PS4DR: a multimodal workflow for identification and prioritization of drugs based on pathway signatures

Background During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

[1]  Sridhar Ramaswamy,et al.  Landscape of Targeted Anti-Cancer Drug Synergies in Melanoma Identifies a Novel BRAF-VEGFR/PDGFR Combination Treatment , 2015, PloS one.

[2]  L. Cardon,et al.  Use of genome-wide association studies for drug repositioning , 2012, Nature Biotechnology.

[3]  Andrew D. Rouillard,et al.  The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins , 2016, Database J. Biol. Databases Curation.

[4]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[5]  Matthew R. Nelson,et al.  STOPGAP: a database for systematic target opportunity assessment by genetic association predictions , 2017, Bioinform..

[6]  Jennifer D. Hintzsche,et al.  Kinase gene fusions in defined subsets of melanoma , 2017, Pigment cell & melanoma research.

[7]  Gautier Koscielny,et al.  Open Targets: a platform for therapeutic target identification and validation , 2016, Nucleic Acids Res..

[8]  Jayanta Debnath,et al.  Inhibition of mTOR by Rapamycin Abolishes Cognitive Deficits and Reduces Amyloid-β Levels in a Mouse Model of Alzheimer's Disease , 2010, PloS one.

[9]  Zhiyong Lu,et al.  A survey of current trends in computational drug repositioning , 2016, Briefings Bioinform..

[10]  Peter Schmid,et al.  A study of vistusertib in combination with selumetinib in patients with advanced cancers: TORCMEK phase Ib results. , 2017 .

[11]  Alexander A. Morgan,et al.  Discovery and Preclinical Validation of Drug Indications Using Compendia of Public Gene Expression Data , 2011, Science Translational Medicine.

[12]  Iwei Yeh,et al.  Kinase fusions are frequent in Spitz tumours and spitzoid melanomas , 2014, Nature Communications.

[13]  Lin He,et al.  DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome , 2011, Nucleic Acids Res..

[14]  Zhen Hua Wu,et al.  Praziquantel Synergistically Enhances Paclitaxel Efficacy to Inhibit Cancer Cell Growth , 2012, PloS one.

[15]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[16]  Publisher's Note , 2018, Anaesthesia.

[17]  Pak Chung Sham,et al.  GWASdb: a database for human genetic variants identified by genome-wide association studies , 2011, Nucleic Acids Res..

[18]  Yang Yang,et al.  Use of Genome-Wide Association Studies for Cancer Research and Drug Repositioning , 2015, PloS one.

[19]  Yan Geng,et al.  Requirement for CDK4 kinase function in breast cancer. , 2006, Cancer cell.

[20]  J. Morillas-Ruiz,et al.  A Review: Inflammatory Process in Alzheimer's Disease, Role of Cytokines , 2012, TheScientificWorldJournal.

[21]  R. M. Owen,et al.  An analysis of the attrition of drug candidates from four major pharmaceutical companies , 2015, Nature Reviews Drug Discovery.

[22]  Hiroyuki Tomita,et al.  The utility of vitamin K3 (menadione) against pancreatic cancer. , 2008, Anticancer research.

[23]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[24]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[25]  Kathleen M Jagodnik,et al.  Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd , 2016, Nature Communications.

[26]  D. Backos,et al.  Targeting WEE1 Kinase in Cancer. , 2016, Trends in pharmacological sciences.

[27]  Ryan Miller,et al.  WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research , 2017, Nucleic Acids Res..

[28]  Andrew D. Rouillard,et al.  LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures , 2014, Nucleic Acids Res..

[29]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[30]  Pankaj Agarwal,et al.  Connecting genetics and gene expression data for target prioritisation and drug repositioning , 2018, BioData Mining.

[31]  M. Dickson,et al.  Key factors in the rising cost of new drug discovery and development , 2004, Nature Reviews Drug Discovery.

[32]  Melissa A. Basford,et al.  Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data , 2013, Nature Biotechnology.

[33]  P. Gimsing.  Belinostat: a new broad acting antineoplastic histone deacetylase inhibitor , 2009, Expert opinion on investigational drugs.

[34]  Gabriele Sales,et al.  graphite - a Bioconductor package to convert pathway topology to gene network , 2012, BMC Bioinformatics.

[35]  Pankaj Agarwal,et al.  Systematic Drug Repositioning Based on Clinical Side-Effects , 2011, PloS one.

[36]  W. Blankesteijn,et al.  Inhibition of Wnt/β-catenin signaling by p38 MAP kinase inhibitors is explained by cross-reactivity with casein kinase Iδ/ɛ. , 2011, Chemistry & biology.

[37]  C. Moussa,et al.  Nilotinib-induced autophagic changes increase endogenous parkin level and ubiquitination, leading to amyloid clearance , 2013, Journal of Molecular Medicine.

[38]  Sachin P. Patil,et al.  Identification of antipsychotic drug fluspirilene as a potential p53-MDM2 inhibitor: a combined computational and experimental study , 2015, Journal of Computer-Aided Molecular Design.

