2019 01-19 Retina symposium 2019, Amsterdam, Alain van Gool

Innovations towards
Personalized Health(care)
Prof Alain van Gool
Professor Personalized Healthcare
Head Translational Metabolic Laboratory
Strategic advisor of the executive board
Coordinator Radboudumc Technology Centers
Retina symposium
Amsterdam, 19 Jan 2019

3 Alain van Gool, Retina symposium, Amsterdam, 19 Jan 2019

Exponential developments – lab technology
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]

Exponential developments - data

Consider individual differences in life science research

Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Medicine
• 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

Crash course in molecular biology
DNA, protein, cell, tissue, system biology

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 !

Personalized medicine in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longerTumors disappearCells stop growing
B-RAFinhibitor

Role of molecular biomarkers in Personalized Healthcare
Personalized diagnosis
Personalized therapy
= Personalized healthcare
+Patient participation
X-Omics
Therapy monitoring

Genomic impact in Personalized Health(care)
Personalized medicine:
B-RAFV600E drugs for melanoma
Personalized health:
BRCA-driven preventive surgery

The power of omics
single
omics
Option 1
Option 2

Diagnostic progress by Whole Exome Sequencing
Human Genetics Nijmegen (Lisenka Vissers, Marcel Nelen, Han Brunner et al)
Retrospective analysis of Intellectual Disability cohort (n=150)
Sanger sequencing
Gene-by-gene
5.4 tests / patient (1-28)
Whole Exome Sequencing
All genes at once
1 test / patient

Next: Whole Genome Sequencing
Circus plots of Whole Genome Sequences of two metastatic cancer patients
Source: Edwin Cuppen, Hartwig Medical Foundation

The DNA-RNA-protein-metabolite dogma
Text book
DNA
RNA
Protein
Metabolite
Reality
{Karr, Cell 2012}Environment

‘DNA’ variants: changes in code
• Benign: 370 mL → 371 mL
• Pathogenic, easy to notice: 370 mL → 971 mL
• Pathogenic, hard to notice: sugar → salt
RNA variants: translation and communication
Protein variants: interpretation and execution

Complexity in protein biology
21.000 genes
1.000.000 -2.000.000
protein forms
N-Glycosylation
Truncation
Phosphorylation
Acetylation
Ubiquitination
…

Functional Omics platforms
Research Biomarkers Diagnostics
Translational Metabolic Laboratory (www.youtube.com/watch?v=yhLbuX0H7rg)
Glycoproteomics
500
1000
1500
2000
m/z
5 10 15 20 25 30 35 40 Time [min]
Metabolomics
400
600
800
1000
1200
1400
m/z
10 20 30 40 50 60 Time [min]
Bottom-up proteomics
Glycomics
Top-down proteomics Functional genomics

Example: Complement system
Classical pathway Lectin pathway Alternative pathway
C3
C5
C6
C7
C8
C9
C1
factor D
factor B
Terminal pathway: Lysis
Inflammation Opsonization
C3bC3a
C2
C4
MBL
MASP
Mannoses, fucoses
and N-acetylated
sugars on cell surface
Spontaneous and foreign
substances
CRP, IgG, IgM
Apoptotic cells
{Esther Willems
TML/LMI, unpubl}

