aetionomy overview ad/pd conference 2015 nice
TRANSCRIPT
Mission
To increase knowledge of the causes of Alzheimer´s and Parkinson´s Disease by genera<ng a mechanism-‐based taxonomy; to validate the taxonomy in a prospec<ve clinical study that demonstrates its suitability for iden<fying pa<ent subgroups (based on discrete disease mechanisms); to support future drug development and lay the founda<on for improved iden<fica<on and treatment of pa<ent subgroups currently classified as having AD or PD.
AETIONOMY: A Big Data Approach for the Genera:on of a Mechanism-‐Based Taxonomy for AD and PD
AD/PD conference, March , 2014
Duncan McHale Martin Hofmann-Apitius
In 2011, Kola and Bell published a remarkable paper in Nature Reviews Drug Discovery. With their “Call to reform the taxonomy of human disease” they proposed a new, mechanism-‐based classifica:on of human disease.
Kola, I., & Bell, J. (2011). A call to reform the taxonomy of human disease. Nature Reviews Drug Discovery, 10(9), 641-‐642.
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Cartoon kindly provided by Reinhard Schneider, LCSB
N OM Y The AETIONOMY
concept
The AETIONOMY concept is based on a BIG DATA paradigm:
• Comprehensive and systema:c harves:ng, re-‐annota:on and cura:on of all relevant public data
• GeneraZon of semanZc frameworks that support the integra:on and interoperability of data through metadata annota:on
• SystemaZc capturing of relevant knowledge and modelling of disease in dedicated, knowledge-‐based disease models
• IdenZficaZon of testable hypotheses represenZng putaZve disease mechanisms through data-‐ and knowledge-‐driven model valida:on and mining
• ValidaZon of a selecZon of testable hypotheses in the course of a prospec:ve clinical study
The AETIONOMY Concept
• Organising all relevant data with proper curaZon and annotaZon in an indicaZon-‐specific data cube
• ConstrucZon of disease models that capture and represent available knowledge and make it amenable for mining purposes
• Development of disease ontologies providing the semanZc framework for proper metadata – annotaZon of data and supporZng informaZon retrieval and knowledge discovery
• ImplementaZon of these modules in a service-‐oriented architecture linking data and knowledge to appropriate data analysis, data visualisa:on and knowledge discovery services
The Problem-‐Solving Approach in AETIONOMY
The AETIONOMY Knowledge Base: Organising Data, Models and Knowledge to make them amenable for modelling and mining
Causal Relationship Models Data Cube (the „axis model“)
Taxonomies (ordering principles; metadata)
Analysis workflows / visualisation
Mechanisms, measurable features and stra:fica:on ....
Mechanisms, measurable features and stra:fica:on ....
There is not a single coherent data set covering all aspects ....
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Where does AETIONOMY stand a^er 1 year of work?
The AETIONOMY Knowledge Base is online !
The AETIONOMY Knowledge Base is online !
The Alzheimer Disease Ontology (ADO)
Malhotra, Ashutosh, et al. Alzheimer's & Demen1a (2013)
Preparatory Work: The Parkinson´s Disease Map
PD Disease Map generated in CellDesigner Contributed by AETIONOMY partner LCSB (Luxembourg) See: h`p://hdl.handle.net/10993/2261
Capturing Knowledge on Causes and Effects: OpenBEL
a(CHEBI:corticosteroid) -| bp(NCI:”Tissue Damage”)
The abundance of molecules designated by the name “corticosteroid” in the CHEBI namespace.
The biological process designated by the name “Tissue Damage” in the NCI namespace.
decreases
Term Expression RelaZonship Term Expression
Subject Predicate Object
The World´s largest Computable Knowledge Representa:on on AD
Kodamullil, et al. Alzheimer's & Demen1a, in press
N OM Y First Candidate
Mechanisms
OpenBEL – based Mechanism-‐Iden:fica:on
!
OpenBEL model-‐model comparison results in the first mechanism-‐hypothesis generated in AETIONOMY: a possible involvement of the NGF-‐NGFR-‐BDNF pathway in early decision-‐making of the neuron on Neuron Survival vs. Apoptosis. Note the integra:on of gene:c variance informa:on in OpenBEL Kodamullil et al., Alzheimer´s & DemenZa, in press
OpenBEL – based Mechanism-‐Iden:fica:on
Mining of co-‐morbidity informaZon results in the second mechanism-‐hypothesis generated in AETIONOMY: a possible link between insulin receptor pathway, mTOR-‐induced autophagy and APP pepZde clearance Suppor:ve evidence from SNPs that are shared by AD and T2DM
!
SNP-‐based iden:fica:on of candidate mechanisms
Contributed by Luc Canard, Gordon Ball, Jesper Tegner, Ma` Page, Suhas.Vasaikar
N OM Y The Final Outcome of
AETIONOMY
A knowledge base comprising curated, re-‐annotated and well-‐organised data; knowledge and disease models that will sustain over the next 10 years
Modelling and Mining Workflows suited for the generaZon of new mechanism-‐hypotheses with a special focus on early dysregulaZon events
A set of testable hypotheses on disease mechanisms of which a subset has been validated in a clinical study. The clinical study links us to the EPAD project
A first version of a mechanism-‐based taxonomy for AD and PD providing the high level structure for a future taxonomy of neurodegeneraZve diseases that will be populated with more and more disease mechanisms over Zme
AETIONOMY Expected Outcome
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The People and the InsZtuZons behind the Project
AETIONOMY Partners
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UCB
EMC
BI, Fraunhofer, LUH, UKB
ICM, SARD
IDIBAPS
UCL
NeuroRad PHI
AE, LCSB (UL)
KI
Novar:s
AETIONOMY is Part of the IMI AD Research Plaborm