Examination of apoptosis signaling in pancreatic cancer by computational signal transduction analysis.
ABSTRACT Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction prediction.
Apoptosis pathway related genes were assembled from electronic databases. To assess expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were analysed using public databases and computational prediction of protein interactions. Gene expression data were implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further showed possible new interactions between the single pathway members, which demonstrate the complexity of the apoptosis pathway.
Our data shows that by computational evaluation of public accessible data an acceptable virtual image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.
- [Show abstract] [Hide abstract]
ABSTRACT: The IL-1 family of ligands and receptors has a central role in both innate and adaptive immune responses and is tightly controlled by antagonists, decoy receptors, scavengers, dominant negative molecules, miRNAs and other mechanisms, acting extracellularly or intracellularly. During evolution, the development of multiple mechanisms of negative regulation reveals the need for tight control of the biological consequences of IL-1 family ligands in order to balance local and systemic inflammation and limit immunopathology. Indeed, studies with gene targeted mice for negative regulators and genetic studies in humans provide evidence for their non-redundant role in controlling inflammation, tissue damage and adaptive responses. In addition, studies have revealed the need of negative regulation of the IL-1 family not only in disease, but also in homeostatic conditions. In this review, the negative regulation mediated by decoy receptors are presented and include IL-1R2 and IL-IL-18BP as well as atypical receptors, which include TIR8/SIGIRR, IL-1RAcPb, TIGIRR-1 and IL-1RAPL. Particular emphasis is given to IL-1R2, since its discovery is the basis for the formulation of the decoy paradigm, now considered a general strategy to counter the primary inflammatory activities of cytokines and chemokines. Emphasis is also given to TIR8, a prototypical negative regulatory receptor having non-redundant roles in limiting inflammation and adaptive responses.Seminars in Immunology 11/2013; · 5.93 Impact Factor
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ABSTRACT: Development of head and neck squamous cell carcinoma (HNSCC) is characterized by accumulation of mutations in several oncogenes and tumor suppressor genes. We have formerly described the mutation pattern of HNSCC and described NOTCH signaling pathway alterations. Given the complexity of the HNSCC, here we extend the previous study to understand the overall HNSCC mutation context and to discover additional genetic alterations. We performed high depth targeted exon sequencing of 51 highly actionable cancer-related genes with a high frequency of mutation across many cancer types, including head and neck. DNA from primary tumor tissues and matched normal tissues was analyzed for 37 HNSCC patients. We identified 26 non-synonymous or stop-gained mutations targeting 11 of 51 selected genes. These genes were mutated in 17 out of 37 (46%) studied HNSCC patients. Smokers harbored 3.2-fold more mutations than non-smokers. Importantly, TP53 was mutated in 30%, NOTCH1 in 8% and FGFR3 in 5% of HNSCC. HPV negative patients harbored 4-fold more TP53 mutations than HPV positive patients. These data confirm prior reports of the HNSCC mutational profile. Additionally, we detected mutations in two new genes, CEBPA and FES, which have not been previously reported in HNSCC. These data extend the spectrum of HNSCC mutations and define novel mutation targets in HNSCC carcinogenesis, especially for smokers and HNSCC without HPV infection.PLoS ONE 01/2014; 9(3):e93102. · 3.53 Impact Factor
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ABSTRACT: Cytokines are messengers between tissues and the immune system. They play essential roles in cancer initiation, promotion, metastasis, and immunotherapy. Structural pathways of cytokine signaling which contain their interactions can help understand their action in the tumor microenvironment. Here, our aim is to provide an overview of the role of cytokines in tumor development from a structural perspective. Atomic details of protein-protein interactions can help in understanding how an upstream signal is transduced; how higher-order oligomerization modes of proteins can influence their function; how mutations, inhibitors or antagonists can change cellular consequences; why the same protein can lead to distinct outcomes, and which alternative parallel pathways can take over. They also help to design drugs/inhibitors against proteins de novo or by mimicking natural antagonists as in the case of interferon-γ. Since the structural database (PDB) is limited, structural pathways are largely built from a series of predicted binary protein-protein interactions. Below, to illustrate how protein-protein interactions can help illuminate roles played by cytokines, we model some cytokine interaction complexes exploiting a powerful algorithm (PRotein Interactions by Structural Matching-PRISM).Cancers. 01/2014; 6(2):663-83.
