Invest New Drugs DOI 10.1007/s10637-014-0081-x
REVIEW
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery Onat Kadioglu & Thomas Efferth
Received: 22 January 2014 / Accepted: 26 February 2014 # Springer Science+Business Media New York 2014
Summary Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of “-omics” data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval. Keywords Computational biology . Connectivity map . Deep sequencing . Drug discovery . Drug repositioning . Genome-wide association studies . -omics technologies . Systems biology . Whole exon sequencing Abbreviations ENCODE Encyclopedia of DNA elements GWAS Genome wide association studies OMIM Online mendelian ınheritance ın men R&D Research and development O. Kadioglu : T. Efferth (*) Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, University of Mainz, Staudinger Weg 5, 55128 Mainz, Germany e-mail: [email protected]
SNP WES
Single nucleotide polymorphism Whole exome sequencing
Drug development is a long and highly demanding process with various requirements, it is even more difficult for complex diseases such as diabetes and cancer. This is a challenge for pharmaceutical companies since the number of newly approved drugs declined for decades mainly because of their failure in clinical phase 2 trials, despite the fact that time and expenditure on drug research and development (R&D) increased annually [1, 2]. Recently emerged high throughput technologies such as next generation sequencing are helpful to generate enormous amount of data. For instance, gene expression profiles can be combined to yield predictors of traits and large genome wide association studies (GWAS) were helpful in finding disease and trait-related genes, but they explained only a small fraction of the trait heritability. Whole exome sequencing (WES) further contributed to understand trait heritability and was successful in finding disease genes in some monogenic or oligogenic traits but had limited ability in complex diseases [3]. In complex disorders, much more data are needed to obtain reliable results, as in the case of enormous international effort made to produce a catalogue of somatic mutations in cancer [4]. Efficient strategies are required to interpret the outputs from high throughput technologies in order to provide information relevant for the disease mechanisms, which is essential to develop new drugs and combat with the complex diseases (Fig. 1). For this purpose, sophisticated computational tools integrating genomic data [5] together with models relating biological components of the cell among them depending on the networks of functional, regulatory and physical interactions to interpret the experimental genomic data in the context of the biological system are required [6, 7]. Moreover, different
Invest New Drugs Fig. 1 Contribution of bioinformatics to drug research and development
Bioinformatics
Preclinical drug discovery and development (in silico, in vitro, in vivo) • Chemical libraries • Target identification • Networks between drugs,
Clinical drug development
• Phase 1-4 trials
genes and diseases
• Virtual drug screening • Highthroughput screening • Pharmaco-/toxicogenomics • Animal experimentation
Individualized therapy of complex diseases Genetic profiling: • Gene copy number variations • Single nucl. polymorphisms Epigenetic profiling: • DNA methylation • Histone acetylation • micro-RNAs • Prions Expression profiling: • Transcriptomics • Proteomics • Metabolomics
Drug repositioning
gene prioritization approaches contribute to the determination of disease genes [8]. Existing drugs with a well-known safety and pharmacokinetic profile that failed against certain diseases might serve as valuable sources, since it is possible to find new applications for them. They might be used against other diseases affected by the same pathway. This phenomenon is referred as drug repositioning, which is also known as drug reprofiling [9] with a conceptually different way to understand a therapeutic target, not as a single, isolated molecule but as a component of a more complex functional module [3]. There are two main strategies to identify new uses for drugs. If the structures of the drug and the target proteins are available, properties and molecular interactions of them can be used to perform molecular docking studies to predict physical interactions [10]. Another strategy depends on the perspective of the disease or the pathology [11]. Identification of effectiveness for thalidomide against severe erythema nodosum leprosum and retinoic acid against acute promyelocytic leukemia are two successful examples for drug repositioning [12, 13]. The main task of drug repositioning is to effectively establish a connection between molecular details and phenotypes under specific conditions. Softwares and databases useful for drug discovery and repositioning are depicted in Table 1 [14]. A critical step in drug development is the determination of effective use and dosage of the drug for each individual. Prospective, comprehensive approach to effectively prevent, diagnose, and treat disease in each individual is referred as personalized medicine that is dependent on
