2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool

Biomarkers in personalized health(care):
past, present and future
Professor of Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers
Applied Biomarker Scientist
Prof Alain van Gool
Building Bridges Autumn 2015:
Biomarkers in Clinical and Translational Research
-‐ the ABCs of Biomarkers
Helsinki
6 Oct 2015

My mixed perspectives in personalized health(care)
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
4 years med school (NL)
(personalized healthcare, Omics, biomarkers)
4 years applied research institute (NL, EU)
(biomarkers, personalized health, nutrition)
A person / citizen / family man
(adventures in EU, USA, Asia)
1991-1996
(PhD)
1996-1998
(post-doc)
2009-2012
(visiting prof)
1999-2007 2007-2009 2009-2011
2011-now
2011-now
2 Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 2015

Biomarkers in personalized health(care)
• From Diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Health(care)
• Some do’s and don’ts
Alain van Gool, Building Bridges Biomarker Symposium, Helsinki, 6 October 20153

Biomarkers in the early days
600 BC:
Sushruta, famous Indian surgeon
Diagnosis of diabetes diabetes, then called “sweet urine disease”, by determining if
ants were attracted to a person’s urine.

Biomarkers in the early days
{Kumar and van Gool, RSC, 2013}
1506:
The urine wheel
Use color, smell and taste of urine
to diagnose disease and decide
best treatment
Ullrich Pinder
Epiphanie Medicorum

Biomarker need from pharma industry
• In old days little understanding of cause of disease
• Use of natural compounds from plant and animal
• Limited testing in laboratory + trial and error in clinic
• Frequently not effacious and/or serious side effects in patients
• Unacceptable approach (ethical, financial)
• Start rational and translational drug discovery and development

Translational medicine in pharma
Basic Research
In Vitro Studies
Target Validation
Animal Models
Phase I and Phase II
-PoC- Studies
Phase III Studies
Clinical Research
Forward TranslationForward Translation
Reverse TranslationReverse Translation
(View drug development
as customer)
(Feed back clinical needs
and samples)
[van Gool et al, Drug Disc. Today 2010]

Limited view from the outside
Source: Gary Larson
Animal models Patient-related outcomes
Source: National University Hospital Singapore

Key is to have a good view inside
High need for molecular tools that allow a look into the black box
and improve disease management: biomarkers
Drug exposure ?
Diagnosis ?
Cross-species differences ?
Patient classification ?
Prognosis ?
Target engagement ?
Modulation of mechanism ?
Off-target drug effects ?
Treatment Phenotype
Mechanism ?
Other (latent) diseases ?
Person

Biomarkers
{Biomarkers definition working group, 2001 }
Definition: ‘a characteristic that is objectively measured and evaluated as
an indicator of normal biological processes, pathogenic processes, or
pharmacologic responses to a therapeutic intervention’
Or ‘Whatever works in adding value’
Molecular biomarkers provide a molecular impression of a biological system
(cell, animal, human)
Biomarkers can be various sorts of data, or combinations thereof
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Lecture LKCH, UMC Utrecht
29 October 2013
Alain van Gool

Biomarker-based translational medicine
• Does the compound get to the site of action?
• Does the compound cause its intended
pharmacological/ functional effects?
• Does the compound have beneficial effects on disease
or clinical pathophysiology?
• What is the therapeutic window (how safe is the drug)?
• How do sources of variability in drug response in target
population affect efficacy and safety?
Lead
Optimization
Exploratory
Development PoC
Lead
Discovery
Target
Discovery
 Exposure ?
 Mechanism ?
 Efficacy ?
 Safety ?
 Responders ?
{van Gool et al, Drug Disc Today 2010}
{Kumar, van Gool, RSC biomarkers, 2013}
2ND intl Pharma-Nutrition Conference
Singapore, 17 April 2013
Alain van Gool
Lecture LKCH, UMC Utrecht
29 October 2013
Alain van Gool
One strategy
11

Biomarker strategy: Data-driven decisions
To be made during testing of drug in preclinical and clinical disease models:
Target engagement? Effect on disease?
yes yes !
no no
• No need to test current
drug in large clinical trial
• Need to identify a more
potent drug
• Concept may still be
correct
• Concept was not correct
• Abandon approach
• Proof-of-Concept
• Proceed to full
clinical
development
“Stop early, stop cheap”
“More shots on goal”
12

