Closing lecture on progress in biomarker land, given at the Oncology clinical chemistry society.

1. Biomarkers in Oncology:
from cells to systems
Prof Alain van Gool
Head Radboud Center for Proteomics, Glycomics
and Metabolomics
Coordinator Radboud Technology Centers
Head Biomarkers in Personalized Healthcare
NVKCL Lustrum symposium
‘From Tumormarkers to Oncological Biomarkers’
Utrecht, 14th Nov 2013

2. Alain’s mixed perspectives
8 years academia (NL, UK)
(research, methods)
13 years pharma (EU, USA, Asia)
(biomarkers, Omics)
2 years applied research institute (NL, EU)
(biomarkers, personalized health)
2 years med school (NL)
(Omics, biomarkers, personalized healthcare)
A person / citizen / family man
(adventures in EU, USA, Asia)
1991-1996 1996-1998 2009-2012
1999-2007 2007-2009 2009-2011
2011-now
2011-now

3. Singularity University’s FutureMed 2013 conference

4. Singularity University’s FutureMed 2013 speakers
Exponential
technologies
Digital
medicine
Integrated
care
Artifical
intelligence
Robotics
Patients
included
Lifestyle
Self
quantification
Global
health
WatsonArtifical
intelligence
Regenerative
medicine
23andme
Robotics
and Jamie Heywood (Patientslikeme)

5. Singularity University’s FutureMed 2013 conference

6. Exponential technologies
“The only constant is change,
and the rate of change is
increasing”
We are at the knee
of the exponential curve

7. 1. Imaging of every part of human body in high resolution
2. Smartphone as the most important pieve of clothing
3. Self-diagnosis as a continous monitoring to quantified self
4. Artifical intelligence and robots
5. Digital medicine, Big Data and wisdom of the crowd
6. Our body as a lego box using 3D printing for spare parts
7. Our brain online using brainsensing headbands to transfer thoughts
Exponential trends

8. Digital medicine

9. Quantified self

11. 3 days high speed innovation in one slide
• Buzzwords:
• Exponential technologies
• Disruptive innovation
• Progress and beyond
• Digital quantified self
• Focus on:
• Where will we be in 5-20 years?
• Technologies, genomics, robotics, Big Data, eHealth, translating data to
knowledge, patient empowerment
• Less focus on:
• What to do next year?
• Biomarkers, robustness assays for decision, innovation in clinical drug
testing

12. 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)
12

13. A problem in biomarker land
• Imbalance between biomarker discovery and application.
• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress
beyond initial publication to multi-center clinical validation.
• Gap 2: Insufficient demonstrated added value of new clinical biomarker and
limited development of a commercially viable diagnostic biomarker test.
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
13
The innovation gap in biomarker
research & development

14. Some numbers
Data obtained from Thomson Reuters Integrity
Biomarker Module, April 2013
Alzheimer’s Disease
Chronic Obstructive
Pulmonary Disease
Type II Diabetes
Mellitis
Eg Biomarkers in time: Prostate cancer
May 2011: 2,231 biomarkers
Nov 2012: 6,562 biomarkers
Oct 2013: 8,358 biomarkers
EU: CE marking
USA: LDT, 510(k), PMA
14

15. The innovation gap in biomarker research & development
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2
– Many new biomarkers are panels (RNA, protein, biochemical, imaging)
– Not wise to discover yet an other biomarker
– Focus on selecting the best biomarker (panels) among those already
found (scientific and patent literature, databases, etc)
– Develop those biomarkers tot clinically applicable tests
15

16. 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}
16

17. “It is simply no longer possible to believe much of the clinical
research that is published, or to rely on the judgment of trusted
physicians or authoritative medical guidelines.
I take no pleasure in this conclusion, which I reached slowly and
reluctantly over my two decades as an editor of The New
England Journal of Medicine.”
Marcia Angell, MD
Former Editor-in-Chief NEJM
Oct 2010
17

18. Biomarkers in oncology
{Miller and Mihm
NEJM, 2006}
Example:
Melanoma
• Genetic risk
factors
• Secondary
events
• Transition benign
to malignant
• Transition local
to metastatic

19. Biomarker need in oncology
High need for molecular tools that allow a look into the black box
and improve personalized disease management:
biomarkers and companion diagnostics
Drug exposure ?
(Early) diagnosis ?
Cross-species differences ?
Patient classification ?
Prognosis ?
Target engagement ?
Modulation of mechanism ?
Off-target drug effects ?
Treatment Patients like me
Mechanism ?
Other (latent) diseases ?
Person
19
Disease stage?
Benign to malignant ?

