Sharing my views on how X-omics biomarker analyses through next gen sequencing and mass spectrometry will change the landscape of diagnostics and clinical chemistry in the near future.

1. Translational X-omics diagnostics to
drive Personalized Healthcare
Klankbordgroep NVKC Werkgroep Massa Spectrometrie
Utrecht, 19 June 2019
Professor Personalized Healthcare
Head Translational Metabolic Laboratory
Coordinator Radboudumc Technology Centers
Chair EATRIS Biomarker Platform
PI Biomarker Development Center
Lead PI of Netherlands X-omics Initiative

2. Role of molecular biomarkers in Personalized Healthcare
Personalized diagnosis
Personalized therapy
= Personalized healthcare
+Patient participation
X-Omics
Therapy monitoring
2 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

3. Genomic impact in Personalized Health(care)
Personalized medicine:
B-RAFV600E drugs for melanoma
Personalized health:
BRCA-driven preventive surgery
3 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

4. 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
4 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

5. Next: Whole Genome Sequencing
Circus plots of Whole Genome Sequences of two metastatic cancer patients
Source: Edwin Cuppen, Hartwig Medical Foundation
5 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

6. 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
6 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

7. Need for Functional Omics to study complex systems
Text book
DNA
RNA
Protein
Metabolite
Reality
{Karr, Cell 2012}Environment
7 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

8. Complexity in protein biology
21.000 genes
1.000.000 -2.000.000
protein forms
N-Glycosylation
Truncation
Phosphorylation
Acetylation
Ubiquitination
…
8 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

9. Biomarker analyses approaches
9 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

10. Our functional Omics platforms
Research Biomarkers Diagnostics
Translational Metabolic Laboratory (www.youtube.com/watch?v=yhLbuX0H7rg)
Glycomics/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]
Proteomics
Top-down proteomics Functional genomics
Targeted analyses
10 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

11. Precision medicine in genetic-metabolic disease – current
Personalized diagnosis
Genomics
(WES)
Metabolomics
New disease
mechanisms
Personalized therapies
Nature Genetics 2018
NEJM 2014
Nature 2016
NEJM 2014
Genet Med 2017
Glycomics
11 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

12. 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 !?
Center of Excellence
For Translational Omics12 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

13. Diagnostic glycoproteomics pipeline
Comprehensive QC analytics

14. Diagnostic glycoproteomics pipeline
Comprehensive QC analytics

15. Precision medicine in genetic-metabolic disease - future
Personalized diagnosis
Genomics
(WGS)
Metabolomics
New personalized
therapies
Glycomics
Glycoproteomics
Deep Learning
Artificial Intelligence
System biology
Nature Genetics 2016 Pilot 2017
New disease
mechanisms
15 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

16. Biomarker innovation gaps !
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
• Too much biomarker discovery
• Too little development to application
• Omics diagnostics can (partly) fill this gap
16 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

17. High need to bring clinical X-omics to higher level
• Technologies: Quality, harmonised, standardised, cheaper, higher throughput
• Translation: Clinical interpretation and regulatory acceptance
• Genomics is advanced
• Proteomics, metabolomics (and other omics) much less so
17
Dynamic
Static 1. Biology
2. Analytics
• Samples
• Preanalytical factors
• Analytics
• Data analysis
• Interpretation
3. Implementation
(democratization)

18. Vision view #1
More Omics in diagnostics
• Higher diagnostic yield
• Contextualisation of a change
• Multiple markers in 1 test
• Relative quantitation
• Use profile as assay output
Single
biomarker
Omics
panel
Patient 1
Patient 2
↑ increase
↓ decrease
18 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

19. Vision view #2
More focus on intact biomarkers
• Whole genomes
• Intact proteins (+ PTMs)
• Structural analysis
proteins/metabolites
Epitope / fragment analysis
Intact protein analysis
19 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

