2018 08-16 Masterclass Advances in Genomics Research, ErasmusMC, Rotterdam, Alain van Gool

X-omics in Personalized Healthcare
Prof Alain van Gool
Professor Personalized Healthcare
Head Translational Metabolic Laboratory
Strategic advisor of the executive board
Coordinator Radboudumc Technology Centers
Masterclass
ErasmusMC, Rotterdam
16 Aug 2018

My path in translational health research
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Lecture LKCH, UMC Utrecht
29 October 2013
Alain van Gool
Knowledge
validation &
application
Knowledge
translation
Knowledge
exploitation
Fundamental
knowledge
Universities IndustryUniversity medical
centers
Applied Research
Institutes
2 Alain van Gool, Masterclass: Advances in Genomics Research, ErasmusMC Rotterdam , 16 Aug 2018

My path in translational health research
Dutch CC meeting ‘Personalized Health Care”
Ede, 2 October 2013
Alain van Gool
Lecture LKCH, UMC Utrecht
29 October 2013
Alain van Gool
Knowledge
validation &
application
Knowledge
translation
Knowledge
exploitation
Fundamental
knowledge
Universities IndustryUniversity medical
centers
Applied Research
Institutes
1991
2018

Consider individual differences in life science research

Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Healthcare
• The right drug for right patient at right dose at right time
• = Precision medicine or Personalized medicine
• Biomarkers as Companion Diagnostics

Example: personalized healthcare 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 healthcare in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longerTumors disappearCells stop growing
B-RAFinhibitor

Emerging Personalized / Precision / Targeted Medicine
2010:
5%of drugs in pipeline had companion diagnostic biomarker test
2016:
80%
50%

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

Genome diagnostics
Agilent V4 → V5
0
100
200
300
400
500
600
700
january
februari
march
april
may
june
july
august
september
october
november
december
january
februari
march
april
may
june
july
august
september
october
november
december
january
februari
march
april
may
june
july
august
september
october
november
december
january
februari
march
april
may
june
july
2013
(n=1,533)
2014
(n=4,213)
2015
(n=5,964)
Q1-Q2 2016
(n=3,543)
#clinicalexomes/month
Outsourcing to BGI
Agilent V4
Hiseq2000 Hiseq
2000 →
4000
Human Genetics Nijmegen (Lisenka Vissers, Marcel Nelen, Han Brunner et al)

Added value of WES
Total number of patients
with a possible diagnosis: 41
Diagnostic yield WES in retrospective ID cohort (n=150)
Whole Exome Sequencing testing
Total number of tests: 150 (x3)
Average #tests/patient: 1
Genetic cause identified: 44
Standard GENETIC testing
Total number of tests: 810
Average #tests/patient: 5.4 (1-28)
Genetic cause identified: 11
Human Genetics Nijmegen (Lisenka Vissers, Marcel Nelen, Han Brunner et al)

Interpretation of genetic variants
“Functional studies can be a powerful tool in
support of pathogenicity”
Richards et al., Genet Med. (2015)17:405-24
Variant classification:
1. Benign
2. Likely benign
3. Uncertain significance
4. Likely pathogenic
5. Pathogenic
Guidelines American College of
Medical Genetics and Genomics
(ACMG)
Variation between two normal people: ~200.000 by WES, ~1.000.000 by WGS

How big is this
problem anyway?
Richard Rodenburg (TML) + Sinje Geuer (Genetics)
Analysis of all diagnostic WES data 2011-
June 2017 @Radboudumc (>15,000
exomes)
Based on reported genetic variants
Rough estimate: 400 class 3 variants per
year in Nijmegen alone
Trend: “exome first”
Number of class 3 variants
Introduction of WGS:
Number of class 3 variants

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

Complexity in protein biology
Gene
Protein
N-Glycosylation

Our functional Omics platforms
Research Biomarkers Diagnostics
Translational Metabolic Laboratory (www.youtube.com/watch?v=yhLbuX0H7rg)
Glycopeptidomics
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

Uric acid
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
Xanthine
Whole Exome Sequencing
Karlien Coene
Leo Kluijtmans
Ron Wevers
Genomics & Metabolomics

Genomics & Glycomics
SLC8A6
GlycoModelControl(n=40)
SLC10A7-CDG
(n=4)
O
therCDG
-II(n=21)
-150
-100
-50
0
50
100
P33
P32
P17
P39
Modelvalues
99 patients; 47 without known gene defect
- Glycomics profiling on 99
- Intact transferrin glycoprofiling on 99
- Whole Exome Sequencing for 32 cases
PGM1: New Eng J Med 2014
Man1B1: Brain 2014
More subtle changes: here
(manuscript in prep)
Dirk Lefeber
Monique van
Scherpenzeel

Genomics & Glycoproteomics
• Mass spectrometry analysis of glycoproteins in human plasma
• 1/20 microliter analysis: detection of 1.000.000 signals in one scan (1,4 Gb)
• ~40.000 peptides of which >80% contain sugar modification
• Potential to screen 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:
Biomarkers !?
Hans Wessels, Alain van Gool, Dirk Lefeber

