Computational Drug Repositioning for Gastric Cancer using Reversal Gene Expression Profiles

Circ_0004592: An auxiliary diagnostic biomarker for gastric cancer

BACKGROUND

Gastric cancer (GC) has a high mortality rate, and robust diagnostic biomarkers are currently lacking. However, the clinical relevance of circular RNAs (circRNAs) as GC biomarkers remains largely unexplored.

AIM

To evaluate the potential of novel circRNA circ_0004592 in the early screening and prognosis of GC.

METHODS

High-throughput sequencing of circRNAs was performed to screen for potential target molecules. Circ_0004592 expression was examined in GC tissues, cells, and plasma. Plasma samples were collected from healthy subjects’ patients, as well as from patients with benign lesions, precancerous lesions, and GC, whereafter the diagnostic accuracy of circ_0004592 was evaluated. The correlation between circ_0004592 levels in plasma and clinicopathological data of patients with GC was further analyzed.

RESULTS

Circ_0004592 was upregulated in both the tissue and plasma of patients with GC. Further, circ_0004592 expression was higher in patients with precancerous lesions than in healthy controls while being highest in patients with GC. In the same patient, the postoperative plasma level of circ_0004592 was lower than that in the preoperative period. Moreover, circ_0004592 level was significantly correlated with tumor differentiation, tumor depth, and lymph node metastasis. The area under the curve (AUC) of plasma circ_0004592 exhibited high sensitivity and specificity for differentiating patients with GC from healthy donors. Diagnosis based on circ_0004592, carcinoembryonic antigen, and cancer antigen 199 achieved a superior AUC and was highly sensitive.

CONCLUSION

Plasma circ_0004592 may represent a potential non-invasive auxiliary diagnostic biomarker for patients with GC.

Circ_0004592: An auxiliary diagnostic biomarker for gastric cancer

BACKGROUND

Gastric cancer (GC) has a high mortality rate, and robust diagnostic biomarkers are currently lacking. However, the clinical relevance of circular RNAs (circRNAs) as GC biomarkers remains largely unexplored.

AIM

To evaluate the potential of novel circRNA circ_0004592 in the early screening and prognosis of GC.

METHODS

High-throughput sequencing of circRNAs was performed to screen for potential target molecules. Circ_0004592 expression was examined in GC tissues, cells, and plasma. Plasma samples were collected from healthy subjects' patients, as well as from patients with benign lesions, precancerous lesions, and GC, whereafter the diagnostic accuracy of circ_0004592 was evaluated. The correlation between circ_0004592 levels in plasma and clinicopathological data of patients with GC was further analyzed.

RESULTS

Circ_0004592 was upregulated in both the tissue and plasma of patients with GC. Further, circ_0004592 expression was higher in patients with precancerous lesions than in healthy controls while being highest in patients with GC. In the same patient, the postoperative plasma level of circ_0004592 was lower than that in the preoperative period. Moreover, circ_0004592 level was significantly correlated with tumor differentiation, tumor depth, and lymph node metastasis. The area under the curve (AUC) of plasma circ_0004592 exhibited high sensitivity and specificity for differentiating patients with GC from healthy donors. Diagnosis based on circ_0004592, carcinoembryonic antigen, and cancer antigen 199 achieved a superior AUC and was highly sensitive.

CONCLUSION

Plasma circ_0004592 may represent a potential non-invasive auxiliary diagnostic biomarker for patients with GC.

Relationship between drug targets and drug-signature networks: a network-based genome-wide landscape

Drugs produce pharmaceutical and adverse effects that arise from the complex relationship between drug targets and signatures; by considering such relationships, we can begin to understand the cellular mechanisms of drugs. In this study, we selected 463 genes from the DSigDB database corresponding to targets and signatures for 382 FDA-approved drugs with both protein binding information for a drug-target score (KDTN, i.e., the degree to which the protein encoded by the gene binds to a number of drugs) and microarray signature information for a drug-sensitive score (KDSN, i.e., the degree to which gene expression is stimulated by the drug). Accordingly, we constructed two drug-gene bipartite network models, a drug-target network and drug-signature network, which were merged into a multidimensional model. Analysis revealed that the KDTN and KDSN were in mutually exclusive and reciprocal relationships in terms of their biological network structure and gene function. A symmetric balance between the KDTN and KDSN of genes facilitates the possibility of therapeutic drug effects in whole genome. These results provide new insights into the relationship between drugs and genes, specifically drug targets and drug signatures.

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.

