Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory

BackgroundNon-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for identifying potential new uses for existing drugs and accelerating the development process of drugs, as well as reducing side effects.ResultsThis work integrates two approaches - machine learning algorithms and topological parameter-based classification - to develop a novel pipeline of drug repositioning to analyze four lung cancer microarray datasets, enriched biological processes, potential therapeutic drugs and targeted genes for NSCLC treatments. A total of 7 (8) and 11 (12) promising drugs (targeted genes) were discovered for treating early- and late-stage NSCLC, respectively. The effectiveness of these drugs is supported by the literature, experimentally determined in-vitro IC50 and clinical trials. This work provides better drug prediction accuracy than competitive research according to IC50 measurements.ConclusionsWith the novel pipeline of drug repositioning, the discovery of enriched pathways and potential drugs related to NSCLC can provide insight into the key regulators of tumorigenesis and the treatment of NSCLC. Based on the verified effectiveness of the targeted drugs predicted by this pipeline, we suggest that our drug-finding pipeline is effective for repositioning drugs.

[1]  Li-Jen Su,et al.  Protein arginine methyltransferase 5 is a potential oncoprotein that upregulates G1 cyclins/cyclin‐dependent kinases and the phosphoinositide 3‐kinase/AKT signaling cascade , 2012, Cancer science.

[2]  David S. Wishart,et al.  DrugBank: a knowledgebase for drugs, drug actions and drug targets , 2007, Nucleic Acids Res..

[3]  Zuping Zhang,et al.  Network-Based Inference Methods for Drug Repositioning , 2015, Comput. Math. Methods Medicine.

[4]  J. Sznajder,et al.  Hypercapnia impairs lung neutrophil function and increases mortality in murine pseudomonas pneumonia. , 2013, American journal of respiratory cell and molecular biology.

[5]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[6]  A. Berman,et al.  Proton Beam Therapy for Non-Small Cell Lung Cancer: Current Clinical Evidence and Future Directions , 2015, Cancers.

[7]  Patrick Aloy,et al.  Recycling side-effects into clinical markers for drug repositioning , 2012, Genome Medicine.

[8]  A. Chiang,et al.  Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses , 2009, Clinical pharmacology and therapeutics.

[9]  Janica C Wong,et al.  Cyclic GMP/protein kinase G type‐Iα (PKG‐Iα) signaling pathway promotes CREB phosphorylation and maintains higher c‐IAP1, livin, survivin, and Mcl‐1 expression and the inhibition of PKG‐Iα kinase activity synergizes with cisplatin in non‐small cell lung cancer cells , 2012, Journal of cellular biochemistry.

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

[11]  Jana Marie Schwarz,et al.  MutationTaster2: mutation prediction for the deep-sequencing age , 2014, Nature Methods.

[12]  Pier Paolo Di Fiore,et al.  Endocytosis and Cancer: an ‘Insider’ Network with Dangerous Liaisons , 2008, Traffic.

[13]  Julio Saez-Rodriguez,et al.  DvD: An R/Cytoscape pipeline for drug repurposing using public repositories of gene expression data , 2012, Bioinform..

[14]  C. Holmes,et al.  The platelet contribution to cancer progression , 2011, Journal of thrombosis and haemostasis : JTH.

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

[16]  Igor Jurisica,et al.  Prioritizing Therapeutics for Lung Cancer: An Integrative Meta-analysis of Cancer Gene Signatures and Chemogenomic Data , 2015, PLoS Comput. Biol..

[17]  Stephen L. Abrams,et al.  Roles of the Raf/MEK/ERK pathway in cell growth, malignant transformation and drug resistance. , 2007, Biochimica et biophysica acta.

[18]  Justin Lamb,et al.  The Connectivity Map: a new tool for biomedical research , 2007, Nature Reviews Cancer.

[19]  Chuhsing Kate Hsiao,et al.  Identification of a Novel Biomarker, SEMA5A, for Non–Small Cell Lung Carcinoma in Nonsmoking Women , 2010, Cancer Epidemiology, Biomarkers & Prevention.

[20]  Ashok R Venkitaraman,et al.  Cancer Susceptibility and the Functions of BRCA1 and BRCA2 , 2002, Cell.

[21]  Gene Ontology Consortium,et al.  The Gene Ontology (GO) project in 2006 , 2005, Nucleic Acids Res..

[22]  Chi-Ying F. Huang,et al.  Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme , 2007, BMC Genomics.