Rational selection of best targets and drugs works
The 5R’s assessment:
• Right Target
• Right Tissue
• Right Safety
• Right Patients
• Right Commercial Potential
13

Adopt rational target selection in pharma research
CarTarDis = Cardiovascular Target Discovery
Public-private partnership, 13 partners, 8 countries, project budget 8.0M Eur
Started 1 Oct 2013 for 4 years
Adopting AstraZeneca’s 5R strategy in drug target selection
(Coordinator)
CarTarDis.eu
14

Successes of drug development
Antibiotics Vaccins
Reproductive medicine Oncology

Source: John Arrowsmith: Nature Reviews Drug Discovery 2011
• Success rates of clinical proof-of-concept have dropped from 28% to 18%
• Insufficient efficacy as the most frequent reason
• Targeted therapy through Personalized Medicine may be the solution
• Patient selection using companion diagnostics
A need for Personalized Medicine
(Analysis of 108 failures in phase II)
Reason for failure Therapeutic area

• From Diagnosis

Consider individual differences in life science research
18
18 Alain van Gool, Pharma-Nutrition 2015, 13 March 2015

{Source: Chakma. Journal of Young Investigators. 2009}
Principle of Personalized/Precision/Targeted Medicine

Personalised Medicine @Europe
European Science Foundation
30 Nov 2012
Innovative Medicine Initiative 2
8 July 2013
EC Horizon2020
10 Dec 2013
20

Precision medicine @USA
President Obama
State of Union 2015

22 Alain van Gool, NanoNext.NL, 3 July 2015
Optimal Personalized / Precision / Targeted Medicine

Case study: B-RAF mutations and melanoma
{Miller and Mihm,
2006}

Mechanism of pathophysiology in BRAF mutated tumors
V600E
Kinase domain
{Roberts and Der, 2007}
• B-RAFV600E mutation: constitutively active kinase, oncogenic addiction
• Overactivate ERK pathway drives cell proliferation
• RAF inhibitors shown to block growth of tumors with B-RAFV600E mutation
• Prevalence of B-RAFV600E is base for patient selection:
• Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer

Clinical efficacy of Vemurafenib (PLX-4032, Zelboraf)
Key biomarkers:
Stratification: BRAFV600E mutation
Mechanism: P-ERK
Cyclin-D1
Efficacy: Ki-67
18FDG-PET, CT
Clinical endpoint: progression-free survival (%)
{Source: Flaherty et al, NEJM 2010}{Source: Chapman et al, NEJM 2011}
SelectBio Biomarkers 2014
Cambridge
8 July 2014
Alain van Gool

Development of Vemurafenib (Zelboraf)
{Source: Davis M J , Schlessinger J J Cell Biol 2012}

Clinical efficacy of Vemurafenib
{Wagle et al, 2011, J Clin Oncol 29:3085}
Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks
• Strong initial effects vemurafenib
• Emerging drug resistancy
• Reccurence of aggressive tumors

Tumor tissue heterogeneity
• BRAFV600D/E is considered as
driving mutation in melanoma
• However, also no BRAFV600D/E
mutation found in regions of a
primary melanoma
• Molecular heterogeneity in
diseased tissue
• Biomarker levels in tissue vary
• Biomarker levels in body fluids
will vary
• Major challenge for
(companion) diagnostics in
solid cancers
{Source: Yancovitz, PLoS One 2012}

Exponential technologies
“The only constant is change,
and the rate of change is
increasing”
We are at the knee
of the exponential curve

Demo room

Exponential developments in biomarker technologies
• 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
Some examples of innovation:

Next Generation Sequencing
{Nature, July 17 2014, 511: 344-}

The epigenome

The microbiome

Emerging protein biomarkers
36
Current diagnostic protein assays:
• Mostly protein abundance
Emerging:
• Post-translational modifications
• Ratio protein isoforms
• Protein complexes

Glycomics
Intact
glycoproteins
Free glycans
Glycopeptides
500
750
1000
1250
1500
1750
m/z
10 15 20 25 30 35 40 Time [min]
PGM1 profile
CID fragmentation spectrum

Discovering new glycoprotein biomarkers
• 1D LC-MS/MS glycoproteomics in plasma
• Detection of ~12.000 unique deconvoluted monoisotopic masses per
single analysis (> 50% are glycopeptides)
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Proof of principle study:

