Organized by the Dutch Techcenter for Life Sciences (www.dtls.nl).

1. Towards Next Generation Life Science
a case study
Prof. Alain van Gool
Netherlands Organisation for Applied Scientific Research (TNO)
Radboud University Nijmegen Medical Centre
Radboud University Nijmegen
DTL Next Generation Life Sciences Workshop
Utrecht, 23 October 2013

2. Next Generation Life Science through
Open Innovation Networks
2
(Source: Model TNO’s Holst Center)
Old New

3. Features of an Open Innovation Network
3
Pharma cies
Biobanks
Analytical
laboratories
Diagnostic cies
D
C
BA
Others
Access to a network of relevant scientists, reagents and technologies
Access to relevant markets and secondary networks
Flexible use of expertise and capabilities in network under standardised agreements
Funding for joined and bilateral projects
Accelerated translation of knowledge to innovation
C1
C2
C3
C..
Technology 1
Technology 2
Technology 3
Technology ..
Eg for biomarker R&D:

4. Outline
4
Backgrounds
TNO
Radboudumc
Biomarker Development Center
Next Generation Life Science
Case study
Identification of microRNA targets for miRNA-based cancer therapeutics
Radboudumc Urology
InteRNA
Radboud Proteomics Centre
TNO System Biology

5. TNO =
Netherlands Organisation for Applied Scientific Research
Mission = to drive ideas to reach their full market value.
We partner with:
Governmental & regulatory organisations
Universities
Pharma, chemical and food companies
International consortia
Knowledge
development
Knowledge
application
Knowledge
exploitation
Develop
fundamental
knowledge
With
universities
With
partners
With
customers
Embedded in the
market
TNO TNO Triskelion
5
Biomarker Europe Summit 2013, GTC BIO, Berlin
10 October 2013
Alain van Gool

6. TNO
Facts &Figures:
Founded in 1932
Member of EARTO
Non-for-profit research institute
~3500 employees
19 sites in Netherlands
+ 18 sites/countries globally
Funding:
• Government (NL)
• Contract research (world)
• Public-private partnerships (world)
7 main themes
www.tno.nl
6
Biomarker Europe Summit 2013, GTC BIO, Berlin
10 October 2013
Alain van Gool

7. TNO in European public-private partnerships
Healthy Living
Defence, Safety & Security
Transport & Mobility
Information Society
Industrial Innovation
Energy
Built Environment
Participation in EU projects:
(Jan 2013)
260 projects (3100 partners)
Roles of TNO:
Technical expertise
Focus on applications
PPP management skills
(in 10% role as coordinator)
32% success rate
(average FP7 is 21%)
Biomarker Europe Summit 2013, GTC BIO, Berlin
10 October 2013
Alain van Gool

8. Year 1
Applying lessons learned across fields
Biomarker Europe Summit 2013, GTC BIO, Berlin
10 October 2013
Alain van Gool
e.g. System Biology @TNO
Year 2
Year 3

9. Radboudumc
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 50 departments
• 3.000 students
• 1.000 beds (ambition to close 500 by improving
healthcare)
• First academic centre outside US to fully implement EPIC

10. Radboudumc Technology Infrastructure
Preclinical Imaging Centre (PRIME) DTL
Microscopic Imaging Centre (MIC) DTL
Centre for Molecular and Biomolecular Informatics (CMBI) DTL
Genetics DTL
Centre for Proteomics, Glycomics and Metabolomics DTL
Centraal Dierenlaboratorium (CDL)
Translationeel Malaria Onderzoek
Translational research and cellular therapy
Flow cytometry
Clinical Research Centre Nijmegen (CRCN)
Radboud Biobank
Translational Neuroscience Unit (TNU)
Medical Innovation and Technology expert Centre (MITeC)

11. Centre for Proteomics, Glycomics & Metabolomics
Radboud
Proteomics
Center
Radboud
Metabolomics
Group
Radboud
Glycomics
Facility
Research Biomarkers Diagnostics
Mass spectrometry – NMR based, 16 dedicated fte, part of diagnostic laboratory (Department
Laboratory Medicine), close interaction with Radboudumc scientists and external partners

12. Biomarker Development Center
• A focus on application of innovation, not on biomarker discovery or new technologies
• The innovation is a clinically validated and applicable biomarker test method
• Bring together available state-of-the art biomarker expertise in an industrial process flow
• Sponsors and end-users define objectives (a.o. pharma, diagnostics, patients)
• Shared biomarker R&D in Open Innovation Network based on Public-Private-Partnership
Shared knowledge, technologies
and objectives
Accelerate biomarker development

13. Focus
Emerging
Discovery Clinical validation
and confirmation
Diagnostic
application
Number of
biomarkers
Experimental
Discovery
Assay kit
development
Assay
development
Early Late
Academia
(discovery)
Industry
(development)
Shared R&D in biomarkers:
1. Assay development of (diagnostic) biomarkers
2. Clinical biomarker validation (quantification/confirmation, multicenter)
Leading to standardised clinical applications

14. Biomarker Development Center (Netherlands)
STW perspectief application
Biomarker Development Center
Public-private partnership 4 years
Project budget 4.3M Eur
Close interactions with:
- Clinicians (biomarker application)
- Industry (+ 0.94MEur cash + 1.24MEur kind)
- Patient stakeholder associations