patient’s “-omics”-based data profile consisting of computational models together with biological knowledge [3, 15]. Emerging new genomics and transcriptomics technologies allowed to perform large GWAS and thus yield information on genomic variations such as single nucleotide polymorphisms (SNPs), copy number variations and other structural variations associated with disease progression and drug response (reported association of about 300 SNPs to pharmacogenomics traits) [3]. The recently reported ENCODE project has yielded valuable information regarding association of genomic variations and epigenetic modifications with disease progression and occurrence [16]. High levels of variability have been discovered to be related with differential drug responses among individuals [17, 18]. Those differential responses depend on a network of complex relationships among proteins, thus conventional biomarkers are limited in this regard [19]. An example for drug repositioning in a network framework is the online server, DRAR-CPI [20]. Firstly, a comprehensive drug-protein interaction network is constructed by combining primary drug–target interactions with off drug–target associations inferred by the embedded target prediction tool. Then the protein association profiles of drugs are compared and indications of drugs with similar profiles are matched [20]. Another example is a Bayesian partition method to identify drug– gene–disease co-modules [21]. Protein-protein interaction network is assembled by referring to various databases. After that, disease-related genes retrieved from OMIM and drug targets from DrugBank are mapped onto the protein–protein interaction network [21].
Invest New Drugs Table 1 Selected databases and tools for drug discovery and repositioning Data type
Database/tool
URL
Gene-disease association
DisGeNET GAD OMIM ToppGene Suite HMDD miR2disease AutoDock 4 BindingDB ChEMBL ChemProt ProtChemSI PyRx STITCH BioGRID ClusPro DIP DOMINE HitPredict HPRD KUPS MINT MIPS STRING Wiki-Pi TRANSFAC TRED miRBase TargetScan GO Aracne Arrayexpress CMAP GEO ClinicalTrial.gov Drugs@FDA PubChem SIDER ACToR DCDB canSAR CTD DrugBank IBIS IntAct ITM Probe KEGG NPC PharmGKB PID
http://ibi.imim.es/web/DisGeNET/v01;jsessionid=17sgecaqwyltq1d7p5golfni7f http://geneticassociationdb.nih.gov/ http://www.ncbi.nlm.nih.gov/omim http://toppgene.cchmc.org/enrichment.jsp http://cmbi.bjmu.edu.cn:8080/html/tools/hmdd2.html http://www.mir2disease.org/ http://autodock.scripps.edu/ http://www.bindingdb.org/bind/index.jsp https://www.ebi.ac.uk/chembl/ http://www.cbs.dtu.dk/services/ChemProt-2.0/ http://pcidb.russelllab.org/ http://pyrx.sourceforge.net/ http://stitch.embl.de/ http://wiki.thebiogrid.org http://cluspro.bu.edu/login.php http://dip.doe-mbi.ucla.edu/dip/Main.cgi http://domine.utdallas.edu/cgi-bin/Domine http://hintdb.hgc.jp/htp/ http://hprd.org/ http://www.ittc.ku.edu/chenlab/kups/ http://mint.bio.uniroma2.it/mint/Welcome.do http://mips.helmholtz-muenchen.de/proj/ppi/ http://string.embl.de/ http://severus.dbmi.pitt.edu/wiki-pi/ http://www.gene-regulation.com/pub/databases/transfac/doc/ http://rulai.cshl.edu/cgi-bin/TRED/tred.cgi?process=home http://www.mirbase.org/ http://www.targetscan.org/ http://www.geneontology.org/ http://wiki.c2b2.columbia.edu/califanolab/index.php/Software/ARACNE http://www.ebi.ac.uk/arrayexpress/ http://www.broadinstitute.org/cmap/ http://www.ncbi.nlm.nih.gov/geo/ http://clinicaltrials.gov/ http://www.accessdata.fda.gov/scripts/cder/drugsatfda/ https://pubchem.ncbi.nlm.nih.gov/ http://sideeffects.embl.de/ http://actor.epa.gov/ http://www.cls.zju.edu.cn/dcdb/ https://cansar.icr.ac.uk/ http://ctdbase.org/ http://www.drugbank.ca/ http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi http://www.ebi.ac.uk/intact/ http://www.ncbi.nlm.nih.gov/CBBresearch/qmbp/mn/itm_probe/ http://www.genome.jp/kegg/ http://tripod.nih.gov/npc/ http://www.pharmgkb.org/ http://pid.nci.nih.gov/index.shtml
miRNA-disease association Chemical-protein interaction
Protein-protein interaction
Transcription factor-gene interaction miRNA-gene interaction Functional annotation Microarray
Drug-disease association Bioassay of compound Drug-side effect association Chemical-toxicity association Drug combination-disease association Integrative databases
Invest New Drugs
In order to develop effective and personalized cancer therapy strategies, enormous amount of “-omics” data together with drug combination studies and biological network data are required, since tumors are dynamic ensembles of subpopulations with distinct mutations [22, 23]. The connectivity map project is a contributing factor to establish a connection between chemicals and gene expression profiles in different cancer cell lines for more than 1700 compounds [24].
