On invitation of the Novo Nordisck Foundation, an interactive story on personalized healthcare, biomarkers and innovation in proteomics for a group of enthousiastic young scientists.

1. Translating molecular biomarkers to
novel diagnostics and
impact in personalized health(care)
Prof Alain van Gool
Copenhagen Bioscience Lecture
8 September 2016

2. My path 1989-now
• Molecular biology
• Mechanisms of disease
• Biomarkers
• Omics / technologies
• Translational medicine
• Personalized healthcare
Senior Scientist Integrator Biomarkers
Scientific lead DTL-Technologies
Head EATRIS Biomarker Platform
Professor of Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics & Metabolomics
Coordinator Radboud Technology Centers
2 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

3. Consider individual differences in life science research
3
3 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

4. Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Medicine
4
• The right drug for right patient at right dose at right time
• Molecular biomarkers as key drivers of patient selection
• = Precision medicine or Targeted medicine
4 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

5. Example: Personalized medicine in melanoma
B-RAFV600E mutation Strong growth of cell Growth of tumor
• B-RAFV600E cells always grow and become cancer cells
• RAF inhibitors will block pathway, block cell growth
and inhibit cancers that have a B-RAFV600E mutation
• 60% of melanoma patients have B-RAFV600E mutation
• Basis for a personalized medicine !
5 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

6. Personalized medicine in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longerTumors disappearCells stop growing
B-RAFinhibitor
6 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

7. Emerging Personalized / Precision / Targeted Medicine
2010:
5%of drugs in pipeline had companion diagnostic biomarker test
2015:
80%
50%
7 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

8. Moving to personalized health(care)
{Source: Barabási 2007 NEJM 357; 4}
• People are more than linear pathways
• Different systems and networks
• Different risk factors
• Different preferences
8 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

9. Societal need in efficient personalized health(care)
{Source: prof Jan Kremer}
Towards cost effective care, less cure
9 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

10. Highest need in efficient personalized health(care)
It’s personal !
‘I want to stay healthy.’
‘If not, how do I get healthy?’
10 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

11. 3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
11 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

12. 12 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
Translating Personalized Health(care) in society

13. We need a personalized data-driven GPS for health
• Monitor on background
• Alert when you are at risk
• Advice what to do
13 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

14. 3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
14 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

15. 15 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
15
New data
(generators, owners)

16. What does my DNA tell me?
23% chance blond hair 3.1% Neanderthaler DNA
16 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

17. Genetic risk lung cancer
→ don’t smoke !
What does my DNA tell me?
No expected adverse reaction
to Warfarin
17 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

19. … but not all data is useful data !

20. Challenge: translate laboratory to society
• Heart beat
• Steps / movement
• Glasses water/coffee• 1.000.000 molecules per analysis
20 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

21. Exponential technological developments
• Next generation sequencing
• DNA, RNA
• Risk analysis and therapy selection
• Mass spectrometry
• Proteins, metabolites
• Monitoring of disease and treatment effects
• Imaging
• Non invasive images, real time
• Spatial view of intact organs and organisms
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
21 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

22. Principle: One genome → multiple proteomes
Body fluids
Tissues
Cells
Plasma Urine CSF
Lung Colon Adrenal gland
THP-1 Jurkat Granulosa cells
22 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

23. 23 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
Conventional or “Bottom-Up” proteomics • Single proteins
• Protein complexes
• Cells
• Tissues
• Organelles
• Membranes
• Circulating vesicles
• Body fluids

24. Example cellular proteome profiling
Sample: HEK293 whole cell proteome (1 µg tryptic digest of urea extract)
1D LC-M/MS bottom-up proteomics analysis
Retention time
m/z
400
600
800
1000
1200
1400
m/z
10 20 30 40 50 60 Time [min]
Blue: signal intensity in MS
Pink dots: precursors selected for MS/MS
Detected peaks in MS spectra 1.584.599
Detected isotope patterns in MS spectra 130.172
Total number of MS/MS spectra 22.743
Av. Absolute Mass Deviation [ppm] 2,8972
Matched MS/MS spectra 5.603
Identified NR peptides 4.537
Identified proteins 1.321
False Discovery Rate 0,98%
In 1 scan:
24 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