20. Companion Diagnostics
Right drug
in right patient
at right dose
at right time
In other words:
Apply a well characterized therapy in a biological system you know well to
treat a disease you understand well, in a way that you know works.
Often: (molecular) biomarkers as diagnostic companions of a drug.
Actually: biomarkers are diagnostic companions to a person !
20

21. Companion Diagnostics – some numbers
At present in pharmaceutical development:
40.000 clinical trials ongoing
16.000 trials in oncology
8.000 trials in oncology have a companion diagnostic
At present on market:
113 Biomarker in drug label (2012; up from 69 in 2010 = +64%)
16 CDx testing needed (2012; up from 4 in 2010 = +400%)
Costs of development:
>1.000 MUSD per drug
~10 MUSD per diagnostic
Source: www.fda.gov
21

22. Companion Diagnostics
Metabolism
Efficacy or
safety
Source: www.fda.gov
{Kumar and van Gool, 2013}
22

23. Case: Biomarkers in Oncology
V600D/E
Kinase domain
{Roberts and Der, 2007}
• B-RAFV600D/E mutation: constitutively active kinase, oncogenic addiction
• Overactivate ERK pathway drives cell proliferation
• RAF inhibitors block growth of tumor xenografts with B-RAFV600D/E mutation
• Prevalence of B-RAFV600D/E
• Melanoma (60%), colon (15%), ovarian (30%), thyroid (30%) cancer
• Develop B-RAF inhibitors with B-RAFV600D/E as companion diagnostic
23

24. 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}
24

25. 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
25

26. Tumor tissue heterogeneity
26
• BRAFV600D/E is driving mutation
• However, also no BRAFV600D/E
mutation found in regions of a
primary melanoma
• Molecular heterogeneity in
diseased tissue
• Biomarker levels in tissue will
vary
• Biomarker levels in body fluids
will vary
• Real challenge for (companion)
diagnostics
{Source: Yancovitz, PLoS One 2012}

27. Biomarkers in oncology
Oncological biomarker
(system)
Tumor marker
(cell)
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation

28. Biomarkers in oncology
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation
Oncological biomarker
(system)
Tumor marker
(cell)

29. Alternative molecular profiling approaches
• Genetics (DNA)
• SNPs, indels, CNV, mDNA
• Transcriptomics (RNA)
• mRNA, miRNA, ncRNA
• Proteomics
• Expression, isoforms (PTMs),
complexome
• Metabolomics
• Abundance, isoforms, flux
• Cellomics
• Morphology, enzymes
• …

30. Case: stratification by mRNA 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

31. Primary colorectal cancer xenograft study
Mastertable 20 Colon Cancer Specimens CrownBio Collaboration
Model ID Passage # Growth Kinetic
for Mutation Days to BRAF
Analysis/RNA 500 mm
3
Exon2 Exon3 EXON15 Exon9 Exon20
CRF004 P6 41 WT WT
1799 T>A
Val600Glu
1633G>A,
Glu545Lys
WT
CRM010 P3 53 WT WT WT
1633G>A,
Glu545Lys
WT
CRF012 P5 65
38G>A,
Gly13Asp
WT WT WT WT
CRF024 P1 63 (difficult to grow) WT WT WT WT WT
CRM028 P3 64
35G>A,
Gly12Asp
WT WT WT WT
CRX231 P3 93
38G>A,
Gly13Asp
WT WT WT
3140A>G,
His1047Arg
CRX455 P5 32
35G>A,
Gly12Asp
WT WT WT
3140A>T,
His1047Leu
CRM588 P3 31
38G>A,
Gly13Asp
WT WT WT WT
CRF692 P2 NA
35G>A,
Gly12Asp
WT WT WT WT
CRX047 P3 55
34G>T,Gly12Cy
s
WT WT
1633G>A,
Glu545Lys
WT
CRM245 P3 42 WT WT WT WT WT
CRM205 P5 43 WT WT
1781 A>G
Asp594Gly
WT
3062A>G,
Tyr1021Cys
CRF150 P4 64
35G>A,
Gly12Asp
WT WT WT WT
CRM146 P3 60 WT WT WT
1634A>G,
Glu545Gly
WT
CRF560 P5 34 WT WT WT WT WT
CRF126 P5 33
35G>T,
Gly12Val
WT WT WT WT
CRF029 P5 68 WT WT
1799 T>A
Val600Glu
WT WT
CRM170 P5 37 WT WT WT WT WT
CRF193 P5 35
38G>A,
Gly13Asp
WT 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 selection parameters:
1. Growth analysis
2. Mutation analysis hotspots
– KRASG12, G13, Q61
– BRAFV600
– PI3KCAE542, E545, H1047
3. Pathway gene expression

