Identification of drug candidate against prostate cancer from the aspect of somatic cell reprogramming

Transcriptionally Conditional Recurrent Neural Network for De Novo Drug Design

Computational molecular generation methods that generate chemical structures from gene expression profiles have been actively developed for de novo drug design. However, most omics-based methods involve complex models consisting of multiple neural networks, which require pretraining. In this study, we propose a straightforward molecular generation method called GxRNN (gene expression profile-based recurrent neural network), employing a single recurrent neural network (RNN) that necessitates no pretraining for omics-based drug design. Specifically, our method utilizes the desired gene expression profile as input for the RNN, conditioning it to generate molecules likely to induce a similar profile. In a case study involving ten target proteins, GxRNN exhibited superior structural reproducibility of known ligands, surpassing several existing methods. This advancement positions our proposed method as a promising tool for facilitating de novo drug design.

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.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

G-protein signaling of oxytocin receptor as a potential target for cabazitaxel-resistant prostate cancer

Although the treatment armamentarium for patients with metastatic prostate cancer has improved recently, treatment options after progression on cabazitaxel (CBZ) are limited. To identify the mechanisms underlying CBZ resistance and therapeutic targets, we performed single-cell RNA sequencing of circulating tumor cells (CTCs) from patients with CBZ-resistant prostate cancer. Cells were clustered based on gene expression profiles. In silico screening was used to nominate candidate drugs for overcoming CBZ resistance in castration-resistant prostate cancer. CTCs were divided into three to four clusters, reflecting intrapatient tumor heterogeneity in refractory prostate cancer. Pathway analysis revealed that clusters in two cases showed up-regulation of the oxytocin (OXT) receptor-signaling pathway. Spatial gene expression analysis of CBZ-resistant prostate cancer tissues confirmed the heterogeneous expression of OXT-signaling molecules. Cloperastine (CLO) had significant antitumor activity against CBZ-resistant prostate cancer cells. Mass spectrometric phosphoproteome analysis revealed the suppression of OXT signaling specific to CBZ-resistant models. These results support the potential of CLO as a candidate drug for overcoming CBZ-resistant prostate cancer via the inhibition of OXT signaling.

Lansoprazole inhibits the development of sessile serrated lesions by inducing G1 arrest via Skp2/p27 signaling pathway

BACKGROUND

Although the serrated-neoplasia pathway reportedly accounts for 15-30% of colorectal cancer (CRC), no studies on chemoprevention of sessile serrated lesions (SSLs) have been reported. We searched for effective compounds comprehensively from a large series of compounds by employing Connectivity Map (CMAP) analysis of SSL-specific gene expression profiles coupled with in vitro screening using SSL patient-derived organoids (PDOs), and validated their efficacy using a xenograft mouse model of SSL.

METHODS

We generated SSL-specific gene signatures based on DNA microarray data, and applied them to CMAP analysis with 1309 FDA-approved compounds to select candidate compounds. We evaluated their inhibitory effects on SSL-PDOs using a cell viability assay. SSL-PDOs were orthotopically transplanted into NOG mice for in vivo evaluation. The signal transduction pathway was evaluated by gene expression profile and protein expression analysis.

RESULTS

We identified 221 compounds by employing CMAP analysis of SSL-specific signatures, which should cancel the gene signatures, and narrowed them down to 17 compounds. Cell viability assay using SSL-PDOs identified lansoprazole as having the lowest IC50 value (47 µM) among 17 compounds. When SSL-PDO was orthotopically transplanted into murine intestinal tract, the tumor grew gradually. Administration of lansoprazole to mice inhibited the growth of SSL xenograft whereas the tumor in control mice treated with vehicle alone grew gradually over time. The Ki67 index in xenograft lesions from the lansoprazole group was significantly lower compared with the control group. Cell cycle analysis of SSL-PDOs treated with lansoprazole exhibited a significant increase in G1 phase cell population. Microarray and protein analysis revealed that lansoprazole downregulated Skp2 expression and upregulated p27 expression in SSL-PDOs.

CONCLUSIONS

Our data strongly suggest that lansoprazole is the most effective chemopreventive agent against SSL, and that lansoprazole induces G1 cell cycle arrest by downregulating Skp2 and upregulating p27 in SSL cells.

