Biomarkers in Personalized Health(care), time for quality, not quantity
Prof Alain van Gool
NVKC congres 15 April 2016
My background in personalized health(care)
8 years academia (NL, UK)
(molecular mechanisms of disease)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
4.5 years med school (NL)
(personalized healthcare, Omics, biomarkers)
4.5 years applied research institute (NL)
(biomarkers, personalized health, nutrition)
1991-1996 (PhD)
1996-1998 (post-doc)
2009-2012 (visiting prof)
1999-2007 2007-2009 2009-2011
2011-now (Senior Scientist Integrator Biomarkers)
2011-now (prof)
A person / citizen / family man 2016 (Scientific lead DTL-Technologies)
2016 (Head Biomarker Platform)
Professor of Personalized Healthcare Head Radboud Proteomics Center Coordinator Radboud Technology Centers
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Consider individual differences in life science research
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Source: Chakma, Journal of Young Investigators, 16, 2009
Principle of Personalized Medicine
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• 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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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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Personalized medicine in melanoma
Treat patients with
B-RAFV600E mutation Inhibit growth of cell
Patients live longer Tumors disappear Cells stop growing
B-RAF inhibitor
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Emerging Personalized / Precision / Targeted Medicine
2010:
5% of drugs in pipeline had companion diagnostic biomarker test
2015:
80%
50%
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Moving to Personalized Health(care)
“The term "personalized medicine" is often described as providing "the
right patient with the right drug at the right dose at the right time."
More broadly, "personalized
medicine" may be thought of as the tailoring of medical treatment to the individual characteristics, needs, and
preferences of a patient during all stages of care, including prevention,
diagnosis, treatment, and follow-up.”
(FDA, October 2013)
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Societal need in efficient personalized health(care)
{Source: prof Jan Kremer}
Towards cost effective care, less cure
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Personal 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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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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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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News from a molecular biomarker lab • Mass spectrometry analysis of glycoproteins in human plasma • 0,05 microliter analysis: detection of 100.000 signals in one scan • ~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:
{Translational Metabolic Laboratory, Radboudumc, unpublished data}
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Bridge research to novel protein biomarkers
Current diagnostic protein assays:
• Mostly protein abundance
Emerging:
• Post-translational modifications
• Intact proteins
• Protein complexes
Intact protein analysis
Bottum-up proteomics
Top-down proteomics
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Glycomics
Intact glycoproteins
Free glycans
Glycopeptides 500
750
1000
1250
1500
1750
m/z
10 15 20 25 30 35 40 Time [min]
PGM1 profile
CID fragmentation spectrum
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New diagnostic glycoprotein biomarker
• Rare metabolic disease cases (liver disease and dilated cardiomyopathy)
• Combination glycoproteomics and exome sequencing
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic mass spec test
{Monique van Scherpenzeel, Dirk Lefeber}
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Intact complexome analysis 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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23% chance blond hair
What does my DNA tell me?
3.1% Neanderthaler DNA
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Genetic risk lung cancer → don’t smoke !
What does my DNA tell me?
No expected adverse reaction to Warfarin
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Need for optimal quality in health biomarker analyses
Test, interpret, advice
“Post-traumatic Test Syndrome” ?
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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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healthy disease disease + treatment
2. How much can it change?
Subgroups
100%
Individual
Population
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Personalized health(care) model Personalized Intervention
of patients-like-me Personal thresholds of persons-like-me
Big Biomarker Data
Molecular Non-molecular Environment …
Ho
meo
sta
sis
A
llo
sta
sis
D
isease
Time
Disease
Health
Selfmonitoring
Adapted from Jan van der Greef, TNO
Personal profile
Personalized health
Personalized medicine
{See eg Chen … Snyder, Cell 2012, 148: 1293}
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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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3. What should be the follow-up for me?
Personal profile data
Knowledge
Understanding
Decision
Action
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Personalized health(care) monitoring as part of our lives
• Monitor on background
• Alert when you are at risk
• Advice what to do
Examples from car dashboard:
• Empty gas tank
• Traffic jam ahead
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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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Translational medicine using simulation
Simulating YOUR health
Heleen Wortelboer Herman van Wietmarschen Jan van der Greef Esther Zondervan Wim van Hartingsveldt
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Translational medicine using simulation + feedback
Simulating YOUR health + Personalized advice + Feedback
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However …
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
• Too much biomarker discovery • Too little development to application
Biomarker innovation gaps!
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Biomarker innovation gaps: some numbers
5 biomarkers/ working day
1 biomarker/ 1-3 years
1 biomarker/ 3-10 years
?
