Invited lecture at a great technical university, specialising in nanotechnology and innovative devices to analyse biomarkers.

1. Personalized Health(care):
more than just targeted medicines
Professor of Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers
Senior Scientist Integrator Biomarkers
Prof Alain van Gool
University Twente
Enschede, 19 Jan 2016

2. My background 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)
1991-1996
(PhD)
1996-1998
(post-doc)
2009-2012
(visiting prof)
1999-2007 2007-2009 2009-2011
2011-now
2011-now (prof)
2
A person / citizen / family man
(adventures in EU, USA, Asia)
Alain van Gool, University Twente, Enschede, 19 Jan 2016

3. 3 Alain van Gool, University Twente, Enschede, 19 Jan 2016

4. Consider individual differences in life science research
4
4 Alain van Gool, University Twente, Enschede, 19 Jan 2016

5. Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized/Precision/Targeted Medicine
5
Molecular biomarkers as key drivers to select right patient
for right drug at right dose at right time
5 Alain van Gool, University Twente, Enschede, 19 Jan 2016

6. Example: Personalized Medicine in melanoma
Key biomarkers:
Stratification: BRAFV600E DNA mutation assay
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}
6 Alain van Gool, University Twente, Enschede, 19 Jan 2016

7. Emerging companion diagnostics
Good examples personalized medicine in Oncology and Neurosciences:
• Cyp450, Her2/neu, BRCA, BRAF, EGFR, EML4/ALK, etc
Emerging companion diagnostics, also linked to non-drug therapies:
• Volker: Intestinal surgery → XIAP → Cord blood
• Beery twins: Cerebral palsy → SPR → Diet 5HTP
• Wartman: Leukemia → FLT3 → Sunitinib
• Gilbert: Healthy → BRCA → Mas/Ovarectomy
• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise
• Lauerman: Scotoma, leg → JAK2 → Aspirin
• Bradfield: Healthy → CDH1 → Gastrectomy
Coming up: metabolic biomarkers, imaging biomarkers
7 Alain van Gool, University Twente, Enschede, 19 Jan 2016

8. Rational selection of best targets and biomarkers works
The 5R’s assessment:
• Right Target
• Right Tissue
• Right Safety
• Right Patients
• Right Commercial Potential
8 Alain van Gool, University Twente, Enschede, 19 Jan 2016

9. Adopt rational target/biomarker 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
9

10. 10
Optimal Personalized / Precision / Targeted Medicine

11. People are more than linear pathways

12. People are more than linear pathways
{Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks and influences
• Different risk factors
• Different preferences
12 Alain van Gool, University Twente, Enschede, 19 Jan 2016

13. Personalized health(care) in a systems view
13
13 Alain van Gool, University Twente, Enschede, 19 Jan 2016

14. Societal need in efficient personalized health(care)
{Source: prof Jan Kremer}
Towards cost effective care, less cure
14 Alain van Gool, University Twente, Enschede, 19 Jan 2016

15. Personal need in efficient personalized health(care)
It’s personal !
‘I want to stay healthy.’
‘If not, how do I get healthy?’
15 Alain van Gool, University Twente, Enschede, 19 Jan 2016

16. A changing world in Personalized Medicine@ USA
“The term "personalized medicine" is
often described as providing "the
right patient with the right drug at
the right dose at the right time."
More broadly, "personalized
medicine" may be thought of as the
tailoring of medical treatment to the
individual characteristics, needs, and
preferences of a patient during all
stages of care, including prevention,
diagnosis, treatment, and follow-up.”
(FDA, October 2013)
16
16 Alain van Gool, University Twente, Enschede, 19 Jan 2016

17. 17 Alain van Gool, University Twente, Enschede, 19 Jan 2016

18. My route to Personalized Health(care)
18 Alain van Gool, University Twente, Enschede, 19 Jan 2016

19. Analogy: TOMTOM
GPS to a location
Amsterdam
Traffic jam
Amsterdam
Route 1 Route 2
= Default Traffic jam near Utrecht Alternative route
19 Alain van Gool, University Twente, Enschede, 19 Jan 2016

20. Personalized Health(care) model
GPS to health
Health
Route 1 Route 2
= Default
First signs of
disease risk
Alternative route
Now
Disease risk
Health
Now
Health
20 Alain van Gool, University Twente, Enschede, 19 Jan 2016