Intact protein analysis
39
Bottum-up proteomics
Top-down proteomics

Intact complexome analysis as new biomarker?
• 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
40

© ImaBiotech 2014
m/z
Int.
X1, Y1
m/z
Int.
X2, Y1
m/z
Int.
X3, Y1
m/z
Int.
X4, Y1
m/z
Int.
X4, Y2
m/z
Int.
X3, Y2
m/z
Int.
X2, Y2
‘Nb of pixels’ = ‘nb of mass spectra’
Mass Spectrometry Imaging (MSI)?
{Source: Gregory Hamm}

© ImaBiotech 2014
m/z
Int.
Each m/z signal = One specific MS image

© ImaBiotech 2014
‘Nice images’ & Reliable information
H.E.
Staining
Molecular
images
overlay

©2014 ImaBiotech
PDPKToxicologyMSI-QMSI
400 µm
resolution
40 µm
resolution
20 µm resolution
Mass Spectrometry Imaging (MSI): Spatial Resolution
Spatial resolution = difference between two pixels of the image

© ImaBiotech 2014
Lipid markers
distribution:
m/z 538.98
CE = Cholesteryl ester
PC =
Phosphatidylcholine
TG= Triacylglycerol
Heme b=
Hemoglobine b
MALDI-MSI reveals regionally distinct lipid profiles in atherosclerotic plaque
Biomarker Discovery: CVD Disease
CarTarDis

© ImaBiotech 2014
Discovery of CVD biomarkers using MSI?
Lipids relative quantification in histological regions
Shoulder region
Fibrous cap
Calcified region
Necrotic core
LPC (18:1)
…lysophosphatidylcholine (LPC)
CarTarDis
Biomarker Discovery: CVD Disease

48
48
New data !

www.scanadu.com

Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma
DIY monitoring of vital signs

• DIY sequence your genome and/or your microbiome
genome
• at a provider, at a pharmacy, at home
• Take your genome to the doctor
• Have a personalized healthcare advice
DIY sequencing

• Measure your brain waves (EEG)
• Recognize conditions for maximal
concentration or relaxation.
• Use device to train.
DIY EEG imaging

DIY protein biomarker analysis

‘insideables’
‘wearables’

58

BUT … Biomarker innovation gaps
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
59
• Too much biomarker discovery
• Too little development to application

Biomarker innovation gaps: some numbers
Data from Thomson Reuters Integrity database, February 2015
Alzheimer’s Disease
Chronic Obstructive
Pulmonary Disease
Type II Diabetes Mellitis

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
5 Oct 2015: n = 11,856 biomarkers
Discovery Clinical
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3

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
• Bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011}
{Source: Khusru Asadullah, Nat Rev Drug Disc 2011}
Health Valley Event 2014
Nijmegen
13 March 2014
Alain van Gool

“It is simply no longer possible to believe much of the clinical
research that is published, or to rely on the judgment of trusted
physicians or authoritative medical guidelines.
I take no pleasure in this conclusion, which I reached slowly and
reluctantly over my two decades as an editor of The New
England Journal of Medicine.”
Marcia Angell, MD
Former Editor-in-Chief NEJM
Oct 2010
Health Valley Event 2014
Nijmegen
13 March 2014
Alain van Gool

• From Diagnosis

Personalized health(care)
Is more than ‘just’ targeted medicines
It’s personal !
‘I want to stay healthy.’
‘If not, how do I get healthy?’

The route to Personalized Health

Analogy: route planner
GPS to a location
Amsterdam
Traffic jam
Amsterdam
Route 1 Route 2
= Default Traffic jam near Utrecht Alternative route

Personalized Health(care) planner
GPS to health
Health
Route 1 Route 2
= Default
First signs of
disease risk
Alternative route
Now
Health risk
Health
Now
Health

Personalized Health(care) model
Analogies to GPS route planner:
• Technology enabled
• Monitoring should be on the background;
only alert when risk
• Success through participation of user
• Personal choice to actively monitor or not
• Commercial competition of tool builders to
become market leader(s)
• Implementation as standard in society
GPS to health