MNK, mTOR or eIF4E-selecting the best anti-tumor target for blocking translation initiation

Overexpression of eIF4E is common in patients with various solid tumors and hematologic cancers. As a potential anti-cancer target, eIF4E has attracted extensive attention from researchers. At the same time, mTOR kinases inhibitors and MNK kinases inhibitors, which are directly related to regulation of eIF4E, have been rapidly developed. To explore the optimal anti-cancer targets among MNK, mTOR, and eIF4E, this review provides a detailed classification and description of the anti-cancer activities of promising compounds. In addition, the structures and activities of some dual-target inhibitors are briefly described. By analyzing the different characteristics of the inhibitors, it can be concluded that MNK1/2 and eIF4E/eIF4G interaction inhibitors are superior to mTOR inhibitors. Simultaneous inhibition of MNK and eIF4E/eIF4G interaction may be the most promising anti-cancer method for targeting translation initiation.

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.

Discovery of a new candidate drug to overcome cabazitaxel-resistant gene signature in castration-resistant prostate cancer by in silico screening

BACKGROUND

The taxane cabazitaxel (CBZ) is a promising treatment for docetaxel-resistant castration-resistant prostate cancer (CRPC). However, the survival benefit with CBZ for patients with CRPC is limited. This study used screening tests for candidate drugs targeting CBZ-resistant-related gene expression and identified pimozide as a potential candidate for overcoming CBZ resistance in CRPC.

METHODS

We established CBZ-resistant cell lines, DU145CR and PC3CR by incubating DU145 cells and PC3 cells with gradually increasing concentrations of CBZ. We performed in silico drug screening for candidate drugs that could reprogram the gene expression signature of a CBZ-resistant prostate cancer cells using a Connectivity Map. The in vivo effect of the drug combination was tested in xenograft mice models.

RESULTS

We identified pimozide as a promising candidate drug for CBZ-resistant CRPC. Pimozide had a significant antitumor effect on DU145CR cells. Moreover, combination treatment with pimozide and CBZ had a synergic effect for DU145CR cells in vitro and in vivo. Microarray analysis identified AURKB and KIF20A as potential targets of pimozide in CBZ-resistant CRPC. DU145CR had significantly higher AURKB and KIF20A expression compared with a non-CBZ-resistant cell line. Inhibition of AURKB and KIF20A had an antitumor effect in DU145CR xenograft tumors. Higher expression of AURKB and KIF20A was a poor prognostic factor of TGCA prostate cancer cohort. CBZ-resistant prostate cancer tissues in our institution had higher AURKB and KIF20A expression.

CONCLUSIONS

Pimozide appears to be a promising drug to overcome CBZ resistance in CRPC by targeting AURKB and KIF20A.

Resveratrol inhibits development of colorectal adenoma via suppression of LEF1; comprehensive analysis with connectivity map

Although many chemopreventive studies on colorectal tumors have been reported, no effective and safe preventive agent is currently available. We searched for candidate preventive compounds against colorectal tumor comprehensively from United States Food and Drug Administration (FDA)-approved compounds by using connectivity map (CMAP) analysis coupled with in vitro screening with colorectal adenoma (CRA) patient-derived organoids (PDOs). We generated CRA-specific gene signatures based on the DNA microarray analysis of CRA and normal epithelial specimens, applied them to CMAP analysis with 1309 FDA-approved compounds, and identified 121 candidate compounds that should cancel the gene signatures. We narrowed them down to 15 compounds, and evaluated their inhibitory effects on the growth of CRA-PDOs in vitro. We finally identified resveratrol, one of the polyphenolic phytochemicals, as a compound showing the strongest inhibitory effect on the growth of CRA-PDOs compared with normal epithelial PDOs. When resveratrol was administered to Apc^Min/+ mice at 15 or 30 mg/kg, the number of polyps (adenomas) was significantly reduced in both groups compared with control mice. Similarly, the number of polyps (adenomas) was significantly reduced in azoxymethane-injected rats treated with 10 or 100 mg/resveratrol compared with control rats. Microarray analysis of adenomas from resveratrol-treated rats revealed the highest change (downregulation) in expression of LEF1, a key molecule in the Wnt signaling pathway. Treatment with resveratrol significantly downregulated the Wnt-target gene (MYC) in CRA-PDOs. Our data demonstrated that resveratrol can be the most effective compound for chemoprevention of colorectal tumors, the efficacy of which is mediated through suppression of LEF1 expression in the Wnt signaling pathway.

