Keynote lecture at the HUPO 2017 in Dublin, focussing on our innovations in translational omics for applications in our personalized healthcare at Radboudumc.

1. Integrated ‘omics in Personalized Healthcare,
now and in the future
Professor Personalized Healthcare
Head Translational Metabolic Laboratory
Alain van Gool

2. www.radboudumc.nl

3. https://www.youtube.com/watch?v=yhLbuX0H7rg
3 Alain van Gool, HUPO, Dublin, 20 Sept 2017

4. Diagnostics is the GPS for Personalized Healthcare
“we create personalized diagnostics”
4 Alain van Gool, HUPO, Dublin, 20 Sept 2017

5. Exponential technology developments in laboratories
• 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]
5 Alain van Gool, HUPO, Dublin, 20 Sept 2017

6. Comprehensive genomic detailing
Circus plots of Whole Genome Sequences of two metastatic cancer patients
Source: prof Edwin Cuppen, Hartwig Medical Foundation
6 Alain van Gool, HUPO, Dublin, 20 Sept 2017

7. and hardly on proteome or metabolome profiles
Personalized health checks mostly based on genomics
7 Alain van Gool, HUPO, Dublin, 20 Sept 2017

8. Health management requires multilevel approach
{Source: Sturla et al,
Chemical Research in
Toxicology, 2014}
Including
Integrated ‘Omics
(X-omics)
8 Alain van Gool, HUPO, Dublin, 20 Sept 2017

9. X-omics in biomarker validation CarTarDis.eu
Immunohistochemistry
(protein)
In situ hybridisation
(miRNA, mRNA)
MS Imaging
(lipids, metabolites)
Human/mouse
CVD samples
Laser capture microdissection
(mRNA profiles)
Histology
9 Alain van Gool, HUPO, Dublin, 20 Sept 2017

10. X-omics in (functional) 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)
10 Alain van Gool, HUPO, Dublin, 20 Sept 2017

11. Added value of WES
Total number of patients
with a possible diagnosis: 41
Diagnostic yield WES in retrospective 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)
11 Alain van Gool, HUPO, Dublin, 20 Sept 2017

12. Functional Omics platforms in Translational Metabolic Lab
Research Biomarkers Diagnostics
Translational Metabolic Laboratory (www.youtube.com/watch?v=yhLbuX0H7rg)
Targeted proteomics
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
12 Alain van Gool, HUPO, Dublin, 20 Sept 2017

13. 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
Leo Kluijtmans,
Ron Wevers
Genomics & Metabolomics

14. 3.Proteinglycosylation
Activatedsugars
ER/Golgi
TissuespecificGlycoCODE
Cytosol
1. Novel genetic Factors
Carbohydrate
metabolism
Nucleotide
metabolism
Amino acid
metabolism
Lipid
metabolism
Energy
metabolism
Carbohydrate
metabolism
Nucleotide
metabolism
Amino acid
metabolism
Lipid
metabolism
Energy
metabolism
Carbohydrate
metabolism
Nucleotide
metabolism
Amino acid
metabolism
Lipid
metabolism
Energy
metabolism
2.MetabolicNetworks
Center for Disorders of Glycosylation
Genomics & Glycomics / Glycoproteomics
Am J Hum Gen 2009; Cell & Brain 2010; PLosGenetics 2011
Ann Neurol 2012; Nature Genetics 2012; New Eng J Med 2014
Chem Biol 2015; Nature Genetics 2016
Dirk Lefeber
Monique van Scherpenzeel

15. 15 Alain van Gool, HUPO, Dublin, 20 Sept 2017
Genomics & Glycomics
SLC8A6
GlycoModelC
ontrol(n=40)
SLC
10A
7-C
D
G
(n=4)
O
therC
D
G
-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)

16. Analyse protein glycosylation at different levels
1. Intact
glycoproteins
3. Free glycans
2. Glycopeptides
500
750
1000
1250
1500
1750
m/z
10 15 20 25 30 35 40 Time [min]
Glycopeptide spectrum
Nanochip-PGC-QTOF
Nanochip-C8-QTOF
16 Alain van Gool, HUPO, Dublin, 20 Sept 2017

17. Moving to glycoproteins
P Ac
Top-Down proteomics:
• Quantitative information
• Amino acid backbone: Q21 – H132
• Phosphoryl @ S61
• Acetyl @ K80
• Unknown mod @ N121
Glycopeptide analysis:
• Glycan structure: Hex5HexNAc4NeuAc2
• Glycosylation site: N121
LC-MS/MS Digest + enrichment
LC-MS/MS
Simple
modifications
Complex
modifications
{Hans Wessels}

