Transcript Ontology for Neurodegenerative Disorders

ECO
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European Centre for
Ontological Research
Ontology and Referent Tracking
for
Neurodegenerative Disorders
Dr. Werner Ceusters
European Centre for Ontological Research
Saarland University, Saarbrücken,
Germany
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Part I.
Neurodegenerative disorders:
modern history
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Examples of
neurodegenerative diseases
• Involving the central nervous system:
–
–
–
–
–
–
–
Alzheimer's Disease
Parkinson Disease
ALS (Lou Gehrig's Disease)
Frontal Temporal Dementia
Huntington's Disease
Cerebellar Ataxias
Hereditary Spastic Paraplegias
• Involving the peripheral nervous system:
– Charcot-Marie Tooth Hereditary Neuropathies
– Muscular Dystrophy.
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R Some disease characteristics
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• Neurodegeneration is a major element.
– But some disorders with ND are not usually classified as
degenerative e.g. multiple sclerosis, epilepsy, some inborn errors
of metabolism, schizophrenia, and even tumours.
• selective, at least initially, for a particular neuronal
pool
• both genetic and environmental risk factors play a
part in the etiology
• a long run-in period until sufficient protein
accumulates, followed by a cascade of symptoms
over 2-20 years, with increasing disability leading
to death
A. Williams. Defining neurodegenerative diseases. BMJ 2002;324:1465-1466 ( 22 June )
ECO Wide range of phenotypes in same
R category, e.g. cerebellar ataxias (SCA)
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• SCA 1: hypermetric saccades and hyperreflexia.
• SCA 2: reduced velocity of saccadic eye movements,
areflexia and changes similar to those seen in
olivopontocerebellar atrophy on brain imaging.
• SCA 3: protruded eyes, muscle fasciculations, spasticity,
chorea, gaze-evoked nystagmus, parkinsonism and
peripheral neuropathy.
• SCAs 5, 6, 10 and 11: pure cerebellar signs.
• SCA 7: macular degeneration.
• SCA 8: mild sensory neuropathy, late-onset spasticity.
• SCA 10: seizures with ataxia.
• SCA 12: head and hand tremors.
• SCA 17: Intellectual deterioration and dysphagia.
T.E. King. Molecular diagnosis of adult neurodegenerative diseases and movement disorders. April
2005. http://www.bioethics-singapore.org/resources/pdf/GeneticTestingMovementDisorders_tanek.pdf
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Histopathological
phenomena
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in some central NDs
L. Bertram and R.E. Tanzi. The genetic epidemiology of
neurodegenerative disease. J. Clin. Invest. 115:1449-1457 (2005).
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Multi-factorial with cross-relations,
R e.g.
genetic epidemiology of Alzheimer
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L. Bertram and R.E. Tanzi. The genetic epidemiology of
neurodegenerative disease. J. Clin. Invest. 115:1449-1457 (2005).
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Main research epochs
• Early 1900
– Microscopic study of stained tissues
– Alzheimer, Lewy, Pick, ...
– NDs classified as clinicopathological entities
• Last decade:
– Molecular genetics and molecular biology
– Advanced functional and sequential imaging
– NDs classified by means of pathological
biochemical pathways
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Research purposes for ND
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• characterize the clinical, laboratory, and
pathological phenotypes of the various
disorders included in this category;
• identify and clone genes directly causing
or functioning as risk factors for these
disorders;
• understand basic mechanisms underlying
the biochemical and molecular
pathogenesis of these disorders;
• Find application to treatment and
prevention
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Research purposes from
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an –omics perspective
• understand the normal functions of genes
and proteins involved in
neurodegenerative diseases,
• characterize their role in pathogenic
disease mechanisms,
• model their functions in animals,
• explore their roles in the diagnosis,
treatment and prevention
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Technical strategy for
analysing ND pathogenesis
• identify pathogenic genes
– by positional cloning,
– by cloning genes that encode proteins involved
in the disease,
– or by combining the two approaches;
• find pathogenic mutations;
• model and study the disease
– in cells by transfection and
– in mice by transgenesis
D. L. Price, S. S. Sisodia, D. R. Borchelt, Science 282, 1078 (1998)
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E.g.: ‘positional cloning’
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• identify large multigenerational families with
a long history of carrying the disorder
• determine linkage with polymorphic genetic
markers
• look for cytogenetic rearrangements
associated with the disease
• isolate overlapping DNA clones from the
region
• identifying the gene that is responsible
ECO
Another strategy for ND gene detection
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L. Bertram and R.E. Tanzi. The genetic epidemiology of
neurodegenerative disease. J. Clin. Invest. 115:1449-1457 (2005).