32. Transcriptomics 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
Transcripts
BRAF WT
KRAS WT

33. Clustering of primary tumors based on transcriptomics
KRAS Wildtype
BRAF Wildtype
KRAS Mutant
BRAF Wildtype
KRAS Wildtype
BRAF Mutant

34. Biomarkers in oncology
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation
Oncological biomarker
(system)
Tumor marker
(cell)

35. EC DG for Research and Innovation
Alain van Gool
Brussels, 11 Sept 2012
Working in complex human biological systems
requires a systems biology approach
System biology in:
Diagnosis
Prognosis
Treatment
Monitoring

36. Interaction tumor and the adaptive immune system
Source: prof Jan Smit, Radboudumc
Kareva I , and Hahnfeldt. P Cancer Res 2013;73:2737-2742

37. System proteomic
biomarkers
Plasma
Quantitation of
autoantibodies
or proteins
Confirmation
in tissue
slides
Tumor

38. Personal profiles
Source: Barabási 2007 NEJM 357; 4}
• People are different
• Different networks influences
• Different risk factors
38

39. 39
Personal profiles
Source: Thomas Kelder, Marijana Radonjic

40. Environmental factors in oncology healthcare
Source: 11 Sept 2013 @de Volkskrant
• Biological clock
• Smoking
• Pharma-Nutrition
• Drug-drug interaction
• Alternative medicine
• Genetic factors
• …
Prof Ron Matthijssen
ErasmusMC

41. Biomarkers in oncology
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation
Oncological biomarker
(system)
Tumor marker
(cell)

42. Exponential technologies

43. Exponential health(care) technologies
• IBM Watson
• AI system on top of recorded medical data + connected to Big Data clouds
• Independent data-driven clinical diagnosis with very high accuracy
• Artifical intelligence

44. Exponential health(care) technologies
Georg Church, Craig Venter
• 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
• Next next generation sequencing
• Various DNA and RNA species
• Single cell level
• Link molecular diagnosis to therapies

45. Exponential health(care) technologies
• Next next generation sequencing
• Various DNA and RNA species
• Single cell level
• Link molecular diagnosis to therapies
• Synthetic life
• Longevity (sequencing very old people to identify rare protective alleles)
• Personalgenomes.org
Georg Church, Craig Venter

46. Exponential health(care) technologies
• Proteomics
• Bottom-up proteomics (established)
• Protein identification
• Differential peptide expression profiling
• Targeted proteomics (emerging)
• Absolute/relative peptide quantitation
• Top-down proteomics (new)
• Intact protein characterization
• Differential analysis post-translational modifications (like glycosylations)
• Metabolomics
• Untargeted profiling
• Differential metabolite profiling
• Targeted analysis
• Quantitation of subclasses of biochemical analytes

47. Source: Allison Doerr, Nature Methods 9,36 (2012)
Case: Glycoproteomics

48. MAB ESI - MS Intact MAB spectrum
Compound Spectra
147916.0294
148062.0367
148224.0781
148387.2015
148550.0889
148713.2075
+MS, 0.985-10.524min, Smoothed (0.07,6,SG), Baseline subtracted(0.80), Deconvoluted (MaxEnt, 2673.57-3122.37, *1.75, 10000)
0
2000
4000
6000
8000
Intens.
147250 147500 147750 148000 148250 148500 148750 149000 149250 149500 m/z
Case: Glycoproteomics
Analysis of intact monoclonal antibodies by ESI-MS

49. Analysis of intact Trastuzumab by ESI-MS
Multiple charged ion
Single charged ion = intact protein
- Single proteins
- Protein (sub)complexes
Mitochondrial complex 1 (40 subunits)
Quantitative analysis of
intact protein isoforms
- N/C-terminal truncations
- Splice variants
- Post-translational modifications
(glycosylation, phosphorylation,
etc)
148 kDa!