20. Vision view #3
More X-Omics in our diagnostics
• Functional validation of
possible disease-causing
variants
• Functional testing of Variants
of Unknown Significance
• Analysis genetic + non-
genetic causes
• Specific for metabolic
biomarkers:
• Therapy monitoring
• Personalized dosing
20 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

21. Vision view #4
Normalisation to self
healthy disease disease +
treatment
Subgroups
Population
Past
Present
Future100%
Individual data through self-monitoring
treatment time
• People are their best own controles
(time, tissues)
• Personalized diagnostics
21 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

22. Vision view #5
More use of other people’s data
• Larger power of analysis
• More controle subject data
• Robuster outcome
Research:
• Large national genomic screening initiatives (eg GenomeEngland, MEGA)
• Adding analyses of protein, metabolite, microbiome, etc
(not standardised/harmonised yet)
• Data from multiple people (healthy, diseased) will online
• National/European/Global move to make research data FAIR
Diagnostics:
• Ignore or follow/adapt?
• Reference methods and reference standards in the future?
• Multiplex assays (range 2-1.000.000 components)
• Patient samples as standards in assays
• Harmonisation and standardisation?
• How to trust other people’s data?
• Translation of assay outcome to clinical understanding?
22 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

23. Vision view #6
Less lab, more data informatics • Large data sets
• Easier workflows
• Assays
• Omics diagnostic tests are more information-rich by themselves = beyond
human interpretation
• Data pipelines and workflows (HPC, storage, etc)
• Need for chemometrics and statistics (R, Python, Matlab, …)
• Interpretation
• Clinical interpretation of multiple biomarkers in disease
• More information is published and more data gets online
• Need for algorithms / machine learning / deep learning to filter out the
easy ones so specialists can focus on difficult ones
Lab Data Other Lab Data Other
23 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

24. Netherlands X-omics Initiative
Quality
Best practice
Expertise
Standards
FAIR data
Coordinator: Alain van Gool
24 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

25. X-omics infrastructure
Quality
Best practice
Expertise
• Helpdesk
• Training
• Community
• Demonstrators (Cell, Individual, Population)
• Data integration
• Data analysis
• Study design
• Sample handling
• Data stewardship
• Resolution, sensitivity,
coverage
• Harmonisation,
standardisation,
data FAIR-at-source
X-omics
approach
Push omics
technologies
Outreach

26. Afterthought: there is no single one reflection of health
• Funhouse mirror effect
• Multiple sources of your data
• Multiple Omics
• Clinical chemistry
• Electronic Patient Dossier
• Wearables
• Digital biomarkers
• Commercial health tests
• Social media
• Surrounding
• Each are a skewed image of you
• How to deal with all of this for
your personal health(care)?
{Mira Vegter, Hub Zwart, Alain van Gool: submitted}
26 Alain van Gool, NVKC WGMS, Utrecht, 19 Jun 2019

27. Acknowledgements
Translational Metabolic Laboratory
Hans Wessels
Dirk Lefeber
Karlien Coene
Leo Kluijtmans
Richard Rodenburg
Jolein Gloerich
Roel Tans
Esther Willems
Omar Tutakhel
Marek Noga
Albert Gerritsen
Purva Kulkarni
and others
Edwin Cuppen
Albert Heck
Thomas Hankemeier
Peter Bram ‘t Hoen
Daniella Kasteel
and others
Collaborators/funders
Human Genetics Nijmegen
Helger Ijntema
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
Dapha Habets
Irene Keularts
and others
Rainer Bischoff
Theo Luider
and others

28. 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 sciences
3. Biomarkers in translational sciences
4. Biomarker-informed clinical trials
5. The road ahead in precision medicine
6. Lessons from the past and pioneers of the
future
7. Emerging technologies
8. The next frontiers in therapeutic target areas
9. Lessons learned and what’s next?
Publication data May 3rd 2019
28 Alain van Gool, Benelux Precision Medicine Forum, Utrecht, 13 June 2018