14. 15. 16.
17. Conflict of interest The authors declare that they have no conflict of interest whatsoever.
References 1. Mullard A (2012) 2011 FDA drug approvals. Nat Rev Drug Discov 11:91–94 2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715 3. Dopazo J (2013) Genomics and transcriptomics in drug discovery. Drug Discov Today pii:S1359–6446(13):00166–9 4. Stratton MR (2011) Exploring the genomes of cancer cells: progress and promise. Science 331:1553–1558 5. Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486 6. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2 7. Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68 8. Moreau Y, Tranchevent LC (2012) Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 13:523–536 9. Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683 10. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20 11. Dudley JT, Deshpande T, Butte AJ (2011) Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform 12:303–311 12. Aronson JK (2007) Old drugs–new uses. J Clin Pharmacol 64:563–565 13. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ (2011) Discovery and preclinical validation of
18.
19. 20.
21.
22.
23.
24.
drug indications using compendia of public gene expression data. Sci Transl Med 3:96ra77 Wu Z, Wang Y, Chen L (2013) Network-based drug repositioning. Mol BioSyst 9:1268 Feero WG, Guttmacher AE, Collins FS (2010) Genomic medicine— an updated primer. N Engl J Med 362:2001–2011 ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74 Xue Y, Chen Y, Ayub Q, Huang N, Ball EV, Mort M, Phillips AD, Shaw K, Stenson PD, Cooper DN, Tyler-Smith C, 1000 Genomes Project Consortium (2012) Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. Am J Hum Genet 91: 1022–1032 Carbonell J, Alloza E, Arce P, Borrego S, Santoyo J, Ruiz-Ferrer M, Medina I, Jiménez-Almazán J, Méndez-Vidal C, González-Del Pozo M, Vela A, Bhattacharya SS, Antiñolo G, Dopazo J (2012) A map of human microRNA variation uncovers unexpectedly high levels of variability. Genome Med 4:62 Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113 Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L (2011) DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Res 39:W492–W498 Zhao S, Li S (2012) A co-module approach for elucidating drugdisease associations and revealing their molecular basis. Bioinformatics 28:955–961 Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892 Creixell P, Schoof EM, Erler JT, Linding R (2012) Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 30: 842–848 Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel M, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander E, Golub TR (2006) The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science 313:1929–1935
REVIEW
Contributions from emerging transcriptomics technologies and computational strategies for drug discovery Onat Kadioglu & Thomas Efferth
Received: 22 January 2014 / Accepted: 26 February 2014 # Springer Science+Business Media New York 2014
Summary Drug discovery involves various steps and is a long process being even more demanding for complex diseases such as cancer. Tumors are ensembles of subpopulations with different mutations, require very specific and effective strategies. Conventional drug screening technologies may not be adequate and efficient anymore. Drug repositioning is a useful strategy to identify new uses for previously failed drugs. High throughput and deep sequencing technologies provide valuable support by yielding enormous amounts of “-omics” data and contribute to understanding the molecular mechanisms responsible for drug action. Computational methods coupled with systems biology represent a promising step to interpret pharmacogenomic data and establish strong connections with drug discovery. Genomic variations have been found to be linked with differential drug response among individuals. Large genome wide association studies are necessary to identify reliable connections between genomic variations and drug response since personalized medicine has been accepted as an important phenomenon in the drug discovery and development process post approval. Keywords Computational biology . Connectivity map . Deep sequencing . Drug discovery . Drug repositioning . Genome-wide association studies . -omics technologies . Systems biology . Whole exon sequencing Abbreviations ENCODE Encyclopedia of DNA elements GWAS Genome wide association studies OMIM Online mendelian ınheritance ın men R&D Research and development O. Kadioglu : T. Efferth (*) Department of Pharmaceutical Biology, Institute of Pharmacy and Biochemistry, University of Mainz, Staudinger Weg 5, 55128 Mainz, Germany e-mail: [email protected]