50. Application glycoproteomics in rare diseases
50
• 12 families with liver disease and dilated cardiomyopathy (5-20 years)
• Initial clinical assessment didn’t yield clear cause of symptoms
• Specific sugar loss of serum transferrin identified via glycoproteomics
• Genetic defect in glycosylation enzyme identified via exome sequencing
• Outcome 1: Explanation of disease
• Outcome 2: Dietary intervention as succesful personalized therapy
• Outcome 3: Glycoprofile developed as diagnostic test by mass spectrometry
Dietary
intervention
Incomplete glycosylation Complete glycosylation
{Dirk Lefeber et al,
NEJM 2013}

51. Biomarkers in oncology
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation
Oncological biomarker
(system)
Tumor marker
(cell)

52. Need for biomarker validation
52
Discovery Clinical
validation/confirmation
Diagnostic
test
Number of
biomarkers
Gap 1
Gap 2

53. Case: validation of soluble biomarkers for melanoma
Source: Yurkovetsky et al. Clin Cancer Res 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 & 379 healthy individuals
Secreted protein
biomarkers
→ Goal: clinically validate IL-8 as biomarker for melanoma

54. Validation study to confirm IL-8 in melanoma
Tissue Plasma
Normal Healthy Controls 40 (Tissue Solutions Inc) 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
• Genetic analysis in tissue samples for BRAFV600E/D mutation
• Measure IL-8 in tissue samples by in situ hybridisation (bRNA) and immuno-
histochemistry (protein)
• Measure IL-8 protein in matching body fluids (by ELISA, Luminex, Mesoscale)

55. 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?
• Cannot publish results, cannot communicate widely to biomarker field.
• No lesson learned and same study is likely to be done again by others.
• Inefficient and expensive practice.

56. Shared biomarker research through open innovation
We need to set up a open innovation network to share biomarker knowledge and
jointly develop and validate biomarkers (at level of NL and EU):
1. Assay development of (diagnostic) biomarkers
Share resources and time to develop a robust quantitative assay
2. Clinical biomarker quantification/validation/confirmation
Share resources and time by joined multi-center biomarker studies
Shared knowledge,
technologies and objectives

57. Biomarkers in oncology
1. Also include other means than genetics for screening
2. Consider tumor cells as part of a system
3. Embrace novel technologies
4. Focus on biomarker validation
Oncological biomarker
(system)
Tumor marker
(cell)

58. But most importantly …
58
Data
Knowledge
Understanding
Decision
Action
Translation is key !

59. The future is nearly there …
59
Personalized advice
Action
Selfmonitor
Cloud
Lifestyle
Nutrition
Pharma

60. A different model of personalized healthcare

61. Personalized healthcare
61
Ways forward:
• Data sharing
• Selfmonitoring
• Big Data
• System biology
• Lifestyle + Nutrition + Pharma

62. Acknowledgements
Jan van der Greef
Ben van Ommen
Peter van Dijken
Ton Rullmann
Lars Verschuren
Bas Kremer
Marijana Radonjic
Thomas Kelder
Robert Kleemann
Suzan Wopereis
and others
Ron Wevers
Jolein Gloerich
Dirk Lefeber
Monique Scherpenzeel
Leo Kluijtmans
Udo Engelke
Ulrich Brandt
Lucien Engelen
and others
Lutgarde Buydens
Jasper Engel
Lionel Blanchet
Jeroen Jansen
and others
Radboud umc Personalized Healthcare Taskforce:
Andrea Evers, Alain van Gool, Joris Veltman, Jan Kremer, Bas
Bloem, Maroeska Rovers, Jack Schalken, Paul Smits, Gerdi
Egberink, Viola Peulen, Martijn Hoogboom, Martijn Gerretsen
alain.vangool@tno.nl
alain.vangool@radboudumc.nl