Date post: | 11-Sep-2014 |
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Health & Medicine |
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eTRIKS: A Knowledge Management Service for PPP Translational Research
Yike GuoeTRIKS academic coordinator
Example Stratified Medicine Consortium
2 Billion €
Partnership
I Billion €
Public
I Billion €
Private
Stratified Medicine:• GAUCHERITE Consortium• Stop HCV• MATURA
• RA-Map• COPD-Map
22 CTMM research projects are active, involving a total of 119 partners and a research budget of 302.7 M€.
Scientific Output
Patient enters medical center
Intellectual Property
Improved Healthcare
Experimental data
Downstreamanalysis
Clinical Procedures
Imaging Samples ExperimentsElectronicHealth Record
DataIntegration
External data
Image database Biobank database
Clinical database
Strat Med Project Process
• Clinical Data Capture (& Anonymisation)
• Sample tracking
• Biological assay data capture & processing
• Consortia Data & KM Platform
• Data Analytics tools
• Consortia Collaboration toolbox
Data Management Components
The eTRIKS Project
• Service Project – not Research. ~80% of project activities driven by demand from supported IMI projects (customer driven).
• Mandate to support PPP Translational studies with data & KM services:• Open Platform development, enhancement and support• Installation support• Training• Curation ETL support• Standards development• Data hosting• Limited retrospective content curation to support studies
• Budget: €23.79m for 5 years (Oct 2012---Sept 2017)
• Members:– 10 Pharma, 3 Academic, 1 standards, 2 Commercial Suppliers
The Consortium…
10 Pharma 6 Partners
+
Work Packages
WP1
WP2
WP3
WP4
WP5
WP6
WP7
Platform Deployment
Platform Development
Data Standards
Curation and Analysis
Management and Sustainability
Community and Outreach
Ethics
CNRS/Janssen
Imperial/Pfizer
Roche/IDBS/Merck/CDISC
Luxembourg/Sanofi
AstraZeneca/BioSci Consulting
Janssen/BioSci Consulting
CNRS/SanofiBios
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WP Number WP Name WP Leads
Projects Engaging eTRIKS
Oncology Safety
InflammationRA-Map
InfectionND4BB
• Discoverable Data – Basis for an IMI archive
• Enables re-usable innovation – common plug’n’play interface. Enables entrepreneurial Biz Models
• Minimises re-invention of the wheel by each project: e.g. ‘Big Data’ omic challenges or data security once.
• Facilitates easy interoperation & integration for partners with each new consortia
• Cost effective use of tax payers €s – operational efficiency
• Drives standardisation in data capture and management
Business Logic
This is what we are starting with
Data gets capturedOrganized / ManagedStoredAnalyzedViewed / disseminatedAssimilation / Synthesis
This is what we really need to support
• A key to translational research advancement is allowing a• continuous feedback loop between outcomes of basic and
clinical • research to accelerate translation of data into knowledge
eTRIKS Platform
DM KM
Product Management
Demand
Execution
Progress Reports
IMI Client
Project
Demand1
Demand2
Demand3
Platform Deployment
Platform Development
Data Standards
Curation and Analysis
Community and Outreach
Ethics
eTRIKS Resour
ces
Decision
Delivery Packages
Deliveries
Progress UpdatesProject Input
3-6 Month Cycle
Product Management Process
• All requests for new features (via forms) are be submitted 6 weeks before the Resource Team meeting, in order to be considered. An appointed member of the PMP will consolidate the requests and place on the eTRIKS PM wiki.
• PMP Decision making TC meeting is held 4 weeks before upcoming Resource team meeting, where the ranking proposal will be agreed upon.
• The PMP reviews all requests for entering into eTRIKS product backlog and selects a set of features from the product backlog to be implemented in the following development period following Resource Team meeting approval.
• Potentially, there is an additional PMP meeting (TC), 3 week before the Resource team meeting, in case the PMP decides they require further information and/or a user demo of the requested feature.
• The ranked list from the PMP is be placed on the eTRIKS PM wiki 2 weeks prior to the Resource team meeting, where all eTRIKS participants can comment.
Schematic Representation on PM
Internal Stakeholder Request
External Stakeholder Request
Consolidation of Project Requests of by AM1
Competition Analysis
PM-wiki
Consolidation of all requests by PM2
Ranking of requests by PMP
Proposal to Resource Team
Approval
Development Roadmap
Requirement GatheringRequirementConsolidation
RequirementRanking & Proposal
DevelopmentRoadmap
*3
*3
1: AM - Account Manager, 2: PM – Product Manager, 3: Clarification of request by requesting stakeholder/AM
User Requirement Gathering
Platform
http://requirements.etriks.org/twiki/bin/view/RequestManagement/
User Requirement GatheringKey fields
• Benefit Estimate: Stakeholders must provide for each request an estimate of the relative benefit that each feature provides to the users and/or to achieving the eTRIKS objectives (e.g. establishing a centralized European data base) on a scale from 1 to 5, with 1 indicating very little benefit and 5 being the maximum possible benefit.
• Cost Estimate: Stakeholders interface with the WP2 Architect to provide a rough estimate of the effort (in person month) or required financial investment (in Euros). PMP will transform absolute costs into a relative cost value, again on a scale ranging from a low of 1 to a high of 5. Cost ratings are be based on factors such as the requirement complexity, the extent of user interface work required, the potential ability to reuse existing designs or code, and the levels of testing and documentation needed.
