Natural and MedicalSciences Institute at the University of Tübingen

Dr. Markus Templin

Protein Microarray Technologies –Signalling Networks in Tumours

Tecan Symposium 2011Shanghai, November 3 2011

Miniaturized Parallel Immunoassays

Applications Mechanisms of disease Mechanisms of drug action Biomarkers for

- early diagnosis- pharmacodynamics- prediction (exploratory)

PhaseI

Development

Clinical Trials

PhaseIII

PhaseII

Pre-Clinical Trials

In VivoTesting

In VitroTesting

Hit to Lead

Screening Optimi-sationValidationIdenti-

fication

Approval

Discovery

Target Enzyme

Protein Array Technologies Reverse-phase Protein Arrays Sandwich Immunoassays

YYYYYYYY

Parallelization…… driven by high sample load

Multi-Micro-Cells

automaticcell changer

Micro-arrays

Cuvettes

Macro-arraysMicrotiter plates

Single endpoint (1) Semi-global (100s) Global (1000s)

needle biopsies

tissue sections

solid tissue (parts)

~ 20 mg

< 1 mg

body fluids

cell cultures

material > 100 mg

Microarray based Protein Profiling

Miniaturization… driven by limiting sample material

- features are 10x10µm²

- allows more than 100.000

different capture reactions

allows the description of

complex systems

Miniaturization… driven by parallelisation

mRNA Expression Profiling

Miniaturized parallel immunoassays

Systems Biology approach to analyze cell signaling

Drug Discovery Support

Protein Arrays

Low sample consumption

Throughput / Automation

Broad panel of validated assays

Two Platforms - Many Applications

Protein Microarrays… a variation of the immunoassay

solid tissuessections biopsiesblood serum cell cultures

96-wellplatebody

fluidscells

tissue

Reverse Phase Arrays (RPA)µELISA

Beads

Antibody Array

Many analytes in 1 sample

Sample Array(Lysate Array)

YYYYYYYY

1 analyte in many samples

Chip

Assay Formats – A Comparison

Forward Phase Protein Microarray (µELISA)

Y Y Y Ycapture

antibody

YY

Y YY

+ YY+

antigenmix

Y Y Y Y

YY

Y

YY

Y

YY

detection

Forward Phase Protein Microarray (µELISA)

Y Y Y Ycapture

antibody

Y Y Y Ycapture

antibody

YY

Y YY

+ YY

YYYY

YY YYYY

+ YYYY+

antigenmix

+

antigenmix

Y Y YY Y Y Y

YY

Y

YY

Y

YY

detectionYY

YYYY

YY

YYYY

YY

YYYY

detection

Measuresmany analytesin one sample

Measuresone analytein many samples

Reverse Phase Protein Microarray (Lysate Array)

antigen (lysate) mix

YY+

YY YY

YY

Y

+

YYYY YY

antibody

Y YYY

detection

Reverse Phase Protein Microarray (Lysate Array)

antigen (lysate) mix

YY+

YY YY

YYYYYY+

YYYY YYYY

YYYY

Y

+

YYYY YY

antibody

YY YYY

detection

YYYYY

detection

Reverse Phase Protein Microarrays

Normal vs Tumour Different Treatments

measurement of protein activation status

complex signal transduction analysis

compound evaluation on molecular basis

specific protein pattern

cells/tissue

denaturing lysis

Signal transduction analysis byreverse phase protein microarray

miniaturized dot blot high throughput and sensitivity

analysis of complex signalling pathways

Y

Cell/tissuelysate

+ YY

YY YY

YY

Y

+

YY

Y YY

antibody detection

YY

Y

Signal transduction analysis byreverse phase protein microarray

Y

Cell/tissuelysate

+ YY

YY YY

YY

Y

+

YY

Y YY

antibody detection

YY

Y

crudeproteinextracts

high sensitivityreadout

specificdetectionreagents

Analysis of sample signalfrom 4 dilutions curve (N = 8 spots):

Some Numbers – Analysis & Sensitivity

1cell equivalent per spoti.e. ~100 pg of crude protein

1000analyte copies per spot

10E-6analyte fraction detectable

Robust signalgeneration frommultiple spots

sensitivity

0.4 mg/ml total protein0.1

sign

al(R

FI)

meansignal

Validation of antibodies … for use with RPA

ANTIBODY REQUIREMENTS

Specific single band(s) in Western Blot

Correlation of Western Blot and RPA signals

WHY?