Examination of Apoptosis Signaling in Pancreatic Cancer
by Computational Signal Transduction Analysis
Felix Ru ¨ckert1*, Gihan Dawelbait2, Christof Winter2, Arndt Hartmann3, Axel Denz1, Ole Ammerpohl4,
Michael Schroeder2, Hans Konrad Schackert5, Bence Sipos6, Gu ¨nter Klo ¨ppel6, Holger Kalthoff4,
Hans-Detlev Saeger1, Christian Pilarsky1., Robert Gru ¨tzmann1.
1Department of Visceral, Thoracic and Vascular Surgery, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany, 2Bioinformatics Group,
Biotechnological Centre, Technical University Dresden, Dresden, Germany, 3Department of Pathology, University of Erlangen, Erlangen, Germany, 4Division of Molecular
Oncology, Clinic for General Surgery and Thoracic Surgery, Schleswig-Holstein University Hospitals, Kiel, Germany, 5Department of Surgical Research, University Hospital
Carl Gustav Carus, Technical University Dresden, Dresden, Germany, 6Division of Molecular Oncology, Institute for Experimental Cancer Research, Schleswig-Holstein
University Hospitals, Kiel, Germany
Background: Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of cancer death. Changes in apoptosis
signaling in pancreatic cancer result in chemotherapy resistance and aggressive growth and metastasizing. The aim of this
study was to characterize the apoptosis pathway in pancreatic cancer computationally by evaluation of experimental data
from high-throughput technologies and public data bases. Therefore, gene expression analysis of microdissected pancreatic
tumor tissue was implemented in a model of the apoptosis pathway obtained by computational protein interaction
Methodology/Principal Findings: Apoptosis pathway related genes were assembled from electronic databases. To assess
expression of these genes we constructed a virtual subarray from a whole genome analysis from microdissected native
tumor tissue. To obtain a model of the apoptosis pathway, interactions of members of the apoptosis pathway were
analysed using public databases and computational prediction of protein interactions. Gene expression data were
implemented in the apoptosis pathway model. 19 genes were found differentially expressed and 12 genes had an already
known pathophysiological role in PDAC, such as Survivin/BIRC5, BNIP3 and TNF-R1. Furthermore we validated differential
expression of IL1R2 and Livin/BIRC7 by RT-PCR and immunohistochemistry. Implementation of the gene expression data in
the apoptosis pathway map suggested two higher level defects of the pathway at the level of cell death receptors and
within the intrinsic signaling cascade consistent with references on apoptosis in PDAC. Protein interaction prediction further
showed possible new interactions between the single pathway members, which demonstrate the complexity of the
Conclusions/Significance: Our data shows that by computational evaluation of public accessible data an acceptable virtual
image of the apoptosis pathway might be given. By this approach we could identify two higher level defects of the
apoptosis pathway in PDAC. We could further for the first time identify IL1R2 as possible candidate gene in PDAC.
Citation: Ru ¨ckert F, Dawelbait G, Winter C, Hartmann A, Denz A, et al. (2010) Examination of Apoptosis Signaling in Pancreatic Cancer by Computational Signal
Transduction Analysis. PLoS ONE 5(8): e12243. doi:10.1371/journal.pone.0012243
Editor: Syed A. Aziz, Health Canada, Canada
Received June 25, 2010; Accepted July 20, 2010; Published August 19, 2010
Copyright: ? 2010 Ru ¨ckert et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by Deutsche Krebshilfe and the MedDrive38 program of the medical faculty of Technische Universita ¨t Dresden. The funders
had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Felix.Rueckert@uniklinikum-dresden.de
. These authors contributed equally to this work.
Pancreatic ductal adenocarcinoma (PDAC) is the 8th most
common cancer in the western world . Its mortality almost
equals its incidence rate of 6.3/100,000 . Despite combined
modality therapy pancreatic carcinoma shows a unsatisfactory
response to treatment . Recently, a comprehensive genomic
analysis of Jones et al. could identify apoptosis as a core signaling
pathway in pancreatic cancer. The pathway was genetically
altered in most of 24 primary pancreatic cancer cell lines .