• Risk Estimate / Mitigation Analysis: Stakeholders and WP2 architect should provide a brief description of possible risks associated with the feature development and mitigation strategy for each risk. In addition, Stakeholders and WP2 Architect to estimate the relative degree of technical or other risk associated with each feature on a scale from 1 to 5. An estimate of 1 means you can program it in your sleep, while 9 indicates serious concerns about feasibility, the availability of staff with the needed expertise, or the use of unproven or unfamiliar tools and technologies.
Click here for Request Page
eTRIKS DM Component
Example : GUI Design for Study Repository
One data tree for all investigations- Cross study searches- Every data type viewed in context
Consistent Data Organization
Consistent Vocabulary
Consistent Layout
• Becoming functional – recruitment, op norms, reporting, legal docs, etc
• Production tranSMART v1.1 released this month.– PostgreSQL open platform
• Installations at: Imperial (UBIOPRED), Liverpool (PredictTB), Alacris (Oncotrack), QMUL (RA-Map), Luxembourg (eTRIKS), and CC-IN2P3 (eTRIKS)
• Curation of retrospective public content: – EBI Atlas gene fold change data (subset): SearchApp– Public Asthma & RA related GEO data: DataSetExplorer App– TCGA Datasets (Clinical and Gene Expression Data):
• Breast invasive carcinoma [BRCA]• Colon adenocarcinoma [COAD]• Uterine Corpus Endometrial Carcinoma [UCEC]• Ovarian serous cystadenocarcinoma [OV]
• Support of 5 IMI projects to date:– January: UBIOPRED: server set-up at ICL, 625 patients to date (screening & baseline), Low density
Eicosanoid Lipidomic data, gene expression data, proteomic data and animal model clinical data loaded. Training provided.
– May: Oncotrack, ABIRISK, PredictTB and ABPI/MRC RA-Map: tranSMART installations and training to date.
• Active discussions with 4-5 other projects re requirements and support
Progress (1)
• Requirements gathering1. 2 x User requirement workshops:
• 1. tranSMART developer and user meeting , Amsterdam, June 2013 (tranSMART foundation collaboration)• 2. eTRIKS User requirement session, London, 2013 (eTRIKS Request)
2. Requirements reviewed and consolidated (identical request merged into one)3. eTRIKS Product Management WIKI (functions such as voting and automatic pre-prioritisation: based on number or
requests, benefit, cost and risk estimates, enabled) implemented and requirements uploaded to it
Progress (2)
Proposed Future Model
Patient Stratification
Predicting Therapy Response
Biomarker Discovery: Correlating Signatures to Clinical Outcome
Animal: Human model validation
IMIArchive
DiseaseModelling
COPD -
Local Instance lMI Instance
DataTransfer
ABIRISK
UBIOPRED
StratMed X
SecureFederated
Search(data & samples)
Metadata Query
What Longitudinal Arteriosclerosis
studies have been run in the UK involving >
500 subjects?
• Robust• Responsive• Fit for Purpose• Stable• Supported• Backed-up• Secure• Re-usable• Sustainable• Community Led• Efficient
• Accessible Common Infrastructure
• Federation of searchable archives
of translational study information
• Ability to transfer data securely between
organisations within consortia
• Healthy ecosystem of commercial and
NFP service providers supporting projects
and institutions
• Large and diverse innovative
analytics & visualisation toolbox
Ultimately…
Medical CentresAnalytics
Specialists
DiseaseSpecialists
IT Services
CRO
AssaySpecialists
Regulatoryauthorities
Patient organization
P
P
P
Fully Integrated Stratified
Medicine Ecosystem
1. Ensure the legacy of project data/results 2. Facilitate dataset integration 3. Increase operational efficiency 4. Establish a common set of standards
www.eTRIKS.orgLinked In Discussion Group: eTRIKS Twitter @etriks1
End
• Clinical Data Capture: – EDCs for the capture and validation of the clinical assay and patient data. Needs to be standardised
for project across all recruiting centres. e.g. OpenClinica, REDCap
• Sample tracking: – human and animal biopsy/fluid and cell line samples need tracking as they are stored and shipped
between the consortia partners for assays. Operational logistics tool.
• Biological assay data capture: – Multiple LIMS (laboratory information management systems) platforms for the different assay
technologies (NGS, omic, etc). Either vendor supplied, open source or locally developed. Important that data pre-processing is transparent and results in processed data in standard formats.
• Consortia Data & KM Platform: – A consortia wide platform for normalized data storage, integration, querying, and long term
archiving. Requires multiple ETL processes to export data from the local EDCs & LIMS. Needs a pluggable interface to allow the integration of analytics tools. Archive – but what to keep???
• Data Analytics tools: – Range of commercial, open and local data analysis tools to find signals in the phenotypic and
biological information.
• Consortia Collaboration toolbox: – Tools to support communication, project management and document sharing across the consortia
partners. Basic collaboration tools such as calendar, document management, project management, tc facilities etc, including a common ELN (E-Lab Notebook) for the capture of experimental design, analysis processes, results and conclusions.
Data Management Components