2016-02-01 ECPM Basel Alain van Gool

Biomarkers in Personalized Healthcare

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

Senior Scientist Integrator Biomarkers

Prof Alain van Gool

European Center Pharmaceutical Medicine course Pharmacenter Basel, February 1-3, 2016

My background

8 years academia (NL, UK) (molecular mechanisms of disease) 13 years pharma (EU, USA, Asia) (biomarkers, Omics) 4 years med school (NL) (personalized healthcare, Omics, biomarkers) 4 years applied research institute (NL, EU) (biomarkers, personalized health, nutrition)

1991-1996 (PhD)

1996-1998 (post-doc)

2009-2012 (visiting prof)

1999-2007 2007-2009 2009-2011

2011-now

2011-now (prof)

2

Biomarkers, Molecular Profiling, Translational Medicine, Personalized Healthcare

Metabolic, Oncology, Neuroscience, Cardiovascular, Reproductive Medicine

Alain van Gool, ECPM course, Basel, 1 Feb 2016

The pharmaceutical R&D phases (since 2000)

• A rational and step-wise approach

• ‘reverse pharmacology’

• Cleaner and more specific drugs

3 Alain van Gool, ECPM course, Basel, 1 Feb 2016

Translational medicine in pharma

Basic Research

In Vitro Studies

Target Validation

Animal Models

Phase I and Phase II

-PoC- Studies

Phase III Studies

Clinical Research

Forward Translation Forward Translation

Reverse Translation Reverse Translation

(View drug development

as customer)

(Feed back clinical needs

and samples)

[van Gool et al, Drug Disc. Today 2010]


Biopsies

Tissues

Translational medicine in pharma

etc

Monkey

Pig

etc

Rabbit

Mice

Ex vivo

Rodents

Dog Tissues

Cell lines

Primary

cells

Diseased

human

Healthy

human

Rat Cells HTS

(solution)

assays

Cell lines

• High attrition from Research to Development (90%)

• Frequent crossing of systems barriers during drug development

• High need for translational models and biomarkers to bridge R&D and

determine drug exposure, efficacy and safety


Limited view on complex systems from the outside

Source: Gary Larson

Animal models Patient-related outcomes

Source: National University Hospital Singapore


Key is to have a good view inside

High need for molecular tools that allow a look into the black box

and improve disease management: biomarkers

Drug exposure ?

Diagnosis ?

Cross-species differences ?

Patient classification ? Prognosis ?

Target engagement ?

Modulation of mechanism ?

Off-target drug effects ?

Treatment Phenotype

Mechanism ?

Other (latent) diseases ?

Person


Biomarkers

{Biomarkers definition working group, 2001 }

Definition: ‘a characteristic that is objectively measured and evaluated as

an indicator of normal biological processes, pathogenic processes, or

pharmacologic responses to a therapeutic intervention’

Or ‘Whatever works in adding value’

Molecular biomarkers provide a molecular impression of a biological system

(cell, animal, human)

Biomarkers can be various sorts of data, or combinations thereof

Dutch CC meeting ‘Personalized Health Care”

Ede, 2 October 2013

Alain van Gool

Lecture LKCH, UMC Utrecht

29 October 2013

Alain van Gool


http://www.google.com/imgres?imgurl=http://upload.wikimedia.org/wikipedia/en/thumb/4/49/Cholesterol_structure.png/800px-Cholesterol_structure.png&imgrefurl=http://picsdigger.com/image/9bbfe1e8/&usg=__lgIeM7XqXX386731BUQz3NedgF8=&h=359&w=800&sz=24&hl=en&start=4&sig2=Zx35nHL-T3JMZ-sJMEym5A&um=1&itbs=1&tbnid=gcQNbxseIbu3DM:&tbnh=64&tbnw=143&prev=/images?q=cholesterol+structure&um=1&hl=en&tbs=isch:1&ei=PPYRTPbEFsKvrAea0dW3BA

http://www.google.com/imgres?imgurl=http://www.mc.uky.edu/Structuralbiology/psa_for_jack.jpg&imgrefurl=http://www.mc.uky.edu/Structuralbiology/&usg=__YXLZwCJp85cTWQYJzer1AzfgMHE=&h=535&w=750&sz=234&hl=en&start=1&sig2=AqVXjE_3CNeBw04PJ2IlZA&um=1&itbs=1&tbnid=tVXfc3lHcX1jHM:&tbnh=101&tbnw=141&prev=/images?q=psa+structure&um=1&hl=en&tbs=isch:1&ei=UPYRTPSiNcK-rAee6Mm0BA

http://www.google.com/imgres?imgurl=http://levis.tongji.edu.cn/gzli/image/t1-mri-brain-glioma.jpg&imgrefurl=http://levis.tongji.edu.cn/gzli/direction.htm&usg=__I1zN5qHq6Dy-XeIoV7PEErv_U50=&h=512&w=512&sz=59&hl=en&start=15&sig2=bkDWF22aiEMBpA-IuA0aZw&um=1&itbs=1&tbnid=rzCYX-mOOCfnxM:&tbnh=131&tbnw=131&prev=/images?q=mri+brain+tumor&um=1&hl=en&tbs=isch:1&ei=vfYRTPqYBorCrAfJ2bDDBA

Evolving role of biomarkers

• From Diagnosis

• To Translational Medicine

• To Personalized/Stratified/Precision Medicine

• To Personalized Health(care)


Biomarker-based translational medicine

• Does the compound get to the site of action?