21. 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?
21 Alain van Gool, University Twente, Enschede, 19 Jan 2016

22. 1. What to measure?
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]
22 Alain van Gool, University Twente, Enschede, 19 Jan 2016

23. 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
Department of Laboratory Medicine
*CCKL accreditation/RvA/EFI
www.laboratorymedicine.nl
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Alain van Gool, University Twente, Enschede, 19 Jan 201623

24. Emerging protein biomarkers
24
Current diagnostic protein assays:
• Mostly protein abundance
Emerging:
• Post-translational modifications
• Ratio protein isoforms
• Protein complexes
Alain van Gool, University Twente, Enschede, 19 Jan 2016

25. 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
25 Alain van Gool, University Twente, Enschede, 19 Jan 2016

26. Discovering new glycoprotein biomarkers
• 1D LC-MS/MS glycoproteomics in plasma
• Detection of 100K features in one scan
• ~20.000 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:
26 Alain van Gool, University Twente, Enschede, 19 Jan 2016

27. New diagnostic glycoprotein biomarker
• Rare metabolic disease cases (liver disease and dilated cardiomyopathy)
• 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}
27 Alain van Gool, University Twente, Enschede, 19 Jan 2016

28. Intact protein analysis
28
Bottum-up proteomics
Top-down proteomics
Alain van Gool, University Twente, Enschede, 19 Jan 2016

29. Intact complexome analysis as new biomarker?
Proof of principle study:
• 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
29

30. Next Generation Sequencing
{Nature, July 17 2014, 511: 344-}
30 Alain van Gool, University Twente, Enschede, 19 Jan 2016

31. Human
samples
Plasma, CSF (urine)
Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography
- Positive and negative mode
- Features
XCMS
Alignment
Peak comparison
> 10,000 Features
Personalized metabolic diagnostics
Xanthine Uric acid
Full metabolite profile:
Highly suspected of
xanthinuria
31
Alain van Gool, University Twente, Enschede, 19 Jan 201631

32. 32
32
New data
(generators, owners)
Alain van Gool, University Twente, Enschede, 19 Jan 2016

35. However …
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
• Too much biomarker discovery
• Too little development to application
35
Biomarker innovation gaps!
Alain van Gool, University Twente, Enschede, 19 Jan 2016

36. 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
36 Alain van Gool, University Twente, Enschede, 19 Jan 2016

37. 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}
37 Alain van Gool, University Twente, Enschede, 19 Jan 2016

38. 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)
Alain van Gool, University Twente, Enschede, 19 Jan 201638

39. Ongoing independent biomarker activities
39
Europe
USA
{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}
Alain van Gool, University Twente, Enschede, 19 Jan 201639

40. 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.
40
Alain van Gool, University Twente, Enschede, 19 Jan 201640

41. 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?
41 Alain van Gool, University Twente, Enschede, 19 Jan 2016

42. 2. How much can it change?
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 (2013)
Personal profile
Personalized health
Personalized medicine
42 Alain van Gool, University Twente, Enschede, 19 Jan 2016

43. 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
43 Alain van Gool, University Twente, Enschede, 19 Jan 2016

44. healthy disease disease +
treatment
Interpret data with self-normalisation
Subgroups
100%
Normalisation of responders
44 Alain van Gool, University Twente, Enschede, 19 Jan 2016

45. Quality of self-testing
45
Test, interpret, advice
“Post-traumatic Test Syndrome” ?
Alain van Gool, University Twente, Enschede, 19 Jan 2016

46. 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?
46 Alain van Gool, University Twente, Enschede, 19 Jan 2016

47. 3. What should be the follow-up for me?
Personal profile data
Knowledge
Understanding
Decision
Action
47 Alain van Gool, University Twente, Enschede, 19 Jan 2016

48. Translation is key in Personalized Healthcare !
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
?
48 Alain van Gool, University Twente, Enschede, 19 Jan 2016

49. 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
49

50. Objectives patient and clinican may be different
50
Select personalized therapy
Alain van Gool, University Twente, Enschede, 19 Jan 2016

