2016 11-15 lygature partnership meetup, utrecht, alain van gool
TRANSCRIPT
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Biomarkers for clinical application: time for quality, not quantity
Prof Alain van Gool
Lygature Utrecht 15 Nov 2016
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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 !
*
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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
RA
Fi #
1
RA
Fi #
2
RA
Fi #
3
RA
Fi #
4
MEK
i #1
MEK
i #2
ERK
i #1
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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
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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
?
5
OK
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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{Unpublished, 2010}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)
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Lessons learned? Source: Youtube - Burn after reading ending}
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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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Alain’s path 1989-now • Basis in molecular biology
• Academia, pharma, medical center, applied research institutes
• Biomarkers / Omics / technologies
• Mechanisms of disease
• 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
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Alain’s path 1989-now • Basis in molecular biology
• Academia, pharma, medical center, applied research institutes
• Biomarkers / Omics / technologies
• Mechanisms of disease
• 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
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Consider individual differences in life science research
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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]
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Advances in mass spectrometry • 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 • 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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System biology β-cell Pathology
gluc Risk factor
{Source:
Ben van Ommen
TNO}
therapy
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A changing world: Getting digital
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New data ! (generators, owners)
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Ongoing: translate laboratory to society
• Point-of-care analysis of few biomarkers
• 1.000.000 signals per proteomics analysis
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and many many many more …
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Demo room
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Biomarkers for personalized health(care)
time for quality, not quantity
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Need for optimal quality in health biomarker analyses
Test, interpret, advice
“Post-traumatic Test Syndrome” ?
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Need to change our focus
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Start with the end user: the patient / citizen
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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
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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 14 Nov 2016: 14,481 biomarkers
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
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Choice for biomarker scientists: discover or confirm?
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Good example of multi-laboratory biomarker validation
3 biomarkers: • Aβ42 • T-Tau • P-Tau
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Good example of multi-laboratory biomarker validation
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Adoption of best biomarker practice ???
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Share negative outcomes
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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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Critical component in biomarker R&D: Data
{Wilkinson et al, Nature Scientific Data, 2016}
• Data capture • Data stewardship (FAIR)
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Prof Barend Mons Leiden University Medical Center Dutch Techcenter for Life Sciences Netherlands eScience Center
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Way forward Quote Freedman paper:
{Freedman et al, PLOS Biology, 2015}
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Need to build biomarker development communities
Standardisation, harmonisation, knowledge sharing needed in:
1. Assay development
2. Clinical validation
3. Application
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Research Biomarkers Diagnostics
Department of Laboratory Medicine Integrated Translational Research and Diagnostic Laboratory, 250 fte, yearly budget ~ 28M euro. Close interaction with Dept of Genetics, Pathology, Pharmacy 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
*CCKL accreditation/RvA/EFI
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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 data analysis
Statistics
Human performance
Data stewardship
Molecule
Flow cytometry
3D lab
Institutional community
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National communities (1)
NL Roadmap Molecular Diagnostics (2012) NL Grant 4.3M Eur (2014)
EU Grant 1M Eur (2016)
+ …
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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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National communities (2)
Funding of Large scale Scientific Infrastructures (>10M euro)
Data4LifeSciences (organising biomedical data
in academic hospitals)
National Technology Infrastructure (from 40+ partners in DTL)
National Science Agenda (originated from citizens)
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Joining forces in Netherlands To drive personalized medicine & health research
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https://youtu.be/MpMSkbu9YQg
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Health-RI Congress
1 December 2016 De Flint in Amersfoort Register: www.nlhealthresearch.nl
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Acknowledgements
Hans Wessels Jolein Gloerich
Roel Tans Esther Willems
Maurice van Dael Jenneke Keizer
Dirk Lefeber Monique van
Scherpenzeel
Leo Kluijtmans Ron Wevers
Marcel Verbeek Lucien Engelen
Jan Kremer Bas Bloem
Nathalie Bovy Paul Smits
the Radboudumc Technology Centers
and many others
www.radboudumc.nl/personalizedhealthcare
www.radboudumc.nl/research/technologycenters
www.radboudresearchfacilities.nl
www.linkedIn.com
www.slideshare.net/alainvangool
Many collaborators and funders
Jan van der Greef Ben van Ommen
Ivana Bobeldijk Hans Princen
Lars Verschuren Marjan van Erk
Suzan Wopereis Heleen Wortelboer
Wessel Kraaij Ronald Mooi
Peter van Dijken Cyrille Krul
and many others
CarTarDis
Ruben Kok Barend Mons
Jaap Heringa Merlijn van Rijswijk and
many others
Anton Ussi Florence Bietrix
Laura Bermejo Andreas Scherer
Sulev Koks Marian Hajduch
Giovanni Migliaccio
and many others
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