Examination of apoptosis signaling in pancreatic cancer by computational signal transduction analysis

Abstract

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 prediction.

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 apoptosis pathway.

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.

Figure 1. Graphic display of the study design.

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.

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).

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 ×40)(D). * indicates p-value <0.05.