Precision medicine’s goal of achieving a better response rate by avoiding ineffective therapies has sparked new approaches, including the testing of patient-derived 3d (PD3Ds) tumor cultures for modeling individual patient response, and the use of various molecular pathology techniques for advanced tumor profiling. Well-established genomics methodologies cannot directly assess cell-signaling activity within the tumor cells defined by the phosphorylation status of cellular signaling pathway networks. Here we present the development of a robust protein profiling strategy utilizing the DigiWest immuno-assay platform, to obtain data on the activation status of key cellular signaling networks implicated in cancer, and on proteins targeted by FDA-approved drugs including a number of targeted cancer therapies for e.g. EGFR, HER2, PI3K, mTOR, ALK and AKT. We compiled a list of relevant pathway nodes and their phosphorylation sites that yield activity information on RAS/RAF/ERK, PI3K/AKT and mTOR signaling pathways. Based on this, we validated 242 total and phospho-antibodies in a pre-set oncoproteomic DigiWest panel that yields information on the activity of these signaling networks from the receptor level down to transcription factors, apoptosis and proliferation. This oncoproteomic panel can be utilized for elucidating drug response in pre-clinical cell models, in PD3D organoid models or in clinical tumor samples. Exemplary, we show differential effects of PI3K kinase inhibitor copanlisib vs MEK inhibitor trametinib at various levels within their signaling networks. Also, we validated this panel in PD3D organoids that were subjected to screening against common targeted therapies.

1. Dr. Gerrit Erdmann
NMI TT Pharmaservices
c/o CoLaborator, S141
Müllerstraße 178
13353 Berlin, Germany
E: erdmann@nmi-tt.de
T: +49 173 751 0620
www.nmi-tt.de/pharmaservices
Abstract
Generating an oncoproteomic pathway activity panel
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1.SDS-PAGE and 2. Western blot.
3.Per lane, 96 molecular weight fractions
are eluted and loaded onto 96 color-
coded Luminex® bead populations.
4. Each sample’s pooled bead mix goes
into hundreds of antibody-based
immunoassays.
5.Readout on Luminex® FlexMAP 3D
instrument and data processing.
6.Comparative profiling analysis for up to
800 proteins/sample.
How DigiWest Works
Gerrit Erdmann1, Anja Arndt1, Przemysław Dudys1, Ulrike Pfohl2, Markus F Templin3, Christian RA Regenbrecht2,4,5, Christoph Sachse1
1 NMI TT Pharmaservices, 2 Institute of Pathology, University Clinic Magdeburg, 3 NMI Natural and Medical Sciences Institute at the University of Tübingen, 4 CELLphenomics GmbH, 5 ASC Oncology GmbH
A ‛Beyond Genomics’ Approach to Precision
Oncology: A Multiplex Protein Profiling Platform
for Tumor and Tumor Organoid Samples
Purpose: Precision medicine’s goal of achieving a better response rate by avoiding ineffective therapies has sparked new approaches, including the testing of
patient-derived 3d (PD3Ds) tumor cultures for modeling individual patient response, and the use of various molecular pathology techniques for advanced tumor
profiling. Well-established genomics methodologies cannot directly assess cell-signaling activity within the tumor cells defined by the phosphorylation status of
cellular signaling pathway networks.
Methods: Here we present the development of a robust protein profiling strategy utilizing the DigiWest immuno-assay platform, to obtain data on the activation
status of key cellular signaling networks implicated in cancer, and on proteins targeted by FDA-approved drugs including a number of targeted cancer therapies for
e.g. EGFR, HER2, PI3K, mTOR, ALK and AKT.
Results: We compiled a list of relevant pathway nodes and their phosphorylation sites that yield activity information on RAS/RAF/ERK, PI3K/AKT and mTOR
signaling pathways. Based on this, we validated 242 total and phospho antibodies in a pre-set oncoproteomic DigiWest panel that yields information on the activity
of these signaling networks from the receptor level down to transcription factors, apoptosis and proliferation. This oncoproteomic panel can be utilized for
elucidating drug response in pre-clinical cell models, in PD3D organoid models or in clinical tumor samples. Exemplary, we show differential effects of PI3K kinase
inhibitor copanlisib vs MEK inhibitor trametinib at various levels within their signaling networks. Also, we tested this panel in PD3D organoids that were subjected to
screening against common targeted therapies.
Conclusions: While the initial results are promising, further work on evaluating how such an oncoproteomic panel profiling might contribute to the rationale for
personalized therapy decisions is required.
