This document provides an overview of the TraIT project and existing demonstrators using tranSMART. It discusses the TraIT roadmap and user stories being implemented at the Netherlands Cancer Institute. Key points include: - TraIT aims to support translational research through integrated data and tools across clinical, imaging, biobanking and experimental domains. - Existing demonstrators using tranSMART include DeCoDe (colorectal cancer) and PCMM (prostate cancer). - The roadmap involves enhancing tranSMART functionality based on user needs and integrating additional data sources. - At NKI, tranSMART will provide an integrated research data warehouse with clinical and research data from various sources and departments.

1. TraIT user stories for tranSMART
tranSMART User Meeting; Paris
Jan-Willem Boiten; Jelle ten Hoeve
7 Nov 2013

2. 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)

3. Global positioning of TraIT
Facts & figures:
• Netherlands
(AKA Holland)
300
Km
• 40.000 km2
• 17 million
people
• 8 UMCs (
150
Km
)

4. CTMM, TIPharma and BMM
offer an integrated approach for innovations in
the Dutch health care sector
TIPharma: drugs
• Translational research on novel
pharmaceutical therapies
CTMM: diagnosis
• Early detection of disease by invitro and in-vivo diagnostics
Biomarkers
• Target finding, animal models and
lead selection
• Stratification of patients for
personalized treatment
• Drug formulation, delivery and
targeting
• Assessing efficiency and efficacy
of medicines by imaging
Image guided
drug delivery
• Image guided delivery of
medication
• Focus on cancer, cardiovascular,
neurodegenerative and infectious
/autoimmune disease.
• Special Theme focusing on the
efficiency of the process of drug
development
Imaging for
regenerative
medicine
Drug
delivery
BMM: devices
• Smart drug delivery systems
• Innovations in contemporary organ replacement
therapies
• Passive and active scaffolds, including cell
signalling functions

5. CTMM projects
Stroke
Heart
Failure
Breast
Arrhythmia
Diabetes
Kidney Failure
Lung
Thrombosis
Peripheral Vascular
Disease
Prostate
Colon
Leukemia
Alzheimer
Rheumatoid Arthritis
Sepsis

6. Growth of active participation in TraIT:
2011  2013: increase from 11  26 partners
EUR 16 million / 4 years
Growing TraIT project team

7. TraIT aims to support the translational
research process by means of IT
Epi/Genetics
DNA Variants,
Copy Number
modifications
Transcriptome
mRNA, ncRNA
miRNA
Peripheral Markers
Proteins, Metabolites
Cells, Microbes
Organ Systems

8. Patient enters
medical center
Clinical
Procedures
Electronic
Health Record
Imaging
Samples
Experiments
Clinical database
Image database
Biobank
database
Experimental
data
Data
Integration
External data
Scientific Output
Downstream
analysis
Intellectual
Property
Improved
Healthcare

9. Connecting initiatives
z
13 November 2013
9

10. the middle ages
the 21st century

11. 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

12. TraIT tools & applications: the landscape
Hospital (IT)
HIS
PACS
LIS
Samples (IT)
BIMS
Public Data
P
s
e
u
d
o
n
y
m
i
z
a
t
i
o
n
Translational Research (IT)
data domains
clinical data
integrated
data
OpenClinica
translational
analytics
workbench
imaging data
tranSMART/
cohort explorer
NBIA + XNAT
biobanking
CBM-NL
tranSMART/i2b2
dataware house
R
experimental data
e.g.
Galaxy,
Chipster
e.g.
PhenotypeDB,
coLIMS
Galaxy

13. Uptake of OpenClinica
55 studies
84 sites
300 users
OpenClinica Use
number of studies
35
31 studies
30 sites
185 users
Pre TraIT effect:
all multicenter
VUmc studies
30
25
20
15
Also multicenter studies
UMCU, UMCN, EMC,
Meander MC
10
5
0
1
2
2008 3
4
5
6
7
8
9
10 11 12 13 14 15 16 17 18 19
calendar years
July
2008
Oct
2011
Start
DeCoDe
OpenClinica
Start
TraIT
OpenClinica
Oct
2012
Sept
2013
Today

14. TraIT Data Integration Roadmap
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)
2014/
2015
2013
2012
2012
2013
2014/2015

15. TraIT foundation team
Foundation team:
• TraIT core development
team
NKI
2 FTE
2 FTE
4 FTE
2 FTE
• Adapt & adopt existing
solutions like tranSMART
• Distributed Scrum Team
• Four core centers and
several associated (
)
ones

16. 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

17. 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

18. 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)

19. Examine study data
Overall survival summary statistics in ‘Results’

20. Comparison of different groups
Overall survival in subjects with MSI vs MSS

21. Comparison of different groups
Overall survival in subjects with MSI vs MSS

22. Survival analysis
Overall survival of subjects < and >60 years of age

23. Survival analysis
Overall survival of subjects < and >60 years of age

24. Comparison of chromosomal alterations
between different groups
Are there significant differences between two groups, e.g. MSS vs MSI?