Dot blot => no separation by molecular weight

HOW TO ACHIEVE?

Western Blot for specificity determination(single band characteristics targeted)

Standard set of 10 relevant tissue and cell line samples

Scoring system to judge antibody quality

30 kDa

Western blot

score 3 2 1

ab 1 2 3 kDa

Dynamic cell signaling events

Quantification of ≥ 20% changes of

protein abundance and activation state

(e.g. phosphorylation)

Good correlation to Western results

Good reproducibility0.00

0.05

0.10

0.150.4

time (min)0 60201052

arra

y si

gnal

(RFI

)

phospho-p44/42p44/42

Jurkat cells treated with CD3/CD28to induce MAP kinase signaling

Superior Assay Performance

Profiling key nodes of signaling in treated cell cultures

CV spot-to-spot signal 3-5%

CV intra-array signal 6-13%

CV inter-array signal 7-14%

Complex Networks:Signal Transduction in Cellular Systems

Tracing the networksof signaling pathways

Screening & Validation fordisease-specific diagnosticand prognostic biomarkers

Mode-of-actionfor pharmaceutical treatments(efficacy, adverse effects)

Differential protein expression and activation analysis

Tumour Analysis in the Clinic

Drug induced Carcinogenesis

Reverse Phase Arrays in Drug Development

0

100

200

300

400

500mAU

-10.0 -5.0 0.0 5.0 10.0 15.0 20.0 25.0mlA2 A3 A4 A5

“Non-Survivers” “Survivers”“Non-Survivers” “Survivers”

0,1 1 10 100 10000

5000

10000

15000

20000

Inhibitor [nM]

MFI

[AU

]

0,1 1 10 100 10000

5000

10000

15000

20000

Inhibitor [nM]

MFI

[AU

]

52199.13

1328.57

0

0.4

0.8

x10

1000 2000m/z

52199.13

1328.57

0

0.4

0.8

x10

1000 2000

2199.13

1328.57

0

0.4

0.8

x10

1000 2000m/z

Adverse Effects in pre-clinical Studies

16.02.201117

Animal study

Mode of action of Non-GenotoxicCarcinogens (NGC)

100+ genetically characterizedliver tumors, ca 2 mm diam.

Defined mutations: Wnt pathwayMAPK pathway

180 Proteins

Phospho-proteome :60 assays for phosphorylatedproteins

Transcription factors

Downstream markers

(GS, Cyp‘s)

Material RPA expression studies

Prof. Schwarz, Toxicology, Tübingen

Signal Transduction in Tumours

IMI - BMBF, Berlin18

Why focus on Non-Genotoxic Carcinogens ?

The MARCAR project received funding from the European Union Innovative Medicines Initiative (IMI JU) under Grant Agreement no 11500125.10.2010

The Innovative Medicine InitiativeMARCAR

5 years (start January 2010) Volume: 15 Mio € 8,2 Mio € Pharma contribution 5,2 Mio € EU 1,6 Mio € SME & Academia

NMI

Signal Tansduction Analysis Phosphoproteomics

Novel Biomarkers forCancer Risk Assessment

Early detection and enhancedrisk assessment of non-genotoxic carcinogens

-3 3

Colour code:

log2• Evaluation:

variance analysis: normal, Ha-Ras and B-Rafvariance analysis: Ha-Ras and B-Raf

Size: 10.400 datapoints

Signal Transduction in Mouse Liver TumorsSub-classification of Ha-ras and B-raf mutations

Signal Transduction in TumorsHa-Ras versus B-Raf Mutations (mouse liver)

