promote FOXO nuclear translocation without interfering with the nuclear export machinery and induce the transcription of FOXO target genes. Our results highlight the great potential of this approach for drug repurposing and suggest that harmine and piperlongumine or similar compounds might be useful in the clinic to overcome TRIB2-mediated therapy resistance in cancer patients.

Identification and validation of a six-lncRNA prognostic signature with its ceRNA networks and candidate drugs in lower-grade gliomas

Gliomas account for 75% of the primary malignant brain tumors and a majority of lower-grade gliomas (LGG) inevitably develop into glioblastoma. The dysregulation of lncRNAs play a crucial role in LGG. In the present study, we first screened out six differentially expressed lncRNAs (AC021739.2, AL031722.1, AL354740.1, FGD5-AS1, LINC00844, and NEAT1) based on TCGA and GTEx RNA-seq databases. LncRNA prognostic signature was then established by Kaplan-Meier and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. After lncRNA-miRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with results enriched in various malignancy-related functions and pathways. Finally, six putative drugs (irinotecan, camptothecin, mitoxantrone, azacitidine, mestranol, and enilconazole) were predicted by Connectivity Map. In conclusion, we identified a 6-lncRNA prognostic signature with its ceRNA networks, and six candidate drugs against LGG.

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.

Drug Repositioning for P-Glycoprotein Mediated Co-Expression Networks in Colorectal Cancer

Colorectal cancer (CRC) is one of the most fatal types of cancers that is seen in both men and women. CRC is the third most common type of cancer worldwide. Over the years, several drugs are developed for the treatment of CRC; however, patients with advanced CRC can be resistant to some drugs. P-glycoprotein (P-gp) (also known as Multidrug Resistance 1, MDR1) is a well-identified membrane transporter protein expressed by ABCB1 gene. The high expression of MDR1 protein found in several cancer types causes chemotherapy failure owing to efflux drug molecules out of the cancer cell, decreases the drug concentration, and causes drug resistance. As same as other cancers, drug-resistant CRC is one of the major obstacles for effective therapy and novel therapeutic strategies are urgently needed. Network-based approaches can be used to determine specific biomarkers, potential drug targets, or repurposing approved drugs in drug-resistant cancers. Drug repositioning is the approach for using existing drugs for a new therapeutic purpose; it is a highly efficient and low-cost process. To improve current understanding of the MDR-1-related drug resistance in CRC, we explored gene co-expression networks around ABCB1 gene with different network sizes (50, 100, 150, 200 edges) and repurposed candidate drugs targeting the ABCB1 gene and its co-expression network by using drug repositioning approach for the treatment of CRC. The candidate drugs were also assessed by using molecular docking for determining the potential of physical interactions between the drug and MDR1 protein as a drug target. We also evaluated these four networks whether they are diagnostic or prognostic features in CRC besides biological function determined by functional enrichment analysis. Lastly, differentially expressed genes of drug-resistant (i.e., oxaliplatin, methotrexate, SN38) HT29 cell lines were found and used for repurposing drugs with reversal gene expressions. As a result, it is shown that all networks exhibited high diagnostic and prognostic performance besides the identification of various drug candidates for drug-resistant patients with CRC. All these results can shed light on the development of effective diagnosis, prognosis, and treatment strategies for drug resistance in CRC.

TNF-α enhances TGF-β-induced endothelial-to-mesenchymal transition via TGF-β signal augmentation

The tumor microenvironment (TME) consists of various components including cancer cells, tumor vessels, cancer-associated fibroblasts (CAFs), and inflammatory cells. These components interact with each other via various cytokines, which often induce tumor progression. Thus, a greater understanding of TME networks is crucial for the development of novel cancer therapies. Many cancer types express high levels of TGF-β, which induces endothelial-to-mesenchymal transition (EndMT), leading to formation of CAFs. Although we previously reported that CAFs derived from EndMT promoted tumor formation, the molecular mechanisms underlying these interactions remain to be elucidated. Furthermore, tumor-infiltrating inflammatory cells secrete various cytokines, including TNF-α. However, the role of TNF-α in TGF-β-induced EndMT has not been fully elucidated. Therefore, this study examined the effect of TNF-α on TGF-β-induced EndMT in human endothelial cells (ECs). Various types of human ECs underwent EndMT in response to TGF-β and TNF-α, which was accompanied by increased and decreased expression of mesenchymal cell and EC markers, respectively. In addition, treatment of ECs with TGF-β and TNF-α exhibited sustained activation of Smad2/3 signals, which was presumably induced by elevated expression of TGF-β type I receptor, TGF-β2, activin A, and integrin αv, suggesting that TNF-α enhanced TGF-β-induced EndMT by augmenting TGF-β family signals. Furthermore, oral squamous cell carcinoma-derived cells underwent epithelial-to-mesenchymal transition (EMT) in response to humoral factors produced by TGF-β and TNF-α-cultured ECs. This EndMT-driven EMT was blocked by inhibiting the action of TGF-βs. Collectively, our findings suggest that TNF-α enhances TGF-β-dependent EndMT, which contributes to tumor progression.

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).