• Does the compound cause its intended

pharmacological/ functional effects?

• Does the compound have beneficial effects on disease

or clinical pathophysiology?

• What is the therapeutic window (how safe is the drug)?

• How do sources of variability in drug response in target

population affect efficacy and safety?

Lead

Optimization

Exploratory

Development PoC Lead

Discovery

Target

Discovery

Exposure ?

Mechanism ?

Efficacy ?

Safety ?

Responders ?

{van Gool et al, Drug Disc Today 2010}

{Kumar, van Gool, RSC biomarkers, 2013}

2ND intl Pharma-Nutrition Conference

Singapore, 17 April 2013

Alain van Gool

Lecture LKCH, UMC Utrecht

29 October 2013

Alain van Gool

One strategy

10

Standardised biomarker strategy & development planning

Start for biomarker discovery, validation, development

11

Biomarker strategy: Data-driven decisions

To be made during testing of drug in preclinical and clinical disease models:

Target engagement? Effect on disease?

yes yes !

no no

• No need to test current

drug in large clinical trial

• Need to identify a more

potent drug

• Concept may still be

correct

• Concept was not correct

• Abandon approach

• Proof-of-Concept

• Proceed to full

clinical

development

“Stop early, stop cheap”

“More shots on goal”

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{Kumar, van Gool, RSC biomarkers, 2013}

Rational selection of best targets and drugs works

The 5R’s assessment:

• Right Target

• Right Tissue

• Right Safety

• Right Patients

• Right Commercial Potential

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Adopt rational target selection in pharma research CarTarDis = Cardiovascular Target Discovery Public-private partnership, 13 partners, 8 countries, project budget 8.0M Eur Started 1 Oct 2013 for 4 years Adopting AstraZeneca’s 5R strategy in drug target selection

(Coordinator)

CarTarDis.eu

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Successes of drug development

Antibiotics Vaccins

Reproductive medicine Oncology


Source: John Arrowsmith: Nature Reviews Drug Discovery 2011

• Success rates of clinical proof-of-concept have dropped from 28% to 18% • Insufficient efficacy as the most frequent reason • Targeted therapy through Personalized Medicine may be the solution • Patient selection using companion diagnostics

A need for Personalized Medicine

(Analysis of 108 failures in phase II)

Reason for failure Therapeutic area


{Source: Chakma. Journal of Young Investigators. 2009}

Principle of Personalized/Precision/Targeted Medicine


Case study: B-RAF mutations and melanoma

{Miller and Mihm,

2006}


Mechanism of pathophysiology in BRAF mutated tumors

V600E

Kinase domain

{Roberts and Der, 2007}

• B-RAFV600E mutation: constitutively active kinase, oncogenic addiction

• Overactivate ERK pathway drives cell proliferation • RAF inhibitors shown to block growth of tumors with B-RAFV600E mutation • Prevalence of B-RAFV600E is base for patient selection:

• Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer


Clinical efficacy of Vemurafenib (PLX-4032, Zelboraf)

Key biomarkers: Stratification: BRAFV600E mutation Mechanism: P-ERK Cyclin-D1 Efficacy: Ki-67 18FDG-PET, CT Clinical endpoint: progression-free survival (%)

{Source: Flaherty et al, NEJM 2010} {Source: Chapman et al, NEJM 2011}


Development of Vemurafenib (Zelboraf)

{Source: Davis M J , Schlessinger J J Cell Biol 2012}


Vemurafenib trials

22

108 clinical studies


Emerging companion diagnostics

Good examples personalized medicine in Oncology and Neurosciences:

• Cyp450, Her2/neu, BRCA, BRAF, EGFR, EML4/ALK, etc

Emerging companion diagnostics, also linked to non-drug therapies:

• 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

Coming up: metabolic biomarkers, imaging biomarkers


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Optimal Personalized / Precision / Targeted Medicine

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]


Next Generation Sequencing

{Nature, July 17 2014, 511: 344-}


Emerging protein biomarkers

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Current diagnostic protein assays:

• Mostly protein abundance

Emerging:

• Post-translational modifications

• Ratio protein isoforms

• Protein complexes


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


Discovering new glycoprotein biomarkers

• 1D LC-MS/MS glycoproteomics in plasma • Detection of 100K features in one scan • ~20.000 unique deconvoluted monoisotopic masses per single analysis

(> 50% are glycopeptides)

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]

Proof of principle study:

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{Hans Wessels, Monique van Scherpenzeel, Dirk Lefeber, Alain van Gool, unpubl}


