Making the most of phenotypes in ontology-based biomedical knowledge discovery

Making the most of phenotypes
in ontology-based biomedical
knowledge discovery
1
Michel Dumontier, Ph.D.
Associate Professor of Medicine (Biomedical Informatics)
Stanford University
@micheldumontier::Biostats:19-02-15

Topics
• Computable Phenotypes
• Methods to compare Phenotypes
• Cross-Species Phenotype Integration
• Applications
– Undiagnosed Diseases
– Drug Target Identification
– Drug Repurposing

Phenotypes
• A phenotype is an observable characteristic of
an individual and typically pertains to its
morphology, function, and behavior.
– qualitative, deals with normal and abnormal phen.
– red eye color, abnormal gait, enlarged colon

Diagnosis
uses observable/measured phenotypes
“Phenotypic Profile”

Matching patients to diseases
Patient
Disease X
Differential diagnosis with similar but non-matching phenotypes is difficult
Flat back of head Hypotonia
Abnormal skull morphology Decreased muscle mass

Differential diagnosis becomes challenging
with rare and complex disorders
• Over 7000 rare diseases
• < 1 in 1500-2500
• Most have fewer than 50
case reports
• Nearly 1 in 10 Americans
suffer from one or more
rare diseases
• Only 250 medicinal
products have been
approved to diagnose and
treat rare diseases
Carpenter Syndrome
- acrocephalopolysyndactyly (ACPS)
disorder
- 40 cases described in the literature
- <1 in 1M

Genotypes + Phenotypes
Improves Diagnosis
Remove off-target, common variants,
and variants not in known disease
causing genes
http://compbio.charite.de/PhenIX/
Target panel of 2741 known
Mendelian disease genes
Compare
phenotype
profiles from:
Clinvar, OMIM,
Orphanet
Zemojtel et al. Sci Transl Med. 2014. 6(252):252ra123

PhenIX helped diagnose 11/40 patients

So how did they do it?
1. Computable representation of phenotypes
2. Methods to compare phenotype profiles
3. Using model organisms to increase coverage
of the phenotype space

Difficult to find all results
using text searches

The Human Phenotype Ontology:
A Computable Representation of Human Phenotypes
11,000+ classes
Follows the True Path Rule
Used to annotate:
• Patients
• Disorders/Diseases
• Genes, Gene Variants,
& Genotypes
Reduced pancreatic
beta cells
Abnormality of
pancreatic islet
cells
Abnormality of endocrine
pancreas physiology
Pancreatic islet
cell adenoma
Pancreatic islet cell
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
Abnormality of exocrine
pancreas physiology
Köhler et al. Nucleic Acids Res. 2014 Jan 1;42(1):D966-74.

HPO has unique terms
Winnenburg and Bodenreider, ISMB PhenoDay, 2014

Increased numbers of
diseases are described
using the HPO
Phenotype annotations per species
http://www.monarchinitiative.org

Phenotype “BLAST”: Which phenotypic
profile is most similar?
Disease X
Patient
Disease Y

Phenotips: Getting high quality
patient phenotypes
Girdea et al. (2013), PhenoTips: Patient Phenotyping Software for Clinical and Research
Use. Hum. Mutat., 34: 1057–1065. doi: 10.1002/humu.22347

Semantic Similarity
• Semantic similarity is a metric defined over a set of
terms, where the distance between them is based on
their meaning.
• It can be estimated by examining, for instance,
– Topological similarity
– Information content
– Statistical co-occurrence
• Widely used in bioinformatics for gene enrichment,
function prediction, network screening, clustering,
etc.

= X
similarity

Measures of Semantic Similarity
Edge-Based Measures
– Shortest path (Rada)
– Common path
– Scaling by depth, etc.
• Requires uniform distribution
of nodes and edges
Node-based Measures
– Shared terms
– Common ancestors
– Information content (IC)
• Better able to account for
structural heterogeneity
Set comparisons
• Pairwise
– Max/average/sum
– All or best pairs
• Groupwise
– Set, graph, vector
– Various combinations
Implementations
– Semanticmeasureslibrary.org
– OWL-SIM
Semantic Similarity in Biomedical Ontologies
PLoS Comput Biol. 2009 Jul; 5(7): e1000443.