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.

[Development of Decomposition Approach for Comprehensive Understanding of Drug Effects]

As the term polypharmacology suggests, there are multiple actions of small-molecule compounds. We proposed a decomposition and understanding concept that sheds light on the small effects in comparison to the large effects by decomposing these multiple effects. This concept was embodied by describing the effects of the compounds in a transcriptome profile, followed by factor analysis to extract latent variables as decomposed effects. Application of this approach to public datasets resulted in the inferences of compound effects consistent with existing knowledge such as gene ontologies and pathways. In one experimental validation, the potential inducibility of endoplasmic reticulum stress of several commercial drugs was detected by decomposition. Another study successfully discriminated the effects of a natural product and its derivatives despite their structural similarity. In the era of big data, it is important to infer conceptual elements composed of measurable elements as a higher layer than the given data of a specimen, which can expand our perception and understanding of the specimen. This review introduces an example of such a philosophy by applying it to the multiple effects of drugs to contribute to the understanding.

Targeting phase separation on enhancers induced by transcription factor complex formations as a new strategy for treating drug-resistant cancers

The limited options for treating patients with drug-resistant cancers have emphasized the need to identify alternative treatment targets. Tumor cells have large super-enhancers (SEs) in the vicinity of important oncogenes for activation. The physical process of liquid-liquid phase separation (LLPS) contributes to the assembly of several membrane-less organelles in mammalian cells. Intrinsically disordered regions (IDRs) of proteins induce LLPS formation by developing condensates. It was discovered that key transcription factors (TFs) undergo LLPS in SEs. In addition, TFs play critical roles in the epigenetic and genetic regulation of cancer progression. Recently, we revealed the essential role of disease-specific TF collaboration changes in advanced prostate cancer (PC). OCT4 confers epigenetic changes by promoting complex formation with TFs, such as Forkhead box protein A1 (FOXA1), androgen receptor (AR) and Nuclear respiratory factor 1 (NRF1), inducing PC progression. It was demonstrated that TF collaboration through LLPS underlying transcriptional activation contributes to cancer aggressiveness and drug resistance. Moreover, the disruption of TF-mediated LLPS inhibited treatment-resistant PC tumor growth. Therefore, we propose that repression of TF collaborations involved in the LLPS of SEs could be a promising strategy for advanced cancer therapy. In this article, we summarize recent evidence highlighting the formation of LLPS on enhancers as a potent therapeutic target in advanced cancers.

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.

Chemical-induced gene expression ranking and its application to pancreatic cancer drug repurposing

Chemical-induced gene expression profiles provide critical information of chemicals in a biological system, thus offering new opportunities for drug discovery. Despite their success, large-scale analysis leveraging gene expressions is limited by time and cost. Although several methods for predicting gene expressions were proposed, they only focused on imputation and classification settings, which have limited applications to real-world scenarios of drug discovery. Therefore, a chemical-induced gene expression ranking (CIGER) framework is proposed to target a more realistic but more challenging setting in which overall rankings in gene expression profiles induced by de novo chemicals are predicted. The experimental results show that CIGER significantly outperforms existing methods in both ranking and classification metrics. Furthermore, a drug screening pipeline based on CIGER is proposed to identify potential treatments of drug-resistant pancreatic cancer. Our predictions have been validated by experiments, thereby showing the effectiveness of CIGER for phenotypic compound screening of precision medicine.

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A new deep-learning method (CIGER) for chemical-induced gene expression ranking

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CIGER can predict gene expression for de novo chemicals from chemical structures

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We discovered drugs for the treatment of drug-resistant pancreatic cancer

In recent years, a phenotype-based drug discovery approach using chemical-induced gene expressions has shown to be effective in drug discovery and precision medicine. However, it is not feasible to experimentally determine chemical-induced gene expressions for all available chemicals of interest, thereby hindering the application of gene expression-based compound screening on a large scale. Thus, it is crucial to design a computational approach that can generate gene expression information for any chemicals. We proposed a new, deep-learning framework named chemical-induced gene expression ranking (CIGER) to predict a landmark gene expression profile (i.e., gene ranking) induced by de novo chemicals based on their chemical structures. Leveraging CIGER, we predicted and experimentally validated that several existing drugs can increase the therapeutic response on drug-resistant pancreatic cancer. Our results demonstrated the effectiveness of CIGER for precision drug discovery in practice.