51. Shared decision making
Select personalized therapy
Treatment options
Successrates
Example from Prostate cancer patient guide
51
{Source: Peep Stalmeier}
Alain van Gool, University Twente, Enschede, 19 Jan 2016

52. Shared decision making
Treatment options
Pro’sCon’s
Select personalized therapy
Example from Prostate cancer patient guide
52
{Source: Peep Stalmeier}
Alain van Gool, University Twente, Enschede, 19 Jan 2016

53. Flipping the coin
Via the ónly constante in
healthcare: The patient
Via HC ICT systems
53
{Source: Lucien Engelen}
Alain van Gool, University Twente, Enschede, 19 Jan 2016

54. • Nijmegen, The Netherlands
• Mission: “To have a significant impact on healthcare”
• Strategic focus on Personalized Healthcare through
“the patient as partner”
• Core activities:
• Patient care
• Research
• Education
• 11.000 colleagues
• 52 departments
• 3.300 students
• 1.000 beds
Radboud university medical center
54
Radboud campus:
- Radboudumc
- Radboud University
- Hogeschool Arnhem Nijmegen
- Max Planck Institute
- Multiple companies
Alain van Gool, University Twente, Enschede, 19 Jan 2016

55. Patient
Radboud
Personalized Healthcare
A significant impact
on healthcare
Molecule
Population
55 Alain van Gool, University Twente, Enschede, 19 Jan 2016

56. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+Patient’s preference of treatment
Exchange experiences in
care communities Select personalized therapy
Population
Man
Molecule
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57. Research themes and institutes
www.radboudumc.nl/Research/Themes/Pages/default.aspx
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58. Orientation across the spectrum
from molecule to man to population
Orientationacross
thespectrumofdiseases
Researcher
Research Theme
TechnologyCenter
Research support by Technology
58 Alain van Gool, University Twente, Enschede, 19 Jan 2016

59. External role
Internal role
• Knowledge hub for technological expertise
• Maximise use of available technical capabilities and knowledge (‘duurzaamheid’)
• Advise scientists with technological expertise
• Advise management on strategic investments and opportunities
• Drive innovations by working with each other, theme’s and Valorisation
• Easy access to Radboudumc’s technological expertise
• Represent Radboudumc as one in external technology networks
• Increase funding (grants, contract research) with Valorisation
Internal / external role
Radboudumc Technology Centers
59
Alain van Gool, University Twente, Enschede, 19 Jan 2016

60. www.radboudumc.nl/research/technologycenters
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-based
research
Statistics
Human
performance
Data
stewardship
Molecule
Flow
cytometry
60 Alain van Gool, University Twente, Enschede, 19 Jan 2016

61. About 250 dedicated people working in 18 Technology Centers, ~2000 users (internal, external), ~150 consortia
www.radboudumc.nl/research/technologycenters/
• 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
• Big Data
• Best practice
• In vivo
• Functional
diagnostics
• iPSC
• Organoids
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62. Access
Combination of
• Science
• Technology
• Business
• Innovation
• Impact in health
www.radboudumc.nl/research/technologycenters/
(1H2015: 1.900 unique visitors, 26.000 page views)
62 Alain van Gool, University Twente, Enschede, 19 Jan 2016

63. Working together on the Radboud campus
(Spin-out)
companies
63 Alain van Gool, University Twente, Enschede, 19 Jan 2016

64. Radboud Research Facilities
• Shared facilities across Radboud campus, also made part of Gelderland facilities
• Initiated by funding 6.2M Eur Gelderland + 6.2M Eur Radboud University/ Radboudumc
www.ru.nl/radboudresearchfacilities/
64 Alain van Gool, University Twente, Enschede, 19 Jan 2016

65. Working with other networks
Region, nation, Europe, world
65 Alain van Gool, University Twente, Enschede, 19 Jan 2016

66. Takehome message
• Strategic focus on implementing Personalized Healthcare
• Strong technological and methodological infrastructure
• Continuous exploration of functional networks
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67. Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Lucien Engelen
Nathalie Bovy
Paul Smits
Maroeska Rovers
Bas Bloem
the Technology Centers
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudresearchfacilities.nl
www.radboudumc.nl/research/technologycenters
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
67 Alain van Gool, University Twente, Enschede, 19 Jan 2016

68. 68
www.scanadu.com