25. Targeted proteomics for biomarker validation
• Select potential protein biomarkers
• Select suitable peptides and fragment ions
• Develop multiplex MRM assay
• Measure (very complex) samples
Nature Methods:
Method of the year 2012
Protein A isoform 1
Protein A isoform 2
Protein B
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26. Innovation in protein biomarker diagnostics
Current diagnostic protein assays:
• Mostly protein abundance
• Often unknown epitope of detection
• Ignore occurence of proteoforms
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27. Intact protein analysis
Bottom-up proteomics
Top-down proteomics
27 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

28. 28 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
Intact protein analysis
Top-down proteomics
{Hans Wessels, Alain van Gool}

29. Top-Down proteomics: characterization of protein-ligand products
Measured vs simulated spectrumChromatographic trace of Exendin-NODAGA
Annotated CID MS/MS spectrum of Exendin-NODAGA with 100% sequence coverage
29 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

30. Top-Down proteomics: analysis of in-vitro protein processing
HIS-tagremovalbyprotease
30 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

31. Top-Down proteomics: analysis of intact antibody
G0: GlcNAc2Man3GlcNAc2
G0F: GlcNAc2Man3GlcNAc2Fuc1
G1F: GalGlcNAc2Man3GlcNAc2Fuc1
G2F: Gal2GlcNAc2Man3GlcNAc2Fuc1
148.000 Da m/z !
Glycosylations
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32. Top-Down proteomics: analysis of highly purified complex I
Sequence coverage of NIDM subunit
MS/MS sequence coverage: 85.7% MS sequence coverage: 100%
m/z z MASCOT Score Residues Fragmentation Modifications
841.2617 13 128 S2-K92 ETD Truncated Met, Acetyl: 1
16
29
19
7411 8860 18355 10810977 155137 17598 1406 1431411381138 13146 128 164146123 170149134 17851 120 187191
13
39
23
18 70
64
61
43
36
34
66
27 42
89
21
49 93 99 173104
7231 83 1855838 91102 1111019679 161152117474 135 168121 125 15786 167158144 1627525 18815013082 18154 177103 115
CI filtered Captive 3ul 05FA_Tray02-E1_01_1071.d
0.5
1.0
1.5
2.0
7x10
Intens.
10 20 30 40 50 Time [min]
Extracted Ion Chromatograms
NIDM subunit
Mr 10.923,3198 Da
Mass error: 0.0088Da (0.81 ppm)
• Unambiguous identification and quantitation of mature molecular forms of Complex I subunits
• 42 subunits but >250 proteoforms detected !
• Insights into the combined dynamics of all available PTMs in a single experiment
(Complex I crystal-grade purified from Yarrowia lipolytica)
32 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
{Hans Wessels, Ulrich Brandt, Alain van Gool}

33. Top-Down proteomics: analysis of isolated membrane complexes
Chromatographic separation of complex V and identification of subunit ATP5J
Ragged N-terminus of ATP5J
Nativegelelectrophoresis
33 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
{Hans Wessels, Sergio Guerrero-Castillo, Ulrich Brandt, Alain van Gool}

34. Top-Down proteomics: map subunit-PTMs in specific complexes
Different proteoforms of subunit COX5A within complex IV
Nativegelelectrophoresis
Annotation Mr (Da) Relative
occurrence
Proteoform ID
by top down
MS2?
Modification ID
by bottom-up
MS2?
N terminus ID
by bottom-up
MS2?
44-END phospho 12.509,3623 52,1 % Yes - Yes
44-END 12.429,3950 41,6 % Yes Yes
46-END phospho 12.285,2733 2,9 % - - -
43-END deamidated 12.592,4715 1,4 % - Deamidation
N77 and N102
-
43-END 12.592,4715 1,1 % - -
44-END acetyl 12.471,3661 0,5 % - Acetyl N-term
Acetyl K89
Yes
44-END acetyl+acetyl 12.513,3739 0,4% - Acetyl N-term
Acetyl K89
Yes
(Sequence 100% proteoforms identified using top down and bottom-up MS and MS/MS)
(Mass error top-down MS2  0,6-0,7 ppm)
• Accurate relative quantitation of all proteoforms of one subunit in complex IV
• Much better insight as compared to:
• Bottom-up analysis of peptide fragments alone (missing protein data)
• Analysis after enrichment for specific PTMs (eg phosphoproteomics)
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35. Next: intact complexome proteins as new biomarkers?
• Native tissue biopsies
• Isolate intact membrane complexes
• Separate and isolate complexes using native gels
• LC-MS/MS analysis of intact proteins
• Data analysis
Tissue 1
(n=3)
Tissue
2 (n=3)
Subunit
Subunit – tissue 1
Subunit – tissue 2
• Identified protein sequence of subunit
• Deduce simulated sequences from database
• Determine fit with experimental data
35
{Hans Wessels, Sergio Guerrero-Castillo, Susanne Arnold, Ulrich Brandt, Alain van Gool}

