The disease relevance of human hepatocellular xenograft models: molecular characterization and review of the literature

Mouse models of liver cancer

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide, and the third leading cause of cancer mortality. The great majority of patients are not eligible for curative therapies, and therapeutic approaches for advanced disease show only limited efficacy. Difficulties to treat HCC are due to the heterogenous genetic alterations of HCC, profound alterations in the hepatic microenvironment, and incomplete understanding of HCC biology. Mouse models of HCC will be helpful to improve our understanding of HCC biology, the contributions of the specific pathways and genetic alterations to carcinogenesis. In addition, mouse models of HCC may contribute to elucidate the role of the tumor microenvironment, and serve as models for preclinical studies. As no single mouse model is appropriate to study all of the above, we discuss key features and limitations of commonly used models. Furthermore, we provide detailed protocols for select models, in which HCC is induced genetically, chemically or by transplantation of tumor cells.

enRoute: dynamic path extraction from biological pathway maps for exploring heterogeneous experimental datasets

Jointly analyzing biological pathway maps and experimental data is critical for understanding how biological processes work in different conditions and why different samples exhibit certain characteristics. This joint analysis, however, poses a significant challenge for visualization. Current techniques are either well suited to visualize large amounts of pathway node attributes, or to represent the topology of the pathway well, but do not accomplish both at the same time. To address this we introduce enRoute, a technique that enables analysts to specify a path of interest in a pathway, extract this path into a separate, linked view, and show detailed experimental data associated with the nodes of this extracted path right next to it. This juxtaposition of the extracted path and the experimental data allows analysts to simultaneously investigate large amounts of potentially heterogeneous data, thereby solving the problem of joint analysis of topology and node attributes. As this approach does not modify the layout of pathway maps, it is compatible with arbitrary graph layouts, including those of hand-crafted, image-based pathway maps. We demonstrate the technique in context of pathways from the KEGG and the Wikipathways databases. We apply experimental data from two public databases, the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) that both contain a wide variety of genomic datasets for a large number of samples. In addition, we make use of a smaller dataset of hepatocellular carcinoma and common xenograft models. To verify the utility of enRoute, domain experts conducted two case studies where they explore data from the CCLE and the hepatocellular carcinoma datasets in the context of relevant pathways.

A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells

Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.

Comparative analysis of multidimensional, quantitative data

When analyzing multidimensional, quantitative data, the comparison of two or more groups of dimensions is a common task. Typical sources of such data are experiments in biology, physics or engineering, which are conducted in different configurations and use replicates to ensure statistically significant results. One common way to analyze this data is to filter it using statistical methods and then run clustering algorithms to group similar values. The clustering results can be visualized using heat maps, which show differences between groups as changes in color. However, in cases where groups of dimensions have an a priori meaning, it is not desirable to cluster all dimensions combined, since a clustering algorithm can fragment continuous blocks of records. Furthermore, identifying relevant elements in heat maps becomes more difficult as the number of dimensions increases. To aid in such situations, we have developed Matchmaker, a visualization technique that allows researchers to arbitrarily arrange and compare multiple groups of dimensions at the same time. We create separate groups of dimensions which can be clustered individually, and place them in an arrangement of heat maps reminiscent of parallel coordinates. To identify relations, we render bundled curves and ribbons between related records in different groups. We then allow interactive drill-downs using enlarged detail views of the data, which enable in-depth comparisons of clusters between groups. To reduce visual clutter, we minimize crossings between the views. This paper concludes with two case studies. The first demonstrates the value of our technique for the comparison of clustering algorithms. In the second, biologists use our system to investigate why certain strains of mice develop liver disease while others remain healthy, informally showing the efficacy of our system when analyzing multidimensional data containing distinct groups of dimensions.

SYSGENET: a meeting report from a new European network for systems genetics

The first scientific meeting of the newly established European SYSGENET network took place at the Helmholtz Centre for Infection Research (HZI) in Braunschweig, April 7-9, 2010. About 50 researchers working in the field of systems genetics using mouse genetic reference populations (GRP) participated in the meeting and exchanged their results, phenotyping approaches, and data analysis tools for studying systems genetics. In addition, the future of GRP resources and phenotyping in Europe was discussed.

BRE over-expression promotes growth of hepatocellular carcinoma

BRE, also known as TNFRSF1A modulator and BRCC45, is an evolutionarily highly conserved protein. It is a death receptor-associated protein in cytoplasm and a component of BRCA1/2-containing DNA repair complex in nucleus. BRE was found to have anti-apoptotic activity. Over-expression of BRE by transfection promoted survival of cell lines against apoptotic induction; whereas depletion of the protein by siRNA resulted in the opposite. In vivo anti-apoptotic activity of BRE was demonstrated by significant attenuation of Fas-induced acute fulminant hepatitis in transgenic mice expressing the human protein specifically in the liver. BRE was also implicated in tumor promotion by the accelerated tumor growth of Lewis Lung carcinoma transfected with human BRE; and by high expression of BRE specifically in the tumoral regions of human hepatocellular carcinoma (HCC). The present study was to test directly if transgenic expression of BRE in livers could promote HCC development in neonatal diethylnitrosamine model. By 8months after tumor induction, the maximal sizes of tumor nodules of transgenic mice were significantly larger than those of the non-transgenic controls, although the numbers of tumor nodules between the two groups did not significantly differ. Importantly, as in human HCC, the mouse endogenous BRE level was up-regulated in mouse HCC nodules. These results show that BRE over-expression can indeed promote growth, though not initiation, of liver tumors. Furthermore, the common occurrence of BRE over-expression in human and mouse HCC suggests that up-regulation of BRE is functionally important in liver tumor development.