A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Tailored modulation of S100A1 and RASSF8 expression by butanediamide augments healing of rotator cuff tears

Objectives

This investigation sought to elucidate promising treatment modalities for rotator cuff tears (RCTs) by delving into the molecular machinations instigating the affliction. The focus was on differentially expressed genes (DEGs) linked to RCTs, and the exploration of their roles and operative pathways.

Methods

DEGs were discerned from GEO datasets, followed by the establishment of a protein-protein interaction (PPI) network. Subsequently, the network's core genes were determined employing a Venn diagram. Enrichment analysis facilitated the unveiling of the biological roles and signal transduction pathways of these pivotal genes, thus shedding light on molecular strategies for RCT-targeted treatment. The Discovery Studio 2019 software was employed to sift through FDA-sanctioned drugs targeting these essential proteins. Moreover, the efficaciousness of these FDA-endorsed drugs vis-à-vis RCTs was corroborated by the construction of an in vivo animal model of the injury and the in vitro cultivation of tendon-derived stem cells.

Results

Bioinformatics outcomes revealed a significant overexpression of S100A1 and RASSF8 in RCT patients. The FDA drug repository indicated that Butanediamide has a selective affinity for S100A1 and RASSF8. Subsequent in vivo and in vitro experimentation demonstrated that Butanediamide could suppress S100A1 expression and bolster TDSC proliferation, thereby facilitating RCT healing.

Conclusions

S100A1 and RASSF8 are pivotal genes implicated in RCTs, and their roles have been elucidated. The FDA-approved compound, Butanediamide, may represent a prospective therapeutic agent for RCTs by targeting S100A1 and RASSF8, respectively.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

NMSDR: Drug repurposing approach based on transcriptome data and network module similarity

Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.

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.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Drug repurposing in silico screening platforms

Over the last decade, for the first time, substantial efforts have been directed at the development of dedicated in silico platforms for drug repurposing, including initiatives targeting cancers and conditions as diverse as cryptosporidiosis, dengue, dental caries, diabetes, herpes, lupus, malaria, tuberculosis and Covid-19 related respiratory disease. This review outlines some of the exciting advances in the specific applications of in silico approaches to the challenge of drug repurposing and focuses particularly on where these efforts have resulted in the development of generic platform technologies of broad value to researchers involved in programmatic drug repurposing work. Recent advances in molecular docking methodologies and validation approaches, and their combination with machine learning or deep learning approaches are continually enhancing the precision of repurposing efforts. The meaningful integration of better understanding of molecular mechanisms with molecular pathway data and knowledge of disease networks is widening the scope for discovery of repurposing opportunities. The power of Artificial Intelligence is being gainfully exploited to advance progress in an integrated science that extends from the sub-atomic to the whole system level. There are many promising emerging developments but there are remaining challenges to be overcome in the successful integration of the new advances in useful platforms. In conclusion, the essential component requirements for development of powerful and well optimised drug repurposing screening platforms are discussed.

EGFR Ligand Clustering on E2 Bionanoparticles for Targeted Delivery of Chemotherapeutics to Breast Cancer Cells

Naturally occurring protein nanocages are promising drug carriers because of their uniform size and biocompatibility. Engineering efforts have enhanced the delivery properties of nanocages, but cell specificity and high drug loading remain major challenges. Herein, we fused the SpyTag peptide to the surface of engineered E2 nanocages to enable tunable nanocage decoration and effective E2 cell targeting using a variety of SpyCatcher (SC) fusion proteins. Additionally, the core of the E2 nanocage incorporated four phenylalanine mutations previously shown to allow hydrophobic loading of doxorubicin and pH-responsive release in acidic environments. We functionalized the surface of the nanocage with a highly cell-specific epidermal growth factor receptor (EGFR)-targeting protein conjugate, 4GE11-mCherry-SC, developed previously in our laboratories by employing unnatural amino acid (UAA) protein engineering chemistries. Herein, we demonstrated the benefits of this engineered protein nanocage construct for efficient drug loading, with a straightforward method for removal of the unloaded drug through elastin-like polypeptide-mediated inverse transition cycling. Additionally, we demonstrated approximately 3-fold higher doxorubicin internalization in inflammatory breast cancer cells compared to healthy breast epithelial cells, leading to targeted cell death at concentrations below the IC50 of free doxorubicin. Collectively, these results demonstrated the versatility of our UAA-based EGFR-targeting protein construct to deliver a variety of cargoes efficiently, including engineered E2 nanocages capable of site-specific functionalization and doxorubicin loading.