Clinicopathologically, this defective apoptosis signaling contributes
to the tumor’s poor response to chemotherapy, ionizing radiation
and immunotherapy  and affects the metastasizing capacity and
growth rate of the tumor [6,7]. Therefore, understanding of
apoptosis resistance is a prerequisite for improving cancer therapy.
Apoptosis, or cell death program, can be activated by various
mechanisms within the extrinsic and the intrinsic pathway. While
activation of cell death receptors leads to the engagement of the
extrinsic pathway, the intrinsic pathway is activated by mitochondria
during cellular stress, both resulting in an activation of caspases .
Today, the apoptosis pathway is one of the best investigated
intracellular pathways. However, interpretation of experimental
PLoS ONE | www.plosone.org1August 2010 | Volume 5 | Issue 8 | e12243
data is hindered by the multitude of signaling molecules and
complex interactions of the pathway. In this study we tried to
approach the cell death pathway in pancreatic cancer by a
computational analysis of experimental data from highthroughput
technologies and public databases. We tried to use the great
amount of information to model the intracellular information flow
of the apoptosis pathway in pancreatic cancer. For a graphic
display of the study design see Figure 1.
The implementation of gene expression data into a model of the
apoptosis pathway obtained by protein interaction databases and
protein interaction prediction showed a consistent pattern of
higher-level defects in the intrinsic pathway and on the level of cell
death receptors that can potentially result in the phenotype of
apoptosis resistance in pancreatic cancer.
Computational construction of the apoptosis pathway
Interactions of the 103 apoptosis associated genes from our
database search were initially evaluated by screening of protein-
protein interaction databases. The search resulted in 940 known
interactions. Those interactions represented experimentally prov-
en interactions between defined proteins. This data was used to
construct a pathway map, as mentioned above (Figure 2).
In a second step we tried to find previously unknown
interactions between the 103 apoptosis-associated genes. We
therefore had to assign structural families to the gene products
because most of the structures were previously unknown. The
structural assignment and family classification for the apoptotic
associated genes resulted in the assignment of 53 genes. Applying
the interface conservation evaluation to possible interactions
between the products of those 53 genes resulted in 21 novel
interactions (for examples see File S2, for whole data see
which are not yet experimentally proven. All new interactions
were implemented in the first map of the cell death pathway
We constructed a virtual subarray to identify gene expression
changes of the 93 apoptotic genes for which an identifier could be
obtained. To evaluate the performance of this approach we
compared the results for the apoptotic gene set with whole genome
and virtual subarray analysis. Of 23 probe sets identified with the
virtual subarray analysis only 18 were detected in the whole
genome analysis. The mean expression intensities of the probesets
detected only by subarray analysis was 125 compared to 346 for
probe sets detected with both methods, indicating that the virtual
subarray analysis was more sensitive. The 23 probesets represent-
ed 19 differentially expressed genes (Figure 3). Of these, 11 genes
were overexpressed and 8 were underexpressed in PDAC
compared to microdissected normal ductal cells. Among the
nineteen genes, 12 were already reported by other groups in
PDAC (Table 1).
Figure 1. Graphic display of the study design.
Apoptosis in PDAC
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Verification of differential expression
We selected two genes, Livin/BIRC7 and IL1R2, for validation
by quantitative RT-PCR and/or immunohistochemistry. We
confirmed an significant upregulation in PDAC cells or IL1R2
(p=0.035) and LIVIN/BIRC7 (p=0.01) (Figure 4 A,B). Parallel
to RT-PCR 16 samples from patients with PDAC and 16 normal
pancreatic tissues were stained for Livin/BIRC7. 89% of the
PDAC cells were tested positive for Livin/BIRC7, in contrast to
only 62% of the of the normal ductal cells (p=0.001). PDAC tissue
showed also more intensive staining than normal tissue, and the
results were statistically significant (p,0.001) (Figure 4 C,D).
Pancreatic cancer is a malignancy with very poor prognosis and
no significant improvement in therapy over the last 30 years .
Recently, a comprehensive genomic analysis identified apoptosis
as a core signaling pathway in pancreatic cancer .