36. Glycoproteomics workflow
• Mass spectrometry analysis of glycoproteins in human plasma
• 0,05 microliter analysis: detection of 1.000.000 signals in one scan (1,4 Gb)
• ~40.000 peptides of which >80% contain sugar modification
• Use to diagnose patients and identify new biomarkers
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:
{Hans Wessels, Monique van Scherpenzeel, Dirk Lefeber, Alain van Gool}
Biomarkers !?
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37. New diagnostic glycoprotein biomarker
• Rare metabolic disease cases (liver disease and dilated cardiomyopathy)
• Combination glycoproteomics and exome sequencing
• Identification of deficient enzyme in glycosylation pathway
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glyco -transferrin profile developed as diagnostic mass spec test
{Monique van Scherpenzeel, Dirk Lefeber}
37 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

38. 3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?

39. Personalized health(care) model
Personalized
Intervention
of patients-like-me
Personal thresholds
of persons-like-me
Big
Biomarker
Data
Molecular
Non-molecular
Environment
…
HomeostasisAllostasisDisease
Time
Disease
Health
Selfmonitoring
Adapted from Jan van der Greef, TNO
Personal profile
Personalized health
Personalized medicine
39 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016

40. 3 key aspects of personalized health(care)
‘I want to stay healthy. If not, how do I get healthy?’
1. What to measure?
2. How much can it change?
3. What should be the follow-up for me?
40 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016

41. 3. What should be the follow-up for me?
Personal profile data
Knowledge
Understanding
Decision
Action
41 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016

42. 42 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

43. Biomarker innovation gaps !
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
• Too much biomarker discovery
• Too little development to application
43 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

44. Biomarker innovation gaps: some numbers
5 biomarkers/
working day
1 biomarker/
1-3 years
1 biomarker/
3-10 years
?
Eg Biomarkers in time: Prostate cancer
May 2011: n= 2,231 biomarkers
Nov 2012: n= 6,562 biomarkers
Oct 2013: n= 8,358 biomarkers
Nov 2014: n= 10,350 biomarkers
Oct 2015: n = 11,856 biomarkers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
44 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

45. Reasons for biomarker innovation gap
• Not one integrated pipeline of biomarker R&D
• Publication pressure towards high impact papers
• Lack of interest and funding for confirmatory biomarker studies
• Hard to organize multi-lab studies
• Biology is complex on organism level
• Data cannot be reproduced
• Publishing bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011}
{Source: Prinz, Schlange, Asadullah, Nat Rev Drug Disc 2011}
Slide from:
Alain van Gool, Eur Commission advice, 11 Sept 2012
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46. Irreproducibility of data
{Freedman et al, PLOS Biology, 2015}
{2012} {2011} {2013} {2008}{2012}
46 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

47. Categories of errors leading to irreproducibility
{Freedman et al,
PLOS Biology, 2015}
47 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

48. Add to this: bad Data Stewardship
{Wilkinson et al,
Nature Scientific Data, 2016}
80% of data is not FAIR: Findable, Accessible, Interoperable, Reusable
48 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

49. Consequences downstream
49 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
{Freedman et al, PLOS Biology, 2015}

