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tranSMART Community Meeting 5-7 Nov 13 - Session 3: The TraIT user stories for tranSMART
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Transcript of tranSMART Community Meeting 5-7 Nov 13 - Session 3: The TraIT user stories for tranSMART
TraIT user stories for tranSMART
tranSMART User Meeting; Paris
Jan-Willem Boiten; Jelle ten Hoeve
7 Nov 2013
Contents
• Introduction TraIT project
• A taster of the existing tranSMART demonstrators
– DeCoDe: colorectal cancer
– PCMM; prostate cancer
• Current user stories on TraIT roadmap
• Implementation within Netherlands Cancer Institute
(Jelle ten Hoeve)
Global positioning of TraIT
Facts & figures:
• Netherlands
(AKA Holland)
• 40.000 km2
• 17 million
people
• 8 UMCs ( )
300
Km
150
Km
CTMM, TIPharma and BMM
offer an integrated approach for innovations in
the Dutch health care sector
CTMM: diagnosis
• Early detection of disease by in-
vitro and in-vivo diagnostics
• Stratification of patients for
personalized treatment
• Assessing efficiency and efficacy
of medicines by imaging
• Image guided delivery of
medication
• Focus on cancer, cardiovascular,
neurodegenerative and infectious
/autoimmune disease.
TIPharma: drugs
• Translational research on novel
pharmaceutical therapies
• Target finding, animal models and
lead selection
• Drug formulation, delivery and
targeting
• Special Theme focusing on the
efficiency of the process of drug
development
BMM: devices
• Smart drug delivery systems
• Innovations in contemporary organ replacement
therapies
• Passive and active scaffolds, including cell
signalling functions
Image guided
drug delivery
Biomarkers
Drug
delivery
Imaging for
regenerative
medicine
CTMM projects
Breast
Prostate Colon
Lung
Leukemia
Heart
Failure
Stroke
Diabetes
Kidney Failure
Arrhythmia
Peripheral Vascular
Disease
Thrombosis
Alzheimer Rheumatoid Arthritis
Sepsis
Growth of active participation in TraIT:
2011 2013: increase from 11 26 partners
Growing TraIT project team
EUR 16 million / 4 years
TraIT aims to support the translational
research process by means of IT
Epi/Genetics
Transcriptome
Peripheral Markers
Organ Systems
DNA Variants,
Copy Number
modifications
mRNA, ncRNA
miRNA
Proteins, Metabolites
Cells, Microbes
Scientific Output
Patient enters medical center
Intellectual Property
Improved Healthcare
Experimental data
Downstream analysis
Clinical Procedures
Imaging Samples Experiments Electronic
Health Record
Data Integration
External data
Image database Biobank database
Clinical database
13 November 2013
9
z
Connecting initiatives
the 21st century
the middle ages
TraIT incentives
• Increase efficiency of translational research
– End to end workflow
– Multicenter studies
– Connect initiatives (ESFRI, IMI, national programs, etc)
• Cope with data challenges
– Volume
– Silo’s
– Interoperability
– Stewardship
– (open) access
• QA/QC
– Improve validity of proof of concepts
– Diminish scientific misconduct
TraIT tools & applications: the landscape
Hospital (IT) Translational Research (IT)
data domains
clinical data
imaging data
experimental data
biobanking
integrated
data
translational
analytics
workbench
HIS
PACS
LIS
Galaxy
tranSMART/
cohort explorer
R tranSMART/i2b2
dataware house CBM-NL
OpenClinica
NBIA + XNAT
e.g.
PhenotypeDB,
coLIMS
e.g.