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.

The (Still Unknown) Hypothetical Protective Role of COVID-19 Therapy in Bladder Cancer

The COVID-19 pandemic continues to put a strain on the entire world population. The common features of bladder cancer (BCa) and COVID infection have been widely reported and discussion may continue regarding treatment as well. We have highlighted how COVID-19 therapy has many implications with BCa therapy, in particular with potential protective role.

Topoisomerase II alpha inhibition can overcome taxane-resistant prostate cancer through DNA repair pathways

Cabazitaxel (CBZ) is approved for the treatment of docetaxel-resistant castration-resistant prostate cancer (CRPC). However, its efficacy against CRPC is limited, and there are no effective treatments for CBZ-resistant CRPC. This study explored the optimal treatment for CRPC in the post-cabazitaxel setting. PC3 (CBZ-sensitive) and PC3CR cells (CBZ-resistant) were used in this study. We performed in silico drug screening for candidate drugs that could reprogram the gene expression signature of PC3CR cells. The in vivo effect of the drug combination was tested in xenograft mice models. We identified etoposide (VP16) as a promising treatment candidate for CBZ-resistant CRPC. The WST assay revealed that VP16 had a significant antitumor effect on PC3CR cells. PC3CR cells exhibited significantly higher topoisomerase II alpha (TOP2A) expression than PC3 cells. Higher TOP2A expression was a poor prognostic factor in The Cancer Genome Atlas prostate cancer cohort. In the Fred Hutchinson Cancer Research Center dataset, docetaxel-exposed tissues and metastatic tumors had higher TOP2A expression. In addition, VP16 significantly inhibited the growth of tumors generated from both cell lines. Based on these findings, VP16-based chemotherapy may be an optimal treatment for CPRC in the post-CBZ setting.

Subtype-specific collaborative transcription factor networks are promoted by OCT4 in the progression of prostate cancer

Interactive networks of transcription factors (TFs) have critical roles in epigenetic and gene regulation for cancer progression. It is required to clarify underlying mechanisms for transcriptional activation through concerted efforts of TFs. Here, we show the essential role of disease phase-specific TF collaboration changes in advanced prostate cancer (PC). Investigation of the transcriptome in castration-resistant PC (CRPC) revealed OCT4 as a key TF in the disease pathology. OCT4 confers epigenetic changes by promoting complex formation with FOXA1 and androgen receptor (AR), the central signals for the progression to CRPC. Meanwhile, OCT4 facilitates a distinctive complex formation with nuclear respiratory factor 1 (NRF1) to gain chemo-resistance in the absence of AR. Mechanistically, we reveal that OCT4 increases large droplet formations with AR/FOXA1 as well as NRF1 in vitro. Disruption of TF collaborations using a nucleoside analogue, ribavirin, inhibited treatment-resistant PC tumor growth. Thus, our findings highlight the formation of TF collaborations as a potent therapeutic target in advanced cancer.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing

Phenotype-based compound screening has advantages over target-based drug discovery, but is unscalable and lacks understanding of mechanism. Chemical-induced gene expression profile provides a mechanistic signature of phenotypic response. However, the use of such data is limited by their sparseness, unreliability, and relatively low throughput. Few methods can perform phenotype-based de novo chemical compound screening. Here, we propose a mechanism-driven neural network-based method DeepCE, which utilizes graph neural network and multi-head attention mechanism to model chemical substructure-gene and gene-gene associations, for predicting the differential gene expression profile perturbed by de novo chemicals. Moreover, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves superior performances to state-of-the-art methods. The effectiveness of gene expression profiles generated from DeepCE is further supported by comparing them with observed data for downstream classification tasks. To demonstrate the value of DeepCE, we apply it to drug repurposing of COVID-19, and generate novel lead compounds consistent with clinical evidence. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data and screening novel chemicals for the modulation of a systemic response to disease.

Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

The dysregulation of RNA binding proteins (RBPs) regulates the progression of several cancers. However, information on the overall functions of RBPs in prostate cancer (PCa) remains largely understudied. Therefore, based on the TCGA dataset, this study identified 144 differentially expressed RBPs in tumors compared to normal tissues. Subsequently, through univariate, LASSO and multivariate Cox regression analysis, 6 RBP genes among them, MSI1, MBNL2, LENG9, REXO2, RNASE1, and PABPC1L were screened as prognostic hub genes and prognostic signature was further identified. Further analysis indicated that the high-risk group was significantly associated with poor RFS, which was validated in the MSKCC cohort. Besides, patients in the high-risk group were closely associated with dysregulation of DNA damage repair pathway, copy number alteration, tumor burden mutation, and low-response to cisplatin (P < 0.001), and bicalutamide (P < 0.001). Using the Connectivity Map, we finally predicted 3 drugs including, ribavirin, carmustine, and carbenoxolone. In summary, we identified six-RBP gene signature and 3 potential drugs against PCa, which might promote the individualized treatment strategies and further improve the quality of life among PCa patients.

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.

Decomposition profile data analysis of multiple drug effects identifies endoplasmic reticulum stress-inducing ability as an unrecognized factor

Chemicals have multiple effects in biological systems. Because their on-target effects dominate the output, their off-target effects are often overlooked and can sometimes cause dangerous adverse events. Recently, we developed a novel decomposition profile data analysis method, orthogonal linear separation analysis (OLSA), to analyse multiple effects. In this study, we tested whether OLSA identified the ability of drugs to induce endoplasmic reticulum (ER) stress as a previously unrecognized factor. After analysing the transcriptome profiles of MCF7 cells treated with different chemicals, we focused on a vector characterized by well-known ER stress inducers, such as ciclosporin A. We selected five drugs predicted to be unrecognized ER stress inducers, based on their inducing ability scores derived from OLSA. These drugs actually induced X-box binding protein 1 splicing, an indicator of ER stress, in MCF7 cells in a concentration-dependent manner. Two structurally different representatives of the five test compounds exhibited similar results in HepG2 and HuH7 cells, but not in PXB primary hepatocytes derived from human-liver chimeric mice. These results indicate that our decomposition strategy using OLSA uncovered the ER stress-inducing ability of drugs as an unrecognized effect, the manifestation of which depended on the background of the cells.

A deep learning framework for high-throughput mechanism-driven phenotype compound screening

Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Development of a Novel Platform of Proteome Profiling Based on an Easy-to-Handle and Informative 2D-DIGE System

Proteome profiling based on two-dimensional (2D)-DIGE might be a useful tool for investigating drug-like compounds and the mode of action of drugs. However, obtaining data for profiling requires high labor costs, and it is difficult to control the reproducibility of spot positions because 2D-DIGE usually requires large-size glass plates and spot alignments are greatly affected by the quality of DryStrips and polyacrylamide gels (PAGs). Therefore, we have developed a novel platform by employing small size DryStrips and PAGs, and an image analysis strategy based on dual correction of spot alignment and volume. Our system can automatically detect a large number of consistent spots through all images. Cytosol fractions of HeLa cells treated with dimethyl sulfoxide (DMSO) or bortezomib were analyzed, 1697 consistent spots were detected, and 775 of them were significantly changed with the treatment. Deviations between different days and lot sets of DryStrips and PAGs were investigated by calculating the correlation coefficients. The mean values of the correlation between days and lot sets were 0.96 and 0.94, respectively. Clustering analysis of all the treatment data clearly separated the DMSO or bortezomib treated groups beyond day deviations. Thus, we have succeeded in developing an easy-to-handle 2D-DIGE system that can be a novel proteome profiling platform.

In silico chemical screening identifies epidermal growth factor receptor as a therapeutic target of drug-tolerant CD44v9-positive gastric cancer cells

Background

Tumours consist of heterogeneous cancer cells and are likely to contain drug-tolerant cell subpopulations, causing early relapse. However, treatment strategies to eliminate these cells have not been established.