The Discovery of New Drug-Target Interactions for Breast Cancer Treatment

Drug–target interaction (DTIs) prediction plays a vital role in probing new targets for breast cancer research. Considering the multifaceted challenges associated with experimental methods identifying DTIs, the in silico prediction of such interactions merits exploration. In this study, we develop a feature-based method to infer unknown DTIs, called PsePDC-DTIs, which fuses information regarding protein sequences extracted by pseudo-position specific scoring matrix (PsePSSM), detrended cross-correlation analysis coefficient (DCCA coefficient), and an FP2 format molecular fingerprint descriptor of drug compounds. In addition, the synthetic minority oversampling technique (SMOTE) is employed for dealing with the imbalanced data after Lasso dimensionality reduction. Then, the processed feature vectors are put into a random forest classifier to perform DTIs predictions on four gold standard datasets, including nuclear receptors (NR), G-protein-coupled receptors (GPCR), ion channels (IC), and enzymes (E). Furthermore, we explore new targets for breast cancer treatment using its risk genes identified from large-scale genome-wide genetic studies using PsePDC-DTIs. Through five-fold cross-validation, the average values of accuracy in NR, GPCR, IC, and E datasets are 95.28%, 96.19%, 96.74%, and 98.22%, respectively. The PsePDC-DTIs model provides us with 10 potential DTIs for breast cancer treatment, among which erlotinib (DB00530) and FGFR2 (hsa2263), caffeine (DB00201) and KCNN4 (hsa3783), as well as afatinib (DB08916) and FGFR2 (hsa2263) are found with direct or inferred evidence. The PsePDC-DTIs model has achieved good prediction results, establishing the validity and superiority of the proposed method.

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs' mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs' effect on a given patient.

Modularity in Biological Networks

Network modeling, from the ecological to the molecular scale has become an essential tool for studying the structure, dynamics and complex behavior of living systems. Graph representations of the relationships between biological components open up a wide variety of methods for discovering the mechanistic and functional properties of biological systems. Many biological networks are organized into a modular structure, so methods to discover such modules are essential if we are to understand the biological system as a whole. However, most of the methods used in biology to this end, have a limited applicability, as they are very specific to the system they were developed for. Conversely, from the statistical physics and network science perspective, graph modularity has been theoretically studied and several methods of a very general nature have been developed. It is our perspective that in particular for the modularity detection problem, biology and theoretical physics/network science are less connected than they should. The central goal of this review is to provide the necessary background and present the most applicable and pertinent methods for community detection in a way that motivates their further usage in biological research.

Drug repurposing: Iron in the fire for older drugs

Repositioning or "repurposing" of existing therapies for indications of alternative disease is an attractive approach that can generate lower costs and require a shorter approval time than developing a de novo drug. The development of experimental drugs is time-consuming, expensive, and limited to a fairly small number of targets. The incorporation of separate and complementary data should be used, as each type of data set exposes a specific feature of organism knowledge Drug repurposing opportunities are often focused on sporadic findings or on time-consuming pre-clinical drug tests which are often not guided by hypothesis. In comparison, repurposing in-silico drugs is a new, hypothesis-driven method that takes advantage of big-data use. Nonetheless, the widespread use of omics technology, enhanced data storage, data sense, machine learning algorithms, and computational modeling all give unparalleled knowledge of the methods of action of biological processes and drugs, providing wide availability, for both disease-related data and drug-related data. This review has taken an in-depth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning.

A Machine Learning-Based Biological Drug-Target Interaction Prediction Method for a Tripartite Heterogeneous Network

Drug repositioning is the identification of interactions between drugs and target proteins in pharmaceutical sciences. Traditional large-scale validation through chemical experiments is time-consuming and expensive, while drug repositioning can drastically decrease the cost and duration taken by traditional drug development. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning methods have been used to systematically identify potential drug-target interactions. Some of them are based on a particular class of machine learning algorithms called kernel methods. In this paper, we propose a new machine learning prediction method combining multiple kernels into a tripartite heterogeneous drug-target-disease interaction spaces in order to integrate multiple sources of biological information simultaneously. This novel network algorithm extends the traditional drug-target interaction bipartite graph to the third disease layer. Meanwhile, Gaussian kernel functions on heterogeneous networks and the regularized least square method of the Kronecker product are used to predict new drug-target interactions. The values of AUPR (area under the precision-recall curve) and AUC (the area under the receiver operating characteristic curve) of the proposed algorithm are significantly improved. Especially, the AUC values are improved to 0.99, 0.99, 0.97, and 0.96 on four benchmark data sets. These experimental results substantiate that the network topology can be used for predicting drug-target interactions.

