Clinical X-omics biomarkers to drivePersonalized Healthcare
Prof Alain van Gool
2nd Conference Validation of BiomarkersCOST CliniMARKBasel, 28 Mar 2019
Professor Personalized HealthcareHead Translational Metabolic LaboratoryCoordinator Radboudumc Technology CentersChair EATRIS Biomarker PlatformLead PI of Netherlands X-omics Initiative
www.radboudumc.nl
Personalized healthcare in rare metabolic diseases
Normal Dutch parents
Son Brian, 2002, low birth weight, lactic acidosis, hypoglycaemia
Intellectual diability, movement disorder, epilepsy
† age 3,5 yr (respiratory failure)
Son Joel, 2009, same clinical phenotype
† age 1,5 yr (epilepsy)
Clinical phenotype:
Suspicion of mitochondrial dysfunction
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{Wortmann et al, Human Biology 2017}
Case study
Lab tests • ATP production ↓, Creatine phosphate production ↓
• But OXPHOS enzyme complex I-V normal
• Candidate gene sequencing: no variant
• Mechanism of disease?
• In 2010: Whole Exome Sequencing - WARS2 mutations{medicalxpress.com}
{Rodenburg, BiochimBiophys Acta, 2016}
Case study
New mechanism of disease • WARS2 is mtDNA-codedtryptophanyl-tRNA synthases
• Novel mutation causesinstability of WARS2 protein
• Less charging of Trp-tRNATrp
• New prenatal genetic test !
Case study
{Wortmann et al, Human Biology 2017}
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Meet & greet @ Translational Metabolic Laboratory
Case study
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Lessons learned
• Fast translational of biomarker research to implementation in academic clinical laboratories
• Technology innovation is driving impact in personalizedhealthcare
• Crucial to combine different molecular views (X-omics)
Case study
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Role of X-omics biomarkers in Personalized Healthcare
Personalized diagnosis
Personalized therapy
= Personalized healthcare
+Patient participation
X-Omics
Therapy monitoring
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Genomic impact in Personalized Health(care)
Personalized medicine:B-RAFV600E drugs for melanoma
Personalized health:BRCA-driven preventive surgery
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The power of omics in diagnostics
Singlebiomarker
Omicspanel
Patient 1
Patient 2
• Higher diagnostic yield• Contextualisation of change
↑ increase
↓ decrease
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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 sequencingGene-by-gene5.4 tests / patient (1-28)
Whole Exome SequencingAll genes at once1 test / patient
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Next: Whole Genome Sequencing
Circus plots of Whole Genome Sequences of two metastatic cancer patientsSource: Edwin Cuppen, Hartwig Medical Foundation
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Interpretation of genetic variants becomes the issue
“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)
More sequence data =more unknown variants
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Complexity of biological systems
Text book
DNA
RNA
Protein
Metabolite
Reality
{Karr, Cell 2012}Environment
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Complexity in protein biology
21.000 genes
1.000.000 -2.000.000 protein forms
N-Glycosylation
TruncationPhosphorylationAcetylationUbiquitination…
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‘DNA’ variants: changes in code• Benign: 370 mL → 371 mL• Pathogenic, easy to notice: 370 mL → 970 mL• Pathogenic, hard to notice: sugar → salt
RNA variants: translation and communication
Protein variants: interpretation and execution
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
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Uric acidHuman samples
Plasma, CSF (urine)Controls vs. patient
QTOF Mass Spectrometry
- Reverse phase liquid chromatography- Positive and negative mode- Features
XCMS
AlignmentPeak comparison> 10,000 Features
Xanthine
Whole Exome Sequencing
Karlien CoeneLeo KluijtmansRon Wevers
Genomics & Metabolomics
Translational Metabolic Laboratory
Genomics & Glycomics
SLC8A6
GlycoModel
Contr
ol (n=4
0)
SLC
10A7-
CDG (n
=4)
Oth
er C
DG-II
(n=2
1)
-150
-100
-50
0
50
100
P33
P32
P17P39
Mo
del valu
es
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 2014Man1B1: Brain 2014More subtle changes: here
(manuscript in prep)
Dirk LefeberMonique van
Scherpenzeel
Translational Metabolic Laboratory
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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
Translational Metabolic Laboratory
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Functional genomics
functional validationof variants with unknownsignificance
CRISPR/Cas9
rescue
biomarkers
animal model
iPSC
+nGFP +nGFP +TAZcontr BTH BTH
PTPS deficiency PTPS deficiency-Corr#1
TH/β
III-
tub
ulin
/DA
PI
com
ple
men
tati
on
Control clpb MO
mdh1Δ
mdh1-P128L
mdh1-P202L
mdh1-G30R
hMDH2
MDH1
-
YPDfermentation
YPDrespiration
α-a
ctin
in
α-ManNAcCH3
β-ManNAcCH3
2.10 2.05 2.00
{Rodenburg, J Inherit Metab Dis, 2018}
micro-organism model