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New NDs are discovered
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• a previously unrecognized adult-onset dominantly
inherited ND that affects the basal ganglia
associated with iron accumulation.
• Phenotype:
– extrapyramidal symptoms and low ferritin serum levels.
– lesions in the globus pallidus with abundant spherical
inclusions containing aggregates of ferritin and iron.
– axonal swellings throughout the brain
– organs such as the pancreas, liver, and heart that are
typically affected in iron accumulation disease, appear
to function normally
Curtis ARJ, Fey C, Morris CM, et al. Mutation in the gene
encoding ferritin light polypeptide causes dominant adult-onset
basal ganglia disease. Nature Genetics 2001; 28: 350-354.
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Intermediate conclusions
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• NDs are challenging with respect to ‘reality
representation’
– Involve entities of diverse nature
– ‘true’ nature of some entities not yet understood
– Type of relationships unclear
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Part II.
Role of ontology
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Ontology
• ‘Ontology’: the study of being as a science
• ‘An ontology’ : a representation of some preexisting domain of reality which
– (1) reflects the properties of the objects within its
domain in such a way that there obtains a systematic
correlation between reality and the representation itself,
– (2) is intelligible to a domain expert
– (3) is formalized in a way that allows it to support
automatic information processing
• ‘ontological’ (as adjective):
– Within an ontology.
– Derived by applying the methodology of ontology
– ...
ECO Need for widely accepted
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Top Level Ontology (TLO)
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• TLO: an ontology that describes by means
of theories or specifications the most
general, domain-independent categories of
reality such as time, space, inherence,
instantiation, identity, processes, events,
attributes, relations, ...
• Ongoing efforts:
– BFO
– DOLCE
– SUMO
ECO Need for widely accepted
R Biomedical Domain Ontology (BMO)
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• domain ontology:
– an ontology that describes the most general categories
within a specific domain, using the framework of the top
level ontology. In our case: the domain is biomedicine.
• Where a top level ontology describes entities such
as objects and processes, a biomedical domain
ontology:
– describes entities such as genes and insulin, and
transcription and hormon secretion.
– further classifies these entities within the framework of
the top level ontology, thereby adding new descriptive
elements that are relevant at that level of reality.
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Essential (and missing) components
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for a biomedical domain ontology
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– Ontology for functions and processes
• levels of granularity for functions:
• localizing functions and processes to understand their mutual
relationships:
• functional states of molecules
– Ontology for anatomical levels of granularities
• levels of granularity based on grains and structure
• determination of parthood relations across entities in different
levels of granularity
– Pathophysiology ontology
• Dependence relations between physiological entities and
pathology
• Determination of parthood relations for pathological entities
– An upper ontology for health information stored in
public health information databases
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Links to ongoing efforts
• Use BMO:
– To make more DB semantics explicit and formal
– to make the various databases semantically
interoperable at both structure and content level.
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Example:
KEGG Pathway Database on
Neurodegenerative Disorders
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•
•
•
•
•
Alzheimer's disease
Parkinson's disease
Amyotrophic lateral sclerosis (ALS)
Huntington's disease
Dentatorubropallidoluysian atrophy
(DRPLA)
• Prion disease
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KEGG Pathway ND
ECO KEGG Pathway Alzheimer
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KEGG Pathway notation
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Part III.
Referent Tracking
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The missing link
• ‘From genotype to phenotype’
– Most DBs contain data without reference to
particular patients
– Some DBs (usually not publicly accessible) just
have snapshots of correlations
– No DBs provide a dynamically growing pool of
data about interrelated patient phenomena
– Note:
• NOT “interrelated data”
• Relationships between data are distinct from
relationships amongst the entities the data are about
– The right approach: Referent Tracking
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•
Referent Tracking ...
Corrects the overemphasis on data and
information and too little attention to reality:
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–
•
“data modelling”
“information modelling”
Does right what the “Object Oriented” model
claims to do right.
–
–
objects are said to be those things that exist in reality
But: ‘The object-oriented model is based on a
collection of objects’
–
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An object contains values stored in instance variables within the
object.
Unlike the record-oriented models, these values are themselves
objects.
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A look at the database:
R Use of SNOMED codes for ‘unambiguous’
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understanding
PtID
Date
ObsCode
Narrative
How
many
closed
fracture
of shaft numerically
of femur
different
disorders are
Fracture,
closed, spiral
closed
fracture
of shaft?of femur
listed
here
5572
04/07/1990
26442006
5572
04/07/1990
81134009
5572
12/07/1990
26442006
5572
12/07/1990
9001224
5572
04/07/1990
79001
0939
24/12/1991
255174002
2309
21/03/1992
26442006
2309
21/03/1992
9001224
47804
03/04/1993
58298795
Other lesion on other specified region
5572
17/05/1993
79001
Essential hypertension
298
22/08/1993
2909872
298
22/08/1993
9001224
5572
01/04/1997
26442006
How many disorders
have
patients
5572, 2309
Closed
fracture
of radial head
and in298
thus
Accident
publiceach
buildinghad
(supermarket)
closed
of shaftlifetime
of femur
farfracture
in their
?