Term specificity
𝑖𝑐 𝑡 = −log(𝑃 𝑡 )
𝑖𝑐 𝑡 = 𝑑𝑒𝑝𝑡ℎ 𝑡 𝑥 1 −
log 𝑑𝑒𝑠𝑐 𝑡 + 1
log 𝑡𝑜𝑡𝑎𝑙 𝑡𝑒𝑟𝑚𝑠
Structure-based
Corpus-based
by: Heiko Muller, CSIRO

Group-wise Similarity
𝐽 𝐴, 𝐵 =
|𝐴 ∩ 𝐵|
|𝐴 ∪ 𝐵|
𝐽 g1, g2 =
6
11
= 0.55

Group-wise Semantic Similarity
IC(g1) = 10.66
IC(g1) = 9.79
IC(g1 ⊕ g2) = 2.79
-------------------
sim(g1,g2)=0.27
𝑠𝑖𝑚 g1, g2 =
1
2
𝐼𝐶(g1 ⊕ g2)
𝐼𝐶(g1)
+
𝐼𝐶(g1 ⊕ g2)
𝐼𝐶(g2)
X. Chen et al. Gene. 2012. 509(1):131-5

Robustness of phenotype annotations

Image credit: Viljoen and Beighton, J Med Genet. 1992
Schwartz-jampel Syndrome, Type I
 Schwartz-jampel Syndrome,
Type I
 Caused by Hspg2 mutation, a
proteoglycan
~100 phenotype annotations

Similarity of Schwartz-jampel Syndrome derivations

Semantic similarity
is robust in the face of missing information
 92% of derived profiles are most similar to original
disease profile
Profile Similarity Derived Profile Rank

Semantic similarity algorithms
are sensitive to specificity of information
 The more general the phenotype, the poorer the
match the disease
Profile Similarity Derived Profile Rank

Annotation Sufficiency Score
http://www.phenotips.orghttp://www.monarchinitiative.org

Problem: less than 40% of human
genes are annotated with phenotypes
GWAS
+
ClinVar
+
OMIM

B6.Cg-Alms1foz/fox/J
increased weight,
adipose tissue volume,
glucose homeostasis altered
ALSM1(NM_015120.4)
[c.10775delC] + [-]
GENOTYPE
PHENOTYPE
obesity,
diabetes mellitus,
insulin resistance
increased food intake,
hyperglycemia,
insulin resistance
kcnj11c14/c14; insrt143/+(AB)
A multi-species inventory of phenotypes
from genetic perturbations

Down Syndrome Mouse
Ts65Dn mice survive to adulthood and express
some characteristics of Down syndrome such
as developmental delay, hyperactivity, weight
problems, craniofacial dysmorphology,
impaired learning, and behavior deficit

Each species uniquely covers a
different set of phenotypes
Provides an opportunity to use this information to inform
human disease @micheldumontier::Biostats:19-02-1531

Human and model phenotypes can be
linked to >75% human genes

Problem: Clinical and model
phenotypes are described differently

lung
lung
lobular organ
parenchymatous
organ
solid organ
pleural sac
thoracic
cavity organ
thoracic
cavity
abnormal lung
morphology
abnormal respiratory
system morphology
Mammalian Phenotype
Mouse Anatomy
FMA
abnormal pulmonary
acinus morphology
abnormal pulmonary
alveolus morphology
lung
alveolus
organ system
respiratory
system
Lower
respiratory
tract
alveolar sac
pulmonary
acinus
organ system
respiratory
system
Human development
lung
lung bud
respiratory
primordium
pharyngeal region
Problem:
Each organism uses different vocabularies
develops_from
part_of
is_a (SubClassOf)
surrounded_by