Galaxy,
Chipster
Samples (IT)
P
s
e
u
d
o
n
y
m
i
z
a
t
i
o
n
Public Data
BIMS
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
nu
mb
er
of stu
die
s
calendar years
OpenClinica Use
2008
July
2008
Oct
2011
Oct
2012
Start
DeCoDe
OpenClinica
Start
TraIT
OpenClinica
Sept
2013
Pre TraIT effect: all multicenter VUmc studies
Also multicenter studies UMCU, UMCN, EMC, Meander MC
31 studies
30 sites 185 users
55 studies
84 sites 300 users
Today
Uptake of OpenClinica
TraIT Data Integration Roadmap
2012 2013 2014/
2015
2012:
Data integration platform
evaluation and selection
tranSMART
2013:
Study driven enhancement
of data integration platform
using “ready to use” data:
=> enhanced functionality
and robustness
(tranSMART++)
2014/2015:
Study-driven system
integration with TraIT data
capturing systems
=> enhanced interoperability
and usability
(TraIT platform)
2012 2013 2014/2015
TraIT foundation team
Foundation team:
• TraIT core development
team
• Adapt & adopt existing
solutions like tranSMART
• Distributed Scrum Team
• Four core centers and
several associated ( )
ones
2 FTE
4 FTE
2 FTE
2 FTE
NKI
Foundation team user stories & epics
• User stories are collected for every potential TraIT customer
project (large research consortia)
• User stories are collected on the TraIT Wiki and broken down
in epics that can be taken up by the foundation team
• Transformed into an actively maintained TraIT roadmap
CAIRO studies
The Dutch Colorectal Cancer Group (DCCG)
provides an excellent infrastructure for the performance of
multicentre clinical studies in patients with colorectal cancer
CAIRO studies: principal investigator Prof.dr. C.J.A. Punt
Collaborative translational research: Prof.dr. G.A. Meijer
combine clinical trial information with molecular profiling data
CAIRO studies
Clinical data, e.g.: - TNM staging - gender
- age - treatment arm of study
Non-omics data, e.g.:
- MSI/MSS - MLH1
- KRAS - BRAF
Genomics:
- Comparative genomic hybridisation microarray (arrayCGH)
Examine study data Overall survival summary statistics in ‘Results’
Comparison of different groups Overall survival in subjects with MSI vs MSS
Comparison of different groups Overall survival in subjects with MSI vs MSS
Survival analysis Overall survival of subjects < and >60 years of age
Survival analysis Overall survival of subjects < and >60 years of age
Comparison of chromosomal alterations
between different groups Are there significant differences between two groups, e.g. MSS vs MSI?
Chromosomal alterations and overall survival
• Most common cancer in men (>900 K ww cases p.a.)
• Every 2.5 minutes a man is newly diagnosed
• Every 19 minutes a man dies from prostate cancer
• Ageing population
The Prostate Cancer Crisis: Statistics
Rudolph Guiliani
diagnosed at age 56
Andrew Lloyd
Webber
diagnosed at age 61
Ryan O’Neal
diagnosed at age
70
Warren Buffet
diagnosed at age
81
26
Data collection
MRI
UltraSound
Clinical
Urine Blood
Tissue
27
Examine study data: summary statistics
Comparison of readcounts (RNA-Seq) between
different groups
Conclusions demo sessions June-Sep 2013
Praise:
• "Oh, wow, you just dragged that in!", "I've never been able to
do this“
• "This is already great for exploring data.“
Conclusions demo sessions June-Sep 2013
But also many new wishes & issues identified:
• Improve user interface
– Standard navigation for all studies
– Zoom in/select (group of) subjects from any plot
• Basic functionality for facilitating data exploration to be
extended
– Better handling of units
– Stratification
– Combinations
• Improve genome/chromosome viewing
– Implement standard genome browser
• Important data sets are still missing
Projects are still not actively using tranSMART
Further roadmap
Current portfolio of projects for a tranSMART implementation:
• DeCoDe: Colorectal cancer (demonstrator available)
• PCMM: Prostate cancer consortium (demonstrator available)
• Maastricht Study: A longitudinal diabetes study
• POSEIDON: A national registry for outcome data in lung cancer
• NKI: Internal data warehouse Netherlands Cancer Institute
• And many more in the queue…….