The apoptosis pathway is one of the best investigated
intracellular pathways. However, the pathway comprises a
multitude of signaling molecules and displays complex interac-
tions. This leaves results of experimental studies hard to interpret.
In this study we tried to approach the cell death pathway in
pancreatic cancer by a computational analysis of experimental
data from high-throughput technologies and by evaluation of
public databases. With the help of these technologies we tried to
better understand the great amount of information and to make
statements about disturbances in the information flow in the
apoptosis pathway in pancreatic cancer. Our results were
compared to previous publications on apoptosis in pancreatic
103 apoptosis associated genes were identified by database
search. To assess interactions between the identified apoptosis
associated genes we evaluated databases and computationally
predicted protein interactions. The fact that apoptosis pathway is
one of the best known pathways was mirrored by the great
Figure 2. Pathway map of the apoptosis pathway. The nodes in these graphs represent receptors, ligands, effectors, kinases and transcription
factors, while each edge describes a relation between these species. In the upper part of the figure the direct apoptosis induction is shown (A),
whereas in the lower part the modulation through gene expression is depicted (B). Black interactions signify known protein interactions from
databases. For better view we did not display all of the 940 known interactions, please see File S3 for a list of all interactions. Blue edges signify
computationally predicted interactions for all 103 apoptosis-associated genes with a high evidence level.
Apoptosis in PDAC
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Apoptosis in PDAC
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amount of experimental proven protein interactions in public
databases. Our approach yielded 940 interactions and we could
further identify 21 previously unknown interactions computa-
tionally by the sequence-based and structure-based prediction of
protein interactions. Especially MCL-1, CASP 3, TRADD,
SEPT 4, AIF, CALM and TRAF 6 showed branchings in the
signaling cascade previously unknown. Although we could not
experimentally proof those interactions, this is an evidence for the
complexity of the information flow within this pathway. By
setting cell death receptors as starting point for the signaling
pathway we constructed a model of the pathway to visualize the
Gene expression analysis of the apoptosis-associated genes was
performed using a virtual subarray in a set of microdissected tissue
from normal pancreatic ducts and PDAC. Comparing the data
from the subarray with whole genome analysis revealed consid-
erably more probe sets to be differentially expressed using the
virtual subarray approach. Interestingly the probe sets not
identified by whole genome analysis displayed lower expression
values, demonstrating that the construction of a virtual subarray
might result in an enhanced sensitivity for detection at the lower
end of gene expression intensities. This is mainly due to the smaller
number of probe sets tested, reducing the possible noise of
fluctuation during the analysis.
Gene expression analysis showed 19 differentially expressed
genes. Of these, 11 genes were overexpressed and 8 were
underexpressed in PDAC compared to microdissected normal
ductal cells. Among the nineteen genes, 12 were already reported
by other groups in PDAC.
Survivin, Livin, MCL-1, and DcR3 were upregulated and
showed very good accordance to previous reports (see File S1).
TNF-R1, BNIP3, and Caspase 9 were downregulated, those genes
also showed good accordance to previous studies (see File S1).
However, XIAP, a member of the IAP family of proteins was
downregulated in our analysis contrary to previous studies. This
discrepancy might be due to tumor heterogeneity, a fundamental
facet of all solid tumors . It might also be due to the differences
in study designs, because most of the previous studies on XIAP
used pancreatic carcinoma cell lines, while we used microdissected
native tumor tissue.
However, our data yielded interesting result, as we found two
major foci of dysregulations within the cell death pathway.
One of the major foci was at the level of cell receptors.
Generally, there seems to be a downregulation of cell death
receptors, and an upregulation of decoy-receptors. The downreg-
ulation of the cell death receptor TNRF-1 and the upregulation of
the Fas-decoy receptor DcR3 in our data was already reported
earlier . This dysregulation is meant to help the tumor evade
Table 1. Results of the GeneChip analysis.