45 proteins classifytumor vs normal tissue

4 activated (phosphorylated) proteins classify sub-types of tumors (Ha-ras, B-raf)

signifikant regulation in MAPK signaling pathway

Normal Ha-rasB- raf

Ha-ras normal B-raf

N 1

7

N 1

9

N 6

N 1

2

B 6

2T1

B 6

2T2

B 2

9T4

B 4

5T1

H 3

7T7

H 3

9T2

H 1

6T5

H 7

8T1

N 1

2

N 6

N 1

9

N 1

7

B 6

2T1

B 6

2T2

B 2

9T4

B 4

5T1

H 3

7T7

H 3

9T2

H 1

6T5

H 7

8T1

N 1

2

N 6

N 1

9

N 1

7

B 6

2T1

B 6

2T2

B 2

9T4

B 4

5T1

H 3

7T7

H 3

9T2

H 1

6T5

H 7

8T1

Erk1/2 pT202/Y204

Mek1/2 pS217/221

RSK 1 pS380

Verification of RPPM Results with Western BlotHa-Ras versus B-Raf Mutation

Ha-rasNormal B-raf N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0 . 0

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4

RFI

[AU

]

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0

1

2

3

4

5

6

RFI

[AU

]

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

RFI

[AU

]

Ha-rasNormal B-raf

MAPKsignaling pathway

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0.0

0.3

0.6

0.9

1.2

1.5

1.8

2.1

2.4

RFI

[AU

]

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0

1

2

3

4

5

6

RFI

[AU

]

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 4

5T2

B 5

0T3

B 6

2T1

B 6

2T2

B 6

6T1

B 7

5T1

0 . 0

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4

RFI

[AU

]

MAPK signalling pathwayHa-Ras versus B-Raf mutation

N 1

2

N 6

N 1

9

N 1

7

B 6

2T1

B 6

2T2

B 2

9T4

B 4

5T1

H 3

7T7

H 3

9T2

H 1

6T5

H 7

8T1

Erk1/2 pT202/Y204

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 45

T2B

50T

3B

62T

1B

62T2

B 6

6T1

B 7

5T1

0.00.20.40.60.81.01.21.41.61.82.0

RFI

[AU

]

JNK/SAPK pT183/Y185

N 3

N 6

N 7

N 9

N 1

0N

12

N 1

7N

19

N 2

0N

24

H 1

T1H

1T2

H 1

1T1

H 1

6T5

H 2

1T1

H 3

3T1

H 3

7T7

H 3

7T10

H 3

8T1

H 3

9T2

H 5

5T4

H 6

1T4

H 6

6T3

H 7

8T1

H 7

9T1

B 1

1T6

B 1

3T1

B 2

9T2

B 2

9T4

B 3

9T1

B 4

5T1

B 45

T2B

50T

3B

62T

1B

62T2

B 6

6T1

B 7

5T1

0.00.20.40.60.81.01.21.41.61.82.0

RFI

[AU

]

JNK/SAPK pT183/Y185

Normal Ha-Ras B-Raf

N 1

2

N 6

N 1

9

N 1

7

B 6

2T1

B 6

2T2

B 2

9T4

B 4

5T1

H 3

7T7

H 3

9T2

H 1

6T5

H 7

8T1

JNK

NormalHa-ras B-raf

Example: MAP Kinase SignallingHa-ras versus B-raf mutation in mouse liver tumours

Results

• Strong differences seen in the activation status ofsignal transduction networks

• Differences not clearly seen in mRNA expression data, but make sense after reanalysis

• New crosstalk between differentMAPK pathways

RSK

16.02.2011 Meeting Bayer-NMI26

Case study: CDK2 inhibitor induced retinal toxicityExpression Profiling & Validation in LCM tissues

Saturno G., Pesenti M., Cavazzoli C., Rossi A., Giusti M., Gierke B., Pawlak, M. and Venturi M. (2007) Expression of Serine/Threonine Protein-Kinases and Related Factors in Normal Monkey and Human Retinas: The Mechanistic Understanding of a CDK2 Inhibitor Induced Retinal Toxicity. Toxicologic Pathology 35, 972-983.