[39]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .

[40]  Hon-Cheong So,et al.  Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry , 2017, Nature Neuroscience.

[41]  Kakajan Komurov,et al.  Inhibition of mTORC1/2 overcomes resistance to MAPK pathway inhibitors mediated by PGC1α and oxidative phosphorylation in melanoma. , 2014, Cancer research.

[42]  H. Stefánsson,et al.  Genetics of gene expression and its effect on disease , 2008, Nature.

[43]  D. di Bernardo,et al.  Identification of small molecules enhancing autophagic function from drug network analysis. , 2010, Autophagy.

[44]  J. Castle,et al.  An integrative genomics approach to infer causal associations between gene expression and disease , 2005, Nature Genetics.

[45]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[46]  P. Panula,et al.  Histamine in neurotransmission and brain diseases. , 2010, Advances in experimental medicine and biology.

[47]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[48]  L J Peters,et al.  Prevention of second primary tumors with isotretinoin in squamous-cell carcinoma of the head and neck. , 1990, The New England journal of medicine.

[49]  M. Sasamata,et al.  Antitumor Activity of YM155, a Selective Small-Molecule Survivin Suppressant, Alone and in Combination with Docetaxel in Human Malignant Melanoma Models , 2011, Clinical Cancer Research.

[50]  Michael J. Keiser,et al.  Predicting new molecular targets for known drugs , 2009, Nature.

[51]  C. Chong,et al.  New uses for old drugs , 2007, Nature.

[52]  Stuart Gillies,et al.  Menadione-induced Reactive Oxygen Species Generation via Redox Cycling Promotes Apoptosis of Murine Pancreatic Acinar Cells* , 2006, Journal of Biological Chemistry.

[53]  Anna Zhukova,et al.  Modeling sample variables with an Experimental Factor Ontology , 2010, Bioinform..

[54]  Liang-Jun Yan,et al.  Rapamycin, Autophagy, and Alzheimer's Disease. , 2013, Journal of biochemical and pharmacological research.

[55]  John Kirkwood,et al.  A multi-center phase II evaluation of the small molecule survivin suppressor YM155 in patients with unresectable stage III or IV melanoma , 2011, Investigational New Drugs.

[56]  Massimo Broggini,et al.  Breast Cancer–Derived Bone Metastasis Can Be Effectively Reduced through Specific c-MET Inhibitor Tivantinib (ARQ 197) and shRNA c-MET Knockdown , 2011, Molecular Cancer Therapeutics.

[57]  A. Malhotra,et al.  Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic , 2015, Molecular Psychiatry.

[58]  Thawfeek M. Varusai,et al.  The Reactome Pathway Knowledgebase , 2017, Nucleic acids research.

[59]  James M. Gallo,et al.  Preclinical Pharmacokinetic and Pharmacodynamic Evaluation of Novel Anticancer Agents, ON01910.Na (Rigosertib, Estybon™) and ON013105, for Brain Tumor Chemotherapy , 2012, Pharmaceutical Research.

[60]  Samantha L. Scudder,et al.  Synaptic structure and function are altered by the neddylation inhibitor MLN4924 , 2015, Molecular and Cellular Neuroscience.

[61]  M. Stefanelli,et al.  The mechanism of action of methylprednisolone in the treatment of multiple sclerosis , 2005, Multiple sclerosis.

[62]  Leonardo Pardo,et al.  Serotonin 5-HT6 receptor antagonists for the treatment of cognitive deficiency in Alzheimer's disease. , 2014, Journal of medicinal chemistry.

[63]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[64]  Gary K. Schwartz,et al.  Crizotinib, a c-Met Inhibitor, Prevents Metastasis in a Metastatic Uveal Melanoma Model , 2013, Molecular Cancer Therapeutics.

[65]  Pooja Mittal,et al.  A novel signaling pathway impact analysis , 2009, Bioinform..

[66]  Ekaterina Rogaeva,et al.  Drug Repositioning for Diabetes Based on 'Omics' Data Mining , 2015, PloS one.

[67]  Katrina Pedersen,et al.  A phase I/II study combining tosedostat with capecitabine in patients with metastatic pancreatic ductal adenocarcinoma (PDAC). , 2017 .

[68]  Jihye Kim,et al.  DSigDB: drug signatures database for gene set analysis , 2015, Bioinform..

[69]  Yi Pan,et al.  Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[70]  Min Jiang,et al.  Fenofibrate inhibited pancreatic cancer cells proliferation via activation of p53 mediated by upregulation of LncRNA MEG3. , 2016, Biochemical and biophysical research communications.

[71]  Ting Chen,et al.  Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach , 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[72]  Ann Richmond,et al.  Augmenting Chemosensitivity of Malignant Melanoma Tumors via Proteasome Inhibition , 2004, Cancer Research.

