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Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Integration of knowledge for personalizedmedicine: a pharmacogenomics case-study
Robert Hoehndorf, Michel Dumontier and George Gkoutos
University of CambridgeCarleton University
Aberystwyth University
18 September 2012
2007 2008 2009 2010 2011 2012
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Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Translational research
National Cancer Institute:
Translational research transforms scientific discoveries arising fromlaboratory, clinical, or population studies into clinical applicationsto reduce [disease] incidence, morbidity, and mortality.
Pharmacogenomics databases
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Ontology
Gruber (1993):
An ontology is the explicit specification of a conceptualization of adomain.
controlled vocabulary
provide background knowledge
hierarchically organized
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
OntologyOntologies in pharmacogenomics
drugs and chemicals:
ATCChEBIMeSHUMLS
diseases:
HumanDOHuman Phenotype OntologyICDMeSHSNOMED CTUMLS
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
OntologyOntologies alone do not resolve heterogeneity.
Euzenat (2007):
“[M]erely using ontologies [...] does not reduce heterogeneity: itjust raises heterogeneity problems to a higher level.”
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
OntologyData-driven approach to integration
data- and question-driven integration of ontologies
integration of data and databases through integratedontologies
reduction of complexitybackground knowledgehierarchical abstraction
ontology-based data analysis
semantic similaritystatistical testsgraph-/network-based algorithms
data- and question-driven evaluation
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Aims: queries and integrated analysis
integrate and query knowledge in pharmacogenomics
identify aberrant pathways and patho-physiology underlyingdisease
identify drug pathways (pharmacokinetics andpharmacodynamics)
personalized treatment and dosage guidelines based on geneexpression profile
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Required: integration of multiple data sources
drugs and drug targets
pathways, genetic interactions, protein interactions, generegulation
drug–disease associations
gene–disease associations
genotypes–drug response
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Approach to data integration in pharmacogenomics
integration of databases containing drug, gene, genotype,disease and pathway information
DrugBank: drugs and drugs targetsPharmGKB: genotype and drug responsePathway Interaction Database: biological pathwaysCTD: toxicogenomics information (chemical–gene–disease)
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Queries
What drugs can be used to treat parasitic infectious diseases(DOID:1398)?
Chloroquine
Arthemeter
...
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Queries
What drugs are effective for diseases affecting the joints(FMA:7490)?
Folic acid (for arthritis)
Chloroquine (for Chikungunya virus)
...
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Queries
What genotypes are related to diseases affecting the joints(FMA:7490)?
RSID:rs70991108 (with arthritis)
RSID:rs1207421 (Osteoarthritis, Knee)
...
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Queries
What genotypes are related to response to steroids(CHEBI:35341)?
RSID:rs45566039 (with estrogen)
RSID:rs1042713 (with budesonide)
...
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathwaysOntology enrichment analysis can identify over-represented ontology classes.
ontology-based, statistical approach to identify drug anddisease pathways
use graph structure of ontology to identify statistically over-and under-represented ontology classes
aims:
identify over-represented disease classes (in disease ontology)for genes in a pathway (disease pathways)identify over-represented chemical classes (from chemicalontology) for genes in a pathway (drug pathways)
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathwaysOntoFUNC enables enrichment analyses over OWL ontologies.
OntoFUNC: http://ontofunc.googlecode.com
based on FUNC (http://func.eva.mpg.de)
supports
hypergeometric testWilcoxon rank testbinomial testMcDonaldKreitman (2x2 contingency) testcorrection for multiple testing (FWER, FDR)
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathwaysOntoFUNC identifies disease classes that are enriched in pathways.
hypergeometric test over Disease Ontology
genes participating in pathway P vs. all other genes
carcinosarcoma (DOID:4236) and Zidovudine Pathway(PharmGKB:PA165859361) (p < 10−10).
mood disorder (DOID:3324) and Zidovudine Pathway(PharmGKB:PA165859361) (p < 0.01).
(All results at http://pharmgkb-owl.googlecode.com)
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Gene expression and drug responseOngoing research
Based on a (differential) gene expression profile, can we findcandidate drugs that act (only) on the aberrant pathways?
aberrant pathways from (differential) gene expression
Wilcoxon signed rank test
(types of) drugs acting on these pathways
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Limitations and future work
only works for known pathways
extension to interaction networks
(experimental) validation
include directionality of interactions
drug–gene/proteingene regulationprotein–protein
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Conclusions
knowledge in pharmacogenomics is distributed across multipledatabases
ontologies can enable data integration and integrated dataanalysis
integration of knowledge is necessary to enable personalizedmedicine
http://pharmgkb-owl.googlecode.com
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Acknowledgements
Michel Dumontier
George Gkoutos
Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Thank you!