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

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

Singularity University’s FutureMed 2013 conference

Singularity University’s FutureMed 2013 speakers

Exponential technologies

Digital medicine

Integrated care

Artifical intelligence

Robotics Patients included

Lifestyle

Self quantification

Global health

Watson Artifical intelligence

Regenerative medicine

23andme Robotics

and Jamie Heywood (Patientslikeme)

Singularity University’s FutureMed 2013 conference

Exponential technologies

“The only constant is change, and the rate of change is

increasing”

We are at the knee of the exponential curve

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

Digital medicine

Quantified self

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

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)

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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

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The innovation gap in biomarker research & development

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

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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

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

“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

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Biomarkers in oncology

{Miller and Mihm NEJM, 2006}

Example: Melanoma

• Genetic risk factors

• Secondary events

• Transition benign to malignant

• Transition local to metastatic

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

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Disease stage? Benign to malignant ?

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

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

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Companion Diagnostics

Metabolism

Efficacy or safety

Source: www.fda.gov {Kumar and van Gool, 2013}

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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

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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}

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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

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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}

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

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)

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

• …

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

Primary colorectal cancer xenograft study Mastertable 20 Colon Cancer Specimens CrownBio Collaboration

Model 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 selection parameters: 1. Growth analysis 2. Mutation analysis hotspots

– KRASG12, G13, Q61

– BRAFV600

– PI3KCAE542, E545, H1047

3. Pathway gene expression

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

Tran

scri

pts

BRAF WT KRAS WT

Clustering of primary tumors based on transcriptomics

KRAS Wildtype

BRAF Wildtype

KRAS Mutant

BRAF Wildtype

KRAS Wildtype

BRAF Mutant

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)

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

Interaction tumor and the adaptive immune system

Source: prof Jan Smit, Radboudumc

Kareva I , and Hahnfeldt. P Cancer Res 2013;73:2737-2742

System proteomic biomarkers

Plasma

Quantitation of autoantibodies or proteins

Confirmation in tissue slides

Tumor

Personal profiles

Source: Barabási 2007 NEJM 357; 4}

• People are different • Different networks influences • Different risk factors

38

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Personal profiles

Source: Thomas Kelder, Marijana Radonjic

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

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)

Exponential technologies

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

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

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

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

Source: Allison Doerr, Nature Methods 9,36 (2012)

Case: Glycoproteomics

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

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!

Application glycoproteomics in rare diseases

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• 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}

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)

Need for biomarker validation

52

Discovery Clinical validation/confirmation

Diagnostic test

Number of biomarkers

Gap 1

Gap 2

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

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)

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? • 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.

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

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)

But most importantly …

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Data

Knowledge

Understanding

Decision

Action

Translation is key !

The future is nearly there …

59

Personalized advice

Action

Selfmonitor Cloud

Lifestyle Nutrition Pharma

A different model of personalized healthcare

Personalized healthcare

61

Ways forward:

• Data sharing

• Selfmonitoring

• Big Data

• System biology

• Lifestyle + Nutrition + Pharma

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