tecan symposium 2011 shanghai, november 3 2011 · tecan symposium 2011 shanghai, november 3 2011....
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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