Know your personal thresholds and intervention options
Personalized
Intervention
of patients-like-me
Risk profiles
of persons-like-me
Big
Biomarker
Data
Molecular
Non-molecular
Environment
…
HomeostasisAllostasisDisease
Time
Disease
Health
Selfmonitoring
Adapted from Jan van der Greef, TNO (2013)
Personal profile
Personalized
Participatory
Pre-emptive
70
Personalized health
Personalized medicine

Example personal profile-based patient assessment
{Chen et al, Cell 2012, 148: 1293}
Concept:
• Continuous monitoring (n=1)
• Routine biomarkers to alert
• Omics to explain
• Early intervention

Simulate and visualise health interventions {Albert de Graaf}

• From Diagnosis
• Some do’s and don’ts

How to move forward?
1. Focus biomarker research on the end user

Plan biomarker studies based on needs of end user
Discovery Clinical
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
75
• Don’t do it because you can
• Do it because it is needed

Connect patient to clinical lab to patient
76
https://www.youtube.com/watch?v=yhLbuX0H7rg

Case: diagnostic glycoprotein biomarker
• Rare metabolic disease cases
• Combination glycoproteomics and exome sequencing
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test
{Tegtmeyer et al, NEJM 370;6: 533 (2014)}
Genomics Glycomics Metabolomics
{Monique van Scherpenzeel, Dirk Lefeber}

Lab values Clinical
outcomes
Pain Mobility Fatigue
INTEGRATE-HTA
Objectives patient and clinican may be different
R van Hoorn, W Kievit, M Tummers, GJ van der Wilt
How to do optimal shared decision making?
Intervention
78

Translation is key in Personalized Healthcare !
Personal profile data
Knowledge
Understanding
Decision
Action

“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
?

Treatment options
Pro’sCon’s
Select personalized therapy

2. System biology

System Biology
β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
therapy
Visceral
adiposity
LDL elevated
Glucose toxicity
Fatty liver
Gut
inflammation
endothelial
inflammation
systemic
Insulin resistance
Systemic
inflammation
Hepatic IR
Adipose IR
Muscle metabolic
inflexibility
adipose
inflammation
Microvascular
damage
Myocardial
infactions
Heart
failure
Cardiac
dysfunction
Brain
disorders
Nephropathy
Atherosclerosis
β-cell failure
High cholesterol
High glucose
Hypertension
dyslipidemia
ectopic
lipid overload
Hepatic
inflammation
Stroke
IBD
fibrosis
Retinopathy
Physical inactivity
Caloric excess
Chronic Stress
Disruption
circadian rhythm
Parasympathetic
tone
Sympathetic
arousal
Worrying
Hurrying
Endorphins
Gut
activitySweet &
fat foods
Sleep disturbance
Inflammatory
response
Adrenalin
Fear
Challenge
stress
Heart rate
Heart rate
variability
High cortisol
α-amylase
Lipids, alcohol, fructose
Carnitine, choline
Stannols, fibre
Low glycemic index
Epicathechins
Anthocyanins
Soy
Quercetin, Se, Zn, …
Metformin
Vioxx
Salicylate
LXR agonist
Fenofibrate Rosiglitazone
Pioglitazone
Sitagliptin
Glibenclamide
Atorvastati
n
Omega3-fatty acids
Pharma
Nutrition
Lifestyle

EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Relating tissue pharmacology – biomarker - therapy
84

Translating knowledge to field labs
1. Implementation-plan ‘Personalized diagnosis of
(pre)diabetic and their lifestyle treatment in
Dutch Health care’.
2. Use of Oral Glucose Tolerance Test as a
stratification biomarker for (pre)diabetic
patients
3. Advice a tailored treatment (lifestyle and/or
medical)
4. Monitor added value of stratification
5. Communicate results and
lessons learned
Being implemented in
1st line care
(region Hillegom,
Netherlands)
Alliance “Expedition Sustainable Care,
starting with diabetes”

Explore personalized interventions by Pharma-Nutrition
Shared Innovation Programs through public-private consortia
Higher efficacy / less side effects
86

2. System biology
3. Build biomarker development pipelines based on
“Good Biomarker Practices”

Build biomarker validation pipelines
Standardisation, harmonisation,
knowledge sharing needed in:
1. Assay development
2. Clinical validation
3. Regulatory acceptance
NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)
Move towards EU funding (2016)

