Biomarker development for

targeted cancer therapeutics,

a real life story

ODDP 2014, Amsterdam

Prof. Alain van Gool

Professor Personalized Healthcare

Coordinator Radboudumc Technology Centers

Head Radboud Center for Proteomics, Glycomics and Metabolomics

Head Biomarkers for Personalized Healthcare

Based on data and slides from projects @Organon, Schering-Plough, MSD

2

My background

8 years academia (NL, UK)

(molecular mechanisms of disease)

13 years pharma (EU, USA, Asia)

(biomarkers, Omics)

3 years applied research institute (NL, EU)

(biomarkers, personalized health)

3 years university medical center (NL)

(personalized healthcare, Omics, biomarkers)

1991-1996 1996-1998 2009-2012

1999-2007 2007-2009 2009-2011

2011-now

2011-now

2

3

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

4

Source: 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 • Better therapies following Personalized Medicine strategies are needed • Key to apply translational biomarkers for personalized therapy

Need for Personalized Medicines

Analysis of 108 failures in phase II

Reason for failure Therapeutic area

4

5

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]

6

Biomarkers

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’

Molecular biomarkers can provide a molecular impression of a biological

system (cell, animal, human)

Biomarkers can be various analytes:

PSA protein – blood, indicator of prostate cancer

Cholesterol – blood, risk indicator for coronary and vascular disease

{Biomarkers definition working group, 2001 }

MRI scan – shows abnormal tissue, like brain tumor

7

Biomarker strategy based on key questions

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 ?

Core of Biomarker Strategy and Development planning

Start in Early Discovery, expand in Lead Optimization, complete in clinical Proof of Concept

{Concept by de Visser and Cohen, CHDR}

{van Gool et al, Drug Disc. Today 2010}

8

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”

Include personalized differences at every stage when possible.

9

High attrition in oncology drug development

{Kola & Landis, Nat. Rev. Drug Disc. (2004) 8: 711}

10

Biomarker need in oncology clinical care

Early detection tumor

Determine mechanism of pathophysiology

Determine tumor stage

Early detection benign to malignant tumor progression

Detect residual disease after therapy

Early and sensitive detection metastatic circulating cells

Early detection metastatic tumor

Understand why people respond differently

Main needs:

Need for biomarkers to develop more targeted therapies

Need for biomarkers for patient selection

11

Biomarker need in oncology drug development

Determine mechanism of pathophysiology of tumor

Verify published data on drug target

Select and develop a drug with

– Sufficient selectivity

– Highest efficacy Lead Optimisation

– Lowest off-target safety risk

Test exposure, efficacy and safety of drug in preclinic model

Test exposure, efficacy and safety of drug in clinical trials

Test efficacy in stratified patients, selected on mechanism

Monitor drug efficacy and safety post-market introduction

Back-translation of clinical findings to research

Consistent application of translational biomarkers

12

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

13

Case study: Development RAF inhibitors for melanoma

{Miller and Mihm,

2006}

14

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

15

15

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

16

Cellular efficacy by selective B-RAF inhibition by siRNA

Wild

type

Moc

k

BRAF

1

BRAF

5

CRAF

1

CRAF

3

ARAF

4

ARAF

8

siCont

rol

GFP

0

25

50

75

100

125

150

Percen

tag

e

Inhibition of RAF-MEK-ERK

pathway and induction of

apoptosis by siRNA (shown effect in A375 cells)

Inhibition of cell proliferation

by siRNA (shown effect in A375 cells)

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

B-RAF C-RAF

A-RAF GFP

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/MS

G1

A0

A0 : 38 %

G1 : 42 %

S : 6 %

G2/M : 5 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G2/M

G1

S

G1 : 65 %

S : 17 %

G2/M : 15 %

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

G1 : 56 %

S : 16 %

G2/M : 17 %

SG2

G1

SG2

G1

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

G2S

G1

G1 : 65 %

S : 17 %

G2/M : 12 %

B-RAF C-RAF

A-RAF GFP

Induction of apoptosis

by siRNA

(shown effect in A375 cells)

Key response selection biomarker is B-RAFV600D/E mutation

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

B - RAF

C - RAF

ERK

B - actin

A - RAF

Mock GFP Si -

control C - RAF A - RAF

PARP

B - RAF WT

p-MEK

p-ERK

-

Mock GFP Si -

control C - RAF A - RAF B - RAF WT

17

Cellular efficacy by RAF kinase inhibitor compounds

Inhibition of proliferation (A375, SK-MEL-24, Colo-205)

Inhibition of anchorage-

independent growth

in soft agar (A375)

Inhibition of RAF-MEK-ERK pathway (A375, SK-MEL-24, Colo-205)

Sorafenib

Sorafenib CI1040 Org240390 SB590885 Org245224 Org245108

Solvent No compound

Sorafenib CI1040 Org240390 SB590885 Org245224 Org245108

A375

Cells:

Compounds:

SK-MEL-24

Colo-205

Sorafenib

(multikinase)

CI-1040

SB 590885

Pe

rce

nta

ge

gro

wth

- 10 - 9 - 8 - 7 - 6 - 5 - 4 - 10

0 10 20 30 40 50 60 70 80 90

100 110 120

Log conc. (M)

CI-1040

(MEK)

SB590885

(B-RAF)

18

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)

24

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)

(Enabling 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

25

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

26

• ~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

27

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

28

Validation study to confirm IL-8 in melanoma

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)

29

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

30

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?

31

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?