Novel therapeutic compounds for prostate adenocarcinoma treatment: An analysis using bioinformatic approaches and the CMap database

INTRODUCTION

Prostate adenocarcinoma is the most frequently diagnosed malignancy, particularly for people >70 years old. The main challenge in the treatment of advanced neoplasm is bone metastasis and therapeutic resistance for known oncology drugs. Novel treatment methods to prolong the survival time and improve the life quality of these specific patients are required. The present study attempted to screen potential therapeutic compounds for the tumor through bioinformatics approaches, in order to provide conceptual treatment for this malignant disease.

METHODS

Differentially expressed genes were obtained from the Gene Expression Omnibus database and submitted into the Connectivity Map database for the detection of potentially associated compounds. Target genes were extracted from the search results. Functional annotation and pathway enrichment were performed for the confirmation. Survival analysis was used to measure potential therapeutic effects.

RESULTS

It was revealed that 3 compounds (vanoxerine, tolnaftate, and gabexate) may help to prolong the disease-free survival time from tumor metastasis of patients with the tumor. A total of 6 genes [also-keto reductase family 1 member C3 (AKR1C3), collagen type III α 1 chain (COL3A1), lipoprotein lipase (LPL), glucuronidase, β pseudogene 11 (GUSBP11), apolipoprotein E (APOE), and collagen type I α 1 chain (COL1A1)] were identified to be the potential therapeutic targets for the aforementioned compounds.

CONCLUSION

In the present study, it was speculated that 3 compounds may function as the potential therapeutic drugs of bone metastatic prostate adenocarcinoma; however, further studies verifying vitro and in vivo are necessary.

Mutational profiling of micro-dissected pre-malignant lesions from archived specimens

BACKGROUND

Systematic cancer screening has led to the increased detection of pre-malignant lesions (PMLs). The absence of reliable prognostic markers has led mostly to over treatment resulting in potentially unnecessary stress, or insufficient treatment and avoidable progression. Importantly, most mutational profiling studies have relied on PML synchronous to invasive cancer, or performed in patients without outcome information, hence limiting their utility for biomarker discovery. The limitations in comprehensive mutational profiling of PMLs are in large part due to the significant technical and methodological challenges: most PML specimens are small, fixed in formalin and paraffin embedded (FFPE) and lack matching normal DNA.

METHODS

Using test DNA from a highly degraded FFPE specimen, multiple targeted sequencing approaches were evaluated, varying DNA input amount (3-200 ng), library preparation strategy (BE: Blunt-End, SS: Single-Strand, AT: A-Tailing) and target size (whole exome vs. cancer gene panel). Variants in high-input DNA from FFPE and mirrored frozen specimens were used for PML-specific variant calling training and testing, respectively. The resulting approach was applied to profile and compare multiple regions micro-dissected (mean area 5 mm²) from 3 breast ductal carcinoma in situ (DCIS).

RESULTS

Using low-input FFPE DNA, BE and SS libraries resulted in 4.9 and 3.7 increase over AT libraries in the fraction of whole exome covered at 20x (BE:87%, SS:63%, AT:17%). Compared to high-confidence somatic mutations from frozen specimens, PML-specific variant filtering increased recall (BE:85%, SS:80%, AT:75%) and precision (BE:93%, SS:91%, AT:84%) to levels expected from sampling variation. Copy number alterations were consistent across all tested approaches and only impacted by the design of the capture probe-set. Applied to DNA extracted from 9 micro-dissected regions (8 PML, 1 normal epithelium), the approach achieved comparable performance, illustrated the data adequacy to identify candidate driver events (GATA3 mutations, ERBB2 or FGFR1 gains, TP53 loss) and measure intra-lesion genetic heterogeneity.

CONCLUSION

Alternate experimental and analytical strategies increased the accuracy of DNA sequencing from archived micro-dissected PML regions, supporting the deeper molecular characterization of early cancer lesions and achieving a critical milestone in the development of biology-informed prognostic markers and precision chemo-prevention strategies.

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Background

It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time.

Methods

In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.

Results

The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.