Define, share and act on“Good Biomarker Practices”
Some items in need of standardisation:
• Reproducibility, quality requirements
• Study design & statistical power
• Variability & heterogeneity
• Specimen acquisition & pre-analytics
• Sample preparation
• Patient & associated clinical data
• Analytical standards & quality control
Not reinvent the wheel.
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
3. Biomarker qualification
• Assay/platform development
• Quality system manufacturing
• Data analysis & management
• Regulatory requirements
• Ethical, IP & legal aspects
• Early HTA
• Quality in documentation & publication

Good example of multi-center biomarker validation

2. System biology
4. Validate more biomarkers in one go

Validate more biomarkers in one go
1. Determine the context of change in a biomarker.
2. Drive validation of multiple biomarkers at once
Multiple
measures
Patient 1 Patient 2
Technologies are already out there:
• Next generation sequencing
• Microarrays
• Multiplex immunoassays
Single
measure
• Targeted mass spectrometry
• Binding assays
• Mass spec imaging

2. System biology
5. Value negative results

Case: Tissue proteomics profiling project Protein expression
(positive controls)
GO Protein distributions
Cellular compartments
LFQ scatter plot
Biological replicates
y= 0.9834x + 130390
R2=0.9842
Q: downstream effects of transgene?
Hippocampus tissue of Transgenic mice
4 Conditions: WT, TG, WT treated, TG treated with drug
5 Biological replicates; 2D LC-MS/MS analysis (20 fractions, 1 hour gradient)
Label-Free Quantitation (LFQ – MaxQuant)
• LC-MS/MS analyses: 400
• MS spectra: 1.937.394
• MS/MS spectra: 2.323.458
• Detected isotope patterns: 66.602.271
• Isotope patterns sequenced: 1.295.489
• Average absolute mass deviation: 1,38 ppm
• 1,3 Terrabyte data
PCA analysis – loading plot
• Matched MS/MS spectra to peptides: 500.317
• Identified proteins: 3.187
• Quantified proteins: 2.365 (≥2 peptides/protein)
• Differential proteins: 276 (p<0.05)
• Average CV < 21%*
* Combining biological and technical reproducability
Transgene
Downstream

2. System biology
6. Interpret data in personalized way

healthy disease disease +
treatment
Interpret data with self-normalisation
Subgroups
100%
Normalisation of responders

2. System biology
7. Use available resources

Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboudumc
Integrated Translational Research and Diagnostic Laboratory, 220 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
• Autoimmunity
• 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
Biomarker development pipeline @ Radboudumc
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl

www.radboudumc.nl/research/technologycenters
Radboudumc Technology Centers

2. System biology
8. Seek interdisciplinary team work

Interdisciplinary team work

Alain van Gool, 2nd Precision Medicine Congress, London, 15 Sept 2015102
Try-outs at REshape Innovation Center
Lucien Engelen

Multi-partner collaborations in Health Informatics

Speak each other’s language

105
Teach how to work inter-disciplinary

2. System biology
9. Copy best practice

Copy best practice
•Nation wide coverage
•66 regional networks
•3000 trained experts
•12 disciplines
prof Bas Bloem
dr Marten Munneke

5. Supportive
technology
1. Network of
experts
2. The patient
as partner
4. Transparant
quality controle
3. Integral reward for outcome, not production
5 key components of ParkinsonNet

Demonstrated added value
Regular care
ParkinsonNet care
% Hip fracture Cost per patient*
*Hospitals, medication, care at home

Dutch export product …
King Willem
Alexander
Bas
Bloem
Marten
Munneke
Queen
Maxima

2. System biology
9. Copy best practice
10. Spread the word

113
Spread the word

114
Spread the word

115
Spread the word

Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Monique Scherpenzeel
Dirk Lefeber
Leo Kluijtmans
Lucien Engelen
Paul Smits
Maroeska Rovers
Nathalie Bovy
Bas Bloem
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
Jan van der Greef
Ben van Ommen
Bas Kremer
Lars Verschuren
Ivana Bobeldijk
Marjan van Erk
Carina de Jongh
Peter van Dijken
Peter Wielinga
Robert Kleemann
Suzan Wopereis
and many others And funders
CarTarDis

2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool

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2015 10-06 Building Bridges Biomarker symposium FIMM Helsinki, Alain van Gool