SNP WES
Single nucleotide polymorphism Whole exome sequencing
Drug development is a long and highly demanding process with various requirements, it is even more difficult for complex diseases such as diabetes and cancer. This is a challenge for pharmaceutical companies since the number of newly approved drugs declined for decades mainly because of their failure in clinical phase 2 trials, despite the fact that time and expenditure on drug research and development (R&D) increased annually [1, 2]. Recently emerged high throughput technologies such as next generation sequencing are helpful to generate enormous amount of data. For instance, gene expression profiles can be combined to yield predictors of traits and large genome wide association studies (GWAS) were helpful in finding disease and trait-related genes, but they explained only a small fraction of the trait heritability. Whole exome sequencing (WES) further contributed to understand trait heritability and was successful in finding disease genes in some monogenic or oligogenic traits but had limited ability in complex diseases [3]. In complex disorders, much more data are needed to obtain reliable results, as in the case of enormous international effort made to produce a catalogue of somatic mutations in cancer [4]. Efficient strategies are required to interpret the outputs from high throughput technologies in order to provide information relevant for the disease mechanisms, which is essential to develop new drugs and combat with the complex diseases (Fig. 1). For this purpose, sophisticated computational tools integrating genomic data [5] together with models relating biological components of the cell among them depending on the networks of functional, regulatory and physical interactions to interpret the experimental genomic data in the context of the biological system are required [6, 7]. Moreover, different
Invest New Drugs Fig. 1 Contribution of bioinformatics to drug research and development
Bioinformatics
Preclinical drug discovery and development (in silico, in vitro, in vivo) • Chemical libraries • Target identification • Networks between drugs,
Clinical drug development
• Phase 1-4 trials
genes and diseases
• Virtual drug screening • Highthroughput screening • Pharmaco-/toxicogenomics • Animal experimentation
Individualized therapy of complex diseases Genetic profiling: • Gene copy number variations • Single nucl. polymorphisms Epigenetic profiling: • DNA methylation • Histone acetylation • micro-RNAs • Prions Expression profiling: • Transcriptomics • Proteomics • Metabolomics
Drug repositioning
gene prioritization approaches contribute to the determination of disease genes [8]. Existing drugs with a well-known safety and pharmacokinetic profile that failed against certain diseases might serve as valuable sources, since it is possible to find new applications for them. They might be used against other diseases affected by the same pathway. This phenomenon is referred as drug repositioning, which is also known as drug reprofiling [9] with a conceptually different way to understand a therapeutic target, not as a single, isolated molecule but as a component of a more complex functional module [3]. There are two main strategies to identify new uses for drugs. If the structures of the drug and the target proteins are available, properties and molecular interactions of them can be used to perform molecular docking studies to predict physical interactions [10]. Another strategy depends on the perspective of the disease or the pathology [11]. Identification of effectiveness for thalidomide against severe erythema nodosum leprosum and retinoic acid against acute promyelocytic leukemia are two successful examples for drug repositioning [12, 13]. The main task of drug repositioning is to effectively establish a connection between molecular details and phenotypes under specific conditions. Softwares and databases useful for drug discovery and repositioning are depicted in Table 1 [14]. A critical step in drug development is the determination of effective use and dosage of the drug for each individual. Prospective, comprehensive approach to effectively prevent, diagnose, and treat disease in each individual is referred as personalized medicine that is dependent on
