Biomarker assessment and clinical utility

Case study 2

EATRIS Biomarker Platform Meeting

Amsterdam 27 November 2014

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

Head Biomarkers in Personalized Healthcare

Prof Alain van Gool

2

Based on data and slides from

projects @Organon, Schering-

Plough, MSD

2006-2010

Prof. Alain van Gool

Validation of IL-8 as

efficacy biomarker

for BRAF inhibitors

3

Case study: Development RAF inhibitors for melanoma

{Miller and Mihm,

2006}

4

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

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

5

Analysis ERK pathway activity

A375 treated with MEKi #1 A375 treated with RAFi #1

RSK RSK RSK

p-MEK

p-ERK

p-RSK

-10 -8 -6

0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f E

RK

ph

os

ph

ory

lati

on

-10 -8 -6 0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f M

EK

ph

os

ph

ory

lati

on

-10 -8 -6

0

50

100

150

DM

SO

Log [SCH 772984, M]

% o

f R

SK

ph

os

ph

ory

lati

on

Log [ , M]

Log [ MEKi #1 , M]

MEKi #1

IC50 = 35.70 nM

IC50 = 14.26 nM

No inhibition

Concentration MEKi #1 Concentration RAFi #1

Immunoassays to monitor phosphorylation biomarkers in ERK pathway

(ELISA, western blotting, mass spectrometry, reverse phase protein arrays)

6

Discovery of improved biomarkers for RAF inhibitors

Aim: identify soluble protein biomarker in blood that reflects

inhibition of ERK pathway in tumor with B-RAFV600D/E mutation

(More practical than p-ERK protein analysis in tumor biopsy)

(Also application in personalized medicine?)

Pharmacogenomics approach:

– A375 melanoma cells

– Homozygote BRAFV600E mutation

– Robust model system for method development

– Investigate effect of 7 inhibitors

• 4x RAFi

• 2x MEKi

• 1x ERKi

on gene expression, proliferation, apoptosis, etc

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Pharmacogenomics in A375 melanoma cells

• Efficient approach

• Highly reproducible data with

robust gene modulation

• Identify compound-specific and

common differential transcripts

• Select candidate biomarkers

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

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• ~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

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Literature

► It is reported in publications that basal levels of serum IL8 are significantly higher in melanoma samples compared to healthy normal controls

► IL8 plays a strong role in melanoma progression and metastasis.

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Zoya R. Yurkovetsky, John M. Kirkwood 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

Elevated levels of IL-8 in Patients with Melanoma

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Validation study to confirm IL-8 in melanoma

FFPE Tissue Plasma

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

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x

Clinical samples used (from two independent commercial biobanks)

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Validation study to confirm IL-8 in melanoma

Stage 1 Stage 2 Stage 3 Stage 4

H&E staining; 20x

Sample analysis:

• Genetic analysis for BRAFV600E/D mutation in genomic DNA from FFPE tissue samples

• IL-8 mRNA analysis in tissue samples by in situ + lysate hybridisation using bDNA probes

(multiplexing with ERK pathway response and housekeeping 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:

• Correlate IL8 expression with BRAF mutation in melanoma (Higher IL8 in mutated BRAF?)

• Correlate IL8 expression with metastasis (Higher in metastatic?)

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Plasma IL-8 levels vs Melanoma Stages

No confirmation of literature: no change in IL-8 protein levels in plasma

samples of melanoma patients. Reason?

14

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)

• Some biomarkers can better be detected in serum than in plasma.

• However, also in serum samples no confirmation of literature: no change in

IL-8 protein levels in melanoma

• Reason?

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Conclusion

No confirmation of literature: no change in IL-8 protein levels in

melanoma

Reasons unclear

– Analytics strong

– Data analysis strong

– Quality/origin of biosamples?

Key response selection biomarker is B-RAFV600D/E mutation

Key pathway biomarker is phosphorylated ERKSer202/204 = p-ERK

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{Source: Prof Khusru Asadullah, Head of Global Biomarkers Bayer Healthcare}

Biomarker innovation gap

• Imbalance between biomarker discovery, validation and application

• Many more biomarkers discovered than available as diagnostic test

Discovery Clinical

validation/confirmation

Diagnostic

test

Number of

biomarkers

Gap 1

Gap 2

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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: Khusru Asadullah, Nat Rev Drug Disc 2011}

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Some numbers

Data obtained from Thomson Reuters Integrity Biomarker Module

Eg Biomarkers in time: Prostate cancer

May 2011: 2,231 biomarkers

Nov 2012: 6,562 biomarkers

Oct 2013: 8,358 biomarkers

Oct 2014: 10,169 biomarkers with 32,093 biomarker uses

EU: CE marking

USA: LDT, 510(k), PMA

Way forward: shared innovation network projects

Standardisation, harmonisation, knowledge sharing needed in:

1. Assay development

2. Clinical validation

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Biomarker Development Center (Netherlands)

STW perspectief grant

Biomarker Development Center

Public-private partnership 4 years

Project grant €4.3M of which € 2.2M government,

and € 2.1M industry (€ 0.9M cash/ € 1.2M kind)

Close interactions with:

- Clinicians (biomarker application)

- Industry partners and stakeholders

- Patient stakeholder associations

Open for

partners !

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Thanks to:

Biomarker strategies Collaborators

Members of:

- Organon Biomarker Platform

- Schering-Plough Biomarker Group

- Merck Research Labs - Molecular Biomarkers

Translational Medicine Research Centre Singapore

Colleagues, particularly:

Erik Sprengers, Shian-Jiun Shih, Brian Henry, Hannes

Hentze, Zaiqi Wang, Rachel Ball, Meena Krishnamoorthi,

Aveline Neo, Sabry Hamza, Nicole Boo, Lee Kian-Chung,

Vidya Anandalaksmi

MSD/Merck

Colleagues, particularly in:

- Oss (Netherlands)

- Rahway, Kenilworth, Boston (East Coast, USA)

- San Francisco, Palo Alto (West Coast, USA)

Many in Asia, Europe, USA, including:

- Academic

- Consortia

- Contract research organizations

- Vendors

Saco de Visser, Adam Cohen Centre for Human Drug Research, Leiden, NL