Reduced pancreatic
beta cells
Abnormality of
pancreatic islet
cells
pancreas physiology
Pancreatic islet
cell adenoma
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
pancreas physiology
abnormal
pancreatic
beta cell
mass
abnormal
pancreatic
beta cell
morphology
abnormal
pancreatic islet
morphology
abnormal
endocrine
pancreas
morphology
abnormal
pancreatic
beta cell
number
abnormal
pancreatic
alpha cell
morphology
abnormal
pancreatic
alpha cell
differentiation
abnormal
pancreatic
alpha cell
number

Enhance lexical approach with OWL
bridging axioms
• Key idea:
– Describe the phenotype in a machine-interpretable
way
• Break it down into digestible chunks!
• Logical definition
– The machine will then be able to help you
• Match phenotypes
• Automate ontology checking and addition of new terms
• Approach:
– Use Web Ontology Language (OWL), a description
logic to describe phenotypes
– Use OWL reasoning to find connections
Mungall et al. (2012). Genome Biology, 13(1), R5
Köhler et al. (2014) F1000Research 2:30
Haendel et al. (2014) JBMS 5:21
Hoendorf et al. (2011). NAR 39(18):e119
Hoendorf et al. (2011) Bioinformatics 27(7):1001

abnormal
pancreatic
beta cell
mass
abnormal
pancreatic
beta cell
morphology
abnormal
pancreatic islet
morphology
type B
pancreatic
cell
islet
of
Langerhans
endocrine
pancreas
part
of
part
of
abnormal
endocrine
pancreas
morphology
part
of

abnormal
pancreatic
beta cell
mass
abnormal
pancreatic
beta cell
morphology
abnormal
pancreatic islet
morphology
type B
pancreatic
cell
islet
of
Langerhans
endocrine
pancreas
part
of
part
of
abnormal
endocrine
pancreas
morphology
part
of
mass
morphology
quality

Reduced pancreatic
beta cells
Abnormality of
pancreatic islet
cells
pancreas physiology
Pancreatic islet
cell adenoma
adenoma
Insulinoma
Multiple pancreatic
beta-cell adenomas
pancreas physiology
abnormal
pancreatic
beta cell
mass
abnormal
pancreatic
beta cell
morphology
abnormal
pancreatic islet
morphology
abnormal
endocrine
pancreas
morphology
abnormal
pancreatic
beta cell
number
abnormal
pancreatic
alpha cell
morphology
abnormal
pancreatic
alpha cell
differentiation
abnormal
pancreatic
alpha cell
number
inferred
from CL
inferred
from PATO
‘abnormal phenotype’ and
has_entity some ‘type B pancreatic cell’ and
has_quality some amount
‘abnormal phenotype’ and
has_entity some ‘type B pancreatic cell’ and
has_quality some ‘reduced amount’

Monarch Cross-Species Similarity

PhenomeDrug
Computational methods that use phenotypes to
predict drug targets, drug effects, and drug
indications

animal models provide insight for on target effects
• In the majority of 100 best selling drugs ($148B in
US alone), there is a direct correlation between
knockout phenotype and drug effect
• Immunological Indications
– Anti-histamines (Claritin, Allegra, Zyrtec)
– KO of histamine H1 receptor leads to decreased
responsiveness of immune system
– Predicts on target effects : drowsiness, reduced
anxiety
Zambrowicz and Sands. Nat Rev Drug Disc. 2003.