50. Example: validation IL-8 as biomarker for melanoma
For use in BRAF-MEK-ERK inhibitor drug development programs
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
50 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

51. Validation study to confirm IL-8 in melanoma
Stage 1 Stage 2 Stage 3 Stage 4
H&E staining; 20x
• 42 melanoma samples (tumor tissue + matching serum & plasma, stage I-IV, from
two independent biobanks
• Genetic analysis for BRAFV600E/D mutation in genomic DNA from tissue samples
• IL-8 mRNA analysis in tissue samples by in situ hybridisation using bDNA probes
(multiplexing with 12 ERK pathway response transcripts)
• IL-8 protein analysis in tissue samples by immunohistochemistry (in parallel with 4
other ERK pathway response proteins, Ki67, Tunnel)
• IL-8 protein analysis in matching plasma and serum by IL-8 immunoassay
(3 formats: ELISA, Luminex, Mesoscale; singleplex and multiplex)
• Statistical data analysis
{Source: Alain van Gool, MSD, unpublished data 2010}
51
Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

52. Example: validation IL-8 as biomarker for melanoma
For use as efficacy biomarker in development BRAF inhibitor drugs
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
Own data
{Unpublished, 2010}
Cause?
(6 months, 4 fte, USD 1.000.000)
52 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
• Sample history ?
• Tumor load?

53. Lessons learned?
Source: Youtube - Burn after reading ending}
53 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

54. Way forward
Quote Freedman paper:
{Freedman et al, PLOS Biology, 2015}
54 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

55. Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboud univerity medical center
Integrated Translational Research and Diagnostic Laboratory, 250 fte, yearly budget ~ 28M euro.
Close interaction with Dept of Genetics, Pathology and Medical Microbiology
Specialities:
• Proteomics, glycomics, metabolomics
• Enzymatic assays
• Neurochemistry
• Cellulair immunotherapy
• Immunomonitoring
Areas of disease:
• Metabolic diseases
• Mitochondrial diseases
• Lysosomal /glycosylation disorders
• Neuroscience
• Nefrology
• Iron metabolism
• Pediatric oncology
• Immunodeficiency
• Transplantation
In development:
• ~500 Biomarkers
• Early and late stage
• Analytical development
• Clinical validation
Assay formats:
• Immunoassay
• Turbidicity assays
• Flow cytometry
• DNA sequencing
• Mass spectrometry
• Experimental human (-ized)
invitro and invivo models for
inflammation and
immunosuppression
Validated assays*:
• ~ 1000 assays
• 3.000.000 tests/year
Areas of application:
• Personalized healthcare
• Diagnosis
• Prognosis
• Mechanism of disease
• Mechanism of drug action
Departmental community
*CCKL accreditation/RvA/EFI
55 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

56. www.radboudumc.nl/research/technologycenters
56
Genomics
Bioinformatics
Animal
studies
Stem
cells
Translational
neuroscience
Image-guided
treatment
Imaging
Microscopy
Biobank
Health
economics
Mass
Spectrometry
Radboudumc
Technology
Centers
Investigational
products
Clinical
studies
EHR data
analysis
Statistics
Human
performance
Data
stewardship
Molecule
Flow
cytometry
3D lab
Institutional community

57. About 280 dedicated people working in 19 Technology Centers, ~1800 users (internal, external), ~150 consortia
www.radboudumc.nl/research/technologycenters/57
• Proteins
• Metabolites
• Drugs
• PK-PD • Preclinical
• Clinical
• Behavioural
• Preclinical
• Animal facility
• Systematic review
• Cell analysis
• Sorting
• Pediatric
• Adult
• Phase 1, 2, 3, 4
• Vaccines
• Pharmaceutics
• Cyclotron
• Radio-isotopes
• Malaria parasites
• Management
• Analysis
• Sharing
• Cloud computing
• DNA
• RNA
• Internal
• External
• Early HTA
• Evidence-
based surgery
• Field lab
• Statistics
• Biological
• Structural
• Preclinical
• Clinical
• Economic
viability
• Decision
analysis
• Experimental design
• Biostatistical advice
• Electronic Health
Records
• Transmural
translation
• Best practice
• In vivo
• Functional
diagnostics
• iPSC
• Organoids
• 3D imaging
• 3D printing
• Virtual reality