1. Identify pathways utilized by
cancer and targeted by drugs
2. Build pathway models and select
antibodies to monitor signaling
states
3. Pre-validate selected antibodies
4. Establish robust assay settings
and controls
5. Verification on cell lines
6. Test panel with compounds
7. Test panel on 3D organoids
0 2 4 6 8
EGFR Inhibitor
Mitosis Inhibitor
Checkpoint Inhibitor
Androgen Receptor Antagonist
CD20 Antibody
ALK Inhibitor
BCR-ABL inhibitor
HDAC Inhibitor
Kinase Inhibitor
Antimetabolite
CDK4/6 Inhibitor
PARP Inhibitor
Proteasome Inhibitor
RTK Inhibitor
selective estrogen receptor modulator
VEGFR Inhibitor
Androgen Receptor Agonist
BRAF Inhibitor
BTK Inhibitor
DNA Methyltransferase Inhibtion
DNA Synthesis Inhibitor
GnRH Agonist
Hedgehog Inihibitor
HER2 Inhibitor
Immune Modulation
Indirect Anti Androgen
Interferon
MEK Inhibitor
mTor Inhibitor
PI3K Inhibitor
retionid receptor agonist
VEGF Inhibitor
Number of Drugs
Drugable Core Panel
1 2 3 4 5 6 7 8 9 10 11 12
# 2559 # 1932 # 2440 # 2414 # 1826 # 2463 # 2189 # 0432 # 2094 # 0443 # 1969 # 2490
# 2343 # 1240 # 2359 # 2365 # 2364 TK # 036 # 2487 # 2358 # 2479 # 1714 # 1920 # 2356
# 2209 # 1824 # 2492 # 2117 # 1897 # 2394 # 2480 # 1167 # 1948 # 1968 # 2195 # 2345
# 1716 # 1218 # 1901 # 1940 # 2206 # 2188 # 1839 # 2081 # 2077 # 0740 # 2160 # 2386
# 2399 # 2182 # 2174 # 2459 # 1717 # 2372 # 2477 # 2323 BG/MP # 109 # 2437 # 1834 # 2095
# 2054 # 2385 # 2388 # 2485 # 1924 # 2170 # 2346 # 1965 # 2491 # 1203 # 2387 # 2203
# 1665 # 1833 TK # 014 # 2397 # 2371 # 1816 # 2154 # 2082 # 1566 # 1835 # 2452 BG/MP # 131
# 2172 # 2451 # 2173 # 2079 # 2197 # 1783 Strep-PE rb-blk ms-blk rb-Cal ms-Cal strep-cal
Drugable Extended Panel
1 2 3 4 5 6 7 8 9 10 11 12
# 2123 # 2142 # 1051 # 2563 # 2497 # 2496 # 2486 # 2481 # 2473 # 2458 # 2401 # 2395
# 2393 # 2375 # 2363 # 2362 # 2357 # 2146 # 1267 # 2376 # 2361 # 2338 # 2380 # 2377
# 2339 BG/MP # 043 # 2063 # 1649 # 2370 # 2400 # 0216 # 2314 # 2488 # 2454 # 1068 # 2462
# 2162 # 0698 # 2468 # 2461 BG/MP # 044 # 0639 # 2560 # 2466 # 2456 # 1988 # 2337 # 1160
# 1215 # 2475 # 2435 # 1967 # 2155 # 2389 # 2495 # 1879 # 2467 # 2484 # 2494 # 2391
# 1219 # 1202 TK # 072 # 1831 # 2304 # 2118 # 0702 # 1823 # 2472 # 2455 # 1601 # 2342
# 2066 # 2157 # 1035 # 2436 # 1862 # 2474 # 2450 # 2379 # 1859 # 2057 # 1844 # 2208
# 2431 # 1610 # 1818 # 2460 # 1819 # 1949 # 1853 # 2378 # 2493 # 2470 gt-blk gt-Cal
Drugable Full Panel
1 2 3 4 5 6 7 8 9 10 11 12
# 0528 # 2407 # 1074 # 1828 # 0749 # 1675 # 2502 # 2501 # 2498 # 2384 # 2352 # 1919
# 1778 # 1242 # 0753 # 1946 # 2471 # 2457 # 1918 # 2373 # 2214 # 1837 # 1829 # 2090
# 1900 # 2499 # 2169 # 1905 # 0448 # 2500 # 2476 # 1567 # 2489 # 0570 # 2465 # 2483
# 1917 # 2402 # 1209 # 1182 # 0411 # 2464 BG/MP # 129 # 0576 # 2273 # 2161 # 1063 # 1365
# 1075 # 2482 # 2059 # 2355 # 2453 # 2020 # 2469 # 2398 # 2349 # 2413
Designation Function
Strep-PE Strepatvidin-PE als loading control (total protein)
rb-blk blank measurement for anti-rb antibody
ms-blk blank measurement for anti-ms antibody
gt-blk blank measurement for anti-gt antibody
rb-Cal
calibrator control for anti rb secondary - antibody cocktail rb on
loaded beads
ms-Cal
calibrator control for anti ms secondary - antibody cocktail rb on
loaded beads
strep-Cal calibrator control for strep-PE
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5
Activity in primary patient-derived 3D tumor organoids
PD3D preparation workflow
Tumor or biopsy from patient Digest and disaggregation
Organoid culture and
expansion in 12-well
format
Cryogenic storage
Tumor-like architecture of PD3D patient-
derived 3D organoids; data from
CELLphenomics.