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 test

{Tegtmeyer et al, NEJM 370;6: 533 (2014)}

Genomics Glycomics Metabolomics

{Monique van Scherpenzeel, Dirk Lefeber}


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31

New data (generators, owners)


healthy disease disease + treatment

Interpret data with self-normalisation

Subgroups

100%

Normalisation of responders


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

35

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}


Identification of ERK pathway biomarkers

• Pharmacogenomics A375 melanoma cells

• Study effect of 4 RAF, 2 MEK, 1 ERK inhibitors

• Select biomarkers based on profile and function

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RAFi #4

MEKi #1MEKi #2

RAFi #3

RAFi #1

RAFi #2

ERKi #1

RA

Fi

#1

RA

Fi

#2

RA

Fi

#3

RA

Fi

#4

ME

Ki

#1

ME

Ki

#2

ER

Ki

#1

RA

Fi

#1

RA

Fi

#2

RA

Fi

#3

RA

Fi

#4

ME

Ki

#1

ME

Ki

#2

ER

Ki

#1

RAFi #1

RAFi #2

RAFi #4

RAFi #1

RAFi #2

RAFi #4

Data for RAFi #4

4x RAFi

2x MEKi

1x ERKi

{Source: Alain van Gool, unpubl. data 2008}


• ~200 genes with >10 fold change.

• Overlap and differences between compound-regulated genes

• Methods applied to select new candidate biomarkers for validation, e.g. as secreted proteins in plasma

• Selection of ERK pathway responsive transcripts, e.g. IL-8

Selection biomarkers from pharmacogenomics A375 cells

RA

Fi

#4

RA

Fi

#1

RA

Fi

#2

ER

Ki

#1

RA

Fi

#3

ME

Ki #

1

ME

Ki #

2

DM

SO



{Yurkovetsky, et al. Clin Cancer Res 2007;13(8) April 15, 2007}

123 pg/ml

9 pg/ml

p < 0.001

Determination of IL-8 levels (one of 29 serum cytokines analyzed) in 179 melanoma patients (stage II & III) & 379 healthy individuals

Literature: elevated levels of IL-8 in melanoma patients


Validation study to confirm IL-8 in melanoma

Tissue Matching plasma + serum

Normal Healthy Controls 40 50

Stage 1 11 11

Stage 2 11 11

Stage 3, non-metastatic 4 4

Stage 3, metastatic 11 11

Stage 4, non-metastatic 3 3

Stage 4, metastatic 19 19

Clinical samples used (from two independent commercial biobanks)

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x



Validation study to confirm IL-8 in melanoma

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x

Analysis done:

• 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



No change in plasma & serum IL-8 levels in melanoma

Serum IL-8 levels in various Stages of Melanoma

Healthy control (n=10) Melanoma (n=37)

0

20

40

60

80

Me

an

IL

-8 l

ev

els

(p

g/m

l)

Plasma IL-8 levels in various Stages of Melanoma

Healthy control (n=20) Melanoma (n=59)

0

5

10

15

20

Me

an

IL

-8 l

ev

els

(p

g/m

l)

No confirmation of literature: no change in IL-8 protein levels in melanoma. Reason? Analytics ok ! Literature correct? Sample ok?



Lessons learned?

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Source: Youtube - Burn after reading ending}


Burn after reading ending.mp4

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)

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

Ongoing independent biomarker activities

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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.

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Take home messages

• Biomarkers have become an integrate part of drug discovery and

development as key drivers of translational medicine and personalized

healthcare

• Planning and execution of Biomarker strategy and development should be

done consistently from early lead compound to clinical proof-of-concept.

• Novel technologies in laboratories and selfmonitoring yield promising

opportunities for new biomarkers and applications

• Mind the biomarker innovation gap!

• Ensure thorough preparation of biomarker discovery, validation and

development and include lessons-learned from others to increase chance of

success

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Acknowledgements

Ron Wevers

Jolein Gloerich

Hans Wessels

Dirk Lefeber

Monique van Scherpenzeel

Leo Kluijtmans

Lucien Engelen

Nathalie Bovy

Paul Smits

Maroeska Rovers

Bas Bloem

and many others

www.radboudumc.nl/personalizedhealthcare

www.radboudumc.nl/research/technologycenters

www.radboudresearchfacilities.nl

[email protected]

[email protected]

www.linkedIn.com

www.slideshare.net/alainvangool

Many collaborators and funders

Jan van der Greef

Ben van Ommen

Bas Kremer

Lars Verschuren

Ivana Bobeldijk

Marjan van Erk

Carina de Jongh

Peter van Dijken

Peter Wielinga

Robert Kleemann

Suzan Wopereis

and many others

CarTarDis


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

https://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjEnJvrsMPKAhVJVBQKHR6ZDXEQjRwIBw&url=https://www.keraweb.nl/webdesign-gelderland/&psig=AFQjCNGhJ7vy6KxfHp83QlZ-NOT09wQYiw&ust=1453756603149974

2016-02-01 ECPM Basel Alain van Gool

Health & Medicine

Transcript of 2016-02-01 ECPM Basel Alain van Gool