58. Regional communities
RADBOUD RESEARCH FACILITIES
58 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
Campus Radboud University
Regional Bioscience Parks
Regional Health Innovation Network

59. National communities
Funding of Large
scale Scientific
Infrastructures
(>10M euro)
Data4LifeSciences
(organising biomedical data)
59 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
National Technology Infrastructure
(from 40+ partners in DTL)
National Science Agenda
(originated from citizens)

60. European communities
@Radboudumc:
Peggy Manders
Gerhard Zielhuis
@Radboudumc:
Peter Friedl
Otto Boerman
@Radboudumc:
Otto Boerman (Head Imaging Platform)
Alain van Gool (Head Biomarker Platform)
@Radboudumc:
Arnoud van der Maas
Alain van Gool
@Radboudumc:
Saskia de Wildt
Paul Smits
60 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

61. Filling the gaps - 1
GAP 1
Public increasingly funding early development, funding gap remains
Public Funding
Private Funding
Moves away from early development
Fundamental
discovery
Early Translational
F.I.M
Phase 1 Phase 2 Phase 3
GAP 1
61 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

62. Filling the gaps - 2
GAP 2
Outputs of publicly funded projects misaligned with privately financed requirements
for further development
Public Funding
Technology push, publications
Private Funding
Product and patient focus, market pull
Fundamental
discovery
Early
Translational
F.I.M
Phase 1 Phase 2 Phase 3
GAP 2
62 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

63. Collaboration & services
Matching client needs to capacities
Experts
Product
PlatformsQA & RA
PM &
Clinical
Legal &
Ethical
compliance
Training & EducationCom & IT
Biomarkers
Group
Vaccine
Group
Tracer &
Imaging
Group
ATMP’s
Group
Small
Molecules
Group
Optimise translational
trajectory
Remove barriers to multi-
disciplinary collaborationDisease
expertise
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64. Ongoing independent biomarker activities
Europe
USA
{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}
64 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

65. Build biomarker validation pipelines
Standardisation, harmonisation,
knowledge sharing in:
1. Assay development
2. Clinical validation
NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)
(Netherlands)
www.biomarkerdevelopmentcenter.nl
65 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

66. The Good Biomarker Practice initiative
Join forces among Europe’s major academic infrastructures + industry to:
1. Establish “Good Biomarker Practice” guidelines
- on translational research, biomarker technologies, biobanking, data stewardship.
2. Efficiently execute high quality biomarker projects
- work together in clinical validation and development of probable biomarkers.
66 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

67. Netherlands: a culture of public-private partnerships
(Centre for Translational Molecular Medicine) (Top Institute Pharma)
(BioMolecular Materials)
2006-2015
67 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

68. Emerging Health Research Infrastructure in Netherlands
68 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

69. Health RI
69 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016

70. Acknowledgements
Hans Wessels Jolein Gloerich
Roel Tans Esther Willems
Dirk Lefeber Monique van
Scherpenzeel
Leo Kluijtmans Ron Wevers
Marcel Verbeek Lucien Engelen
Jan Kremer Bas Bloem
Nathalie Bovy Paul Smits
the Radboudumc Technology Centers
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudumc.nl/research/technologycenters
www.radboudresearchfacilities.nl
alain.vangool@tno.nl
alain.vangool@radboudumc.nl
www.linkedIn.com
www.slideshare.net/alainvangool
Many collaborators and funders
Jan van der Greef Ben van Ommen
Ivana Bobeldijk Hans Princen
Lars Verschuren Marjan van Erk
Suzan Wopereis Heleen Wortelboer
Wessel Kraaij Ronald Mooi
Peter van Dijken Cyrille Krul
and many others
CarTarDis
70 Alain van Gool, Copenhagen Bioscience Lecture, 8 Sept 2016
Ruben Kok Barend Mons
Jaap Heringa Merlijn van Rijswijk
and many others
Anton Ussi Florence Bietrix
Laura Bermejo Andreas Scherer
Sulev Koks Marian Hajduch
Giovanni Migliaccio
and many others