15. Outline
15
Backgrounds
TNO
Radboudumc
Biomarker Development Center
Next Generation Life Science
Case study
Identification of microRNA targets for miRNA-based cancer therapeutics
Radboudumc Urology
InteRNA
Radboud Proteomics Centre
TNO System Biology

16. Next Generation Life Sciences - Case study
16
Department Urology
Dr. Gerard Verhaegh
Prof Jack Schalken
Dr. Iman Schultz
Dr. Paula van Noort
Joined development of miRNA-based cancer therapeutics in prostate cancer

17. Tumor metastasis and EMT
EMT characterized by :
- altered cell morphology
- breakdown of cell junctions
- increased motility / invasion
Tumor metastasis
involves Epithelial to
Mesenchymal
Transition (EMT)
Mechanism of
Action
Target genes
Identification
of EMT
targeting
miRNAs
Inhibition of
Invasion &
Metastasis

18. InteRNA’s miRNA target
identification/validation platform
18© InteRNA Technologies B.V.
State-of-the-art
genomics/sequencing
technologies
&
bioinformatics
Novel miRNAs
In vivo PoC
&
(Pre-)Clinical
development
Functional screening
with library of
known & novel
miRNAs
miRNA drug
candidates
Downstream biology
&
identification of
biomarkers
Mechanism of
Action
IP
IP

19. InteRNA’s unique library of 1100 miRNA
sequences serves as product engine
19© InteRNA Technologies B.V.
 Validated platform for rapid identification of therapeutic miRNAs
miRBase v16
(Sanger)
Proprietary to
InteRNA
550 100 450
Lentiviral-based miRNA
overexpression library
Functional assays
Drug candidates

20. EMT Targeting microRNAs
• 1120 miRNAs screened
(InteRNA library)
• Identification of miRNAs
increasing
EMT LUC reporter
miR-a miR-b miR-c miR-d miR-e
0.00
0.20
0.40
0.60
0.80
1.00
RelativeTumorCellInvasion
Inhibition of Cell Invasion
0
20
40
60
80
100
p = 0.033
miR-d control
Lungmetastaticburden(%)
miR-d
control
Inhibition of Metastasis

21. Next Generation Life Sciences - Case study
Current data insufficient to reliably identify bona-fide miRNA targets
Need to improve understanding of effect miRNA on cancer cell metastasis
Need to combine transcriptomics, proteomics and dedicated bioinformatics
Join forces in ZonMW-DTL project proposal
Department Urology
Dr. Gerald Verhaegh
Prof Jack Schalken
Dr. Iman Schultz
Dr. Paula van Noort
Dr. Jolein Gloerich
Prof. Alain van Gool
Dr. Lars Verschuren
Dr. Thomas Kelder
Dr. Marijana Radonjic
biology miRNA
proteomics bioinformatics

22. Proteomics approach
• Bottom-up proteomics (shotgun)
• Protein identification
• Differential protein expression profiling
Established (>300 projects done)
• Targeted proteomics
• Absolute/relative quantitation
Emerging (5 projects ongoing)
• Top-down proteomics
• Intact protein characterization
• Differential PTM analysis
New

23. Example of cellular proteome profiling project
Results
Samples
Up
regulated
Down
regulated
Differential analysis
-10
-5
0
5
10
∞
∞
Results
Gene ontology: cellular localization
• 3,824 proteins identified in a sample (98% cell specific)
• 2,550 proteins quantified and used for differential
analysis
• 178 proteins differentially expressed due to treatment:
(138 up, 40 down)
Conclusions
Project with TNO
Q: how does proteome
cell line x look like?
Q: First look at effect
treatment on proteome
(feasibility)
→ GeLC-MS approach

24. 1. Quantify 2. Integrate with
prior knowledge
4. Link to health benefit 5. Improved (personalized)
interventions
(Omics assays, Physiology,
Anthropometry, Imaging,
Challenge tests, etc.)
System Biology analysis @TNO: Workflow
LIFESTYLE
NUTRITION
PHARMA
3. Integrate, map and
understand relationships

25. Martina Kutmon, Chris Evelo, Thomas Kelder
Open source software: http://projects.bigcat.unimaas.nl/cytargetlinker/
CyTargetLink: Regulatory Interaction Networks
• Network data integration, analysis
and visualization
• Cytoscape App

26. 26
Martina Kutmon

27. strategy to identify miRNA targets
Phenotypic
confirmation
Western Blot 3’ UTR assayRT qPCR
Homing
Migration
Cytoskeleton
Cell movement
Invasion
Cell death
Apoptosis
Control cells
miRNA-d over
Expressed cells Target
prediction
Experimental
Validation
Transcriptomics
(RNA-Seq)
Proteomics
(LC-MS/MS)
Data Integration
Joined
CyTargetLink

28. Acknowledgements
Lars Verschuren
Thomas Kelder
Marijana Radonjic
Jildau Bouwman
Ben van Ommen
and others
Gerald Verhaegh
Jack Schalken
alain.vangool@tno.nl, alain.vangool@radboudumc.nl
Jolein Gloerich
Hans Wessels
Ron Wevers
Dirk Lefeber
Leo Kluitmans
and others
Iman Schultz
Paula van Noort
Martina Kutmon
Chris Evelo
Rainer Bischoff
Theo Luider
Ron Wevers
and others