Conclusion:

Key response selection biomarker is B-RAFV600D/E mutation

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

32

Alignment with: - Experimental medicine

- Competitive intelligence

- Strategy

- Toxicology

- Formulation

- External experts (clinics, academics)

Predict clinical efficacy in oncology

Cells

Cell line xenografts (PoM, PoP)

Healthy subjects (PoM)

Cancer patients (PoM, PoP)

Selected cancer patients (PoC)

PoM – Proof of Mechanism

PoP – Proof of Principle

PoC – Proof of Concept

Primary tumor xenograft models

Genetically engineered mouse models

(PoM, PoP, non-pivotal PoC)

33

Primary tumor xenograft models

Human tumor biopsies isolated from specific cancer patients

Biopsy fragments transplanted into immunodeficient mice

Passage the tumors to enable parallel testing of dosing groups

Characterize the tumors to mimic patient selection

– DNA mutations

– mRNA expression

Study:

– Colorectal cancers

– Test inhibitors of RAS-RAF-MEK-ERK pathway

in collaboration with:

34

Analysis of primary colorectal cancer tumors

Mastertable 20 Colon Cancer Specimens CrownBio CollaborationModel ID Passage # Growth Kinetic Comments Molecular

for Mutation Days to BRAF on Profiling

Analysis/RNA 500 mm3 Exon2 Exon3 EXON15 Exon9 Exon20 Mutation Results

CRF004 P6 41 WT WT1799 T>A

Val600Glu

1633G>A,

Glu545LysWT

CRM010 P3 53 WT WT WT1633G>A,

Glu545LysWT

CRF012 P5 6538G>A,

Gly13AspWT WT WT WT

CRF024 P1 63 (difficult to grow) WT WT WT WT WT

CRM028 P3 6435G>A,

Gly12AspWT WT WT WT

CRX231 P3 9338G>A,

Gly13AspWT WT WT

3140A>G,

His1047Arg

CRX455 P5 3235G>A,

Gly12AspWT WT WT

3140A>T,

His1047Leu

CRM588 P3 3138G>A,

Gly13AspWT WT WT WT

CRF692 P2 NA35G>A,

Gly12AspWT WT WT WT

CRX047 P3 5534G>T,Gly12Cy

sWT WT

1633G>A,

Glu545LysWT

CRM245 P3 42 WT WT WT WT WT

CRM205 P5 43 WT WT1781 A>G

Asp594GlyWT

3062A>G,

Tyr1021Cys

CRF150 P4 6435G>A,

Gly12AspWT WT WT WT

CRM146 P3 60 WT WT WT1634A>G,

Glu545GlyWT

CRF560 P5 34 WT WT WT WT WT

CRF126 P5 3335G>T,

Gly12ValWT WT WT WT

CRF029 P5 68 WT WT1799 T>A

Val600GluWT WT

CRM170 P5 37 WT WT WT WT WT

CRF193 P5 3538G>A,

Gly13AspWT WT WT WT

CRF196 P5 62 WT WT WT WT WT

Fast (<35) Medium (36-60) Slow (>60)

Mutation Analysis CrownBio

KRAS PIK3CA

Heterozygous Homozygous

20 colon cancer biopsies with

proven response to standard of

care treatment (irinotecan)

• Tumor size

• Pathway engagement (p-ERK)

Tumor selection parameters:

1. Growth analysis

2. Mutation analysis hotspots

– KRASG12, G13, Q61

– BRAFV600

– PI3KCAE542, E545, H1047

3. Pathway gene expression

35

Stratification by gene expression profiling

Absence of DNA mutations in selected genes does not always

mean normal pathway activity

mRNA expression profiling provides alternative way to determine

analysis of pathway status

36

Gene expression profiling of primary colorectal tumors

BRAF mutant

KRAS WT

Cut-off : 5 fold/p-value=0.05

BRAF WT

KRAS mutant

BRAFV600

KRASG12

PI3KCAE542/545

PI3KCAH1047

Tumors

Genes

BRAF WT

KRAS WT

37

Clustering of primary tumors based on gene expression

KRAS Wildtype

BRAF Wildtype

KRAS Mutant

BRAF Wildtype

KRAS Wildtype

BRAF Mutant

Clustering of KRAS wild-type with KRAS mutants

Clustering of KRAS mutant with BRAF mutants

38

Clinical efficacy of Vemurafenib, a novel BRAF inhibitor

Key biomarkers:

Exposure: -

Mechanism: p-ERK, Cyclin-D1

Efficacy: Ki-67, 18FDG-PET, CT

Safety: -

Selection: BRAFV600E mutation

Clinical endpoint: progression-free survival (%)

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

39

History of Zelboraf (Vemurafenib)

Davis M J , Schlessinger J J Cell Biol 2012;199:15-19

© 2012 Davis and Schlessinger

40

Clinical effects of Vemurafenib

{Wagle et al, 2011, J Clin Oncol 29:3085}

Before Rx Vemurafenib, 15 weeks Vemurafenib, 23 weeks

• Strong initial effects vemurafenib

• Drug resistancy

• Reccurence of tumors

41 {Source: Yancovitz, PLoS One 2012}

Tumor tissue heterogeneity

• BRAFV600D/E is the driving

mutation in melanoma

• However, also no BRAFV600D/E

mutation found in parts of a

primary melanoma

• Molecular heterogeneity in

diseased tissue

• Biomarker levels in tissue will

vary

• Biomarker levels in body

fluids will vary

• Major challenge for

(companion) diagnostics

42

Agenda

Background

– Personalized medicine

– Need for biomarkers in oncology

Case study

– Biomarkers to support development of BRAF inhibitors for melanoma

Take home messages:

Choose and validate your biomarkers wisely

Collaborate

Realize human biology is complex

43

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