Open Targets workshop at C4X in 2019

Open Targets: integrating genetics,
omics and chemical data for drug
discovery
Denise Carvalho-Silva, PhD
EMBL-EBI | Open Targets
Wellcome Genome Campus
United Kingdom
C4X Discovery
June 18th 2019

Workshop materials
Presentation slides:
Coursebook with exercises
https://tinyurl.com/slides-c4x
https://tinyurl.com/exercises-c4x

• Introduction to Open Targets
• Open Targets Platform: presentation + live demos
• Hands-on exercises
Lunch break à 12:00-13:00
• Open Targets Genetics: presentation + live demo
• Feedback survey, wrap up, further discussion
This session 10:00-15:00

Drug discovery R&D
Lengthy, costly, high attrition
https://www.genengnews.com/insights/how-crispr-is-accelerating-drug-discovery/
DOI: 10.1016/j.molonc.2012.02.004

Open Targets
A partnership to transform drug discovery
Founded in 2014
Founding partners 2016 2017 20182018
Systematic identification and prioritisation of targets

DATA
GENERATION
Therapeutic
hypothesis
Public data
DATA
INTEGRATION
Experimental projects Bioinformatics projects
Knowledge cycle
https://www.targetvalidation.org
https://genetics.opentargets.org

Data generation: Open Targets
www.opentargets.org/science/
• Organoid knockouts (CRISPR-Cas9) - gut epithelium
• IL22 pathway to treat IBD?
• Wellcome Sanger Institute, GSK, University of Cambridge
• Alzheimer’s and Parkinson’s
• CRISPR/Cas9 screens, iPS cells
• Wellcome Sanger Institute, Biogen, Gurdon Institute
Some examples
• > 1,000 cancer cell lines + biomarkers + tractability
• RNASeq, CRISPR/Cas9 screens
• Wellcome Sanger Institute, GSK and EMBL-EBI

Behan et al (2019)
WRN sustains in vivo growth in:
• colorectal
• ovarian
• endometrial
• gastric
• New candidate target for tumours
with MSI (WRN antagonists)
Data now available in the Open Targets Platform e.g.
https://www.targetvalidation.org/evidence/ENSG000001
00941/EFO_0000305?view=sec:affected_pathway

Data integration: Open Targets
Data integration: web resources
https://www.opentargets.org/resources/#open-targets-platform

Target Disease
Evidence

What is a target?
https://www.targetvalidation.org/target/ENSG00000255248
https://www.targetvalidation.org/target/ENSG00000175482?view=sec:genome_browser
28K
targets
Examples

• Modified version of Experimental Factor Ontology (EFO)
• Controlled vocabulary (Coeliac versus Celiac)
• Hierarchy (relationships)
How do we describe our diseases?
• Promotes consistency
• Increases the richness of annotation
• Allow for easier and automatic integration
10K
diseases

Evidence for our T-D associations
https://docs.targetvalidation.org/data-sources/data-sources

Data sources grouped into data types
Data type
Data source
https://docs.targetvalidation.org/data-sources/data-sources

What can you do with
the Open Targets
Platform?
• Target annotations
• Target-disease associations (+ evidence + score)
• Disease annotations
http://www.targetvalidation.org/target/ENSG00000141510
http://www.targetvalidation.org/diseaset/EFO_0000228
https://www.targetvalidation.org/evidence/ENSG00000141510/EFO_0000228

Demo 1
Searching for a disease à targets
What is the evidence for
the association?
Which targets are
associated with my disease?
Is there any data to help me
with target prioritization e.g.
tractability?

Association score à confidence
Which targets have more evidence for an association?
What is the relative weight of the evidence for different targets?
Overall score Genetic Somatic Drugs Pathways Expression Animal modText mining

ΣH
Calculated at
four levels:
• Evidence
• Data source
• Data type
• Overall
Score: 0 to 1 (max)
Aggregation with
(harmonic sum)
ΣH
Note: Each data set has
its own scoring and
ranking scheme
S1 + S2/22 + S3/32 + S4/42 + Si/i2
European Variation
Archive (germline)
UniProt
Gene2Phenotype
GWAS catalog
Cancer Gene Census
European Variation
Archive (somatic)
IntOGen
ChEMBL
Reactome
Expression Atlas
Europe PMC
PhenoDigm
Genetic associations
Somatic mutations
RNA expression
Animal models
Pathways &
systems biology
Text mining
Drugs
*1.0
*1.0
*1.0
*1.0
*1.0
*1.0
*1.0
*1.0
*1.0
*0.2
*0.2
*0.2
Association
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
ΣH
Genomics England
PheWAS catalog*1.0
*1.0
ΣH
ΣH
weight factor
SLAPenrich
PROGENy
ΣH
*0.5
ΣH
ΣH
SysbioΣH
*0.5
*0.5
Four-tier framework

