Pv H Europroteome BBMRI

Expert center as a private public partnership ‐ The complexity ofpartnership ‐ The complexity of technologies in an SME structure

for expert centersThTh E tE t ii‐‐ The The EuroproteomeEuroproteome experienceexperience‐‐

Dr Paul von HoegenDr. Paul von HoegenManaging Director

Euraccine Consulting GroupBrussels, Belgium

[email protected]

+32 475 913572

Experience as CSO EuroproteomeExperience as CSO Europroteome

Colon Cancer Biobank for marker/targetColon Cancer Biobank for marker/target discovery with patient history and outcome

predictionp• Academic network: High fluctuation – constant training• Needed samples rare – create meaningful patient groups• Technologies constant development and changes from• Technologies – constant development and changes fromproviders: Need to generate internal standards

•Drug targets, biomarker, diagnostics, individualized therapyd f• Complexity: Multiple technologies and Integration of

Bioinformatics (PhaseIt)• Investors and Industry clients: Wanted to see ready togo Biobank; No patients to build Biobank

• Improve management of expectations

2Euraccine Consulting Group (Brussels) ‐ Paul von Hoegen

Pharma’s Needs 2003+

NeedsNeeds

• Anything that reduces development risk, time & costs: FAIL EARLY – FAIL CHEAP

• Priorisation of (genomically derived) targets (500 –5,000) to deal with the VALIDATION BOTTLENECK

• COMPOUNDS

• VALIDATED cancer targets (in vitro and in vivo) with link to a PATHWAY

• Diagnostic bio-markers related to clinical OUTCOME

• Identification of risk groups with ADVERSE EVENTS

Dr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Patient Centered SolutionsPatient Centered SolutionsDIAGNOSTICS THERAPY FOLLOW UP

DEVELOPMENT

Responder ProfilingOncoAssist

Disease Marker

Biomarker

Screening Assays Follow Up

Therapy Control


CCN: Large Scientific Networkg

• Outstanding scientific and

Berlin (Berlin (HennigsdorfHennigsdorf))

• Outstanding scientific and medical expertise as sounding board for research results

MagdeburgMagdeburgDortmundDortmund

KielKiel

Berlin (Berlin (CharitéCharité))

HamburgHamburg

StettinStettin • „Breeding & sourcing ground“ for high‐calibre

GenevaGenevaCottbusCottbus

LeipzigLeipzig

LausanneLausanne

RennesRennes

(( ))

MagdeburgMagdeburgStuttgartStuttgart

ground for high calibre product approaches

• Access to human tissue

ErlangenErlangen

GenevaGeneva LeipzigLeipzigHalleHalle

LyonLyon

• Access to human tissue samples, pathology and related patient data

GrenobleGrenoble

BolognaBologna

ZurichZurich C C N

ChileChileBolognaBologna

THE CLINICAL CANCER NETWORK


Sampling

• Patients consentPrior to surgery

Surgery Pathology *Histology Pathological findings

Patients consent• Blood sampling; Stool sampling

• Grading

g y gy Histology Pathological findings

• pTNM (UICC)

• etc.

*Cell sampling

* Photos of kidney tissue/cells by Prof. F. Dombrowski, Pathology, Magdeburg


Human Tissue Sample Bankp

5.000

6.000s

3 000

4.000

f Sam

ples

2.000

3.000

umbe

r of

0

1.000N

TimeJan 01 Jan 02 Jan 03

Status as of March 10, 2003 Dr. Paul von Hoegen Euraccine Consulting Group (Brussels)

FeaturesO N C O T R A C KCLINICAL CANCER RECORDS &

OUTCOMES DATABASEOUTCOMES DATABASE

Colon, Stomach & Pancreas Cancer (number of variables)Cancer (number of variables)

• Anamnesis and Diagnostics (48)• Surgery & Therapy (50)Surgery & Therapy (50)• Pathology (103)

• Follow Up (21)• Follow Up (21)• Local Recurrence (10)• Distant Metastasis (6)Dr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Cell Samples/Organp / g

Number of Patients:

Rectum

ColonNumber of Patients:

Liver

Stomach

Pancreas

Lung

Oesphagus

Small Bowel

Pancreas

Kidney

Oesphagus

plus ovar, bone, gallbladder

Status as of March 10, 2003 Dr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Research Matrix: Technologiesg