No.Affy ID NameGene Symbol FunctionFCRef. in PDAC
13205403_at IL1-R2 IL1R2Decoy-receptor7.1
36 202094_at 202095_s_atSurvivin/BIRC5 BIRC5 Apoptosis inhibitor 5.0 [19,32]
14219423_x_at 210847_x_atDR3 TNFRSF25 Cell death receptor 4.1
65 204285_s_at 204286_s_atNOXA PMAIP1MP3.1
38220451_s_at Livin/BIRC7BIRC7Apoptosis inhibitor2.9
56 241722_x_atMcl-1MCL1 MP 2.8 [33,34]
181729_at TRADDTRADD Signal molecule2.4
21201587_s_atIrak, pelleIRAK1 Signal molecule2.2
10206467_x_atDcR3 TNFRSF6BDecoy receptor2.1[36,37]
72 226530_at BMFBMF MP2.1
29 220034_atIrak, pelleIRAK3Signal molecule2.0
2201466_s_at AP-1JUNSignal molecule0.48
4207643_s_atTNF-R1TNFRSF1A Cell death receptor0.46
68201848_s_at 201849_atBNIP3 BNIP3 MP0.37 [39,40]
45 240437_atCaspase 9CASP9 Protease0.27
23205558_at TRAF 6TRAF6Signal molecule0.19
Upregulated genes (fold-change .2, q,5%) are listed in the upper part, the downregulated genes in the lower part of the table. The numbers represent the order of
the genes in our supplementary data 2 (MP=mitochondrial protein; FC=Fold change).
Figure 3. Analysis of apoptosis-associated gene expression in PDAC. Heat map of 19 microdissected PDACs (marked red), 13 samples of
microdissected normal ductal cells (marked green), and 13 established pancreatic tumor cell lines (marked magenta) using the 93 differential gene set
and a Euclidian distance matrix. Normal stromal cells served as internal quality control (marked blue).
Apoptosis in PDAC
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the immune system, because of the diminished sensibility towards
apoptotic ligands [11,12].
Another decoy receptor, IL1R2, was chosen for further
validation, because upregulation of this receptor was not reported
previously. By means of quantitative RT-PCR we could for the
first time validate an upregulation of IL1R2 in pancreatic cancer.
IL1, the ligand of IL1R2 is known to be secreted by pancreatic
cancer cells . It has important physiological functions in
inflammation and proliferation but can also trigger apoptosis
through activation of IRAK and MyD88 [14,15,16]. While the
microenvironment could benefit from the angiogenetic and the
proliferative properties of IL1, the decoy-receptor might protect
pancreatic cancers from apoptosis induced by the immune
The second major focus of dysregulations was found on the level
of post-mitochondrial regulatory proteins, the inhibitors of
apoptosis proteins (IAPs). This group of proteins inhibits the
function of caspases and the apoptosome and thereby interferes
both with the extrinsic and the intrinsic pathway. The IAPs are
already known for their important role in carcinogenesis of other
tumor entities and also PDAC [18,19]. In our study, we found an
upregulation of Survivin/BIRC5 and Livin/BIRC7 in microdis-
sected tumor-tissue and we could validate the dysregulation of
Livin/BIRC7 by quantitative RT-PCR and IHC.
The dysregulations in the group of IAPs might have a high
clinical relevance, because the intrinsic pathway normally
mediates the cytotoxic effect of irradiation and many chemother-
The computational analysis of the apoptosis pathway in PDAC
thereby rendered a good accordance of our results with previous
experimental references on apoptosis in pancreatic cancer. Using
existing raw data from high-throughput technologies, we could
partly reproduce experimental data. This data was put in the
context of the complex intracellular apoptosis signaling by
computational interaction analysis. Although a great amount of
information can be assessed fast and descriptive by our approach,
it is economically challenging to experimentally prove the findings.
This must be considered a major disadvantage of our approach.
In Conclusion, the present study shows that by computational
evaluation of data from gene expression analysis and public
databases an acceptable virtual image of the apoptosis pathway
might be given. Comparison of our data to previous publications
rendered good accordance. By this approach we could identify
defects at the level of cell death receptors and the inhibitor of
Figure 4. Validation of differential expressed genes by quantitative RT-PCR. The graphs display the results of the quantitative RT-PCR in
normal tissue of the pancreas and pancreatic adenocarcinoma of Livin/BIRC7 (t-test with p=0.01) (A) and IL1R2 (t-test with p=0.035) (B).
