Lecture on biomarkers (principles, potentials, pitfalls) for 200 pharmaceutical professionals as part of the IMI Pharma Train course organised by the European Center for Pharmaceutical Medicine

1. 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

2. 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

3. 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

4. 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 TranslationForward Translation
Reverse TranslationReverse Translation
(View drug development
as customer)
(Feed back clinical needs
and samples)
[van Gool et al, Drug Disc. Today 2010]
4 Alain van Gool, ECPM course, Basel, 1 Feb 2016

5. Biopsies
Tissues
Translational medicine in pharma
etc
Monkey
Pig
etc
Rabbit
Mice
Ex vivo
Rodents
DogTissues
Cell lines
Primary
cells
Diseased
human
Healthy
human
RatCellsHTS
(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
5 Alain van Gool, ECPM course, Basel, 1 Feb 2016

6. Limited view on complex systems from the outside
Source: Gary Larson
Animal models Patient-related outcomes
Source: National University Hospital Singapore
6 Alain van Gool, ECPM course, Basel, 1 Feb 2016

7. 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
7 Alain van Gool, ECPM course, Basel, 1 Feb 2016

8. 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
8 Alain van Gool, ECPM course, Basel, 1 Feb 2016

9. Evolving role of biomarkers
• From Diagnosis
• To Translational Medicine
• To Personalized/Stratified/Precision Medicine
• To Personalized Health(care)
9 Alain van Gool, ECPM course, Basel, 1 Feb 2016

10. 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

11. Standardised biomarker strategy & development planning
Start for biomarker discovery, validation, development
11

12. 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”
12
{Kumar, van Gool, RSC biomarkers, 2013}

13. Rational selection of best targets and drugs works
The 5R’s assessment:
• Right Target
• Right Tissue
• Right Safety
• Right Patients
• Right Commercial Potential
13

14. 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
14

15. Successes of drug development
Antibiotics Vaccins
Reproductive medicine Oncology
15 Alain van Gool, ECPM course, Basel, 1 Feb 2016

16. 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
16 Alain van Gool, ECPM course, Basel, 1 Feb 2016

17. {Source: Chakma. Journal of Young Investigators. 2009}
Principle of Personalized/Precision/Targeted Medicine
17 Alain van Gool, ECPM course, Basel, 1 Feb 2016

18. Case study: B-RAF mutations and melanoma
{Miller and Mihm,
2006}
18 Alain van Gool, ECPM course, Basel, 1 Feb 2016

19. 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
19 Alain van Gool, ECPM course, Basel, 1 Feb 2016

20. 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}
20 Alain van Gool, ECPM course, Basel, 1 Feb 2016

21. Development of Vemurafenib (Zelboraf)
{Source: Davis M J , Schlessinger J J Cell Biol 2012}
21 Alain van Gool, ECPM course, Basel, 1 Feb 2016

22. Vemurafenib trials
22
108 clinical studies
Alain van Gool, ECPM course, Basel, 1 Feb 2016

23. 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
23 Alain van Gool, ECPM course, Basel, 1 Feb 2016

24. 24
Optimal Personalized / Precision / Targeted Medicine

25. 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]
25 Alain van Gool, ECPM course, Basel, 1 Feb 2016

26. Next Generation Sequencing
{Nature, July 17 2014, 511: 344-}
26 Alain van Gool, ECPM course, Basel, 1 Feb 2016

27. Emerging protein biomarkers
27
Current diagnostic protein assays:
• Mostly protein abundance
Emerging:
• Post-translational modifications
• Ratio protein isoforms
• Protein complexes
Alain van Gool, ECPM course, Basel, 1 Feb 2016

28. 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
28 Alain van Gool, ECPM course, Basel, 1 Feb 2016

29. 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:
29
{Hans Wessels, Monique van Scherpenzeel, Dirk Lefeber, Alain van Gool, unpubl}
Alain van Gool, ECPM course, Basel, 1 Feb 2016

30. 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}
30 Alain van Gool, ECPM course, Basel, 1 Feb 2016

31. 31
31
New data
(generators, owners)
Alain van Gool, ECPM course, Basel, 1 Feb 2016

34. healthy disease disease +
treatment
Interpret data with self-normalisation
Subgroups
100%
Normalisation of responders
34 Alain van Gool, ECPM course, Basel, 1 Feb 2016

35. 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!
Alain van Gool, ECPM course, Basel, 1 Feb 2016

36. 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
36 Alain van Gool, ECPM course, Basel, 1 Feb 2016

37. 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}
37 Alain van Gool, ECPM course, Basel, 1 Feb 2016

38. 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 #1
MEKi #2
RAFi #3
RAFi #1
RAFi #2
ERKi #1
RAFi #4
MEKi #1
MEKi #2
RAFi #3
RAFi #1
RAFi #2
ERKi #1
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#3
RAFi#4
MEKi#1
MEKi#2
ERKi#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}
38 Alain van Gool, ECPM course, Basel, 1 Feb 2016

39. • ~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
{Source: Alain van Gool, unpubl. data 2008}
39 Alain van Gool, ECPM course, Basel, 1 Feb 2016

40. {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
40 Alain van Gool, ECPM course, Basel, 1 Feb 2016

41. 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
{Source: Alain van Gool, unpubl. data 2010}
41 Alain van Gool, ECPM course, Basel, 1 Feb 2016

42. 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
{Source: Alain van Gool, unpubl. data 2010}
42 Alain van Gool, ECPM course, Basel, 1 Feb 2016

43. 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
MeanIL-8levels(pg/ml)
Plasma IL-8 levels in various Stages of Melanoma
Healthy control (n=20) Melanoma (n=59)
0
5
10
15
20
MeanIL-8levels(pg/ml)
No confirmation of literature: no change in IL-8 protein levels in melanoma.
Reason?
Analytics ok ! Literature correct? Sample ok?
{Source: Alain van Gool, unpubl. data 2010}
43 Alain van Gool, ECPM course, Basel, 1 Feb 2016

44. Lessons learned?
44
Source: Youtube - Burn after reading ending}
Alain van Gool, ECPM course, Basel, 1 Feb 2016

45. 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)
45 Alain van Gool, ECPM course, Basel, 1 Feb 2016
(Netherlands)

46. Ongoing independent biomarker activities
46
Europe
USA
{Asadullah et al, Nature Reviews Drug Discovery, Dec 2015}
46 Alain van Gool, ECPM course, Basel, 1 Feb 2016

47. 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.
47
47 Alain van Gool, ECPM course, Basel, 1 Feb 2016

48. 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
48
48 Alain van Gool, ECPM course, Basel, 1 Feb 2016

49. 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
alain.vangool@tno.nl
alain.vangool@radboudumc.nl
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
49 Alain van Gool, ECPM course, Basel, 1 Feb 2016