Unleash transcriptomics to gain insights in disease mechanisms: integration in the Galapagos knowledge platform “Hydra”

1. © Copyright 2018 Galapagos NVCONFIDENTIAL
Unleash transcriptomics to gain insights in
disease mechanisms: integration in the
Galapagos knowledge platform “Hydra”
Maté Ongenaert, PhD
Senior Scientist Bioinformatics
László Kupcsik, Gabriele Sclauzero, Markus Wyss, Joeri Samson, Jan Van Der Schueren
August 30 2018
Leveraging Therapeutic Discovery with Curated Transcriptomic Data - Novartis Campus, Basel, Switzerland

2. 2
Outline
Galapagos in a nutshell
Public / private data integration
Introduction – transcriptomics at Galapagos
StudyExplorer: cross-species / cross- platform /
cross-study integration and multi-level exploration
Hydra: knowledge platform
disease portal application

3. 3
Galapagos
In a nutshell
• Galapagos is a clinical-stage biotechnology company, specialized in the
discovery and development of small molecule medicines with novel modes
of action
• “Flagship” program: filgotinib (JAK-1 inhibitor) in inflammation (Phase 3
studies in Rheumatoid arthritis, Crohn’s disease and ulcerative colitis)
• Advanced programs in lung fibrosis, cystic fibrosis, osteoarthritis and
atopic dermatitis

4. 4
Transcriptomics at Galapagos
Targets, disease and compounds
Target
discovery
Lead
discovery
Lead
optimization
Pre-clinical
development
Clinical
Phase I
Clinical
Phase II
Target Disease Compound
• Target discovery and prioritisation
• Disease mechanism elucidation
• Cellular systems and primary cell disease models characterisation
• Animal models and disease mechanisms
• Compound mode of action (in-vitro, cellular systems, in-vivo)
• Biomarkers

5. 5
Public/private data integration
Curation and integration
Ontologies
Expert
curation
Robust
processing
Internal data
Key assay assets
Primary cell assays
Animal models
Compounds
Compound MoA
Public data
Complementary
data & knowledge
- Disease biomarkers
and biology
- Drug / reference
compounds mode of
actions and disease
relevance
- Drug response
Public and private data integration with proper curation and
ontologies and robust processing and data management is
the foundation for all that is next…

6. 6
Next level of integration
Meta-analysis across platforms
Ontologies
Expert
curation
Robust
processing
Internal data Public data
• Meta-analysis layer
 Integration at the single disease / disease area level for scientific
experts in that area
 Across studies, across platforms
 Apples vs. pears
 Careful selection of appropriate studies
 Careful curation and annotation of the comparisons / contrasts
 Challenges vs. opportunities
 Loss of the power of the original study
 Focus on still accurate and robust results across studies

7. 7
Next level of integration
Meta-analysis across platforms
• Meta-analysis results
 MetaIntegrator R/BioConductor package
 Hedges’ g effect size for each gene in each
dataset
 Summary effect size using a random effect
making use of variance of a gene within a given
dataset and the inter-dataset variation
 Main outputs: effect size and False Discovery
Rate (FDR)
 Example in IPF (idiopathic pulmonary fibrosis)
 10 studies (private and public studies)
 5 different platforms
 > 500 samples

8. 8
Next level of integration
Meta-analysis across platforms - layers
• Meta-analysis: adding levels to the exploration
 Meta-analysis level
 Across studies, across platform
 Loss of accuracy at the original study level
 Focussed on global performance, robustness
 Differential expression level
 Single study level
 Analysis statistics as originally performed
 Expression levels in groups
 Sample/group individual data
 Assessment of heterogeneity, subgroups
StudyExplorer
R/Shiny dynamic
reporting
interfaces
Flexible user-
interfaces for
exploration across
all these data
layers
Suitable graphics
for each datalayer

9. 9
Next level of integration
Meta-analysis across platforms - implementation
Ontologies
Expert
curation
Robust
processing
Manual dataset selection and curation, ontology linking
Aided by Genevestigator frontend
Genevestigator API
Data export / calculation
Meta-analysis
Interface

10. 10
Next level of integration
StudyExplorer application
• StudyExplorer – meta-analysis level

11. 11
Next level of integration
StudyExplorer application
• StudyExplorer – meta-analysis level > study-level > sample level

12. 12
Next level of integration
StudyExplorer application
• StudyExplorer – flexible platform
 Cross-species, human patient data and animal models side by side

13. 13
Next level of integration
StudyExplorer application
• StudyExplorer – flexible platform
 Time-series and other visualisations

14. 14
Next level of integration
StudyExplorer application
• StudyExplorer – flexible platform
 Time-series and other visualisations

15. 15
Next level of integration
StudyExplorer application
• StudyExplorer – flexible platform
 Molecular signatures
 Layering at the ‘gene’ level: exploration at signature level and performance of underlying genes

16. 16
Disease 1
Transcriptomics
Scientific literature
Animal models
Screening data
Disease 2
• “Hydra” knowledge platform
 Integration
 Across diseases and therapeutic areas
 Across different datasources
 Knowledge capturing and sharing
 Modular architecture
Knowledge generation
Hydra platform principles
Transcriptomics
Scientific literature
Animal models
Screening data
• Hydra application: disease portal
 Global overview of relationships
between targets and diseases
 Layered exploration: overviews and
details
 Knowledge capturing and sharing

17. 17
Hydra
Architecture
API
BioPortal
API
Ontology
API
GeneCards
API
Players/genes
TextMining
TenWise
Transcriptomics
GeneVestigator
API
Knowledge Building
Services
Disease Portal

18. 18
Knowledge generation
Hydra - Disease Portal
• Get started! Ontology powered searching and autocompletion

19. 19
Knowledge generation
Hydra - Disease Portal
• Disease portal summary overview pages (target/disease matrix)

20. 20
Knowledge generation
Hydra - Disease Portal
• Disease portal – transcriptomics aspects

21. 21
Knowledge generation
Hydra - Disease Portal
• Disease portal – transcriptomics aspects

22. 22
Knowledge generation
Hydra - Disease Portal
• Disease portal – knowledge generation by combining datasources

23. 23
Public-private data integration: expert annotation and curation are crucial
Flexible framework for integrations – layered exploration benefits from curation
and annotation and powerful API components
Hydra knowledge platform: infrastructure build for hypothesis exploration and
knowledge generation

24. 24
Many thanks to all involved Galapagos
colleagues, Nebion and XAOP