Statistical integration, aggregation and scoring
From evidence to overall score
https://docs.targetvalidation.org/getting-started/scoring
1) Evidence score (e.g. one SNP from a GWAS paper)
2) Data source score (e.g. all SNPs from the GWAS catalog)
3) Data type score (e.g. all sources of Genetic associations)
4) Overall association score

f = sample size (cases and controls)
s = predicted functional consequence (VEP)
c = p value reported in the paper
Computing the score for one evidence
score = f * s * c
f, relative occurrence of a target-disease evidence
s, strength of the effect of the variant
c, confidence of the observation for the target-disease evidence
(Factors affecting the relative strength of GWAS Catalog evidence)

Factorsaffectingtherelativestrengthoftheevidence

Aggregating scores across the data
• Using a mathematical function, the harmonic sum*
where S1,S2,...,Si are the individual sorted evidence scores in descending order
* PMID: 19107201, PMID: 20118918
• Advantages:
A) account for replication
B) deflate the effect of large amounts of data e.g. text mining

Target-Disease Association Score
EuropePMC
(Text Mining)
UniProt
(Manual Curation)
ChEMBL
(Manual Curation)
Overall
association
score
VERY simplified diagram By Andrea Pierleoni

What can you do with
the Open Targets
Platform?
• Target annotations
• Target-disease associations (+ evidence + score)
• Disease annotations
https://www.targetvalidation.org/target/ENSG00000141510
https://www.targetvalidation.org/diseaset/EFO_0000228
https://www.targetvalidation.org/evidence/ENSG00000141510/EFO_0000228

Target annotationsTarget
profile page
https://docs.targetvalidation.org/getting-started/getting-started/target-profile

Target tractability buckets
1 2 3 4 5 6 7 8
• Clinical phases e.g. phase IV (bucket 1)
• Cellular localization e.g. plasma membrane (bucket 4)
• DrugEBIlity – ensemble score e.g. > 0.7 (bucket 5)
• GO cell component (plasma membrane)
1
1 2 3 4 5 6 7 8 99

Target tractability – small molecules
• Clinical precedence:
• Discovery precedence:
• Predictable tractable:
1 2 3
4
5 6
7
8
1 2 3 4 7 5 6 8

Target tractability – antibodies
• Clinical precedence
• Predictable tractable
– high confidence
• Predictable tractable
– med-low confidence
1 2
4 5
6 7 8 99
3
1 2 3 4 5 6 7 8 9

Is my target safe?Target
profile page
https://docs.targetvalidation.org/getting-
started/getting-started/target-profile

Disease annotationsDisease
profile page
https://docs.targetvalidation.org/getting-started/getting-started/disease-profile

Demo 2
Target and disease annotations
Target
profile page
Disease
profile page

Which disease associations are available for my target?
Demo 3
Searching for a target à diseases

Hands-on exercises 1-4
Pages 25-28
Or feel free to explore your target (gene/protein)
and disease of interest

Modes of access → data volume

Batch search
https://www.youtube.com/watch?v=CPkAxnVrt_s

We have a list of 20 possible
targets for multiple myeloma.
https://www.targetvalidation.org/batch-search
Several targets at once
We would like to know if these
targets are represented in
other diseases.
Are there any pathways over
represented in my set of targets?

https://platform-api.opentargets.io/v3/platform/
public/association/filter
?target=ENSG00000163914&size=10000&fields=target.id&fields=disease.id
Server
Endpoint
Parameters
Open Targets Platform REST API*
https://docs.targetvalidation.org/tutorials/rest-api
Open Targets
Python client **
*
** https://docs.targetvalidation.org/tutorials/python-client

User interface or REST API?
• Scale and volume of your search or results
• Flexibility in the search
• Visualisation of the results

How to search
https://platform-api.opentargets.io/v3/platform/public/search?q=PTEN

http://platform-api.opentargets.io/v3/platform
/public/association/filter?
target=ENSG00000171862
&direct=true
How to get all diseases associated with a target

http://platform-api.opentargets.io/v3/platform/
public/association?id=ENSG00000171862
-EFO_0000616
How to get the score for an association