Immunohistochemical staining for Livin/BIRC7 in benign pancreas and invasive adenocarcinoma. Pancreatic carcinoma (arrow) showing intensive
cytoplasmic staining (original magnification x100)(C). Benign ductal epithelium shows a noticeable fainter staining (arrow) (original magnification
640)(D). * indicates p-value ,0.05.
Apoptosis in PDAC
PLoS ONE | www.plosone.org6August 2010 | Volume 5 | Issue 8 | e12243
apoptosis proteins, which might underlie the phenotype of distinct
apoptosis resistance in PDAC. We could further for the first time
identify IL1R2 as possible candidate gene.
Materials and Methods
Interaction prediction of the apoptosis pathway
The apoptosis pathway related genes were assembled from
electronic databases, such as the Kyoto Encyclopedia of Genes and
Genomes (www.genome.ad.jp/kegg), Gene Data Base of the National
Center for Biotechnology Information (www.ncbi.nlm.nih.gov) and
GeneMAPP (www.genmapp.org). Keywords for the search were
‘‘apoptosis’’, ‘‘cell death’’, ‘‘cell death pathway’’, ‘‘cell death
receptor’’ (see File S1).
To evaluate interactions of the apoptosis associated proteins we
initially queried databases with known protein-protein interactions
such as NetPro (www.molecularconnections.com), SCOPPI (www.
scoppi.org) and HPRD (www.hprd.org).
To find novel interactions we used two different methods. First,
we used the structure-based prediction of protein interactions (see
File S2). Most of the 103 apoptosis-associated genes were of
unknown structure. We initially used the Genomic Threading
Database (GTD) as a fold recognition method to assign structural
families to the gene products . Domains of proteins were then
defined by the Structural Classification of Proteins, SCOP. Two
domains are considered interacting if there are at least 5 residue
pairs within 5 A˚, in accord to the interface definitions . Only
domain-assignments with certain and high confidence by GTD
were considered. To predict potential interactions of two given
domains we then used SCOPPI . This database provided
evident domain-domain interactions, which served as structural
templates for our original assigned domains. Two proteins are
considered interacting if each contains a domain where there is a
structural evidence for such a domain-domain interaction
according to SCOPPI. The potential interactions were evaluated
by analysis of the interface conservation. Information of the
residues in the interface was again obtained from the SCOPPI
database. The original protein sequence was aligned against the
SCOPPI template sequence, a conservation of more than 30% of
the interface residues was assumed to be sufficient to share the
same interaction partner.
Second, we used a sequence-based prediction of protein
interactions (see File S2). Therefore, we used NetPro, an expert
curated and annotated database containing around 100,000
protein-protein interactions, for the prediction of an interaction
of our proteins in question. Using this orthologous information
and BLAST we searched for homologous interactions (.80%
sequence identity) for a given protein pair. We only provide new
interactions which were not confirmed before with NetPro or
HPRD [25,26]. To construct our pathway map, we set the cell
death receptors as starting points of the signaling cascade.
Interacting proteins were defined as downstream signaling
proteins. Proteins which are known cell death receptor ligands
were displayed as extra-cellular proteins.
Gene expression analysis
For the construction of the virtual subarray data sets E-MEXP-
950 and E-MEXP-1121 was used [27,28].
Affymetrix probe set identifiers were obtained from Ensembl
resulting in 189 probeset identifiers for 93 genes. For 10 genes no
identifier could be obtained (see File S1). The Cel Files obtained
from the Affymetrix MAS 5.0 software were used for further
analysis. The Cel Files were loaded into dChip2006 (http://www.
dchip.org), then normalized, and expression values were calculated
using the PM/MM model. The expression values of the 189
probesets were exported and further explored using SAM (http://
www-stat.stanford.edu/˜tibs/sam/) and Excel (Microsoft, Red-
mond, WA). We scored genes as differentially expressed if they
met the following criteria: a fold change .2 and a q value ,5%.
Heat maps were generated using dChip.