PLOD3 regulates the expression of YAP1 to affect the progression of non-small cell lung cancer via the PKCδ/CDK1/LIMD1 signaling pathway

Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD3) is a crucial oncogene in human lung cancer, whereas protein kinase C δ (PKCδ) acts as a tumor suppressor. In this study, we aimed to explore the regulation by PLOD3 on the expression of YAP1 to affect the progression of non-small cell lung cancer (NSCLC) via the PKCδ/CDK1/LIMD1 signaling pathway. We found that PLOD3, CDK1, and YAP1 were highly expressed, while LIMD1 was poorly expressed in NSCLC tissues. Mechanistic investigation demonstrated that silencing PLOD3 promoted the cleavage of PKCδ in a caspase-dependent manner to generate a catalytically active fragment cleaved PKCδ, enhanced phosphorylation levels of CDK1, and LIMD1 but suppressed nuclear translocation of YAP1. Furthermore, functional experimental results suggested that loss of PLOD3 led to increased phosphorylation levels of CDK1 and LIMD1 and downregulated YAP1, thereby suppressing the proliferation, colony formation, cell cycle entry, and resistance to apoptosis of NSCLC cells in vitro and inhibiting tumor growth in vivo. Taken together, these results show that PLOD3 silencing activates the PKCδ/CDK1/LIMD1 signaling pathway to prevent the progression of NSCLC, thus providing novel insight into molecular targets for treating NSCLC.

A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results.

Abstract

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Identification of Nifurtimox and Chrysin as Anti-Influenza Virus Agents by Clinical Transcriptome Signature Reversion

The rapid development in the field of transcriptomics provides remarkable biomedical insights for drug discovery. In this study, a transcriptome signature reversal approach was conducted to identify the agents against influenza A virus (IAV) infection through dissecting gene expression changes in response to disease or compounds’ perturbations. Two compounds, nifurtimox and chrysin, were identified by a modified Kolmogorov–Smirnov test statistic based on the transcriptional signatures from 81 IAV-infected patients and the gene expression profiles of 1309 compounds. Their activities were verified in vitro with half maximal effective concentrations (EC₅₀s) from 9.1 to 19.1 μM against H1N1 or H3N2. It also suggested that the two compounds interfered with multiple sessions in IAV infection by reversing the expression of 28 IAV informative genes. Through network-based analysis of the 28 reversed IAV informative genes, a strong synergistic effect of the two compounds was revealed, which was confirmed in vitro. By using the transcriptome signature reversion (TSR) on clinical datasets, this study provides an efficient scheme for the discovery of drugs targeting multiple host factors regarding clinical signs and symptoms, which may also confer an opportunity for decelerating drug-resistant variant emergence.

DDIT: An Online Predictor for Multiple Clinical Phenotypic Drug-Disease Associations

Background: Drug repurposing provides an effective method for high-speed, low-risk drug development. Clinical phenotype-based screening exceeded target-based approaches in discovering first-in-class small-molecule drugs. However, most of these approaches predict only binary phenotypic associations between drugs and diseases; the types of drug and diseases have not been well exploited. Principally, the clinical phenotypes of a known drug can be divided into indications (Is), side effects (SEs), and contraindications (CIs). Incorporating these different clinical phenotypes of drug–disease associations (DDAs) can improve the prediction accuracy of the DDAs.

Methods: We develop Drug Disease Interaction Type (DDIT), a user-friendly online predictor that supports drug repositioning by submitting known Is, SEs, and CIs for a target drug of interest. The dataset for Is, SEs, and CIs was extracted from PREDICT, SIDER, and MED-RT, respectively. To unify the names of the drugs and diseases, we mapped their names to the Unified Medical Language System (UMLS) ontology using Rest API. We then integrated multiple clinical phenotypes into a conditional restricted Boltzmann machine (RBM) enabling the identification of different phenotypes of drug–disease associations, including the prediction of as yet unknown DDAs in the input.

Results: By 10-fold cross-validation, we demonstrate that DDIT can effectively capture the latent features of the drug–disease association network and represents over 0.217 and over 0.072 improvement in AUC and AUPR, respectively, for predicting the clinical phenotypes of DDAs compared with the classic K-nearest neighbors method (KNN, including drug-based KNN and disease-based KNN), Random Forest, and XGBoost. By conducting leave-one-drug-class-out cross-validation, the AUC and AUPR of DDIT demonstrated an improvement of 0.135 in AUC and 0.075 in AUPR compared to any of the other four methods. Within the top 10 predicted indications, side effects, and contraindications, 7/10, 9/10, and 9/10 hit known drug–disease associations. Overall, DDIT is a useful tool for predicting multiple clinical phenotypic types of drug–disease associations.

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

Background

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

Methods

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

Results

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

Conclusions

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

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

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

Human Protein Complex-Based Drug Signatures for Personalized Cancer Medicine

Disease signature-based drug repositioning approaches typically first identify a disease signature from gene expression profiles of disease samples to represent a particular disease. Then such a disease signature is connected with the drug-induced gene expression profiles to find potential drugs for the particular disease. In order to obtain reliable disease signatures, the size of disease samples should be large enough, which is not always a single case in practice, especially for personalized medicine. On the other hand, the sample sizes of drug-induced gene expression profiles are generally large. In this study, we propose a new drug repositioning approach (HDgS), in which the drug signature is first identified from drug-induced gene expression profiles, and then connected to the gene expression profiles of disease samples to find the potential drugs for patients. In order to take the dependencies among genes into account, the human protein complexes (HPC) are used to define the drug signature. The proposed HDgS is applied to the drug-induced gene expression profiles in LINCS and several types of cancer samples. The results indicate that the HPC-based drug signature can effectively find drug candidates for patients and that the proposed HDgS can be applied for personalized medicine with even one patient sample.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