Eg Biomarkers in time: Prostate cancer May 2011: n= 2,231 biomarkers Nov 2012: n= 6,562 biomarkers Oct 2013: n= 8,358 biomarkers Nov 2014: n= 10,350 biomarkers Oct 2015: n = 11,856 biomarkers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
Gap 3
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Reasons for biomarker innovation gap
• Not one integrated pipeline of biomarker R&D
• Publication pressure towards high impact papers
• Lack of interest and funding for confirmatory biomarker studies
• Hard to organize multi-lab studies
• Biology is complex on organism level
• Data cannot be reproduced
• Bias towards extreme results
• Biomarker variability
• …
{Source: John Ioannidis, JAMA 2011}
{Source: Prinz, Schlange, Asadullah, Nat Rev Drug Disc 2011}
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Slide from: Alain van Gool, EC RDG, 11 Sept 2012
Irreproducibility of data
{Freedman et al, PLOS Biology, 2015}
{2012} {2011} {2013} {2008} {2012}
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Categories of errors leading to irreproducibility
{Freedman et al, PLOS Biology, 2015}
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Add to this: bad Data Stewardship
{Wilkinson et al, Nature Scientific Data, 2016}
80% of data is not Findable, Accessible, Interoperable, Reusable
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Example: validation IL-8 as biomarker for melanoma For use in BRAF-MEK-ERK inhibitor drug development programs
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
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Validation study to confirm IL-8 in melanoma
Stage 1 Stage 2 Stage 3 Stage 4
H&E staining; 20x
• 42 melanoma samples (tumor tissue + matching serum & plasma, stage I-IV, from two independent biobanks
• Genetic analysis for BRAFV600E/D mutation in genomic DNA from tissue samples
• IL-8 mRNA analysis in tissue samples by in situ hybridisation using bDNA probes (multiplexing with 12 ERK pathway response transcripts)
• IL-8 protein analysis in tissue samples by immunohistochemistry (in parallel with 4 other ERK pathway response proteins, Ki67, Tunnel)
• IL-8 protein analysis in matching plasma and serum by IL-8 immunoassay (3 formats: ELISA, Luminex, Mesoscale; singleplex and multiplex)
• Statistical data analysis
{Source: Alain van Gool, MSD, unpublished data 2010}
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Example: validation IL-8 as biomarker for melanoma For use as efficacy biomarker in development BRAF inhibitor drugs
Literature
{Yurkovetsky, et al. Clin Cancer Res, 2007}
Own data
{Unpublished, 2010}
Cause?
(6 months, 4 fte, USD 1.000.000)
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Lessons learned?
Source: Youtube - Burn after reading ending}
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Way forward Quote Freedman paper:
{Freedman et al, PLOS Biology, 2015}
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Research Biomarkers Diagnostics
Department of Laboratory Medicine, Radboud univerity medical center Integrated Translational Research and Diagnostic Laboratory, 250 fte, yearly budget ~ 28M euro. Close interaction with Dept of Genetics, Pathology and Medical Microbiology
Specialities: • Proteomics, glycomics, metabolomics • Enzymatic assays • Neurochemistry • Cellulair immunotherapy • Immunomonitoring
Areas of disease: • Metabolic diseases • Mitochondrial diseases • Lysosomal /glycosylation disorders • Neuroscience • Nefrology • Iron metabolism • Pediatric oncology • Immunodeficiency • Transplantation
In development: • ~500 Biomarkers • Early and late stage • Analytical development • Clinical validation
Assay formats: • Immunoassay • Turbidicity assays • Flow cytometry • DNA sequencing • Mass spectrometry • Experimental human (-ized)
invitro and invivo models for inflammation and immunosuppression
Validated assays*: • ~ 1000 assays • 3.000.000 tests/year
Areas of application: • Personalized healthcare • Diagnosis • Prognosis • Mechanism of disease • Mechanism of drug action
Departmental community consensus
*CCKL accreditation/RvA/EFI
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Not all innovations are useful
Patient Adherence Current
Disease progression
Disease stable
€ Effect
NEW innovative platform (app to improve adherence to therapy)
Added value ?
Health Technology Assessment
Hemofilia NO CML YES
Richard van der Broek Sabine Mulders
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www.radboudumc.nl/research/technologycenters
Genomics
Bioinformatics
Animal studies
Stem cells
Translational neuroscience
Image-guided treatment
Imaging
Microscopy
Biobank
Health economics
Mass Spectrometry
Radboudumc Technology
Centers Investigational
products
Clinical studies
EHR-based research
Statistics
Human performance
Data stewardship
Molecule
Flow cytometry
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Institutional community
Build biomarker validation pipelines
Standardisation, harmonisation, knowledge sharing in:
1. Assay development
2. Clinical validation
NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)
(Netherlands)
www.biomarkerdevelopmentcenter.nl
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Ongoing independent biomarker activities
Europe
USA
{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}
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The Good Biomarker Practice initiative
Join forces among Europe’s major academic infrastructures + industry to:
1. Establish “Good Biomarker Practice” guidelines
- on translational research, biomarker technologies, biobanking, data stewardship.
2. Efficiently execute high quality biomarker projects
- work together in clinical validation and development of probable biomarkers.
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Global community:‘Our Future health’ virtual conference
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http://ourfuturehealth.org/news/can-we-change-the-way-we-conference/, 26th May 2016
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.radboudresearchfacilities.nl
www.radboudumc.nl/research/technologycenters
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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Lab values Clinical outcomes
Pain Mobility Fatigue
INTEGRATE-HTA {R van Hoorn, W Kievit, M Tummers, GJ van der Wilt}
Intervention
Participatory healthcare
Shared decision making
66 Alain van Gool, Innovation for Health, 18 Feb 2016
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Working with other networks Region, nation, Europe, world
Alain van Gool, VNFKD, Den Haag, 21 Apr 2016
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