5572
01/04/1997
79001
Essential hypertension
0939
20/12/1998
255087006
malignant polyp of biliary tract
*
Accident in public building (supermarket)
How many different
benign
polypof
of biliary
tract
types
disorders
are
closed fracture of shaft of femur
listed here ?
Essential hypertension
*
*
Accident in public building (supermarket)
* cause, not disorder
ECO
Would
it
be
easier
if
you
R could see the code labels ?
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PtID
Date
ObsCode
Narrative
5572
04/07/1990
26442006
closed fracture of shaft of femur
5572
04/07/1990
81134009
Fracture, closed, spiral
5572
12/07/1990
26442006
closed fracture of shaft of femur
5572
12/07/1990
9001224
Accident in public building (supermarket)
5572
04/07/1990
79001
Essential hypertension
0939
24/12/1991
255174002
benign polyp of biliary tract
2309
21/03/1992
26442006
closed fracture of shaft of femur
2309
21/03/1992
9001224
Accident in public building (supermarket)
47804
03/04/1993
58298795
Other lesion on other specified region
5572
17/05/1993
79001
Essential hypertension
298
22/08/1993
2909872
Closed fracture of radial head
298
22/08/1993
9001224
Accident in public building (supermarket)
5572
01/04/1997
26442006
closed fracture of shaft of femur
5572
01/04/1997
79001
Essential hypertension
0939
20/12/1998
255087006
malignant polyp of biliary tract
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Proposed solution:
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Referent Tracking
• Purpose:
– explicit reference to the concrete individual entities
relevant to the accurate description of each patient’s
condition, therapies, outcomes, ...
• Method:
– Introduce an Instance Unique Identifier (IUI) for each
relevant individual (= particular, = instance).
– Distinguish between
• IUI assignment: for instances that do exist
• IUI reservation: for entities expected to come into existence in
the future
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PtID
No confusion with
explicit references
Date
ObsCode
Narrative
5572
04/07/1990
26442006
IUI-001
closed fracture of shaft of femur
5572
04/07/1990
81134009
IUI-001
Fracture, closed, spiral
5572
12/07/1990
26442006
IUI-001
closed fracture of shaft of femur
5572
12/07/1990
9001224
5572
04/07/1990
79001
IUI-005
Essential hypertension
0939
24/12/1991
255174002
IUI-004
benign polyp of biliary tract
2309
21/03/1992
26442006
IUI-002
closed fracture of shaft of femur
2309
21/03/1992
9001224
IUI-007
Accident in public building (supermarket)
47804
03/04/1993
58298795
Other lesion on other specified region
5572
17/05/1993
79001
IUI-005
Essential hypertension
298
22/08/1993
2909872
IUI-003
Closed fracture of radial head
298
22/08/1993
9001224
5572
01/04/1997
26442006
IUI-012
closed fracture of shaft of femur
5572
01/04/1997
79001
IUI-005
Essential hypertension
0939
20/12/1998
255087006
IUI-004
malignant polyp of biliary tract
IUI-007
Accident in public building (supermarket)
IUI-007
Accident in public building (supermarket)
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R Essentials of Referent Tracking
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• Generation of universally unique identifiers;
• deciding what particulars should receive a IUI;
• finding out whether or not a particular has already
been assigned a IUI (each particular should
receive maximally one IUI);
• using IUIs in the EHR, i.e. issues concerning the
syntax and semantics of statements containing
IUIs;
• determining the truth values of statements in
which IUIs are used;
• correcting errors in the assignment of IUIs.
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Architecture of a
Referent Tracking System (RTS)
• RTS: system in which all statements referring to
particulars contain the IUIs for those particulars
judged to be relevant.
• Ideally set up as broad as possible:
– some metrics:
• % of particulars referred to by means of IUI
• % of HCs active in a region
– Geographic region
– functional region: defined by contacts amongst patients
• % of patients referred to within a region
• Services:
– IUI generator
– IUI repository: statements about assignments and reservations
– Referent Tracking ‘Database’ (RTDB): index (LSID) to statements
relating instances to instances and classes
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Ultimate goal
Ontology
continuant
disorder
person
CAG repeat
EHR
Juvenile HD
#IUI-1 ‘affects’ #IUI-2
#IUI-3 ‘affects’ #IUI-2
#IUI-1 ‘causes’ #IUI-3
...
Referent Tracking
Database