Methods

We established gastric cancer patient-derived cells (PDCs) to examine the contribution of CD44 splicing variant 9 (CD44v9)-positive cells in gastric cancer drug tolerance. We performed gene expression signature-based in silico screening using JFCR_LinCAGE, our anticancer compound gene expression database and subsequent validation in BALB/c-nu/nu mouse xenograft to identify agents targeting the drug-tolerant cancer cells.

Results

CD44v9-positive cancer cells were enriched among residual cancer cells after treatment with SN-38, an active metabolic of irinotecan. CD44v9 protein was responsible for this drug resistance. We identified epidermal growth factor receptor (EGFR) inhibitors as agents that can target CD44v9-positive cell populations in gastric cancer PDCs. CD44v9 promoted cell proliferation, and EGFR inhibition attenuated CD44v9 protein expression through downregulation of the AKT and the ERK signalling pathways, leading to preferential suppression of CD44v9-positive cells. Importantly, EGFR inhibitors significantly reduced the number of residual cancer cells after cytotoxic anticancer drug treatment and enhanced the antitumor effect of irinotecan in vivo.

Conclusions

EGFR inhibitors could be potential agents to eradicate cytotoxic anticancer drug-tolerant gastric cancer cell populations.

Predicting drug-induced transcriptome responses of a wide range of human cell lines by a novel tensor-train decomposition algorithm

MOTIVATION

Genome-wide identification of the transcriptomic responses of human cell lines to drug treatments is a challenging issue in medical and pharmaceutical research. However, drug-induced gene expression profiles are largely unknown and unobserved for all combinations of drugs and human cell lines, which is a serious obstacle in practical applications.

RESULTS

Here, we developed a novel computational method to predict unknown parts of drug-induced gene expression profiles for various human cell lines and predict new drug therapeutic indications for a wide range of diseases. We proposed a tensor-train weighted optimization (TT-WOPT) algorithm to predict the potential values for unknown parts in tensor-structured gene expression data. Our results revealed that the proposed TT-WOPT algorithm can accurately reconstruct drug-induced gene expression data for a range of human cell lines in the Library of Integrated Network-based Cellular Signatures. The results also revealed that in comparison with the use of original gene expression profiles, the use of imputed gene expression profiles improved the accuracy of drug repositioning. We also performed a comprehensive prediction of drug indications for diseases with gene expression profiles, which suggested many potential drug indications that were not predicted by previous approaches.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development of Orthogonal Linear Separation Analysis (OLSA) to Decompose Drug Effects into Basic Components

Drugs have multiple, not single, effects. Decomposition of drug effects into basic components helps us to understand the pharmacological properties of a drug and contributes to drug discovery. We have extended factor analysis and developed a novel profile data analysis method: orthogonal linear separation analysis (OLSA). OLSA contracted 11,911 genes to 118 factors from transcriptome data of MCF7 cells treated with 318 compounds in a Connectivity Map. Ontology of the main genes constituting the factors detected significant enrichment of the ontology in 65 of 118 factors and similar results were obtained in two other data sets. In further analysis of the Connectivity Map data set, one factor discriminated two Hsp90 inhibitors, geldanamycin and radicicol, while clustering analysis could not. Doxorubicin and other topoisomerase inhibitors were estimated to inhibit Na+/K+ ATPase, one of the suggested mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel inducers of autophagy, and other analyses including western blotting revealed that 4 of the 5 actually induced autophagy. These findings indicate the potential of OLSA to decompose the effects of a drug and identify its basic components.

Ribavirin-induced down-regulation of CCAAT/enhancer-binding protein α leads to suppression of lipogenesis

