2016 04-15 netherlands association of clinical chemistry, papendal, alain van gool

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

2 Alain van Gool, NVKC, Papendal, 15 Apr 2016

Consider individual differences in life science research

4


Source: Chakma, Journal of Young Investigators, 16, 2009

Principle of Personalized Medicine

5

• 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


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 !


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


Emerging Personalized / Precision / Targeted Medicine

2010:

5% of drugs in pipeline had companion diagnostic biomarker test

2015:

80%

50%


9 Alain van Gool, Rotary Oss, 3 Mar 2016

Optimal Personalized / Precision / Targeted Medicine

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)

10


Societal need in efficient personalized health(care)

{Source: prof Jan Kremer}

Towards cost effective care, less cure


Personal need in efficient personalized health(care)

It’s personal !

‘I want to stay healthy.’ ‘If not, how do I get healthy?’


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?


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]


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}



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


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


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}


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

20


21

New data (generators, owners)

Science fiction ?


Science reality


Translational medicine in personalized healthcare


23% chance blond hair

What does my DNA tell me?

3.1% Neanderthaler DNA


Genetic risk lung cancer → don’t smoke !

What does my DNA tell me?

No expected adverse reaction to Warfarin


Need for optimal quality in health biomarker analyses

Test, interpret, advice

“Post-traumatic Test Syndrome” ?


Try-outs at REshape Center of Health(care) Innovation

Lucien Engelen

… but not all data is useful data !



1. What to measure?




healthy disease disease + treatment


Subgroups

100%

Individual

Population


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}




1. What to measure?





Personal profile data

Knowledge

Understanding

Decision

Action


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


http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRxqFQoTCJ6kuJXIvcYCFYFcFAodO2oP3A&url=http://arstechnica.com/apple/2014/03/ios-in-the-car-becomes-carplay-coming-to-select-dashboards-this-year/&ei=TsSVVZ7FO4G5UbvUveAN&bvm=bv.96952980,d.d24&psig=AFQjCNHK-5180CQ5Wim0GGTOe8N0tIZRIQ&ust=1435964869551370

Translation is key in Personalized Healthcare !

“I’m afraid you’re

suffering from an

increased IL-1β and

an aberrant miR843

expression”

Adapted from:

?


Translational medicine using simulation

Simulating YOUR health

Heleen Wortelboer Herman van Wietmarschen Jan van der Greef Esther Zondervan Wim van Hartingsveldt


Translational medicine using simulation + feedback

Simulating YOUR health + Personalized advice + Feedback


However …


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!


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


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}

43

Slide from: Alain van Gool, EC RDG, 11 Sept 2012

Irreproducibility of data

{Freedman et al, PLOS Biology, 2015}

{2012} {2011} {2013} {2008} {2012}


Categories of errors leading to irreproducibility



Add to this: bad Data Stewardship

{Wilkinson et al, Nature Scientific Data, 2016}

80% of data is not Findable, Accessible, Interoperable, Reusable


Consequences downstream


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}


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}


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)


Lessons learned?

Source: Youtube - Burn after reading ending}


Burn after reading ending.mp4

Way forward Quote Freedman paper:



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


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


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

55

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


http://www.erasmusmc.nl/index.htm

Ongoing independent biomarker activities

Europe

USA

{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}


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.


Emerging Health Research Infrastructure community NL


Health RI

www.dtls.nl

Community building in Nationale wetenschaps agenda


Global community:‘Our Future health’ virtual conference

62

http://ourfuturehealth.org/news/can-we-change-the-way-we-conference/, 26th May 2016

View in the (very near) future


scanadu.m4v

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

[email protected]

[email protected]

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


http://www.erasmusmc.nl/index.htm

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

67

Working with other networks Region, nation, Europe, world

Alain van Gool, VNFKD, Den Haag, 21 Apr 2016

My world: system biology

68

β-cell Pathology

gluc Risk factor {Source: Ben van Ommen, TNO}

therapy

68 Alain van Gool, VNFKD, Den Haag, 21 Apr 2016

2016 04-15 netherlands association of clinical chemistry, papendal, alain van gool

Health & Medicine