Characterization of Stem-like Circulating Tumor Cells in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most frequent cause of death from cancer. Circulating tumor cells (CTCs) with stem-like characteristics lead to distant metastases and thus contribute to the dismal prognosis of PDAC. Our purpose is to investigate the role of stemness in CTCs derived from a genetically engineered mouse model of PDAC and to further explore the potential molecular mechanisms. The publically available RNA sequencing dataset GSE51372 was analyzed, and CTCs with (CTC-S) or without (CTC-N) stem-like features were discriminated based on a principal component analysis (PCA). Differentially expressed genes, weighted gene co-expression network analysis (WGCNA), and further functional enrichment analyses were performed. The prognostic role of the candidate gene (CTNNB1) was assessed in a clinical PDAC patient cohort. Overexpression of the pluripotency marker Klf4 (Krüppel-like factor 4) in CTC-S cells positively correlates with Ctnnb1 (β-Catenin) expression, and their interaction presumably happens via protein-protein binding in the nucleus. As a result, the adherens junction pathway is significantly enriched in CTC-S. Furthermore, the overexpression of Ctnnb1 is a negative prognostic factor for progression-free survival (PFS) and relapse-free survival (RFS) in human PDAC cohort. Overexpression of Ctnnb1 may thus promote the metastatic capabilities of CTCs with stem-like properties via adherens junctions in murine PDAC.

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.

Understanding and predicting disease relationships through similarity fusion

Motivation

Combining disease relationships across multiple biological levels could aid our understanding of common processes taking place in disease, potentially indicating opportunities for drug sharing. Here, we propose a similarity fusion approach which accounts for differences in information content between different data types, allowing combination of each data type in a balanced manner.

Results

We apply this method to six different types of biological data (ontological, phenotypic, literature co-occurrence, genetic association, gene expression and drug indication data) for 84 diseases to create a ‘disease map’: a network of diseases connected at one or more biological levels. As well as reconstructing known disease relationships, 15% of links in the disease map are novel links spanning traditional ontological classes, such as between psoriasis and inflammatory bowel disease. 62% of links in the disease map represent drug-sharing relationships, illustrating the relevance of the similarity fusion approach to the identification of potential therapeutic relationships.

Availability and implementation

Freely available under the MIT license at https://github.com/e-oerton/disease-similarity-fusion

Supplementary information

Supplementary data are available at Bioinformatics online.

Targeting Cancer Lysosomes with Good Old Cationic Amphiphilic Drugs

Being originally discovered as cellular recycling bins, lysosomes are today recognized as versatile signaling organelles that control a wide range of cellular functions that are essential not only for the well-being of normal cells but also for malignant transformation and cancer progression. In addition to their core functions in waste disposal and recycling of macromolecules and energy, lysosomes serve as an indispensable support system for malignant phenotype by promoting cell growth, cytoprotective autophagy, drug resistance, pH homeostasis, invasion, metastasis, and genomic integrity. On the other hand, malignant transformation reduces the stability of lysosomal membranes rendering cancer cells sensitive to lysosome-dependent cell death. Notably, many clinically approved cationic amphiphilic drugs widely used for the treatment of other diseases accumulate in lysosomes, interfere with their cancer-promoting and cancer-supporting functions and destabilize their membranes thereby opening intriguing possibilities for cancer therapy. Here, we review the emerging evidence that supports the supplementation of current cancer therapies with lysosome-targeting cationic amphiphilic drugs.

Time-Course Transcriptome Analysis for Drug Repositioning in Fusobacterium nucleatum-Infected Human Gingival Fibroblasts

Fusobacterium nucleatum (F. nucleatum) is a crucial periodontal pathogen and human gingival fibroblasts (GFs) are the first line of defense against oral pathogens. However, the research on potential molecular mechanisms of host defense and effective treatment of F. nucleatum infection in GFs remains scarce. In this study, we undertook a time-series experiment and performed an RNA-seq analysis to explore gene expression profiles during the process of F. nucleatum infection in GFs. Differentially expressed genes (DEGs) could be divided into three coexpression clusters. Functional analysis revealed that the immune-related signaling pathways were more overrepresented at the early stage, while metabolic pathways were mainly enriched at the late stage. We computationally identified several U.S. Food and Drug Administration (FDA)-approved drugs that could protect the F. nucleatum infected GFs via a coexpression-based drug repositioning approach. Biologically, we confirmed that six drugs (etravirine, zalcitabine, wortmannin, calcium D-pantothenate, ellipticine, and tanespimycin) could significantly decrease F. nucleatum-induced reactive oxygen species (ROS) generation and block the Protein Kinase B (PKB/AKT)/mitogen-activated protein kinase signaling pathways. Our study provides more detailed molecular mechanisms of the process by which F. nucleatum infects GFs and illustrates the value of the cogena-based drug repositioning method and the potential therapeutic application of these tested drugs in the treatment of F. nucleatum infection.

In silico drug repositioning: from large-scale transcriptome data to therapeutics

Drug repositioning is an attractive alternative to conventional drug development when new beneficial effects of old drugs are clinically validated because pharmacokinetic and safety profiles are generally already available. Since ~ 30% of drugs newly approved by the US food and drug administration (FDA) are developed through drug repositioning, identifying novel usage for existing drugs is an emerging strategy for developing disease treatments. With advances in next-generation sequencing technologies, available transcriptome data related to diseases have expanded rapidly. Harnessing these resources enables a better understanding of disease mechanisms and drug mode of action (MOA), and moves toward personalized pharmacotherapy. In this review, we briefly outline publicly available large-scale transcriptome databases and tools for drug repositioning. We also highlight recent approaches leading to the discovery of novel drug targets, drug response biomarkers, drug indications, and drug MOA.