This document discusses integrating knowledge from pharmacogenomics databases to enable personalized medicine approaches. It describes using ontologies to integrate data on drugs, diseases, pathways, and genotypes from multiple sources. Queries over this integrated knowledge can discover disease and drug pathways and relationships between genotypes and drugs/diseases. The goal is to identify aberrant pathways underlying a disease and personalized treatment options based on a patient's gene expression profile. Future work includes expanding to interaction networks and experimental validation.

1. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Integration of knowledge for personalized
medicine: a pharmacogenomics case-study
Robert Hoehndorf, Michel Dumontier and George Gkoutos
University of Cambridge
Carleton University
Aberystwyth University
18 September 2012

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3. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Translational research
National Cancer Institute:
Translational research transforms scientific discoveries arising from
laboratory, clinical, or population studies into clinical applications
to reduce [disease] incidence, morbidity, and mortality.

6. Pharmacogenomics databases

7. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Ontology
Gruber (1993):
An ontology is the explicit specification of a conceptualization of a
domain.
controlled vocabulary
provide background knowledge
hierarchically organized

8. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Ontology
Ontologies in pharmacogenomics
drugs and chemicals:
ATC
ChEBI
MeSH
UMLS
diseases:
HumanDO
Human Phenotype Ontology
ICD
MeSH
SNOMED CT
UMLS

9. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Ontology
Ontologies alone do not resolve heterogeneity.
Euzenat (2007):
“[M]erely using ontologies [...] does not reduce heterogeneity: it
just raises heterogeneity problems to a higher level.”

10. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Ontology
Data-driven approach to integration
data- and question-driven integration of ontologies
integration of data and databases through integrated
ontologies
reduction of complexity
background knowledge
hierarchical abstraction
ontology-based data analysis
semantic similarity
statistical tests
graph-/network-based algorithms
data- and question-driven evaluation

11. 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 underlying
disease
identify drug pathways (pharmacokinetics and
pharmacodynamics)
personalized treatment and dosage guidelines based on gene
expression profile

12. 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, gene
regulation
drug–disease associations
gene–disease associations
genotypes–drug response

13. 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 targets
PharmGKB: genotype and drug response
Pathway Interaction Database: biological pathways
CTD: toxicogenomics information (chemical–gene–disease)

14. 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
...

15. 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)
...

16. 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)
...

17. 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)
...

18. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathways
Ontology enrichment analysis can identify over-represented ontology classes.
ontology-based, statistical approach to identify drug and
disease 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 chemical
ontology) for genes in a pathway (drug pathways)

19. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathways
OntoFUNC enables enrichment analyses over OWL ontologies.
OntoFUNC: http://ontofunc.googlecode.com
based on FUNC (http://func.eva.mpg.de)
supports
hypergeometric test
Wilcoxon rank test
binomial test
McDonaldKreitman (2x2 contingency) test
correction for multiple testing (FWER, FDR)

20. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Disease and drug pathways
OntoFUNC 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)

23. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Gene expression and drug response
Ongoing research
Based on a (differential) gene expression profile, can we find
candidate 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

24. 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/protein
gene regulation
protein–protein

25. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Conclusions
knowledge in pharmacogenomics is distributed across multiple
databases
ontologies can enable data integration and integrated data
analysis
integration of knowledge is necessary to enable personalized
medicine
http://pharmgkb-owl.googlecode.com

26. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Acknowledgements
Michel Dumontier
George Gkoutos

27. Introduction Integration and querying Discovering disease pathways Outlook and conclusions
Thank you!