Conclusion

Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing

Motivation

Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures.

Results

The novel approach Dr Insight implements a frame-breaking statistical model for the ‘hand-shake’ between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug–target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks.

Availability and implementation

Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Prostate cancer screening research can benefit from network medicine: an emerging awareness

Up to date, screening for prostate cancer (PCa) remains one of the most appealing but also a very controversial topics in the urological community. PCa is the second most common cancer in men worldwide and it is universally acknowledged as a complex disease, with a multi-factorial etiology. The pathway of PCa diagnosis has changed dramatically in the last few years, with the multiparametric magnetic resonance (mpMRI) playing a starring role with the introduction of the “MRI Pathway”. In this scenario the basic tenet of network medicine (NM) that sees the disease as perturbation of a network of interconnected molecules and pathways, seems to fit perfectly with the challenges that PCa early detection must face to advance towards a more reliable technique. Integration of tests on body fluids, tissue samples, grading/staging classification, physiological parameters, MR multiparametric imaging and molecular profiling technologies must be integrated in a broader vision of “disease” and its complexity with a focus on early signs. PCa screening research can greatly benefit from NM vision since it provides a sound interpretation of data and a common language, facilitating exchange of ideas between clinicians and data analysts for exploring new research pathways in a rational, highly reliable, and reproducible way.

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.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

A review of network-based approaches to drug repositioning

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Elucidating the transcriptional program of feline injection-site sarcoma using a cross-species mRNA-sequencing approach

BACKGROUND

Feline injection-site sarcoma (FISS), an aggressive iatrogenic subcutaneous malignancy, is challenging to manage clinically and little is known about the molecular basis of its pathogenesis. Tumor transcriptome profiling has proved valuable for gaining insights into the molecular basis of cancers and for identifying new therapeutic targets. Here, we report the first study of the FISS transcriptome and the first cross-species comparison of the FISS transcriptome with those of anatomically similar soft-tissue sarcomas in dogs and humans.

METHODS

Using high-throughput short-read paired-end sequencing, we comparatively profiled FISS tumors vs. normal tissue samples as well as cultured FISS-derived cell lines vs. skin-derived fibroblasts. We analyzed the mRNA-seq data to compare cancer/normal gene expression level, identify biological processes and molecular pathways that are associated with the pathogenesis of FISS, and identify multimegabase genomic regions of potential somatic copy number alteration (SCNA) in FISS. We additionally conducted cross-species analyses to compare the transcriptome of FISS to those of soft-tissue sarcomas in dogs and humans, at the level of cancer/normal gene expression ratios.

RESULTS

We found: (1) substantial differential expression biases in feline orthologs of human oncogenes and tumor suppressor genes suggesting conserved functions in FISS; (2) a genomic region with recurrent SCNA in human sarcomas that is syntenic to a feline genomic region of probable SCNA in FISS; and (3) significant overlap of the pattern of transcriptional alterations in FISS with the patterns of transcriptional alterations in soft-tissue sarcomas in humans and in dogs. We demonstrated that a protein, BarH-like homeobox 1 (BARX1), has increased expression in FISS cells at the protein level. We identified 11 drugs and four target proteins as potential new therapies for FISS, and validated that one of them (GSK-1059615) inhibits growth of FISS-derived cells in vitro.

CONCLUSIONS

(1) Window-based analysis of mRNA-seq data can uncover SCNAs. (2) The transcriptome of FISS-derived cells is highly consistent with that of FISS tumors. (3) FISS is highly similar to soft-tissue sarcomas in dogs and humans, at the level of gene expression. This work underscores the potential utility of comparative oncology in improving understanding and treatment of FISS.

Discovery of therapeutic agents for prostate cancer using genome-scale metabolic modeling and drug repositioning

BACKGROUND

Genome-scale metabolic models (GEMs) offer insights into cancer metabolism and have been used to identify potential biomarkers and drug targets. Drug repositioning is a time- and cost-effective method of drug discovery that can be applied together with GEMs for effective cancer treatment.

METHODS

In this study, we reconstruct a prostate cancer (PRAD)-specific GEM for exploring prostate cancer metabolism and also repurposing new therapeutic agents that can be used in development of effective cancer treatment. We integrate global gene expression profiling of cell lines with >1000 different drugs through the use of prostate cancer GEM and predict possible drug-gene interactions.