DAPI + Ki-67 + F-Actin
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
Trametinib Copanlisib
FoldChange[log2]
Treatment
Erk1/2 - phospo
Thr202/Tyr204
-9.0
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
Trametinib Copanlisib
FoldChange[log2]
Treatment
RSK 1 - phospho Thr573
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
Trametinib Copanlisib
FoldChange[log2]
Treatment
TBK1 - Phospho Ser172
-2.0
-1.5
-1.0
-0.5
0.0
Trametinib Copanlisib
FoldChange[log2]
Treatment
4E-BP1 phospho
Thr37/Thr46
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
Trametinib Copanlisib
FoldChange[log2]
Treatment
AKT Substrates
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
Trametinib Copanlisib
FoldChange[log2]
Treatment
Rac1/cdc42 - phospho
Ser71
Experiment 1
Experiment 2
Pathway activity effects of targeted drugsPathway activity in cell line models of common cancers
Identifying druggable pathways Building signaling networks Validating antibodies Panel layout and controls
DigiWest analysis of 9 PD3D organoid cultures utilizing 30 (phospho-) proteins together with genotype and
response data. Samples are sorted according to RAS mutation status. Individual IC50 values are shown for tested
compounds. Green marked IC50’s indicate susceptibility to the respective small molecule. Protein expression values
are column-wise color-coded from lowest (yellow) to highest (blue) expression for each analyte. Phosphoproteins
are marked in grey. (Schumacher, D. et al, 2019)
Cell Line Tumor Type
MCF7 Breast Cancer (metastatic)
HT29 Colon Cancer
A549 Lung Adenocarcinom
22Rv1 Prostate Cancer
SCaBER Bladder Cancer
Hela Cervix Cancer
HepG2 Liver Cancer
Mia Paca-2 Pancreas Cancer
The oncoproteomic pathway activity panel was designed from a collection of 1,200+ pre-
validated antibodies. The panel covers actionable cell signaling pathways with pairs of
antibodies against total and phospho proteins and is formatted to three sizes: 90 (core
panel), 184 (extended panel), and 242 analytes (full panel). They yield activity status
information on signaling pathways RTK/RAS/RAF/ERK, PI3K/AKT and mTOR with
increasing coverage.
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0
200
400
600
800
1000
1200
IntegratedPeak[RFU]
EGFR
0
100
200
300
400
500
600
700
800
900
1000
IntegratedPeak[RFU]
ERK2 - pT202/Y204
0
50
100
150
200
250
300
350
400
450
500
IntegratedPeak[RFU]
STAT3 - pS727
0
100
200
300
400
500
600
IntegratedPeak[RFU]
eIF4E - pS209
0
200
400
600
800
1000
1200
IntegratedPeak[RFU]
AMPK - pT172
0
100
200
300
400
500
600
700
IntegratedPeak[RFU]
mTOR - S2448
0
1000
2000
3000
4000
5000
6000
IntegratedPeak[RFU]
SGK - pS78
0
200
400
600
800
1000
1200
1400
1600
1800
IntegratedPeak[RFU]
PRAS40 - T249
0
100
200
300
400
500
600
700
800
900
IntegratedPeak[RFU]
AKT - pS473
0
100
200
300
400
500
600
700
800
900
1000
IntegratedPeak[RFU]
PDK1 - S241
0
500
1000
1500
2000
2500
IntegratedPeak[RFU]
β-Catenin - S552
0
10000
20000
30000
40000
50000
60000
IntegratedPeak[RFU]
RPS6 - S235/S236
DigiWest pathway activity analysis of 8 different cell lines reflecting some of the
most common cancer types. The full pathway activity panel comprised of 242
antibodies was screened, and selected targets from different levels of the
associated signaling networks are shown. The complete dataset was also used
for hierarchical clustering using the MeV software package.
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3
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DigiWest pathway activity analysis of a cell line model treated with the MEK inhibitor Trametinib and the PI3K inhibitor
Copanlisib. Protein signatures illustrate differential compound effects. Hierarchical clustering: MeV software package.
Summary
Signaling in tumors can be best represented as an interactive network that connects various signaling
pathways. In order to understand and predict response to (targeted) therapies, looking beyond
genomics is crucial. Therefore we aim to develop a robust oncoprotemoic signaling pathway activity
panel. Here we show:
 Development of a 242 antibody panel monitoring activity of key druggable cancer pathways,
 Successful test of this panel in various cancer cell lines and in response to reference drugs,
 Ongoing research on oncoproteomic pathway activity profiling in patient derived 3D organoid
models in order to understand drug response.
Contact
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Literature
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