How to get the evidence for an association
http://platform-
api.opentargets.io/v3/platform/public/evidence/filter?target=ENSG00000198947&disease=Orphanet_98896&datat
ype=genetic_association

REST API
https://docs.targetvalidation.org/programmatic-access/rest-api
https://youtu.be/KQbfhwpeEvc

Extra hands-on exercises E1-E4
Pages 29-31
Or feel free to explore your target (gene/protein)
and disease of interest

Open Targets Genetics. Why?
• Refine genetic associations in the Open Targets Platform
• Make sense of GWAS
• Glimpses of the biology behind the association
• Guide target ID?
Lee et al (2017): PMID:29288389

Genetics increases success
Both common and rare variants

Which targets are implicated in disease from common variant
genetic analysis?
Key question

Open Targets Genetics: data sources
https://genetics-docs.opentargets.org/our-approach/data-sources
Functional Genomics
Variant – Gene
Full summary statistics
Neale and colleagues v1
337,199 individuals
2,419 traits
SNP-trait
associations
Human genetics
Variant (GnomAD) – Trait (study)
Sun et al. 2018
Ensembl
VEP
*
**
***
* Javierre et al. 2016
** Andersson et al. 2014
*** Thruman et al. 2012

Open Targets Genetics: data model
S VL G
S Study (traits from UK Biobank and GWAS catalog)
VL Lead variant (associated with traits from GWAS catalog)
VT Tag variant (possible causal, expanded from VL)
G Gene
p-value
GWAS Catalog
UK Biobank
VT
Fine mapping
LD expansion
r2
Posterior Prob.
TSS distance
eQTL (GTEx V7)
pQTL
PCHI-C
FANTOM5
VEP
Aggregated
functional score
https://genetics-docs.opentargets.org/our-approach/pipeline-overview

What can you do
with Open Targets
Genetics?
Variant Trait Gene
• Genes functionally implicated
• Variant association across traits (PheWAS)
• Variants tagged
• Associated traits
• Links to drugs, expression,
pathway, mouse phenotype,
etc in Open Targets Platform
• Independently associated loci
• Overlapping susceptibility loci
V S G
Locus plot
Visualising the associations between traits, variants, and genes
Fine mapping analysis

How to access all this data?
http://genetics-api.opentargets.io/
GraphQL API
Bulk download*
* http://blog.opentargets.org/2019/04/09/open-targets-genetics-release-is-out/
https://genetics.opentargets.io/
User interface

The SNP rs12916 has been
associated with cholesterol LDL
(Teslovich et al 2010)
Demo: searching for a SNP
Can we use Open Targets Genetics
to find which genes are functionally
implicated by this variant?
Which is the nearest protein
coding gene to this variant?
Can we compare Teslovich
et al with other cholesterol
LDL studies?

DATA
GENERATION
Therapeutic
hypothesis
Public data
DATA
INTEGRATION
Experimental projects Bioinformatics
projects
Wrap up

Upcoming data
50K exome sequence data
Community
ML model: learn the weight
to give to the different data
sources e.g. VEP

Our latest publication
http://bit.ly/cite-us

blog.opentargets.org/
@targetvalidate
http://tinyurl.com/opentargets-in
Get in touch
https://tinyurl.com/opentargets-youtube
Open Targets Platform Open Targets Genetics
docs.targetvalidation.org genetics-docs.opentargets.org
support@targetvalidation.org geneticsportal@opentargets.org

Your feedback is important
https://tinyurl.com/london-180619

Details on data sources to associate
targets and diseases
Other Open Targets resources
Extra extra extra

Target safety
• Clinical phases e.g. phase IV (bucket 1)
• Cellular localization e.g. plasma membrane (bucket 4)
• DrugEBIlity – ensemble score e.g. > 0.7 (bucket 5)1
https://docs.targetvalidation.org/getting-started/getting-started/target-profile

Data source: GWAS catalog
• Genome Wide Association Studies
• Array-based chips à genotyping 100,000 SNPs genome wide
Data type

Data source: UniProt
• Protein: sequence, annotation, function
• Manual curation of coding variants in patients
EMBL-EBI train online
Data types

• Variants, genes, phenotypes in rare diseases
• Literature curation à consultant clinical geneticists in the UK
Data source: Gene2Phenotype
https://www.nature.com/articles/s41467-019-10016-3
Data type

Data source: UniProt
• Protein: sequence, annotation, function
• Manual curation of coding variants in patients
Data type