patient’s “-omics”-based data profile consisting of computational models together with biological knowledge [3, 15]. Emerging new genomics and transcriptomics technologies allowed to perform large GWAS and thus yield information on genomic variations such as single nucleotide polymorphisms (SNPs), copy number variations and other structural variations associated with disease progression and drug response (reported association of about 300 SNPs to pharmacogenomics traits) [3]. The recently reported ENCODE project has yielded valuable information regarding association of genomic variations and epigenetic modifications with disease progression and occurrence [16]. High levels of variability have been discovered to be related with differential drug responses among individuals [17, 18]. Those differential responses depend on a network of complex relationships among proteins, thus conventional biomarkers are limited in this regard [19]. An example for drug repositioning in a network framework is the online server, DRAR-CPI [20]. Firstly, a comprehensive drug-protein interaction network is constructed by combining primary drug–target interactions with off drug–target associations inferred by the embedded target prediction tool. Then the protein association profiles of drugs are compared and indications of drugs with similar profiles are matched [20]. Another example is a Bayesian partition method to identify drug– gene–disease co-modules [21]. Protein-protein interaction network is assembled by referring to various databases. After that, disease-related genes retrieved from OMIM and drug targets from DrugBank are mapped onto the protein–protein interaction network [21].
Invest New Drugs Table 1 Selected databases and tools for drug discovery and repositioning Data type
Database/tool
URL
Gene-disease association
DisGeNET GAD OMIM ToppGene Suite HMDD miR2disease AutoDock 4 BindingDB ChEMBL ChemProt ProtChemSI PyRx STITCH BioGRID ClusPro DIP DOMINE HitPredict HPRD KUPS MINT MIPS STRING Wiki-Pi TRANSFAC TRED miRBase TargetScan GO Aracne Arrayexpress CMAP GEO ClinicalTrial.gov Drugs@FDA PubChem SIDER ACToR DCDB canSAR CTD DrugBank IBIS IntAct ITM Probe KEGG NPC PharmGKB PID
http://ibi.imim.es/web/DisGeNET/v01;jsessionid=17sgecaqwyltq1d7p5golfni7f http://geneticassociationdb.nih.gov/ http://www.ncbi.nlm.nih.gov/omim http://toppgene.cchmc.org/enrichment.jsp http://cmbi.bjmu.edu.cn:8080/html/tools/hmdd2.html http://www.mir2disease.org/ http://autodock.scripps.edu/ http://www.bindingdb.org/bind/index.jsp https://www.ebi.ac.uk/chembl/ http://www.cbs.dtu.dk/services/ChemProt-2.0/ http://pcidb.russelllab.org/ http://pyrx.sourceforge.net/ http://stitch.embl.de/ http://wiki.thebiogrid.org http://cluspro.bu.edu/login.php http://dip.doe-mbi.ucla.edu/dip/Main.cgi http://domine.utdallas.edu/cgi-bin/Domine http://hintdb.hgc.jp/htp/ http://hprd.org/ http://www.ittc.ku.edu/chenlab/kups/ http://mint.bio.uniroma2.it/mint/Welcome.do http://mips.helmholtz-muenchen.de/proj/ppi/ http://string.embl.de/ http://severus.dbmi.pitt.edu/wiki-pi/ http://www.gene-regulation.com/pub/databases/transfac/doc/ http://rulai.cshl.edu/cgi-bin/TRED/tred.cgi?process=home http://www.mirbase.org/ http://www.targetscan.org/ http://www.geneontology.org/ http://wiki.c2b2.columbia.edu/califanolab/index.php/Software/ARACNE http://www.ebi.ac.uk/arrayexpress/ http://www.broadinstitute.org/cmap/ http://www.ncbi.nlm.nih.gov/geo/ http://clinicaltrials.gov/ http://www.accessdata.fda.gov/scripts/cder/drugsatfda/ https://pubchem.ncbi.nlm.nih.gov/ http://sideeffects.embl.de/ http://actor.epa.gov/ http://www.cls.zju.edu.cn/dcdb/ https://cansar.icr.ac.uk/ http://ctdbase.org/ http://www.drugbank.ca/ http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi http://www.ebi.ac.uk/intact/ http://www.ncbi.nlm.nih.gov/CBBresearch/qmbp/mn/itm_probe/ http://www.genome.jp/kegg/ http://tripod.nih.gov/npc/ http://www.pharmgkb.org/ http://pid.nci.nih.gov/index.shtml
miRNA-disease association Chemical-protein interaction
Protein-protein interaction
Transcription factor-gene interaction miRNA-gene interaction Functional annotation Microarray
Drug-disease association Bioassay of compound Drug-side effect association Chemical-toxicity association Drug combination-disease association Integrative databases
Invest New Drugs
In order to develop effective and personalized cancer therapy strategies, enormous amount of “-omics” data together with drug combination studies and biological network data are required, since tumors are dynamic ensembles of subpopulations with distinct mutations [22, 23]. The connectivity map project is a contributing factor to establish a connection between chemicals and gene expression profiles in different cancer cell lines for more than 1700 compounds [24].
