2015 2-23 Oxford Global 2015 Manchester

Biomarkers in Personalized Health(care), moving beyond Targeted Medicine

Professor of Personalized Healthcare Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Head Biomarkers in Personalized Healthcare

Prof Alain van Gool

scanadu.m4v

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Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

‘insideables’

‘wearables’

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“Selfmonitoring = Trend of 2014” The future of medicine

Biomarkers in Personalized Health(care) an evolving role

• From only diagnosis

• To Translational Medicine

• To Personalized/Precision/Targeted Medicine

• To Personalized Healthcare

• To Person-centered Health(care)

present

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But …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

3. What should be the follow-up for me?

Exponential technologies

“The only constant is change, and the rate of change is

increasing”

We are at the knee of the exponential curve

Buzzwords Progress and beyond

You are the CEO of your own healthcare team

Exponential technologies

Disruptive innovation

Digitalizing yourself

Sitting is the new smoking

Uber-ization of healthcare

The future is already here, it’s just not evenly distributed …

What’s normal for me is not normal for you

Do things different

Don’t think out of the box, just think there is no box !

Demo room

Exponential developments in biomarker technologies

• Next generation sequencing • Large level of detail on genome level (DNA, RNA) • Sequencing per patient is becoming practice • Allows risk analysis and therapy selection

• Mass spectrometry

• Large level of detail on metabolic level (proteins, metabolites)

• Analysis of blood, urine, cells, tissues, hair, etc all possible • Allows monitoring of disease and treatment effects

• Imaging • Large level of detail on intact in vivo level • Analysis of any tissue, real time

• Allows spatial view of intact organs and organisms

Genome sequencing will become much cheaper !

Next in Next Generation Sequencing • Trends:

₋ Further reduction in sequencing costs ₋ Computational power ₋ Machine learning to analyse (big) data ₋ Link molecular diagnosis to cellular therapies

Also beyond the oncology field:

• Volker: Intestinal surgery → XIAP → Cord blood

• Beery twins: Cerebral palsy → SPR → Diet 5HTP

• Wartman: Leukemia → FLT3 → Sunitinib

• Gilbert: Healthy → BRCA → Mas/Ovarectomy

• Snyder: T2Diabetes → GCKR, KCNJ11 → Diet, exercise

• Lauerman: Scotoma, leg → JAK2 → Aspirin

• Bradfield: Healthy → CDH1 → Gastrectomy

Georg Church, Craig Venter

The microbiome

The epigenome

Consider individual differences in biomarker research

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But …

Knowledge and Innovation gap:

1. What to measure?

2. How much should it change?

3. What should be the follow-up for me?

Most important for biomarkers in Personalized Healthcare:

Focus on the end user: the patient

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Lab values Clinical outcomes

Patient important outcomes

Pain

Pubmed Search query

Critical appraisal tool

Mobility Fatigue

INTEGRATE-HTA

Intervention

Focus on the end user

R van Hoorn, W Kievit, M Tummers, GJ van der Wilt

Clinical outcomes

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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?


Personal profile data

Knowledge

Understanding

Decision

Action

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Select personalized therapy

Treatment options

Succ

ess

rate

s

Example from Prostate cancer patient guide


Treatment options

Pro’s

Con’s

Select personalized therapy

Biomarker innovation gaps

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

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Gap 3

Biomarker innovation gaps: some numbers

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Data from Thomson Reuters Integrity database, February 2015

Alzheimer’s Disease

Chronic Obstructive

Pulmonary Disease

Type II Diabetes Mellitis

Biomarker innovation gaps: some numbers

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

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Gap 3

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

How to move forward?

1. Focus on the end user

2. Validate more biomarkers in one go

3. System biology

4. Define, share and act on “Good Biomarker Practices”

5. Build biomarker development pipelines

6. Develop translational DIY technologies

7. Interpret data with self-normalisation

8. Interdisciplinary teamwork

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2. Validate more biomarkers in one go

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1. Determine the context of change in a biomarker. 2. Drive validation of multiple biomarkers at once

Multiple measures

Patient 1 Patient 2

Technologies are already out there: • Next generation sequencing • Microarrays • Multiplex immunoassays