GSK3 calibration series

3000 1 ng/ml

GSK3 (1:500)

LCM caps whole sections

Assay:

0.2

0.1

030 10 0 n

g/ml

0.3R

FI

cap1

cap2

cap3

Cones and rods cells isolated fromphotoreceptor layers of retina sections(human, monkey eye) using LCM

30´000 cells per cap preparedin 30 µl lysis buffer

Quantification of GSK3 (total, phospho)and Tau levels

Array results corroborated byRT-PCR, IF and IHC

Dr. Miro Venturi

Tumor Analysis in the Clinic

Histology, Histology, Histology ….

Formalin-fixed paraffin-embedded tissue(FFPET) is the routine preserved material

Very limited material from biopsies

Immunohistochemistry (IHC) is the Gold Standard for biomarker analysis

but …labour-intensive, not quantitative

HER2 0

HER2 3+

RPA may be an ideal complementary Low sample consumption Throughput Quantitative

How to bring RPA to Clinical Applicability?

quantification

tissue typefresh frozen FFPE

absolute

relative

clinicalapplicability

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 2 8 29 30 31 32 3 3 34 35 3 6 37 38 39 40

sample # NMI

sign

al (N

FI)

HistonH3-dimethyl-Lys4 1:10000

Objectives & Approaches

Good Antibodies for RPA => Validation

Protein extraction from FFPE tissue sections

Assessment of feasibility to analyze FFPE with RPA=> Comparison RPA with Gold Standard IHC (breast cancer)

Demonstrate clinical applicability of RPA

=> Classification sub-types of Lung Cancer

Feasibility: Analysis of FFPE breast cancertissue with RPA

RPALysis

HER2 0

HER2 3+

HER2 1+

HER2 2+

19 FFPE tissue samples with HER2 scores between 0 and 3+ were lysed, subjected to RPA and compared to IHC scores

RPA Assay of HER2 vs. IHC HER2 Score

HER2 Score

0 1+ 2 + 3 +

Sign

al in

tens

ity fo

r HER

2 [a

u] r= 0.85, p≤0.05

Good correlation of RPA signalswith IHC scores

Biomarker screening to differentiate subtypes of Non-Small Cell Lung Cancer (NSCLC)

Use of RPA for profiling Adenocarcinoma and Squamous Cell Carcinoma:

40 FFPE samples- 20 adenocarcinoma samples- 20 squamous cell carcinoma samples

Protein extracted from single sections

Samples printed on RPA at 4 serial dilutions / duplicates

Samples profiled with about 180 antibodies

Two histopathological markers (Cytokeratin 5, Napsin A) used as quality control for the RPA data

Histopathological markers for distinguishing between adenocarcinoma and squamous cell carcinoma

Cytokeratin 5 Napsin A

adenocarcinoma 0 % positive 79 % positive

squamous cell carcinoma

81 % positive 0 % positive

stains tumor cells stains connective tissue cells

Tacha D et al. USCAP 2010, p1852

Example: RPA images of Cytokeratin 5 and Napsin A

Cytokeratin 5 Napsin A

18.05.2011 EU Biomarkers Summit35

Results

172 antibodies/assays displayed as heat map diagram

7200 data points

Median-centered RPA signals, log2 transformed

Statistical significance analysis performed to identify markers that distinguishthe two carcinoma sub-types

SquamousCell

Adeno

Samples

Antibodies

Results: Protein X

Conclusions

172/180 (95%) assays successfully conductedwith FFPE tissue extracts

=> assays delivered high quality images with signals significantly above blank

Minute amount sample consumed: 10 µg protein (equivalent to one Western Blot lane) sufficient to run complete PoC study (> 300 arrays printed)

Known histopathological markers (Cytokeratin 5, Napsin A) were verified with RPA for distinguishing between adenocarcinoma and squamous cell carcinoma

Quality of FFPE tissue samples depended on the provider

Robust markers found despite differences in fixation conditions

At least one new marker for squamous cell carcinoma was identified

=> data analysis ongoing

Acknowledgements

TRS Proteomics / Biostatistics

Maziar Assadi Jens Lamerz

Pablo Alday Laurent Essioux

Translational Medicine Oncology

Miro Venturi Hubert Paul

NMI Team

Thomas Knorpp Michael Pawlak

Ute Metzger Fridolin Treindl

Ewa Breitinger Yvonne Heubach

Berthold Gierke Thomas Joos

Michael Schwarz