Reverse Transcription Polymerase Reaction (RT-PCR)
1 ng of cDNA was used for a TaqMan assay (Applied
Biosystems, Weiterstadt, Germany). The genes were amplified
with a TaqMan Universal PCR Master Mix according to the
manufacturer’s instructions, with an ABI PRISM 5700 Sequence
Detection System using gene specific primer and probes. Gene
expression was quantified by the comparative cT-Method,
normalizing cT-values to a housekeeping gene (b-actin) and
calculating the relative expression values using the following
primers: RT-PCR: BIRC7/Livin: ACT GAC CAG CCC TGA
TTC C and CTC CAG GGA AAA CCC ACT TT; Actin: AAG
CCA CCC CAC TTC TCT CTA A and AAT GCT ATC ACC
TCC CCT GTG T; IL1R2: ATC AGC TTC TCT GGG GTC
AA and GGT AGG CGC TCT CTA TGT GG .
For immunohistochemistry, a tissue microarray (TMA) con-
taining 16 PDAC samples was constructed. Of this TMA 5 mm
sections were prepared using silanized slides (Menzel Gla ¨ser,
Braunschweig, Germany). Immunohistochemistry for Livin/
BIRC7 was performed using the streptavidin-biotin-peroxidase
method as described previously and antigen retrieval was carried
out in a microwave oven (250 W for 30 min in a citrate solution
pH 6.0) [30,31]. The primary antibody used was a mouse
monoclonal antibody against the Livin/BIRC7 protein (#40958,
Active Motif, Rixensart, Belgium). Normal colon mucosa and
colorectal carcinoma were used as a positive control. As a negative
control specimens were incubated without the primary antibody.
Afterwards the slides were briefly counterstained with hematox-
ylin. Stained and unstained PDAC cells or ductal cells were
counted and the ratio was generated. The staining intensity was
evaluated semi-quantitatively by one pathologist (A.H.) without
knowledge of the histopathologic and molecular data in 3 grades
(negative, moderate and strong).
For statistical analysis the t test and the chi square-test of ‘‘SPSS
13.0’’ for Windows were used.
To better assess changes in gene expression seen in our data, we
conducted a comprehensive literature search on molecular defects
of apoptosis in pancreatic cancer. Keywords were the name of the
gene or protein together with the term ‘‘pancreatic carcinoma’’,
‘‘pancreatic cancer’’, ‘‘pancreas cancer’’ or ‘‘pancreatic ductal
adenocarcinoma’’. Included were studies on the level of the
genome, gene expression and protein/functional studies on tumor
tissue and/or cell lines. The literature search comprised
publications until November 2009. See also File S1.
role of apoptosis-associated genes in pancreatic cancer. The table
displays all genes, which were considered in our study. Please note
that for most of the studies on the level of DNA, which means
Data from our comprehensive literature search for the
Apoptosis in PDAC
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mutational studies, no quantitative statement concerning expres-
sion was made. Included were studies on the level of the genome,
gene expression and protein/functional studies on tumor tissue
and/or cell lines. The literature search comprised publications
until December 2009. (--/-=less/slightly less expression than in
normal tissue; +/-=expression depending on sample/cell-line, no
general statement possible; 0=no difference of expression to
normal tissue/normal function of protein in experimental studies;
++/+=higher/slightly higher expression than in normal tissue;
?=no quantitative statement in this study.)
Found at: doi:10.1371/journal.pone.0012243.s001 (1.49 MB
Examples of structural models of three possible new interactions in
the cell death pathway (more than 30% sequence and interface
identity). The structural alignment between template and
interacting protein structures is ,2 Angstrom. 1=Arts-Apollon;
2=p16-ERK; 3=p16-JNK (B).
Found at: doi:10.1371/journal.pone.0012243.s002 (2.16 MB
Characteristics of our protein interaction prediction (A).
interaction prediction analysis. Sheet one shows already known
interactions from our database search. Sheet two shows putative
interactions, proposed by our interaction prediction model.
Found at: doi:10.1371/journal.pone.0012243.s003 (0.09 MB
Protein interactions from our database search and our
This study was supported by Deutsche Krebshilfe and the MedDrive38
program of the medical faculty of Technische Universita ¨t Dresden. We like
to thank Beatrix Jahnke for technical support.
Conceived and designed the experiments: FR HKS HDS CP RG.
Performed the experiments: FR GD CW AH AD MS CP. Analyzed the
data: FR GD CW OA MS BS GK HK CP. Contributed reagents/
materials/analysis tools: OA BS GK HK. Wrote the paper: FR.
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