Identifying drug targets
from mouse knock-out phenotypes
drug
gene
phenotypes effects
human gene
non-functional
gene model
ortholog
similar
inhibits
Main idea: if a drug’s phenotypes matches the phenotypes of a
null model, this suggests that the drug is an inhibitor of the gene

Terminological Interoperability
(we must compare apples with apples)
Mouse
Phenotypes
Drug effects
(mappings from UMLS to DO, NBO, MP)
Mammalian
Phenotype
OntologyPhenomeNet
PhenomeDrug
poor
coordination
decreased gut
peristalsis
axon
degeneration
decreased
stride length
erotypic
ehavior
Abnormal
EEG
failure to find
food
Unstable
posture
Constipation
Neuronal loss in
Substantia Nigra
Shuffling gait
Resting tremors
REM disorder
Hyposmia
poor rotarod
performance
decreased gut
peristalsis
axon
degeneration
decreased
stride length
sterotypic
behavior
abnormal
EEG
failure to find
food
abnormal
coordination
abnormal
digestive
physiology
CNS neuron
degeneration
abnormal
locomotion
abnormal
motor function
sleep
disturbance
abnormal
olfaction

Semantic Similarity
Given a drug effect profile D and a mouse model M, we
compute the semantic similarity as an information weighted
Jaccard metric.
The similarity measure used is non-symmetrical and
determines the amount of information about a drug effect
profile D that is covered by a set of mouse model
phenotypes M.

Loss of function models predict
targets of inhibitor drugs
• 14,682 drugs; 7,255 mouse genotypes
• Validation against known and predicted inhibitor-target pairs
– 0.76 ROC AUC for human targets (DrugBank)
– 0.81 ROC AUC for mouse targets (STITCH)
• diclofenac (STITCH:000003032)
– NSAID used to treat pain, osteoarthritis and rheumatoid arthritis
– Drug effects include liver inflammation (hepatitis), swelling of liver
(hepatomegaly), redness of skin (erythema)
– 49% explained by PPARg knockout
• peroxisome proliferator activated receptor gamma (PPARg) regulates metabolism,
proliferation, inflammation and differentiation,
• Diclofenac is a known inhibitor
– 46% explained by COX-2 knockout
• Diclofenac is a known inhibitor
Hoehndorf R, Hiebert T, Hardy NW, Schofield PN, Gkoutos GV, Dumontier M. Mouse model phenotypes provide
information about human drug targets. Bioinformatics. 2014 Mar 1;30(5):719-25

Computational Drug Repurposing
• Similarity
– Guilt by association
– If drug i is similar to drug j, and drug i treats
disease x, then drug j may treat disease x
• Complementarity
– if the signature of drug i complements/counters
the signature of disease x, then drug i may treat
disease x

PhenomeDrug:
phenotypic complementarity
• Extends the idea to match opposing drug-
disease phenotypes
– Drugs that induce hypotension may be useful in
treating hypertension
• Problem: We don’t have any information
about phenotypic complementarity
– We generated over 300 antonym pairs for the
Human Phenotype Ontology
– Developed a measure to compute phenotypic
complementarity

Phenotype-Based Drug Repurposing

Preliminary Results
• Suggest that for some
well annotated diseases,
we recapitulate top drug
candidates
• Quality of drug
annotation is an issue
– Some drugs have
insufficient annotations to
find “good” matches
• Full assessment underway
• Pulmonary Arterial
Hypertension

Summary
• Ontologies provide the structure and semantics
by which phenotypes can be accurately
represented and computed with
• Measures of semantic similarity in combination
with terminological integration enable a broad
diversity of ontology-based analyses, including
– Diagnosis of rare diseases
– Identifying human drug targets
– Drug repositioning

Acknowledgements
Dumontier Lab
• Tanya Hiebert
• Joachim Baran
PhenomeDrug
• Robert Hoehndorf
• George Gkoutos
Monarch Initiative
• Melissa Haendel
• Peter Robinson
• Chris Mungall
• the Monarch Team

dumontierlab.com
michel.dumontier@stanford.edu
Website: http://dumontierlab.com
Presentations: http://slideshare.com/micheldumontier
53 @micheldumontier::Biostats:19-02-15

Making the most of phenotypes in ontology-based biomedical knowledge discovery

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Making the most of phenotypes in ontology-based biomedical knowledge discovery