25. Chromosomal alterations and overall survival

26. The Prostate Cancer Crisis: Statistics
• 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
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

27. Data collection
Clinical
MRI
UltraSound
Blood
Tissue
Urine
27

28. Examine study data: summary statistics

29. Comparison of readcounts (RNA-Seq) between
different groups

30. 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.“

31. 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

32. 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

33. Improvement theme: data security
Data security is number one concern for principal investigators
Inter-study security
Intra-study
security
Study
1
Study
2
Intrusion protection
Study
…

34. Improvement theme: molecular viewing
PI / (end)user
wet-lab-person
tech-operator
(bio)informatician
PI / (end)user

35. Recent work: Include Dalliance Genome browser

36. cBioportal example for molecular viewing

37. molecular data
integration
Processed data
 Import to TranSMART
 Suitable for molecular data integration
 Suitable for viewer
 Suitable for data querying

38. Improvement themes: Longitudinal data
Observational studies tend to demand flexible identification of patient events
Diagnosis
Surgery
Chemo
Timeline of disease progression

39. New use cases: sample data
Sample order process
Biobank
Information
System
CBM-NL
Summary data
about samples
Biobank
Information
System
tranSMART
Integration &
study workspace
Collect sample summary data

40. System integration and referenced data
Referencing pathology
scans based on meta
data in tranSMART
Automated upload of
clinical data from OpenClinica
Upload and drill-down into
molecular pipelines using
tools like R and Galaxy
Referencing clinical
images based on meta
data in tranSMART

41. TraIT/tranSMART at the Netherlands Cancer
Institute
Jelle ten Hoeve

42. 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%
technician; 31%
postdoc; 31%
PhD student;
29%
November 2012
+ AvL hospital = Comprehensive Cancer Center

43. High Performance Computing at NKI-AvL
Infrastructure
- 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

44. 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.

45. Translational Research Datawarehouse
IT systems and Curated databases
Data source
Domain
Department
EZIS (Electronic Hospital
Records)
Clinical
Hospital
8,000
Tumor registry
All
Dept. of Biometrics
PALGA, LMS, MolPA
Pathology,
Biobanking
Dept. of Pathology
Pathology,
Biobanking
Biobanking Core
Facility
5,000
Molecular
(Clinical) Genomic
Core facility
3,000
Clinical and research studies
# patients
(per year)
Array and BAM
repositories
Many more
Ready
Domain
# patients
Kinome
Yes
Yes
BOSOM
Yes
Clinical, Biobank, Pathology,
Molecular
Clinical, Biobank, Pathology,
Molecular
Clinical, Molecular
2,500
NKI295
ART
Project
MindAct
Yes
Clinical, Molecular
6,000
80,000
….
Many more
295
8,000
…

46. Translational Research Datawarehouse
IT systems and Curated databases
Data source
Domain
Department
EZIS (Electronic Hospital
Records)
Clinical
Hospital
8,000
Tumor registry
All
Dept. of Biometrics
PALGA, LMS, MolPA
Pathology,
Biobanking
Dept. of Pathology
Pathology,
Biobanking
Biobanking Core
Facility
5,000
Molecular
(Clinical) Genomic
Core facility
3,000
Clinical and research studies
# patients
(per year)
Array and BAM
repositories
Many more
Ready
Domain
# patients
Kinome
Yes
Yes
BOSOM
Yes
Clinical, Biobank, Pathology,
Molecular
Clinical, Biobank, Pathology,
Molecular
Clinical, Molecular
2,500
NKI295
ART
Project
MindAct
Yes
Clinical, Molecular
6,000
80,000
End users ….
Many more
295
8,000
…
TransMart
DATA GOVERNANCE
-
Quality Control
Development
Support
ETLs, ETLs, ETLs
Pa ent Selec on
Browse / Extract
Upload
Templates
Group leaders
(clinical) researchers
Researchers
Researchers
Datamanagers

47. What do we expect from our community?
•
•
•
•
•
•
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
Jelle ten Hoeve
Project leader
NKI
Robbert Hardenberg
Integration specialist
NKI
Jan Hudecek
Scientific programmer
NKI
Marco Janssen
QQ TraIT WP5
Philips

48. Acknowledgements
And many more…