Each project has specific user stories requiring new features
Currently app. 200 resulting epics on the roadmap
Improvement theme: data security
Data security is number one concern for principal investigators
Study
1
Study
2
Study
…
Inter-study security
Intrusion protection
Intra-study
security
Improvement theme: molecular viewing
wet-lab-person
tech-operator
(bio)informatician
PI / (end)user
PI / (end)user
Recent work: Include Dalliance Genome browser
cBioportal example for molecular viewing
molecular data integration
Processed data Import to TranSMART Suitable for molecular data integration Suitable for viewer Suitable for data querying
Improvement themes: Longitudinal data
Timeline of disease progression
Diagnosis
Surgery
Chemo
Observational studies tend to demand flexible identification of patient events
New use cases: sample data
CBM-NL Summary data
about samples
tranSMART Integration &
study workspace
Biobank
Information
System
Biobank
Information
System
Collect sample summary data
Sample order process
System integration and referenced data
Referencing clinical
images based on meta
data in tranSMART
Referencing pathology
scans based on meta
data in tranSMART
Upload and drill-down into
molecular pipelines using
tools like R and Galaxy
Automated upload of
clinical data from OpenClinica
TraIT/tranSMART at the Netherlands Cancer
Institute
Jelle ten Hoeve
The Netherlands Cancer Institute
• 650 employees • Budget: € 80 million/year • 34 professors • 50 PIs (group leaders) in basic research • 33 PIs in clinical research • distribution among positions in basic research
other; 3% group leader; 7%
postdoc; 31%
PhD student; 29%
technician; 31%
November 2012
+ AvL hospital = Comprehensive Cancer Center
High Performance Computing at NKI-AvL
- 10 High Performance Computers (HPCs) and the Life Science Grid - Each HPC: 32-64 cores, 128-512 GB RAM, 20-40 TB storage - 50 research end users - Linux / Ubuntu, R, Matlab and specialized bioinformatics tools - Support together with IT department
Support
Infrastructure
A Research Datawarehouse stores and integrates research data from many data sources across data domains and makes these accessible to researchers. The main challenges for implementing a research datawarehousing are: • Storage: secure central storage of research data • Search and access: govern search of, and data access to, research data • Data integration: integrate research data across projects and domains • System integration: integrate data from clinical and laboratory software • Sustainability: embed into existing IT architecture and into the organization at
large To clarify the concept ‘research data’, we define ‘data domains’ and ‘data sources’. Data sources can be categorized into three categories: ‘project’ data sources, ‘registry’ data sources, and ‘workflow’ data sources.
Translational Research Datawarehouse
Project Ready Domain # patients
Kinome Yes Clinical, Biobank, Pathology, Molecular
2,500
NKI295 Yes Clinical, Biobank, Pathology, Molecular
295
BOSOM Yes Clinical, Molecular 8,000
MindAct Yes Clinical, Molecular 6,000
Many more …
Data source Domain Department # patients (per year)
EZIS (Electronic Hospital Records)
Clinical Hospital 8,000
Tumor registry All Dept. of Biometrics
PALGA, LMS, MolPA Pathology, Biobanking
Dept. of Pathology 80,000
ART Pathology,Biobanking
Biobanking Core Facility
5,000
Array and BAM repositories
Molecular (Clinical) Genomic Core facility
3,000
Many more ….
IT systems and Curated databases
Clinical and research studies
TransMartEndusers
Groupleaders(clinical)researchers
Pa entSelec on
Researchers
Browse/Extract
ResearchersDatamanagers
Templates
Upload
DATAGOVERNANCE
- QualityControl- Development- Support
Translational Research Datawarehouse
Project Ready Domain # patients
Kinome Yes Clinical, Biobank, Pathology, Molecular
2,500
NKI295 Yes Clinical, Biobank, Pathology, Molecular
295
BOSOM Yes Clinical, Molecular 8,000
MindAct Yes Clinical, Molecular 6,000
Many more …
Data source Domain Department # patients (per year)
EZIS (Electronic Hospital Records)
Clinical Hospital 8,000
Tumor registry All Dept. of Biometrics
PALGA, LMS, MolPA Pathology, Biobanking
Dept. of Pathology 80,000
ART Pathology,Biobanking
Biobanking Core Facility
5,000
Array and BAM repositories
Molecular (Clinical) Genomic Core facility
3,000
Many more ….
ETLs, ETLs, ETLs
IT systems and Curated databases
Clinical and research studies
What do we expect from our community?
Jelle ten Hoeve Project leader NKI
Robbert Hardenberg Integration specialist NKI
Jan Hudecek Scientific programmer NKI
Marco Janssen QQ TraIT WP5 Philips
• A comprehensive Datawarehouse (Clinical + Research data) • Active directory and user roles • ETL tooling • “State of the art” exploration of data and basic analysis • Bioinformatician API (TranSMART R/BioC package) • Upload support for end users - stepwise data upload
And many more…
Acknowledgements