Final Report Summary - PPI-MARKER (Interruption of protein-protein interaction and network to cancer biomarkers and therapeutic targets)
Cancer remains the second leading cause of death in the United States, behind heart disease, with an estimated 1.6 million new cases and 580.000 deaths in 2013 alone. It is a highly complex disease, which can encompass multiple genomic alterations, including gene amplifications, epigenetic modifications, point mutations, translocations, aberrant splicing, deletions, and altered gene expression. These changes may be somatically acquired or inherited during progression from a normal to a cancerous cell. In the past decade, it has been discovered how these genomic perturbations drive cancer cell survival by altering the mechanism for apoptosis, cell cycle control, DNA repair, differentiation, and metabolism. By improving the understanding of these molecular mechanisms, scientists have gained greater insight into the initiation of cancer, its progression, and its sensitivity to therapeutics.
Protein-protein interactions are the main keys to executing important cellular functions. We hypothesize that gain or loss of protein-protein interactions plays important roles in the tumor development. We therefore proposed a network-based approach for biomarker discovery combining gene expression data with protein-protein interactions or regulatory transcriptional networks. This approach was used to predict biomarker for prostate cancer and gliomas.
Prostate cancer is the most common form of cancer and the leading cause of cancer death among men in the developed countries. In 2013 roughly 240.000 new cases of prostate cancer were diagnosed. Whereas Glioblastoma multiforme (GBM) is the most common and most aggressive type of primary brain tumor and it accounts for 52% of all primary brain tumor cases.
The androgen receptor is a DNA-binding transcription factor that regulates genes which are critical for the development and maintenance of the male sexual phenotype. A relationship between androgen dependence and the development of prostate cancer has been discovered.
It has been shown that TGF-beta plays a crucial role in the development of glioblastomas, but details of the detailed association are still missing. Novel markers and therapeutic targets are still needed for these two cancers. In this project, we therefore focused on the androgen receptor protein-protein network in prostate cancer and TGF-beta mediated network in gliomas.
Protein interactions can be extracted by text mining, from databases, and from structural data on protein complexes. As most cancer types arise due to mutations in regulatory elements, we focused mainly on gene regulatory networks of key transcription factors in cancers. To identify these transcription factors and their targets in human, ChIP-Chip and ChIP-Seq experiments were carried out. The resulting networks serve as a scaffold on which data of deregulated proteins derived from microarray and next-generation sequencing of patient cancer samples are mapped. The perturbed subnetwork can than be visualized with a new method that identifies and highlights network motifs and modules. Promising biomarker candidates were further examined and validated experimentally. Where possible, candidates were modeled using protein structural data on protein-protein complexes, providing the basis to design ligands that occupy binding sites in order to disrupt cancer-relevant interactions.
Protein-protein interactions are the main keys to executing important cellular functions. We hypothesize that gain or loss of protein-protein interactions plays important roles in the tumor development. We therefore proposed a network-based approach for biomarker discovery combining gene expression data with protein-protein interactions or regulatory transcriptional networks. This approach was used to predict biomarker for prostate cancer and gliomas.
Prostate cancer is the most common form of cancer and the leading cause of cancer death among men in the developed countries. In 2013 roughly 240.000 new cases of prostate cancer were diagnosed. Whereas Glioblastoma multiforme (GBM) is the most common and most aggressive type of primary brain tumor and it accounts for 52% of all primary brain tumor cases.
The androgen receptor is a DNA-binding transcription factor that regulates genes which are critical for the development and maintenance of the male sexual phenotype. A relationship between androgen dependence and the development of prostate cancer has been discovered.
It has been shown that TGF-beta plays a crucial role in the development of glioblastomas, but details of the detailed association are still missing. Novel markers and therapeutic targets are still needed for these two cancers. In this project, we therefore focused on the androgen receptor protein-protein network in prostate cancer and TGF-beta mediated network in gliomas.
Protein interactions can be extracted by text mining, from databases, and from structural data on protein complexes. As most cancer types arise due to mutations in regulatory elements, we focused mainly on gene regulatory networks of key transcription factors in cancers. To identify these transcription factors and their targets in human, ChIP-Chip and ChIP-Seq experiments were carried out. The resulting networks serve as a scaffold on which data of deregulated proteins derived from microarray and next-generation sequencing of patient cancer samples are mapped. The perturbed subnetwork can than be visualized with a new method that identifies and highlights network motifs and modules. Promising biomarker candidates were further examined and validated experimentally. Where possible, candidates were modeled using protein structural data on protein-protein complexes, providing the basis to design ligands that occupy binding sites in order to disrupt cancer-relevant interactions.