Recently, we demonstrated that the anti-viral drug ribavirin (RBV) had the ability to suppress lipogenesis through down-regulation of retinoid X receptor α (RXRα) under the control of the intracellular GTP-level and AMP-activated protein kinase-related kinases, especially microtubule affinity regulating kinase 4 (MARK4). RXRα-overexpression attenuated but did not abolish lipogenesis suppression by RBV, implying that additional factor(s) were involved in this suppressive effect. In the present study, we found that the protein level, but not the mRNA level, of CCAAT/enhancer-binding protein α (C/EBPα) was down-regulated by RBV in hepatic cells. Treatment with proteasome inhibitor attenuated RBV-induced down-regulation of C/EBPα, suggesting that RBV promoted degradation of C/EBPα protein via the ubiquitin-proteasome pathway. Depletion of intracellular GTP through inosine monophosphate dehydrogenase inhibition by RBV led to down-regulation of C/EBPα. In contrast, down-regulation of C/EBPα by RBV was independent of RXRα and MARK4. Knockdown of C/EBPα reduced the intracellular neutral lipid levels and the expression of genes related to the triglyceride (TG) synthesis pathway, especially glycerol-3-phosphate acyltransferase, mitochondrial (GPAM), which encodes the first rate-limiting TG enzyme. Overexpression of C/EBPα yielded the opposite results. We also observed that RBV decreased GPAM expression. Moreover, overexpression of GPAM attenuated RBV-induced reduction in the intracellular neutral lipid levels. These data suggest that down-regulation of C/EBPα by RBV leads to the reduction in GPAM expression, which contributes to the suppression of lipogenesis. Our findings about the mechanism of RBV action in lipogenesis suppression will provide new insights for therapy against the active lipogenesis involved in hepatic steatosis and hepatocellular carcinomas.

Pathway-Based Drug Repositioning for Cancers: Computational Prediction and Experimental Validation

Developing drugs with anticancer activity and low toxic side-effects at low costs is a challenging issue for cancer chemotherapy. In this work, we propose to use molecular pathways as the therapeutic targets and develop a novel computational approach for drug repositioning for cancer treatment. We analyzed chemically induced gene expression data of 1112 drugs on 66 human cell lines and searched for drugs that inactivate pathways involved in the growth of cancer cells (cell cycle) and activate pathways that contribute to the death of cancer cells (e.g., apoptosis and p53 signaling). Finally, we performed a large-scale prediction of potential anticancer effects for all the drugs and experimentally validated the prediction results via three in vitro cellular assays that evaluate cell viability, cytotoxicity, and apoptosis induction. Using this strategy, we successfully identified several potential anticancer drugs. The proposed pathway-based method has great potential to improve drug repositioning research for cancer treatment.

Ribavirin suppresses hepatic lipogenesis through inosine monophosphate dehydrogenase inhibition: Involvement of adenosine monophosphate-activated protein kinase-related kinases and retinoid X receptor α

Ribavirin (RBV) has been widely used as an antiviral reagent, specifically for patients with chronic hepatitis C. We previously demonstrated that adenosine kinase, which monophosphorylates RBV into the metabolically active form, is a key determinant for RBV sensitivity against hepatitis C virus RNA replication. However, the precise mechanism of RBV action and whether RBV affects cellular metabolism remain unclear. Analysis of liver gene expression profiles obtained from patients with advanced chronic hepatitis C treated with the combination of pegylated interferon and RBV showed that the adenosine kinase expression level tends to be lower in patients who are overweight and significantly decreases with progression to advanced fibrosis stages. In our effort to investigate whether RBV affects cellular metabolism, we found that RBV treatment under clinically achievable concentrations suppressed lipogenesis in hepatic cells. In this process, guanosine triphosphate depletion through inosine monophosphate dehydrogenase inhibition by RBV and adenosine monophosphate-activated protein kinase-related kinases, especially microtubule affinity regulating kinase 4, were required. In addition, RBV treatment led to the down-regulation of retinoid X receptor α (RXRα), a key nuclear receptor in various metabolic processes, including lipogenesis. Moreover, we found that guanosine triphosphate depletion in cells induced the down-regulation of RXRα, which was mediated by microtubule affinity regulating kinase 4. Overexpression of RXRα attenuated the RBV action for suppression of lipogenic genes and intracellular neutral lipids, suggesting that down-regulation of RXRα was required for the suppression of lipogenesis in RBV action. Conclusion: We provide novel insights about RBV action in lipogenesis and its mechanisms involving inosine monophosphate dehydrogenase inhibition, adenosine monophosphate-activated protein kinase-related kinases, and down-regulation of RXRα. RBV may be a potential reagent for anticancer therapy against the active lipogenesis involved in hepatocarcinogenesis. (Hepatology Communications 2017;1:550-563).

Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics

The identification of the modes of action of bioactive compounds is a major challenge in chemical systems biology of diseases. Genome-wide expression profiling of transcriptional responses to compound treatment for human cell lines is a promising unbiased approach for the mode-of-action analysis. Here we developed a novel approach to elucidate the modes of action of bioactive compounds in a cell-specific manner using large-scale chemically-induced transcriptome data acquired from the Library of Integrated Network-based Cellular Signatures (LINCS), and analyzed 16,268 compounds and 68 human cell lines. First, we performed pathway enrichment analyses of regulated genes to reveal active pathways among 163 biological pathways. Next, we explored potential target proteins (including primary targets and off-targets) with cell-specific transcriptional similarity using chemical-protein interactome. Finally, we predicted new therapeutic indications for 461 diseases based on the target proteins. We showed the usefulness of the proposed approach in terms of prediction coverage, interpretation, and large-scale applicability, and validated the new prediction results experimentally by an in vitro cellular assay. The approach has a high potential for advancing drug discovery and repositioning.

Computational probing protein-protein interactions targeting small molecules

MOTIVATION

With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets.

RESULTS

Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation.

CONCLUSION

In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework.

AVAILABILITY AND IMPLEMENTATION

PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar.

CONTACT

ycwang@nwipb.cas.cn or ywang@amss.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Knockdown of OCT4 suppresses the growth and invasion of pancreatic cancer cells through inhibition of the AKT pathway

Octamer‑binding transcription factor 4 (OCT4) is one of the factors associated with self‑renewal and differentiation in cancer stem cells, and is crucial for the progression of various types of human malignancy. However, the expression and function of OCT4 in human pancreatic cancer has not been fully elucidated. The purpose of the present study was to investigate the function and molecular mechanisms of OCT4 in pancreatic cancer cells. The clinical significance of OCT4 expression was assessed by an immunohistochemical assay using a tissue microarray procedure in pancreatic cancer tissues and cells with different degrees of differentiation. A loss‑of‑function approach was used to examine the effects of a lentivirus‑mediated OCT4 small hairpin RNA vector on biological behaviors, including cell proliferative activity and invasive potential. The results demonstrated that the expression levels of OCT4 protein in cancer tissues were significantly elevated compared with those in adjacent non‑cancerous tissues (65.0 vs. 42.5%; P=0.005), which was correlated with tumor differentiation (P=0.008). The knockdown of OCT4 inhibited the proliferation and invasion of pancreatic cancer cells (Panc‑1) expressing high levels of OCT4, accompanied with decreased expression of AKT, proliferating cell nuclear antigen (PCNA) and matrix metalloproteinase‑2 (MMP‑2). In conclusion, the present study reveals that the increased expression of OCT4 is correlated with the differentiation of pancreatic cancer, while knockdown of OCT4 suppresses the growth and invasion of pancreatic cancer cells through inhibition of AKT pathway‑mediated PCNA and MMP‑2 expression, suggesting that OCT4 might serve as a potential therapeutic target for the treatment of pancreatic cancer.

Identification of drug candidate against prostate cancer from the aspect of somatic cell reprogramming

Considering the similarities between the transcriptional programming involved in cancer progression and somatic cell reprogramming, we tried to identify drugs that would be effective against malignant cancers. We used the early transposon Oct4 and Sox2 enhancer (EOS) system to select human prostate cancer (PCA) cells expressing high levels of OCT4. Patients with metastatic castration-resistant PCA that does not respond to treatment with docetaxel have few therapeutic options. The OCT4-expressing PCA cells selected using the EOS system showed increased tumorigenicity and high resistance to docetaxel, both in vitro and in vivo. By using their gene expression data, expression signature-based prediction for compound candidates identified an antiviral drug, ribavirin, as a conversion modulator from drug resistance to sensitivity. Treatment of PCA cells with ribavirin decreased their resistance against treatment with docetaxel. This indicated that ribavirin reversed the gene expression, including that of humoral factors, in the OCT4-expressing PCA cells selected using the EOS system. Thereby, ribavirin increased the efficacy of docetaxel for cancer cells. We propose a novel cell reprogramming approach, named drug efficacy reprogramming, as a new model for identifying candidate antitumor drugs.