Data source: PheWAS
• Phenome Wide Association Studies
• A variant associated with multiple phenotypes
• Clinical phenotypes derived from EMR-linked biobank BioVU
• ICD9 codes mapped to EFO
Data type

Data source: GE PanelApp
• Aid clinical interpretation of genomes for the 100K project
• We include ‘green genes’ from version 1+ and phenotypes
Data type

Data source: EVA
• With ClinVar information for rare diseases
• Clinical significance: pathogenic, protective
Data types

Data source: The Cancer Gene Census
• Genes with mutations causally implicated in cancer
• Gene associated with a cancer plus other cancers associated
with that gene
Data type

Data source: IntOGen
• Genes and somatic (driver) mutations, 28 cancer types
• Involvement in cancer biology
• TCGA data
• Rubio-Perez et al. 2015
Data type

Data source: ChEMBL
• Known drugs linked to a disease and a known target
• FDA approved for clinical trials or marketing
Data type

Data source: Reactome
• Biochemical reactions and pathways
• Manual curation of pathways affected by mutations
Data type

Data source: SLAPenrich
• 374 pathways curated and mapped to cancer hallmarks
• Divergence of the total number of (TCGA) cancer samples with
genomic alterations
• Mutational burden and total exonic block length of genes
• Downweighed (x 0.5)
Data type

Data source: PROGENy
• Comparison of pathway activities between normal and primary
samples from TCGA
• Inferred from RNA-seq: 9,250 tumour and 741 normal samples
• EGFR, hypoxia, JAK.STAT, MAPK, NFkB, PI3K, TGFb, TNFa,
Trail, VEGF, and p53
Data type

Data source: SysBio
• Curation of four systems biology papers
• Available for six gene lists: ~ 400 genes
• Late onset Alzheimers, cognitive decline, CHD, and IBD
• Score: p-values or rank-based scores if available, if not s=0.5
Data type
https://docs.targetvalidation.org/data-sources/affected-pathways#sysbio

Data source: CRISPR
• papers
• Available for ~ 400 genes
Data type

Data source: Expression Atlas
• Baseline expression for human genes
- target profile page
• Differential mRNA expression (healthy versus diseased):
- target-disease associations
• No propagation in the disease ontology
Data type

Data source: Europe PMC
• Mining titles, abstracts, full text in research articles
• Target and disease co-occurrence in the same sentence
• Dictionary (not NLP)
Data type

Data sources: PhenoDigm
• Semantic approach to associate mouse models with diseases
• Similarity score between a mouse model and a human disease
(Smedley et al 2013)
Data type

Paste the URL in the location bar of your browser
How to run our REST endpoints (option 1)

Command line e.g. CURL –X GET

Use our free Python client *
* https://docs.targetvalidation.org/tutorials/python-client
Advantage: you can change the way the associations are scored e.g. increase the weight given to
text mining data

REST API calls: some examples*
https://platform-api.opentargets.io/v3/platform/public/search?q=alzheimer%27s
https://platform-api.opentargets.io/v3/platform/public/association/filter?target=ENSG00000142192
https://platform-api.opentargets.io/v3/platform/public/association/filter
?target=ENSG00000142192&direct=true
https://platform-
api.opentargets.io/v3/platform/public/evidence/filter?target=ENSG00000142192&disease=EFO_00002
49&datasource=uniprot&direct=true

Open Targets REST API
Private: methods used by the UI to serve external data. Subject to change without notice
https://platform-api.opentargets.io/v3/platform/docs/swagger-ui

LINK
• LINK: Literature coNcept Knowledgebase
• Subject / predicate / object structured relations
From PubMed abstracts
Proof of Concept
Further developement
http://link.opentargets.io/

Addressing text mining shortcomings
• Entities: genes, diseases, drugs
• Concepts extracted via NLP
(Natural Language Processing)
• 28 M documents, 500 M relations
• http://blog.opentargets.org/link/

DoRothEA
• Candidate TF-drug interactions in cancer
• 1000 cancer cell lines
• 265 anti-cancer compounds
• 127 transcription factors
http://cancerres.aacrjournals.org/content/early/2017/12/09/0008-5472.CAN-17-1679
dorothea.opentargets.io

Example: Rapamycin
• ~ 1000 cancer cell lines
• 265 anti-cancer compounds
• 127 transcription factors

CELLector
http://blog.opentargets.org/2019/02/04/looking-for-cell-line-models-to-predict-anticancer-drug-response-in-
patients-cellector-can-help-you/

Open Targets workshop at C4X in 2019

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