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