Human Hematopoietic Signal peptide-containing Secreted 1 (hHSS1) modulates genes and pathways in glioma: implications for the regulation of tumorigenicity and angiogenesis.

BACKGROUND: Human Hematopoietic Signal peptide-containing Secreted 1 (hHSS1) is a truly novel protein, defining a new class of secreted factors. We have previously reported that ectopic overexpression of hHSS1 has a negative modulatory effect on cell proliferation and tumorigenesis in glioblastoma model systems. Here we have used microarray analysis, screened glioblastoma samples in The Cancer Genome Atlas (TCGA), and studied the effects of hHSS1 on glioma-derived cells and endothelial cells to elucidate the molecular mechanisms underlying the anti-tumorigenic effects of hHSS1. METHODS: Gene expression profiling of human glioma U87 and A172 cells overexpressing hHSS1 was performed. Ingenuity® iReport™ and Ingenuity Pathway Analysis (IPA) were used to analyze the gene expression in the glioma cells. DNA content and cell cycle analysis were performed by FACS, while cell migration, cell invasion, and effects of hHSS1 on HUVEC tube formation were determined by transwell and matrigel assays. Correlation was made between hHSS1 expression and specific genes in glioblastoma samples in the TCGA database. RESULTS: We have clarified the signaling and metabolic pathways (i.e. role of BRCA1 in DNA damage response), networks (i.e. cell cycle) and biological processes (i.e. cell division process of chromosomes) that result from hHSS1effects upon glioblastoma growth. U87-overexpressing hHSS1 significantly decreased the number of cells in the G0/G1 cell cycle phase, and significantly increased cells in the S and G2/M phases (P < 0.05). U87-overexpressing hHSS1 significantly lost their ability to migrate (P < 0.001) and to invade (P < 0.01) through matrigel matrix. hHSS1-overexpression significantly decreased migration of A172 cells (P < 0.001), inhibited A172 tumor-induced migration and invasion of HUVECs (P < 0.001), and significantly inhibited U87 tumor-induced invasion of HUVECs (P < 0.001). Purified hHSS1 protein inhibited HUVEC tube formation. TCGA database revealed significant correlation between hHSS1 and BRCA2 (r = -0.224, P < 0.0005), ADAMTS1 (r = -0.132, P <0.01) and endostatin (r = 0.141, P < 0.005). CONCLUSIONS: hHSS1-overexpression modulates signaling pathways involved in tumorigenesis. hHSS1 inhibits glioma-induced cell cycle progression, cell migration, invasion and angiogenesis. Our data suggest that hHSS1 is a potential therapeutic for malignant glioblastoma possessing significant antitumor and anti-angiogenic activity.

Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets.

Background: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.