[23]  P. Bork,et al.  A method and server for predicting damaging missense mutations , 2010, Nature Methods.

[24]  Richard F. Beltramini,et al.  Meta-Analysis: Quantitative Methods for Research Synthesis , 1987 .

[25]  J. Geschwind,et al.  Tumor glycolysis as a target for cancer therapy: progress and prospects , 2013, Molecular Cancer.

[26]  J. Barrett,et al.  Gap junction function and cancer. , 1993, Cancer research.

[27]  Robert J. Gillies,et al.  Causes and Consequences of Increased Glucose Metabolism of Cancers , 2008, Journal of Nuclear Medicine.

[28]  Ka-Lok Ng,et al.  Improving protein complex classification accuracy using amino acid composition profile , 2013, Comput. Biol. Medicine.

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

[30]  Jeffrey J. P. Tsai,et al.  Prediction of microRNA-regulated protein interaction pathways in Arabidopsis using machine learning algorithms , 2013, Comput. Biol. Medicine.

[31]  Chuang Liu,et al.  Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference , 2012, PLoS Comput. Biol..

[32]  Ka-Lok Ng,et al.  Drug Repositioning Discovery for Early- and Late-Stage Non-Small-Cell Lung Cancer , 2014, BioMed research international.

[33]  M. Carson,et al.  Network-based prediction and knowledge mining of disease genes , 2015, BMC Medical Genomics.

[34]  Yusuke Yoshioka,et al.  Brain metastatic cancer cells release microRNA-181c-containing extracellular vesicles capable of destructing blood–brain barrier , 2015, Nature Communications.

[35]  Alexander A. Morgan,et al.  Computational Repositioning of the Anticonvulsant Topiramate for Inflammatory Bowel Disease , 2011, Science Translational Medicine.

[36]  Ralf Herwig,et al.  ConsensusPathDB—a database for integrating human functional interaction networks , 2008, Nucleic Acids Res..

[37]  S. Shuman,et al.  Structure, mechanism, and evolution of the mRNA capping apparatus. , 2001, Progress in nucleic acid research and molecular biology.

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

[39]  Feng Li,et al.  An Introduction to Metaanalysis , 2005 .

[40]  Ka-Lok Ng,et al.  Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms , 2015, BioMed research international.

[41]  L. Hedges,et al.  Introduction to Meta‐Analysis , 2009, International Coaching Psychology Review.

[42]  Teng-Hung Chen,et al.  Graph theory and stability analysis of protein complex interaction networks. , 2016, IET systems biology.

[43]  Edgar Rivedal,et al.  Downregulation of gap junctions in cancer cells. , 2006, Critical reviews in oncogenesis.

[44]  Y. Fukuoka,et al.  A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs , 2013, Bioinformation.

[45]  P. Bork,et al.  Drug Target Identification Using Side-Effect Similarity , 2008, Science.

[46]  F. Wolf.  Meta-Analysis: Quantitative Methods for Research Synthesis , 1987 .

[47]  Rugang Zhang,et al.  Nucleotide metabolism, oncogene-induced senescence and cancer. , 2015, Cancer letters.

[48]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

[49]  Zhaolei Zhang,et al.  SNPdryad: predicting deleterious non-synonymous human SNPs using only orthologous protein sequences , 2014, Bioinform..

[50]  Andrew J. Doig,et al.  Properties of Protein Drug Target Classes , 2015, PloS one.

[51]  Xiaoqi Liu,et al.  Polo-like kinase (Plk)1 depletion induces apoptosis in cancer cells , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Abdelghani Mazouzi,et al.  DNA replication stress: causes, resolution and disease. , 2014, Experimental cell research.

[53]  Ka-Lok Ng,et al.  In silico identification of potential targets and drugs for non-small cell lung cancer. , 2014, IET systems biology.

[54]  S. Wacholder,et al.  Gene Expression Signature of Cigarette Smoking and Its Role in Lung Adenocarcinoma Development and Survival , 2008, PloS one.

[55]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[56]  Rebecca SY Wong,et al.  Apoptosis in cancer: from pathogenesis to treatment , 2011, Journal of experimental & clinical cancer research : CR.

[57]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[58]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[59]  Ling-Ling Zhu,et al.  Repurposing of bisphosphonates for the prevention and therapy of nonsmall cell lung and breast cancer , 2014, Proceedings of the National Academy of Sciences.

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

[61]  P. Sanseau,et al.  Computational Drug Repositioning: From Data to Therapeutics , 2013, Clinical pharmacology and therapeutics.