Richard RodenburgOmar Tutakhel
Translational Metabolic Laboratory
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Precision medicine in genetic-metabolic disease – current
Personalized diagnosis
Genomics(WES)
Metabolomics
New diseasemechanisms
Personalized therapies
Nature Genetics 2018
NEJM 2014
Nature 2016
NEJM 2014
Genet Med 2017
Translational Metabolic Laboratory
Glycomics
Diagnostic glycoproteomics pipeline
Comprehensive QC analytics
Diagnostic glycoproteomics pipeline
Comprehensive QC analytics
Precision medicine in genetic-metabolic disease - future
Personalized diagnosis
Genomics(WGS)
Metabolomics
New personalizedtherapies
Glycomics
Glycoproteomics
Deep Learning
Artificial Intelligence
System biology
Nature Genetics 2016 Pilot 2017
New diseasemechanisms
Translational Metabolic Laboratory
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High need to bring clinical X-omics to higher level
• Technologies: Quality, harmonised, standardised, cheaper, higher throughput• Translation: Clinical and regulatory acceptance
• Genomics is quite advanced• Proteomics, metabolomics (and other omics) much less so
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Progress further through collaboration
European networks
nationallocal
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www.radboudumc.nl/research/technologycenters29
X-omics
Data
Clinical
Preclinical
Imaging
Collaboration towards Good Biomarker Practices
{van Gool et al, Nature Reviews Drug Discovery, Apr 2017}
COST action CA16113 http://clinimark.eu
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New: Handbook of Biomarkers and Precision Medicine
70 manuscripts from experts in pharma, diagnostics, clinic, technology
1. What is a biomarker and their role in drug development?
2. Biomarkers in preclinical sciences3. Biomarkers in translational sciences4. Biomarker-informed clinical trials5. The road ahead in precision medicine6. Lessons from the past and pioneers of the
future7. Emerging technologies8. The next frontiers in therapeutic target areas9. Lessons learned and what’s next?
Publication data April 29th 2019 Draft pricing (Amazon): hardback USD 285, e-book USD 60
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Collaboration in Netherlands X-omics Initiative
www.x-omics.nl
• Access• Helpdesk / training• Collaboration
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Focus of Netherlands X-omics Initiative
Push omicstechnologies
• Resolution
• Sensitivity
• Coverage
Ge
no
mic
s
Pro
teo
mic
s
Me
tab
olo
mic
s
• Quality
• Standards
• Data FAIR at source
X-omics• Study design
• Sample handling
• Data cataloging
• Quality
• Expertise
• Best practice
• Data integration
• Data analysis• Best practice
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• Access• Helpdesk / training• Collaboration
“Crossy”
www.x-omics.nl/contact/helpdesk
X-omics demonstrators
1. Cell/organoid
• Understanding cancer drug response and resistance
• Drug-induced dynamic pathway analysis
2. Individual
• Understanding personalized differences in rare diseases
• Systemic changes to biological challenges
3. Population
• Understanding genetic variants in Dutch population
• Statistical correlations
To showcase and field test new X-omics capabilities
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www.x-omics.nl
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X-omics summer school July 2019
1. Biomarkers
2. Next gen sequencing
3. Mass spectrometry
4. Data integration & analysis
5. Case studies
5 days @Radboud University Nijmegen
1-5 July 2019
Theory + Hands-on
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www.x-omics.nl
www Radboud University.
Quick link: goo.gl/pyrvS6
Collaboration in United for Metabolic Disease
• Netherlands multidisciplinarycollaboration
• Clinicians + laboratoryspecialists + patients
• Step-wise focus:
1. Awareness
2. Diagnosis
3. Understanding
4. Therapies
5. Care and prevention
• Link to technology in Netherlands X-omics Initiative
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Afterthought: there is no single one reflection of health
• Funhouse mirror effect
• Multiple sources of yourdata
• Multiple Omics• Clinical chemistry• Electronic Patient Dossier• Wearables• Digital biomarkers• Commercial health tests• Social media• Surrounding
• Each give an (incomplete) image of you
• How to deal with all of thisfor your personal health?
{Mira Vegter, Hub Zwart, Alain van Gool, in prep}
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Acknowledgements
Translational Metabolic LaboratoryHans WesselsDirk LefeberKarlien CoeneLeo KluijtmansRichard RodenburgJolein GloerichRoel TansEsther WillemsOmar TutakhelMarek NogaAlbert GerritsenPurva Kulkarniand others
Edwin CuppenAlbert HeckThomas HankemeierPeter Bram ‘t HoenDaniella Kasteeland others
Collaborators/funders
Human Genetics NijmegenHelger IjntemaMarcel NelenAlexander HoischenLisenka VissersChristian GillisenHan Brunnerand others
www.radboudumc.nl/en/people/alain-van-gool
www.slideshare.net/alainvangool
CarTarDis
Dapha HabetsIrene Keulartsand others
Rainer BischoffTheo Luiderand others