18. Issue: Glycosylation creates tremendous protein diversity
Gene
Protein
N-Glycosylation
18 Alain van Gool, HUPO, Dublin, 20 Sept 2017

19. Comprehensive glycopeptide profiling
• 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, Dirk Lefeber}
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20. Comprehensive glycopeptide profiling workflow
{Hans Wessels, Anouk Suppers,
Dirk Lefeber, Monique van Scherpenzeel}
HUPO 2017 - Hans Wessels
MB2-02I 18 Sept
Bruker lunch workshop 19 Sept

21. Comprehensive glycopeptide profiling workflow
Comprehensive QC analytics

22. Glycopeptide profiling cohort study
• 40 Controls
• 118 Patients with 36 unique CDG-II gene defects
• 30 Patients with unknown gene defect
• 12 Patient controls (normal Transferrin)
• 44 Technical analyses
- Sample stability (protein & peptide level)
- Inter & intra essay variability
- Reproducibility
- Different blood collection tubes (EDTA, Na-Heparin, Li-Heparin, Serum)
• Patient and control samples were analyzed by glycopeptide profiling,
glycomics, and Transferrin Q-TOF MS.
• Patient WES data available.
{Hans Wessels, Maurice van Dael, Anouk Suppers, Monique van Scherpenzeel, Alain van Gool , Dirk Lefeber: manuscript in prep}
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23. High diagnostic value of glycopeptide profiles
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24. Performance Glycopeptide profiling versus Glycomics
GlycomicsGlycopeptide
profiling
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25. 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}
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26. 26 Alain van Gool, HUPO, Dublin, 20 Sept 2017

27. However … 3 innovation gaps !
innovation
1. Research to research
2. Research to diagnostics
3. Research to society
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28. 1. Irreproducibility of data
{Freedman et al, PLOS Biology, 2015}
{2012} {2011} {2013} {2008}{2012}
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29. Categories of errors leading to irreproducibility
{Freedman et al,
PLOS Biology, 2015}
29 Alain van Gool, HUPO, Dublin, 20 Sept 2017

30. A short story: 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 !
*
30 Alain van Gool, HUPO, Dublin, 20 Sept 2017

31. Biomarkers to support clinical development
• Within Schering-Plough 4 Lead Optimisation programs in ERK pathway (2009)
• Need for blood-based biomarker that indicated downstream effects of drugs:
• Inhibition ERK pathway (pharmacodynamic)
• Tumor inhibition (efficacy)
• Extensive transcriptomics profiling: IL-8 as promising candidate biomarker
Data for RAFi #4
RAFi
MEKi
ERKi
RAFi#1
RAFi#2
RAFi#3
RAFi#4
MEKi#1
MEKi#2
ERKi#1
31 Alain van Gool, HUPO, Dublin, 20 Sept 2017

32. Validation study to confirm IL-8 in melanoma
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
Objectives:
• Confirm elevated IL-8 in melanoma
• Develop IL-8 assays for clinical use
32 Alain van Gool, HUPO, Dublin, 20 Sept 2017

33. Validation study to confirm IL-8 in melanoma
Stage 1 Stage 2 Stage 3 Stage 4
H&E staining; 20x
59 melanoma samples (tumor tissue (ffpe) + matching serum & plasma, stage I-IV,
from two independent biobanks) + 40 healthy serum & plasma samples
1. Genetic analysis for BRAFV600E/D mutation in genomic DNA from tissue
2. IL-8 mRNA analysis in tissue samples by in situ hybridisation using bDNA
probes (multiplexing with 12 ERK pathway response transcripts)
3. IL-8 protein analysis in tissue samples by immunohistochemistry (in parallel
with 4 other ERK pathway response proteins, Ki67, Tunnel)
4. IL-8 protein analysis in matching plasma and serum by IL-8 immunoassay
(3 formats: ELISA, Luminex, Mesoscale; singleplex and multiplex)
OK
OK
?
OK
33 Alain van Gool, HUPO, Dublin, 20 Sept 2017

34. Validation study to confirm IL-8 in melanoma
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
Own data
{Unpublished, 2010}
Cause?
(6 months, 4 fte, USD 1.000.000)
34 Alain van Gool, HUPO, Dublin, 20 Sept 2017

35. Lessons learned?
Particularly for this case:
1. Know sample history
• IL-8 protein appeared sensitive to freeze-thawing
2. Know all relevant information from the source (patient)
• Tumor load may be too low for our patients
3. Do these type of expensive validation studies together !
• Share burden, increase power, ensure better data
If we want to innovate clinical molecular biomarkers,
we need to increase quality, not quantity of our research.
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36. There is no database for negative outcomes
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37. 2. Critical component in biomarker R&D: Data
{Wilkinson et al,
Nature Scientific Data, 2016}
• Data capture
• Data stewardship (FAIR)
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38. Community building towards FAIR proteomics data
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39. 39 Alain van Gool, HUPO, Dublin, 20 Sept 2017
DATA STEWARDSHIP: INTEROPERABILITY IS KEY
If data are interoperable … … analytics provide new knowledge