A New Computational Approach to Evaluating Systemic Gene–Gene Interactions in a Pathway Affected by Drug LY294002

In this study, we investigate how drugs systemically affect genes via pathways by integrating information from interactions between chemical compounds and molecular expression datasets, and from pathway information such as gene sets using mathematical models. First, we adopt drug-induced gene expression datasets; then, employ gene set enrichment analysis tools for selecting candidate enrichment pathways; and lastly, implement the inverse algorithm package for identifying gene–gene regulatory networks in a pathway. We tested LY294002-induced datasets of the MCF7 breast cancer cell lines, and found a CELL CYCLE pathway with 101 genes, ERBB signaling pathway consisting of 82 genes, and MTOR pathway consisting of 45 genes. We consider two interactions: quantity strength depending on number of interactions, and quality strength depending on weight of interaction as positive (+) and negative (−) interactions. Our methods revealed ANAPC1-CDK6 (−0.412) and ORC2L- CHEK1(0.951) for the CELL CYCLE pathway; INS-RPS6 (−3.125) and PRKAA2-PRKAA2 (+1.319) for the MTOR pathway; and CBLB-RPS6KB1 (−0.141), RPS6KB1-CBLC (+0.238) for the ERBB signaling pathway to be top quality interactions. Top quantity interactions discovered include 12; the CDC (−,+) gene family for the CELL CYCLE pathway, 20; PIK3 (−), 23; PIK3CG (+) for the MTOR pathway, 11; PAK (−), 10; PIK3 (+) for the ERBB signaling pathway.

COL4A family: potential prognostic biomarkers and therapeutic targets for gastric cancer

Background

The type IV collagen alpha chain (COL4A) family is a major component of the basement membrane (BM) that has recently been found to be involved in tumor angiogenesis and progression. However, the expression levels and the exact roles of distinct COL4A family members in gastric cancer (GC) have not been completely understood.

Methods

Here, the expression levels of COL4As in GC and normal gastric tissues were calculated by using TCGA datasets and the predicted prognostic values by the GEPIA tool. Furthermore, the cBioPortal and Metascape tools were integrated to analyze the genetic alterations, correlations and potential functions of COL4As, and their frequently altered neighboring genes in GC.

Results

Notably, the expression levels of COL4A1/2/4 in GC were higher to those in normal gastric tissues, while the expression levels of COL4A3/5/6 were lower in GC than normal. Survival analysis revealed that lower expression levels of COL4A1/5 led to higher overall survival (OS) rate. Multivariate analysis using the Cox proportional-hazards model indicated that age, gender, pathological grade, metastasis and COL4A5 expression, are independent prognostic factors for OS. However, TNM stage, lymph node metastasis, Lauren’s classification, COL4A1-4 and COL4A6 were associated with poor OS but not independent prognostic factors. Function-enriched analysis of COL4As and their frequently altered neighboring genes was involved in tumor proliferation and metastasis in GC.

Conclusions

These results implied that COL4A1/2 were potential therapeutic targets for GC. COL4A3/4/6 might have an impact on gastric carcinogenesis and subsequent progression, whereas COL4A5 was an independent prognostic marker for GC.

Universal Nature of Drug Treatment Responses in Drug-Tissue-Wide Model-Animal Experiments Using Tensor Decomposition-Based Unsupervised Feature Extraction

Gene expression profiles of tissues treated with drugs have recently been used to infer clinical outcomes. Although this method is often successful from the application point of view, gene expression altered by drugs is rarely analyzed in detail, because of the extremely large number of genes involved. Here, we applied tensor decomposition (TD)-based unsupervised feature extraction (FE) to the gene expression profiles of 24 mouse tissues treated with 15 drugs. TD-based unsupervised FE enabled identification of the common effects of 15 drugs including an interesting universal feature: these drugs affect genes in a gene-group-wide manner and were dependent on three tissue types (neuronal, muscular, and gastroenterological). For each tissue group, TD-based unsupervised FE enabled identification of a few tens to a few hundreds of genes affected by the drug treatment. These genes are distinctly expressed between drug treatments and controls as well as between tissues in individual tissue groups and other tissues. We also validated the assignment of genes to individual tissue groups using multiple enrichment analyses. We conclude that TD-based unsupervised FE is a promising method for integrated analysis of gene expression profiles from multiple tissues treated with multiple drugs in a completely unsupervised manner.

Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning

BACKGROUND

Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters.

CONCLUSIONS

The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.

Transcriptome-Guided Drug Repositioning

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies "drug target" spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs' action for approved diseases (ulcerative colitis and Crohn's disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn's disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.