Discussion of a case study of clinical biomarker validation, which was well executed but could not reproduce literature findings.

1. 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. 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. 3
Case study: Development RAF inhibitors for melanoma
{Miller and Mihm,
2006}

4. 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. 5
Analysis ERK pathway activity
A375 treated with MEKi #1
A375 treated with RAFi #1
RSK
p-MEK
p-ERK
p-RSK
-10
-8
-6
0
50
100
150
DMSO
Log [SCH 772984, M]
% of ERK phosphorylation
-10
-8
-6
0
50
100
150
DMSO
Log [SCH 772984, M]
% of MEK phosphorylation
-10
-8
-6
0
50
100
150
DMSO
Log [SCH 772984, M]
% of RSK phosphorylation
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. 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

7. 7
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 #1
MEKi #2
RAFi #3
RAFi #1
RAFi #2
ERKi #1
RAFi #1
RAFi #2
RAFi #3
RAFi #4
MEKi #1
MEKi #2
ERKi #1
RAFi #1
RAFi #2
RAFi #4
Data for RAFi #4
4x RAFi
2x MEKi
1x ERKi

8. 8
•~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
RAFi #4
RAFi #1
RAFi #2
ERKi #1
RAFi #3
MEKi #1
MEKi #2
DMSO

9. 9
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.

10. 10
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

11. 11
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)

12. 12
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?)

13. 13
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. 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
Mean IL-8 levels (pg/ml)
Plasma IL-8 levels in various Stages of Melanoma
Healthy control (n=20) Melanoma (n=59)
0
5
10
15
20
Mean IL-8 levels (pg/ml)
• 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?

15. 15
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

16. 16
16
{Source: Prof Khusru Asadullah, Head of Global Biomarkers Bayer Healthcare}

17. 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
17

18. 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}
18

19. 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

20. Way forward: shared innovation network projects
Standardisation, harmonisation,
knowledge sharing needed in:
1. Assay development
2. Clinical validation
20

21. 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 !
21

22. 22
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