PAK7 blocking peptide

Radiation therapy is an important and effective treatment option for prostate cancer, but high-risk patients are prone to relapse due to radioresistance of cancer cells. Molecular mechanisms that contribute to radioresistance are not fully understood. Novel computational strategies are needed to identify radioresistance driver genes from hundreds of gene copy number alterations. We developed a network-based approach based on lasso regression in combination with network propagation for the analysis of prostate cancer cell lines with acquired radioresistance to identify clinically relevant marker genes associated with radioresistance in prostate cancer patients. We analyzed established radioresistant cell lines of the prostate cancer cell lines DU145 and LNCaP and compared their gene copy number and expression profiles to their radiosensitive parental cells. We found that radioresistant DU145 showed much more gene copy number alterations than LNCaP and their gene expression profiles were highly cell line specific. We learned a genome-wide prostate cancer-specific gene regulatory network and quantified impacts of differentially expressed genes with directly underlying copy number alterations on known radioresistance marker genes. This revealed several potential driver candidates involved in the regulation of cancer-relevant processes. Importantly, we found that ten driver candidates from DU145 (ADAMTS9, AKR1B10, CXXC5, FST, FOXL1, GRPR, ITGA2, SOX17, STARD4, VGF) and four from LNCaP (FHL5, LYPLAL1, PAK7, TDRD6) were able to distinguish irradiated prostate cancer patients into early and late relapse groups. Moreover, in-depth in vitro validations for VGF (Neurosecretory protein VGF) showed that siRNA-mediated gene silencing increased the radiosensitivity of DU145 and LNCaP cells. Our computational approach enabled to predict novel radioresistance driver gene candidates. Additional preclinical and clinical studies are required to further validate the role of VGF and other candidate genes as potential biomarkers for the prediction of radiotherapy responses and as potential targets for radiosensitization of prostate cancer. Author summary Prostate cancer cell lines represent an important model system to characterize molecular alterations that contribute to radioresistance, but irradiation can cause deletions and amplifications of DNA segments that affect hundreds of genes. This in combination with the small number of cell lines that are usually considered does not allow a straight-forward identification of driver genes by standard statistical methods. Therefore, we developed a network-based approach to analyze gene copy number and expression profiles of such cell lines enabling to identify potential driver genes associated with radioresistance of prostate cancer. We used lasso regression in combination with a significance test for lasso to learn a genome-wide prostate cancer-specific gene regulatory network. We used this network for network flow computations to determine impacts of gene copy number alterations on known radioresistance marker genes. Mapping to prostate cancer samples and additional filtering allowed us to identify 14 driver gene candidates that distinguished irradiated prostate cancer patients into early and late relapse groups. In-depth literature analysis and wet-lab validations suggest that our method can predict novel radioresistance driver genes. Additional preclinical and clinical studies are required to further validate these genes for the prediction of radiotherapy responses and as potential targets to radiosensitize prostate cancer.

Endothelial barrier function is carefully controlled to protect tissues from edema and damage inflicted by extravasated leukocytes. RhoGTPases, in conjunction with myriad regulatory proteins, exert both positive and negative effects on the endothelial barrier integrity. Precise knowledge about the relevant mechanisms is currently fragmented and we therefore performed a comprehensive analysis of endothelial barrier regulation by RhoGTPases and their regulators. Combining RNAi with electrical impedance measurements we quantified the relevance of 270 Rho-associated genes for endothelial barrier function. Statistical analysis identified 10 targets of which six promoted-and four reduced endothelial barrier function upon downregulation. We analyzed in more detail two of these which were not previously identified as regulators of endothelial integrity. We found that the Rac1-GEF (Guanine nucleotide Exchange Factor) TIAM2 is a positive regulator and the Cdc42(Rac1)- GAP (GTPase-Activating Protein) SYDE1 is a negative regulator of the endothelial barrier function. Finally, we found that the GAP SYDE1 is part of a Cdc42-centered signaling unit, also comprising the Cdc42-GEF FARP1 and the Cdc42 effector PAK7 which controls the integrity of the endothelial barrier. In conclusion, using a siRNA-based screen, we identified new regulators of barrier function and found that Cdc42 is a dominant positive regulator of endothelial integrity.