14. 15. 16.
17. Conflict of interest The authors declare that they have no conflict of interest whatsoever.
References 1. Mullard A (2012) 2011 FDA drug approvals. Nat Rev Drug Discov 11:91–94 2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715 3. Dopazo J (2013) Genomics and transcriptomics in drug discovery. Drug Discov Today pii:S1359–6446(13):00166–9 4. Stratton MR (2011) Exploring the genomes of cancer cells: progress and promise. Science 331:1553–1558 5. Hawkins RD, Hon GC, Ren B (2010) Next-generation genomics: an integrative approach. Nat Rev Genet 11:476–486 6. Auffray C, Chen Z, Hood L (2009) Systems medicine: the future of medical genomics and healthcare. Genome Med 1:2 7. Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68 8. Moreau Y, Tranchevent LC (2012) Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet 13:523–536 9. Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683 10. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20 11. Dudley JT, Deshpande T, Butte AJ (2011) Exploiting drug–disease relationships for computational drug repositioning. Brief Bioinform 12:303–311 12. Aronson JK (2007) Old drugs–new uses. J Clin Pharmacol 64:563–565 13. Sirota M, Dudley JT, Kim J, Chiang AP, Morgan AA, Sweet-Cordero A, Sage J, Butte AJ (2011) Discovery and preclinical validation of
18.
19. 20.
21.
22.
23.
24.
drug indications using compendia of public gene expression data. Sci Transl Med 3:96ra77 Wu Z, Wang Y, Chen L (2013) Network-based drug repositioning. Mol BioSyst 9:1268 Feero WG, Guttmacher AE, Collins FS (2010) Genomic medicine— an updated primer. N Engl J Med 362:2001–2011 ENCODE Project Consortium, Bernstein BE, Birney E, Dunham I, Green ED, Gunter C, Snyder M (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57–74 Xue Y, Chen Y, Ayub Q, Huang N, Ball EV, Mort M, Phillips AD, Shaw K, Stenson PD, Cooper DN, Tyler-Smith C, 1000 Genomes Project Consortium (2012) Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. Am J Hum Genet 91: 1022–1032 Carbonell J, Alloza E, Arce P, Borrego S, Santoyo J, Ruiz-Ferrer M, Medina I, Jiménez-Almazán J, Méndez-Vidal C, González-Del Pozo M, Vela A, Bhattacharya SS, Antiñolo G, Dopazo J (2012) A map of human microRNA variation uncovers unexpectedly high levels of variability. Genome Med 4:62 Barabasi AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113 Luo H, Chen J, Shi L, Mikailov M, Zhu H, Wang K, He L, Yang L (2011) DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Res 39:W492–W498 Zhao S, Li S (2012) A co-module approach for elucidating drugdisease associations and revealing their molecular basis. Bioinformatics 28:955–961 Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892 Creixell P, Schoof EM, Erler JT, Linding R (2012) Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 30: 842–848 Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel M, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander E, Golub TR (2006) The Connectivity Map: using geneexpression signatures to connect small molecules, genes, and disease. Science 313:1929–1935