40 soluble complement proteins
CRP
Classical pathway
Ag-Ab complex
apoptotic cellsβ-amyloid
C1q-a C1q-b C1q-c
C1r C1s
Lectin pathway
MBL
ficolinscollectin-11
MASP-1 MASP-2 MASP-3
C2 C4
cleavage
C2bC4a
+
C1(C1qr2s2)
C4b2a (C3 convertase)
Mannoses, fucoses
and N-acetylated
sugars on cell surface
Alternative pathway
+
MBL-MASP complex
(bound to cell
surface)
C3a C3 C3a
Spontaneous and foreign
substances (e.g. LPS)
C3
C3a
C3b
factor B
C3bB
factor D
Ba
C3bBb(C3 convertase)
cleavage
cleavagecleavage
C3bC3b
C4b2a3b C3bBb3b
(C5 convertase) (C5 convertase)
C5
C5a C6 C7 C8
TCC or MAC
factor H
(bound to cell surface)
cleavage
+ H2O
P
P
P
cleavage
C9 n
C5bC6C7C8(C9)n
C5b C5bC6C7
C5bC6C7C8(C9)n
Vitronectin
Clusterin
Extrinsic pathway
Tissue factors
thrombin
plasmin
kallikrein
factorXIIa
(soluble form2)
C1-inh
C3a-desR
C5a-desR
CPN
C4a-desR
CPN
cleavage
Factor I
Factor I
C4BP
CD59
CR1
MCP
DAF
CR1
MCP
DAF
factor H-like 1
factor H-like 2
factor H-like 3
factor H-like 4
factor H-like 5
Amplification loop
C1-inh
CPN
OpsonizationTerminal pathway
Inflammation
MBL = Mannose-Binding Lectin
MASP = MBL-Associated Serine Proteases
CPN = Carboxypeptidase N
TCC = Terminal Complement Complex
MAC = Membrane Attack Complex
C4BP = C4 Binding Protein
CR1/CD35 = Complement Receptor 1
MCP/CD46 = Membrane Cofactor Protein
DAF/CD55 = Decay Accelerating Factor
P = Properdin
LPS = Lipopolysaccharide
Figure adapted from several sources1,2,3

A B
C
Targeted proteomics: one multiplex complement assay

Complement in disease: bacterial vs viral infection
Controls Healthy vs Patients

Precision medicine in genetic-metabolic disease – current
Genomics
(WES)
Metabolomics
(IEM panel)
New disease
mechanisms
Personalized therapies
Nature Genetics 2018
NEJM 2014
Nature 2016
NEJM 2014
Genet Med 2017

Precision medicine in genetic-metabolic disease - future
Genomics
(WGS)
Metabolomics
(more panels)
New personalized
therapies
Glycomics
Glycoproteomics
Deep Learning
Artificial Intelligence
System biology
Nature Genetics 2016 Pilot 2017
New disease
mechanisms

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

27
“It’s far more important to
know what person the
disease has
than what disease the
person has.”
Hippocrates, 400 B.C

Personalized health(care) in a systems view
28

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?

We need a personalized data-driven GPS for health
• Monitor on background
• Alert when you are at risk
• Advice what to do
• Doctor as coach?

31
Exponential developments
- digital biomarkers

Do realize there is no single one reflection of health
• Funhouse mirror effect
• Multiple sources of your
data
• Omics
• EPD
• Diagnostics
• Wearables
• Health apps
• Commercial health tests
• Social media
• Surrounding
• Each give an (incomplete)
image of you
• How to deal with this for
personal health?
{Mira Vegter, Hub Zwart, Alain van Gool, in prep}

Acknowledgements
Translational Metabolic Laboratory
Hans Wessels
Dirk Lefeber
Karlien Coene
Leo Kluijtmans
Richard Rodenburg
Jolein Gloerich
Anouk Suppers
Purva Kulkarni
Roel Tans
Esther Willems
Ron Wevers
and others
Edwin Cuppen
Albert Heck
Thomas Hankemeier
Peter Bram ‘t Hoen
Daniella Kasteel
and others
Collaborators/funders
Human Genetics Nijmegen
Marcel Nelen
Alexander Hoischen
Lisenka Vissers
Christian Gillisen
Han Brunner
and others
alain.vangool@radboudumc.nl
www.radboudumc.nl/en/people/alain-van-gool
www.slideshare.net/alainvangool
CarTarDis
Irene Keularts
Hans Scheffer
and others
Rainer Bischoff
Theo Luider
and others

2019 01-19 Retina symposium 2019, Amsterdam, Alain van Gool

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