FINDINGS

We identify the key reactions with altered fluxes based on the gene expression changes and predict the potential drug effect in prostate cancer treatment. We find that sulfamethoxypyridazine, azlocillin, hydroflumethiazide, and ifenprodil can be repurposed for the treatment of prostate cancer based on an in silico cell viability assay. Finally, we validate the effect of ifenprodil using an in vitro cell assay and show its inhibitory effect on a prostate cancer cell line.

INTERPRETATION

Our approach demonstate how GEMs can be used to predict therapeutic agents for cancer treatment based on drug repositioning. Besides, it paved a way and shed a light on the applicability of computational models to real-world biomedical or pharmaceutical problems.

Network-Based Drug Repositioning: Approaches, Resources, and Research Directions

The wealth of knowledge and omic data available in drug research allowed the rising of several computational methods in drug discovery field yielding a novel and exciting application called drug repositioning. Several computational methods try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter we present an in-depth review of data resources and computational models for drug repositioning.

Mebendazole and radiation in combination increase survival through anticancer mechanisms in an intracranial rodent model of malignant meningioma

PURPOSE

Meningiomas are a frequent tumor of the central nervous system. Although mostly benign, approximately 5% present as atypical or malignant tumors. Treatments for atypical meningiomas include gross total resection and radiotherapy, but about 33% of patients have recurrent tumors, sometimes as a higher grade. Recently, the brain penetrant anthelmintic drug, mebendazole, has shown promise as an anticancer agent in rodent models of glioblastoma and medulloblastoma.

METHODS

The half maximal inhibitory concentration (IC₅₀) effect on colony formation, cell proliferation, and caspase-3/7 markers of apoptosis of mebendazole with and without radiation was measured in vitro. Mice intracranially implanted with KT21MG1 human meningioma were administered mebendazole alone or in combination with radiation. Survival benefit was evaluated, while tumors were investigated by immunohistochemical staining for apoptosis, cell proliferation, and vascular density.

RESULTS

In vitro experiments on meningioma cell lines showed the IC₅₀ for mebendazole in the range of 0.26-0.42 µM. Mebendazole alone induced cytotoxicity, however the combination had a greater reduction in colony formation and resulted in higher levels of cleaved caspase-3. The in vivo study showed both, mebendazole alone and the combination, to have a survival benefit with an increase in apoptosis, and decreases in tumor cell and vascular proliferation.

CONCLUSION

These preclinical findings indicate that mebendazole alone or in combination with radiation can be considered for the treatment of malignant meningioma. The mechanism of action for this combination may include an increase in apoptosis, a reduction in proliferation and angiogenesis, or a combination of these effects.

In silico screening for ERα down modulators identifies thioridazine as an anti-proliferative agent in primary, 4OH-tamoxifen-resistant and Y537S ERα-expressing breast cancer cells

PURPOSE

Most breast cancers (BCs) express estrogen receptor α (ERα) and are treated with the endocrine therapy (ET) drugs 4OH-tamoxifen (Tam) and fulvestrant (ICI 182,780; ICI). Unfortunately, a high fraction of ET treated women relapses and becomes resistant to ET. Therefore, additional anti-BC drugs are needed. Recently, we proposed that the identification of novel anti-BC drugs can be achieved using modulation of the intracellular ERα content in BC cells as a pharmacological target. Here, we searched for Food and Drug Administration (FDA)-approved drugs that potentially modify the ERα content in BC cells.

METHODS

We screened in silico more than 60,000 compounds to identify FDA-approved drugs with a gene signature similar to that of ICI. We identified mitoxantrone and thioridazine and tested them in primary, Tam-resistant and genome-edited Y537S ERα-expressing BC cells.

RESULTS

We found that mitoxantrone and thioridazine induced ERα downmodulation and prevented MCF-7 BC cell proliferation. Interestingly, while mitoxantrone was found to be toxic for normal breast epithelial cells, thioridazine showed a preferential activity towards BC cells. Thioridazine also reduced the ERα content and prevented cell proliferation in primary, Tam-resistant and genome-edited Y537S ERα expressing BC cells.

CONCLUSIONS

We suggest that modulation of the intracellular ERα concentration in BC cells can be exploited in in silico screens to identify anti-BC drugs and uncover a re-purposing opportunity for thioridazine in the treatment of primary and metastatic ET resistant BCs.

Network-based machine learning and graph theory algorithms for precision oncology

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology

Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring "big data" applications in pediatric oncology. Computational strategies derived from big data science-network- and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which "big data" and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.