Functional genomics
functional validation
of variants with unknown
significance
CRISPR/Cas9
rescue
biomarkers
animal model
iPSC
+nGFP +nGFP +TAZ
contr BTH BTH
PTPS deficiency PTPS deficiency-Corr#1
TH/βIII-tubulin/DAPI
complementation
Control clpb MO
mdh1Δ
mdh1-P128L
mdh1-P202L
mdh1-G30R
hMDH2
MDH1
-
YPD
fermentation
YPD
respiration
α-actinin
α-ManNAc
CH3
β-ManNAc
CH3
2.10 2.05 2.00
{Rodenburg,
J Inherit Metab Dis, 2018}
micro-organism model
Richard Rodenburg

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

Comprehensive glycopeptide profiling workflow
{Hans Wessels, Anouk Suppers,
Dirk Lefeber, Alain van Gool}

Comprehensive glycopeptide profiling workflow
Comprehensive QC analytics

Performance Glycopeptide profiling versus Glycomics
GlycomicsGlycopeptide
profiling

Next step in Precision diagnostics – long reads
Proteomics:
Multiple post-translational
protein modifications
Gilissen et al. Nature 2014
Genomics:
Insertion duplicated gene
on different chromosome
Epitope / fragment analysis
Intact protein analysis

Translate to intact glycoprotein diagnostic test
• Glycoprofile of intact transferrin protein (80 kDa)
• Identified through combination glycoproteomics and exome sequencing
• Is linked to specific glycosylation disorders (rare metabolic diseases)
• Implemented now in clinical care as diagnostic mass spec test
• Treatable disorder (dietary intervention)
{Dirk Lefeber, Monique van Scherpenzeel}

However … 3 translational innovation gaps !
innovation
1. Research to research
2. Research to diagnostics
3. Research to society
{See www.slideshare.net/alainvangool}

100+ technology experts working in 19 Technology Centers, ~1800 users (internal, external), ~150 consortia32
Progress further through collaboration
www.radboudumc.nl/research/technologycenters

European networks
national
local

• Eur Alliance Personalized Medicine
• CORBEL
• Translate together
• EPEMED
• IC-PERMED
• COST actions
• Int Training Networks
• Genomic Alliances
• Human Proteome Organisation
• European Reference Networks
• ...
• Conferences

Finding best practices
{van Gool et al, Nature Reviews Drug Discovery, Apr 2017}
COST action CA16113
http://clinimark.eu

The Netherlands X-omics Initiative
Alain van Gool
Personalized
Healthcare
Integration of National Large-Scale Research Infrastructures
Albert Heck
Biomolecular
Sciences
Thomas Hankemeier
Analytical Biosciences
Edwin Cuppen
Human Genetics
X-omics Genomics Proteomics Metabolomics

Netherlands X-omics Initiative
Push omics
technologies
• Resolution
• Sensitivity
• Coverage
Genomics
Proteomics
Metabolomics
• Quality
• Standards
• Data FAIR
at source
X-omics
• Study design
• Sample handling
• Data cataloging
• Quality
• Expertise
• Best practice
• Data integration
• Data analysis
• Best practice
• Access
• Helpdesk / training
• Collaboration
Alain van Gool, Masterclass: Advances in Genomics Research, ErasmusMC Rotterdam , 16 Aug 2018

X-omics demonstrators
1. Cell/organoid
• Understanding cancer drug response and resistance
• Hans Clevers, Rene Bernards
2. Individual
• Understanding personalized differences in rare diseases
• Mihai Netea, Han Brunner
3. Population
• Understanding genetic variants in Dutch population
• Cornelia van Duijn, Cisca Wijmenga
To showcase and field test new X-omics capabilities
Alain van Gool, Masterclass: Advances in Genomics Research, ErasmusMC Rotterdam , 16 Aug 2018

We will make a difference by:
www.x-omics.nl39 Alain van Gool, Benelux Precision Medicine Forum, Utrecht, 13 June 2018
1. Advancing X-omics technologies far beyond state-of-the-art
2. Realizing a genuinely integrated X-omics infrastructure in Netherlands

The ‘funhouse mirror’ effect in Precision Medicine
• Multiple sources of
your data
• Omics
• EPD
• Diagnostics
• Wearables
• Health apps
• Commercial health
tests
• Social media
• Surrounding
• Each give an
(incomplete) image of
you
• Some you want to see,
some you don’t
• Not technical issue
• Ethical, philosophical
{Mira Vegter, Hub Zwart, Alain van Gool, in prep}

Collaboration towards nationwide programs

And learn from other fields!

Acknowledgements
Translational Metabolic Laboratory
Hans Wessels
Anouk Suppers
Maurice van Dael
Dirk Lefeber
Monique van Scherpenzeel
Nurulamin Bin Abu Bakar
Roel Tans
Esther Willems
Wynand Alkema
Jenneke Keizer-Garritsen
Jolein Gloerich
Ron Wevers
Alain van Gool
and others
Edwin Cuppen
Albert Heck
Thomas Hankemeier
Peter Bram ‘t Hoen
and others
Collaborators/funders
Human Genetics Nijmegen
Marcel Nelen
Alexander Hoischen
Lisenka Vissers
Christian Gillisen
Han Brunner
and others
alain.vangool@radboudumc.nl
www.linkedIn.com/in/alainvangool
www.slideshare.net/alainvangool
CarTarDis
Irene Keularts
Daphna Habets
and others
Rainer Bischoff
Theo Luider
and others

2018 08-16 Masterclass Advances in Genomics Research, ErasmusMC, Rotterdam, Alain van Gool

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