IDENTIFICATION CHARAC- VALIDATIONIDENTIFICATION TERIZATION VALIDATION

B I O I N F O R M A T I C S

2D-PAGE

MASS SPECMASS SPEC

CIPHERGEN SELDI

DNA MICRO ARRAY & ROSETTA RESOLVER

PROTEIN CHEMISTRY

RT-PCR

CELL CULTURE

YEAST-2-HYBRIDYEAST-2-HYBRID

BIACORE

Si RNA

R E S O U R C E M A N A G E M E N TDr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Technologies for Cancer Target Identification via 2D‐PAGE & MS

SDS‐PAGEIEF ScanningStainingIEF

Image Analysis Spot Picking MS AnalysisImage Analysis


Colon Cancer Gene Expression Analyses: Proof of Concept

MD

134

109

MD

149

MD

150

87M

D13

5M

D82

118

97M

D13

314

195 11

386

MD

121

174

139

112

171

106 20 CRC Patients

M C M M C M M C C9

M C C9

E1 C M C C E 1 C E 1

EP-03-01 MYCTIMP1GAPDH

(DD Mode)

GAPDHCA1FABP1 KRT19KRT20KRT20MUC2CEACAM1

EP-03-01: Induced transmembrane proteinMYC: myc oncogeneTIMP1: Tissue inhibitor of metalloproteinases 1GAPDH: Glycerolaldehyde-3-phosphate dehydrogenase

Color Range Scale

U l t d dehydrogenaseCA1: Carbonic anhydraseFABP1: Liver fatty acid binding protein KRT19: Cytokeratin 19KRT20: Cytokeratin 20MUC2: Mucin 2

Up-regulated

Down-regulated

No gene regulation

*Literature data are from human colon cancer tissue DNA microarray, SAGE and Northern blot experiments

MUC2: Mucin 2CEACAM1: CEA related adhesion molecule 1


… Combining Proteomics & TranscriptomicsTranscriptomics

• 2D PAGE Data• Gene Profiling Data

MD

134

C10

9M

D14

9M

D15

0C

87M

D13

5M

D82

C11

8C

97M

D13

3C

141

C95

E113

C86

MD

121

C17

4C

139

E112

C17

1E1

06

CA1KRT20

KRT20

C C C C C C C C C C

CA1FABP1 KRT20

FABP1

Color Range Scale Up-regulated

Down-regulated

pH 5-8; patient tumor sample

No gene regulation

SwissProt # Protein name2D PAGE data DNA array/SAGE data 2D PAGE DNA array

P00915 Carbonic anhydrase I (CA1) down down up/down up/downP07148 Liver fatty acid binding protein (FABP1) down down down downP35900 Keratin cytoskeletal 20 (KRT20) down down up/down down

EP dataLiterature* reported data

y ( )

* Literature data are from the experiments with human colon cancer tissue


Colon / Metastasis Screen

Colon Rectum Colon RectumTissue Serum

Colon Rectum Colon Rectumwith metastasis 6 3 20 0without metastasis 12 8 16 0

number of patients

Colorectal cancer specimens

Serum Transcriptomics ProteomicsSerum Transcriptomics Proteomics

d ff 223 diff37 differences 289 differences(2 fold change;p‐value < 0.01)

223 differences(2 fold change;40% frequency)

FPLC/HPLC Identification In silico validation In silico validation


Classification of regulated proteins (Wh d h t?)(Who needs what?)

Proteases5%

Kinases1%

Ribosomal1%

Cytokeratins4%

OtherStructural/Cell

adhesion12%

Transport/ P i /

Immunoglobulines

Nucleic Acid Binding

2%

11%

Processing/ Chaperone Function

10%

(and related)5%

EnzymesEnzymes49%


Target Identification & SelectionTarget Identification & SelectionWill vary for each application (client), combined with bioinformatics

3Extensive annotation and

project potential

10 Complex annotation, Target profile

> 50DNA Array,

Bioinformatics

Target profile

50

2D MS

Bioinformatics

> 350 2D, MS

# of Targets (example)Dr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Research Matrix: Technologiesg

IDENTIFICATION CHARAC- VALIDATIONIDENTIFICATION TERIZATION VALIDATION

B I O I N F O R M A T I C S

2D-PAGE

MASS SPECMASS SPEC

CIPHERGEN SELDI

DNA MICRO ARRAY & ROSETTA RESOLVER

PROTEIN CHEMISTRY

RT-PCR

CELL CULTURE

YEAST-2-HYBRIDYEAST-2-HYBRID

BIACORE

Si RNA

R E S O U R C E M A N A G E M E N TDr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Cancer Target ValidationCancer Target ValidationBinding Partners (BiaCore) Yeast Culture (Y‐2‐H)