Single measure

• Targeted mass spectrometry • Binding assays • Mass spec imaging


3. System Biology

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β-cell Pathology

gluc Risk factor

{Source: Ben van Ommen, TNO}

therapy

Visceral

adiposity

LDL elevated

Glucose toxicity

Fatty liver

Gut

inflammation

endothelial

inflammation

systemic

Insulin resistance

Systemic

inflammation

Hepatic IR

Adipose IR

Muscle metabolic

inflexibility

adipose

inflammation

Microvascular

damage

Myocardial

infactions

Heart

failure

Cardiac

dysfunction

Brain

disorders

Nephropathy

Atherosclerosis

β-cell failure

High cholesterol

High glucose

Hypertension

dyslipidemia

ectopic

lipid overload

Hepatic

inflammation

Stroke

IBD

fibrosis

Retinopathy

Physical inactivity Caloric excess

Chronic Stress Disruption

circadian rhythm

Parasympathetic

tone

Sympathetic

arousal

Worrying

Hurrying

Endorphins Gut

activity Sweet & fat foods

Sleep disturbance

Inflammatory

response

Adrenalin

Fear

Challenge

stress

Heart rate Heart rate

variability

High cortisol

α-amylase

Lipids, alcohol, fructose

Carnitine, choline

Stannols, fibre

Low glycemic index

Epicathechins

Anthocyanins

Soy

Quercetin, Se, Zn, …

Metformin

Vioxx

Salicylate LXR agonist

Fenofibrate Rosiglitazone Pioglitazone

Sitagliptin

Glibenclamide

Atorvastatin

Omega3-fatty acids

Pharma

Nutrition Lifestyle


4. Define, share and act on“Good Biomarker Practices”

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Some items in need of standardisation:

• Reproducibility, quality requirements

• Study design & statistical power

• Variability & heterogeneity

• Specimen acquisition & pre-analytics

• Sample preparation

• Patient & associated clinical data

• Analytical standards & quality control

Not reinvent the wheel.

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

3. Biomarker qualification

• Assay/platform development

• Quality system manufacturing

• Data analysis & management

• Regulatory requirements

• Ethical, IP & legal aspects

• Early HTA

• Quality in documentation & publication


5. Build biomarker development pipelines

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

Example: Biomarker Development Center

Open Innovation Network !

Roadmap Molecular Diagnostics

PPP Grant 4.3M Euro

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2015/2016

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

Good example of multi-center biomarker validation

Research Biomarkers Diagnostics

Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 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 • Autoimmunity • 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

Use available resources:

Biomarker development pipeline @ Radboudumc

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl

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• DIY sequence your genome and/or your microbiome genome • at a provider, at a pharmacy, at home

• Take your genome to the doctor • Have a personalized healthcare advice



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• Measure your brain waves (EEG)

• Recognize conditions for maximal concentration or relaxation.

• Use device to train.



healthy disease disease + treatment


7. Interpret data with self-normalisation

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Subgroups

100%

Normalisation of responders


8. Interdisciplinary team work

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www.radboudumc.nl/research/technologycenters


8. Interdisciplinary team work

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• 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 • Radio-isotopes • Malaria parasites

• Management • Analysis • Sharing • Cloud computing

• DNA • RNA

• Internal • External

• HTA • Evidence-based

surgery • Field lab

• Statistics • Biological • Structural

• Preclinical • Clinical

• Economic viability

• Decision analysis

• Experimental design • Biostatistical advice

• Electronic Health Records • Big Data • Best practice

• In vivo • Functional

diagnostics

About 240 dedicated people working in 17 Technology Centers, ~1500 users (internal, external), ~130 consortia

www.radboudumc.nl/research/technologycenters/


Collaboration in Health Informatics

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Start small, think big

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Finally, be passionate !

My professional passions:

Personalized Health(care)

Biomarkers

Molecular Profiling (Omics)

Future of medicine

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Acknowledgements

Lucien Engelen

Jan Kremer

Paul Smits

Maroeska Rovers

Nathalie Bovy

Ron Wevers

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Leo Kluijtmans

Bas Bloem

and others

Lutgarde Buydens

Jasper Engel

Jeroen Jansen

Geert Postma

and others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.Radboudresearchfacilities.nl

[email protected]

[email protected]

www.linkedIn.com

Many external collaborators

Jan van der Greef

Ben van Ommen

Bas Kremer

Lars Verschuren

Ivana Bobeldijk

Marjan van Erk

Peter van Dijken

Marijana Radonjic

Thomas Kelder

Robert Kleemann

Suzan Wopereis

and others

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And funders

2015 2-23 Oxford Global 2015 Manchester

Healthcare

Transcript of 2015 2-23 Oxford Global 2015 Manchester