[73]  M. Wong,et al.  In Silico Identification and In Vitro and In Vivo Validation of Anti-Psychotic Drug Fluspirilene as a Potential CDK2 Inhibitor and a Candidate Anti-Cancer Drug , 2015, PloS one.

[74]  Youbin Kim,et al.  Pimozide reduces toxic forms of tau in TauC3 mice via 5′ adenosine monophosphate‐activated protein kinase‐mediated autophagy , 2017, Journal of neurochemistry.

[75]  S. Nam,et al.  Inflammation and Alzheimer’s disease , 2010, Archives of pharmacal research.

[76]  Sunghoon Kim,et al.  Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug , 2012, BMC Systems Biology.

[77]  Joel Dudley,et al.  Exploiting drug-disease relationships for computational drug repositioning , 2011, Briefings Bioinform..

[78]  William P Schiemann,et al.  Fibroblast growth factor receptor splice variants are stable markers of oncogenic transforming growth factor β1 signaling in metastatic breast cancers , 2014, Breast Cancer Research.

[79]  Peng-Fei Xu,et al.  PI3Kβ inhibitor AZD6482 exerts antiproliferative activity and induces apoptosis in human glioblastoma cells , 2018, Oncology reports.

[80]  Richard Leslie,et al.  GRASP: analysis of genotype-phenotype results from 1390 genome-wide association studies and corresponding open access database , 2014, Bioinform..

[81]  Yasser M. Moustafa,et al.  Olmesartan Potentiates the Anti-Angiogenic Effect of Sorafenib in Mice Bearing Ehrlich's Ascites Carcinoma: Role of Angiotensin (1–7) , 2014, PloS one.

[82]  M. Rask-Andersen,et al.  The druggable genome: Evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. , 2014, Annual review of pharmacology and toxicology.

[83]  Divya Vohora,et al.  Histamine H3 receptor antagonists/inverse agonists on cognitive and motor processes: relevance to Alzheimer's disease, ADHD, schizophrenia, and drug abuse , 2012, Front. Syst. Neurosci..

[84]  Marta Martínez-Vicente,et al.  Fighting neurodegeneration with rapamycin: mechanistic insights , 2011, Nature Reviews Neuroscience.

[85]  Shinji Yamazaki,et al.  Sensitivity of Selected Human Tumor Models to PF-04217903, a Novel Selective c-Met Kinase Inhibitor , 2012, Molecular Cancer Therapeutics.

[86]  Simeon Santourlidis,et al.  Bortezomib/proteasome inhibitor triggers both apoptosis and autophagy-dependent pathways in melanoma cells. , 2013, Cellular signalling.

[87]  Esther I Verhoef,et al.  SYK is a candidate kinase target for the treatment of advanced prostate cancer. , 2015, Cancer research.

[88]  Bart De Moor,et al.  BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis , 2005, Bioinform..

[89]  C E Lipscomb,et al.  Medical Subject Headings (MeSH). , 2000, Bulletin of the Medical Library Association.

[90]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[91]  P. Blandina,et al.  Histamine receptors in the CNS as targets for therapeutic intervention. , 2011, Trends in pharmacological sciences.

[92]  Joel Dudley,et al.  Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets , 2010, PLoS Comput. Biol..

[93]  Reinhard Dummer,et al.  PARP1 inhibitor olaparib (Lynparza) exerts synthetic lethal effect against ligase 4-deficient melanomas , 2016, Oncotarget.

[94]  Robert Petryszak,et al.  UniChem: a unified chemical structure cross-referencing and identifier tracking system , 2013, Journal of Cheminformatics.

[95]  Rob Jones,et al.  Erlotinib in combination with capecitabine and docetaxel in patients with metastatic breast cancer: a dose-escalation study. , 2008, European journal of cancer.

[96]  Shin Miura,et al.  The angiotensin II type I receptor blocker olmesartan inhibits the growth of pancreatic cancer by targeting stellate cell activities in mice , 2013, Scandinavian journal of gastroenterology.

[97]  Krister Wennerberg,et al.  Exploration of databases and methods supporting drug repurposing: a comprehensive survey , 2020, Briefings Bioinform..

[98]  Ismael Torres,et al.  The MLN4924 inhibitor exerts a neuroprotective effect against oxidative stress injury via Nrf2 protein accumulation , 2016, Redox biology.

[99]  L. Liang,et al.  Mapping complex disease traits with global gene expression , 2009, Nature Reviews Genetics.

[100]  J. Clader,et al.  Muscarinic receptor agonists and antagonists in the treatment of Alzheimer's disease. , 2005, Current pharmaceutical design.

[101]  Liwu Fu,et al.  Fluoxetine synergys with anticancer drugs to overcome multidrug resistance in breast cancer cells , 2012, Tumor Biology.

[102]  D. Melton,et al.  The toxicity of nitrofuran compounds on melanoma and neuroblastoma cells is enhanced by Olaparib and ameliorated by melanin pigment. , 2013, DNA repair.