Cell Culture (siRNA, anti sense)Data Integration

Cell Culture (siRNA, anti sense)


Validation: The Technology Wheel2D gels

Validation: The Technology WheelAntibodies 2D gels

Mass Yeast 2Hybrid

SpectrometryHybrid

12

3

RNAi

>350

48DNA ArraysRT PCR

RNAi

CyphergenDr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Cancer Target Discovery StatusCancer Target Discovery Status

Membrane TargetsSeldi Markers Stomach/Colon

3Colon Metastasis

12

3

350

48

Stomach

> 350


Colorectal Cancer: Gene Expression Breakdown by UICC Stage*

• Pre‐UICC Stage III– 112 preferred marker genes expressed– 112 preferred marker genes expressed

• UICC Stage III319 f d k d– 319 preferred marker genes expressed

• UICC Stage IV– 907 preferred marker genes expressed

Up/Down ratio: Clearly negative

*preliminary data based on a limited no. of analysed samples


… as Tool for Functional Target Validation

100

Validation

UICC stage I

60

70

80

90Risk 0Risk 1UICC I

ival

• Decision system based on artificial intelligence

UICC stage I

30

40

50

60

Perc

ent S

urv

• Classification of high‐risk patients for distant metastasis possible

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 2040

10

20

Survival (months)100

Ri k 0

• Based on only 6 clinical parameters

60

70

80

90Risk 0Risk 1UICC II

viva

l

• Tool to be positioned to identify high‐risk patients in Stage I & II

UICC stage II

30

40

50

Perc

ent S

urv

• Discussions with pharma companies for application

0 12 24 36 48 60 72 84 96 108 120 132 144 156 168 180 192 2040

10

20

Survival (months)


From Disease Centered Biobank Research ..

Seldi Proteins HistopathologyCytosolic Proteins

Membrane Proteins

HLA- assoc. Peptides Clinical Data

tumor cell

cDNA Microarray


To Patient Centered Solutions...To Patient Centered SolutionsDIAGNOSTICS THERAPY FOLLOW UP

DEVELOPMENTPROGNOSTICS

Responder ProfilingOncoAssist

Disease MarkerO N C O T R A C K O N C O A S S I S TCLINICAL CANCER RECORDS &

OUTCOMES DATABASE CANCER OUTCOME PREDICTION & STRATIFICATION

Screening Assays Follow UpO N C O P R E P C C N

Therapy Control

CANCER CELL PURIFICATION THE CLINICAL CANCER NETWORK


Life event history calendar for collecting d il d i i f idetailed patient information

Standardized set of cues offer conceptualStandardized set of cues offer conceptual anchors (such as important life themes or domains, e.g., life relationships, jobs, residences, etc.) and encourage top‐down , ) g pprocessing.


LEHC: Parallel Processing of patient information: Complete history of lifestyle and disease possibleComplete history of lifestyle and disease possible

Parallel processing : balanced visual organization allows the individual to look atorganization allows the individual to look at the display, use cues from their own life, and remember events base on their interrelationships between domains.


interrelationships between domains.

Expectations / Ideas / Challengesp / / g

•Integrate all players (patients, ethics academic, agencies, serviceg p y (p , , g ,provider and end-user)•Assure long time engagement under constant conditions•Manage change and integration of new technologiesManage change and integration of new technologies•Multiple platforms for identification and validation•Individual marker versus patterns – making the choice•Standards and reference samples for ring validations•Priorities for sample distributions? How to conserve thematerial best for the future?

•Longitudinal follow up of disease markers (same patient)•Links to clinical trials and treatment outcome•Foresee the needs of the future (of the partners)•Foresee the needs of the future (of the partners)


Biotech, Start upsStart ups

Biotech exampleTechnology AcademicTechnology providers

Academic Research


Need of strong Core AssetsNeed of strong Core Assets

BBMRI

ality

dentia

Confid

CDr. Paul von Hoegen Euraccine Consulting Group (Brussels)

Documents

Pv H Europroteome BBMRI