Interactive lecture with a large group of healthcare professionals, scientists and patients to illustrate developments in personalized healthcare and the role of technology to support science, innovations and medical impact. (slides in English)
2. My path 1989-now
• Molecular biology
• Mechanisms of disease
• Biomarkers
• Omics / technologies
• Translational medicine
• Personalized healthcare
Senior Scientist Integrator Biomarkers
Scientific lead DTL-Technologies
Head EATRIS Biomarker Platform
Professor of Personalized Healthcare
Head Radboud Center for Proteomics, Glycomics & Metabolomics
Coordinator Radboud Technology Centers
2 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016
4. Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Medicine
4
• 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
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5. 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 !
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6. Personalized medicine in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longerTumors disappearCells stop growing
B-RAFinhibitor
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7. Emerging Personalized / Precision / Targeted Medicine
2010:
5%of drugs in pipeline had companion diagnostic biomarker test
2015:
80%
50%
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9. People are more than linear pathways
{Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different systems and networks
• Different risk factors
• Different preferences
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10. Societal need in efficient personalized health(care)
{Source: prof Jan Kremer}
Towards cost effective care, less cure
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11. Highest need in efficient personalized health(care)
It’s personal !
‘I want to stay healthy.’
‘If not, how do I get healthy?’
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12. 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?
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13. 1. What to measure?
Exponential technological developments
• 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]
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14. Crash course in molecular biology
DNA, protein, cell, tissue, system biology
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15. Advances in mass spectrometry
• Mass spectrometry analysis of glycoproteins in human plasma
• 0,05 microliter analysis: detection of 1.000.000 signals in one scan (1,4 Gb)
• ~40.000 peptides of which >80% contain sugar modification
• Diagnose 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:
{Hans Wessels, Monique van Scherpenzeel, Dirk Lefeber, Alain van Gool}
Biomarkers !?
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16. Innovation in protein biomarker diagnostics
Current diagnostic protein assays:
• Mostly protein abundance
Emerging:
• Post-translational modifications
• Intact proteins
• Protein complexes
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17. New diagnostic glycoprotein biomarker
• Rare metabolic disease cases (liver disease and dilated cardiomyopathy)
• Combination glycoproteomics and exome sequencing
• Identification of deficient enzyme in glycosylation pathway
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glyco -transferrin profile developed as diagnostic mass spec test
{Monique van Scherpenzeel, Dirk Lefeber}
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18. 18 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016
Intact protein analysis
Bottom-up proteomics
Top-down proteomics
{Hans Wessels, Alain van Gool}
19. Intact complexome proteins as new biomarker?
• 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
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{Hans Wessels, Susanne Arnold, Uli Brandt, Alain van Gool}
20. 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
Next generation metabolic screening
Xanthine
Whole Exome Sequencing
{Leo Kluijtmans, Ron Wevers (TML) + Genetica}
21. Challenge: translate laboratory to society
• Heart beat
• Steps / movement
• Glasses water/coffee• 1.000.000 molecules per analysis
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23. What does my DNA tell me?
23% chance blond hair 3.1% Neanderthaler DNA
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24. Genetic risk lung cancer
→ don’t smoke !
What does my DNA tell me?
No expected adverse reaction
to Warfarin
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25.
26. Need for optimal quality in health biomarker analyses
Test, interpret, advice
“Post-traumatic Test Syndrome” ?
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29. 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?
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30. healthy disease disease +
treatment
2. How much can it change?
Subgroups
100%
Individual
Population
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Past
Present
Future
31. Personalized health(care) model
Personalized
Intervention
of patients-like-me
Personal thresholds
of persons-like-me
Big
Biomarker
Data
Molecular
Non-molecular
Environment
…
HomeostasisAllostasisDisease
Time
Disease
Health
Selfmonitoring
Adapted from Jan van der Greef, TNO
Personal profile
Personalized health
Personalized medicine
31 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016
32. 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?
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33. 3. What should be the follow-up for me?
Personal profile data
Knowledge
Understanding
Decision
Action
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34. Daily views of molecular system biologists
β-cell Pathology
gluc Risk factor
{Source: Ben van Ommen, TNO}
therapy
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35. 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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36. 36 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016
Translating Personalized Health(care) in society
37. We need a personalized data-driven GPS for health
• Monitor on background
• Alert when you are at risk
• Advice what to do
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38. Translational medicine using simulation + feedback
Simulating YOUR health + Personalized advice + Feedback
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39. Personalized Healthcare @ Radboudumc :
Focus on the end user: the patient / citizen
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41. Personalized Healthcare @ Radboudumc
People are different Stratification by multilevel diagnosis
+Patient’s preference of treatment
Exchange experiences in
care communities Select personalized therapy
Population
Man
Molecule
41 Alain van Gool, Radboudumc Grand Rounds, 22 Aug 2016www.radboudumc.nl/Research/Pages/PersonalizedHealthcare
42. Be proud of what we achieve !
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43. Research themes and institutes
www.radboudumc.nl/Research/Themes/Pages/default.aspx
43
44. Orientation across the spectrum
from molecule to man to population
Orientationacross
thespectrumofdiseases
PI
Research theme
TechnologyCenters
Research support by Technology Centers
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www.radboudumc.nl/research/technologycenters
45. Radboudumc Technology Infrastructure
Get organised:
1. What technological expertise do we have and should we have ?
2. How should we organise this ?
3. How will we communicate this ?
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47. About 280 dedicated people working in 19 Technology Centers, ~1800 users (internal, external), ~150 consortia
www.radboudumc.nl/research/technologycenters/47
• Proteins
• Metabolites
• Drugs
• PK-PD • Preclinical
• Clinical
• Behavioural
• Preclinical
• Animal facility
• Systematic review
• Cell analysis
• Sorting
• Pediatric
• Adult
• Phase 1, 2, 3, 4
• Vaccines
• Pharmaceutics
• Cyclotron
• Radio-isotopes
• Malaria parasites
• Management
• Analysis
• Sharing
• Cloud computing
• DNA
• RNA
• Internal
• External
• Early HTA
• Evidence-
based surgery
• Field lab
• Statistics
• Biological
• Structural
• Preclinical
• Clinical
• Economic
viability
• Decision
analysis
• Experimental design
• Biostatistical advice
• Electronic Health
Records
• Transmural
translation
• Best practice
• In vivo
• Functional
diagnostics
• iPSC
• Organoids
• 3D imaging
• 3D printing
• Virtual reality
48. Working with/within other networks
Region, nation, Europe, world
48
Peggy Manders
Gerhard Zielhuis
Peter Friedl
Otto Boerman
Alain van Gool (Scientific Lead Technologies)
RADBOUD RESEARCH FACILITIES
Otto Boerman (Head Imaging Platform)
Alain van Gool (Head Biomarker Platform)
Via DTL
Grootschalige
Wetenschappelijke
Infrastructuur
49. Acknowledgements
Ron Wevers
Jolein Gloerich
Hans Wessels
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Lucien Engelen
Nathalie Bovy
Paul Smits
Maroeska Rovers
Bas Bloem
the Technology Centers
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudumc.nl/research/technologycenters
www.radboudresearchfacilities.nl
alain.vangool@tno.nl
alain.vangool@radboudumc.nl
www.linkedIn.com
www.slideshare.net/alainvangool
Many collaborators and funders
Jan van der Greef
Ben van Ommen
Ivana Bobeldijk
Lars Verschuren
Marjan van Erk
Peter van Dijken
Heleen Wortelboer
Wessel Kraaij
Peter Wielinga
Ronald Mooij
Suzan Wopereis
and many others
CarTarDis
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