40. However … 3 innovation gaps !
innovation
1. Research to research
2. Research to diagnostics
3. Research to society
40 Alain van Gool, HUPO, Dublin, 20 Sept 2017

41. 1. 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
41 Alain van Gool, HUPO, Dublin, 20 Sept 2017

42. Biomarker innovation gaps: some numbers
~ 5 biomarkers/
working day
1 biomarker/
1-3 years
1 biomarker/
2-10 years
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
Nov 2014: 10,350 biomarkers
Oct 2015: 11,856 biomarkers
Nov 2016: 14,481 biomarkers
19 Sept 2017: 15,463 biomarkers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
42 Alain van Gool, HUPO, Dublin, 20 Sept 2017

43. Choice for biomarker scientists: discover or confirm?
43 Alain van Gool, HUPO, Dublin, 20 Sept 2017

44. Good example of multi-laboratory biomarker validation
3 biomarkers:
• Aβ42
• T-Tau
• P-Tau
44 Alain van Gool, HUPO, Dublin, 20 Sept 2017

45. Good example of multi-laboratory biomarker validation
45
Adoption of best biomarker practice ???

46. 2. Need for innovation in protein biomarker diagnostics
Current diagnostic protein assays:
• Focus on assay simplicity, robustness,
throughput, costs
• Often sandwich/turbidity-immunoassay
format. MRM MS slowly emerging.
• Mostly protein abundance
• Often unknown epitope of detection
• Ignore occurence of proteoforms
Room for innovation !
46 Alain van Gool, HUPO, Dublin, 20 Sept 2017

47. Moving towards intact protein biomarker analysis
Epitope / fragment analysis
Intact protein analysis
47 Alain van Gool, HUPO, Dublin, 20 Sept 2017

48. Intact protein analysis
{Hans Wessels, Roel Tans}
Top-down proteomics
48 Alain van Gool, HUPO, Dublin, 20 Sept 2017
Towards a routine application of Top-Down
approaches for label-free discovery workflows.
Schmit PO, et al. J Proteomics. 2017 Aug 27.

49. Next: intact complexome proteins as new biomarkers?
• 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
49

50. However … 3 innovation gaps !
innovation
1. Research to research
2. Research to diagnostics
3. Research to society
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51. 1. Translation is key in Personalized Healthcare
Personal profile data
Knowledge
Understanding
Decision
Action
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52. 52
2. Selfmonitoring
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53. Challenge: translate laboratory to society
• Point-of-care analysis of
few biomarkers
• 1.000.000 signals per
proteomics analysis
53 Alain van Gool, HUPO, Dublin, 20 Sept 2017

54. Translation is key in Personalized Healthcare
“I’m afraid you’re
suffering from an
increased IL-1β and
an aberrant miR843
expression”
Adapted from:
?
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55. Moving forward
innovation
1. Research to research
2. Research to diagnostics
3. Research to society
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56. 1. Focus on the end user: the patient / citizen
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57. 2. Progress through collaboration
(local)
(European)
(Netherlands)
(global)
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58. Ongoing independent biomarker activities
Europe
USA
{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}
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59. Collaboration towards Good Biomarker Practices
{van Gool et al, Nature Reviews Drug Discovery, Apr 2017}
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COST action CA16113
http://clinimark.eu

60. Collaboration towards a Netherlands X-omics Initiative
www.x-omics.nl
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61. Collaboration towards nationwide programs
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62. And learn from other fields!
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63. Acknowledgments
@ HUPO 2017
Hans Wessels
Lecture MB2-02I - Glycopeptide
profiling
Lecture Bruker lunch – Clinical
proteomics
Roel Tans
Poster D-228 Intact protein
biomarkers
Jolein Gloerich
Alain van Gool
Lecture WE1-01K - X-omics in PHC
‘Meet the expert’ breakfast
Translational Metabolic Laboratory – proteomics group

64. Acknowledgments
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
Pierre-Olivier Schmit
Kristina Marx
Stuart Pengelley
Gary Kruppa
Collaborators/funders
Human Genetics Nijmegen
Marcel Nelen
Alexander Hoischen
Lisenka Vissers
Christian Gillisen
Han Brunner
and others
alain.vangool@radboudumc.nl
www.linkedIn.com
www.slideshare.net/alainvangool
CarTarDis
Irene Keularts
Hans Scheffer
and others