Prediction and testing of biological networks underlying intestinal cancer

Genetic Heterogeneity of Single Circulating Tumour Cells in Colorectal Carcinoma

The aim of the present study was to isolate and investigate the genetic heterogeneities in single circulating tumour cells (CTCs) from patients with colorectal carcinoma (CRC). Twenty-eight single CTCs were collected from eight patients with CRC using a negative immunomagnetic enrichment method. After validation with glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene expression in 3 colon cancer cell lines, a panel of 19 genes were used to analyse the single CTCs (n = 28), primary colorectal carcinoma tissues (n = 8) and colon carcinoma cells (n = 6) using real-time qPCR. Genetic heterogeneities were assessed by comparing gene expression profiles of single CTCs from the different patients and in the same patient, respectively. Genetic profiling of the single CTCs showed extensive heterogeneities of the selected genes among the CTCs. Hierarchical clustering analyses exhibited two clusters of CTCs with differentially expressed genes, which highlighted different modifications from the primary carcinomas. Further, the genetic heterogeneities were observed between different patients or in the same patient. Finally, AKT1 expression was significantly (p = 0.0129) higher in single CTCs from CRC of advanced pathological stages (III or IV) CRC than in CTCs from CRC of early stages (I or II). Our findings suggest that single-cell genetic analysis can monitor the genetic heterogeneities and guide the personalised therapeutic targets in clinical sectors.

MAGNET: MicroArray Gene expression and Network Evaluation Toolkit

MicroArray Gene expression and Network Evaluation Toolkit (MAGNET) is a web-based application that provides tools to generate and score both protein-protein interaction networks and coexpression networks. MAGNET integrates user-provided experimental measurements with high-throughput proteomic datasets, generating weighted gene-gene and protein-protein interaction networks. MAGNET allows users to weight edges of protein-protein interaction networks using a logistic regression model integrating tissue-specific gene expression data, sub-cellular localization data, co-clustering of interacting proteins and the number of observations of the interaction. This provides a way to quantitatively measure the plausibility of interactions in protein-protein interaction networks given protein/gene expression measurements. Secondly, MAGNET generates filtered coexpression networks, where genes are represented as nodes, and their correlations are represented with edges. Overall, MAGNET provides researchers with a new framework with which to analyze and generate gene-gene and protein-protein interaction networks, based on both the user's own data and publicly available -omics datasets. The freely available service and documentation can be accessed at http://gurkan.case.edu/software or http://magnet.case.edu.

Network systems biology for targeted cancer therapies

The era of targeted cancer therapies has arrived. However, due to the complexity of biological systems, the current progress is far from enough. From biological network modeling to structural/dynamic network analysis, network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells. It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks

Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.

Microenvironmental genomic alterations reveal signaling networks for head and neck squamous cell carcinoma

Background

Advanced stage head and neck squamous cell carcinoma (HNSCC) is an aggressive cancer with low survival rates. Loss-of-heterozygosity/allelic imbalance (LOH/AI) analysis has been widely used to identify genomic alterations in solid tumors and the tumor microenvironment (stroma). We hypothesize that these identified alterations can point to signaling networks functioning in HNSCC epithelial-tumor and surrounding stroma (tumor microenvironment).

Results

Under the assumption that genes in proximity to identified LOH/AI regions are correlated with the tumorigenic phenotype, we mined publicly available biological information to identify pathway segments (signaling proteins connected to each other in a network) and identify the role of tumor microenvironment in HNSCC. Across both neoplastic epithelial cells and the surrounding stromal cells, genetic alterations in HNSCC were successfully identified, and 75 markers were observed to have significantly different LOH/AI frequencies in these compartments (p < 0.026). We applied a network identification approach to the genes in proximity to these 75 markers in cancer epithelium and stroma in order to identify biological networks that can describe functional associations amongst these marker-associated genes.

Conclusions

We verified the involvement of T-cell receptor signaling pathways in HNSCC as well as associated oncogenes such as LCK and PLCB1, and tumor suppressors such as STAT5A, PTPN6, PARK2. We identified expression levels of genes within significant LOH/AI regions specific to stroma networks that correlate with better outcome in radiation therapy. By integrating various levels of high-throughput data, we were able to precisely focus on specific proteins and genes that are germane to HNSCC.

DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization

Background

High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.

Results

We demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.

Conclusions

These results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.

PETALS: Proteomic Evaluation and Topological Analysis of a mutated Locus' Signaling

Background

Colon cancer is driven by mutations in a number of genes, the most notorious of which is Apc. Though much of Apc's signaling has been mechanistically identified over the years, it is not always clear which functions or interactions are operative in a particular tumor. This is confounded by the presence of mutations in a number of other putative cancer driver (CAN) genes, which often synergize with mutations in Apc.

Computational methods are, thus, required to predict which pathways are likely to be operative when a particular mutation in Apc is observed.

Results

We developed a pipeline, PETALS, to predict and test likely signaling pathways connecting Apc to other CAN-genes, where the interaction network originating at Apc is defined as a "blossom," with each Apc-CAN-gene subnetwork referred to as a "petal." Known and predicted protein interactions are used to identify an Apc blossom with 24 petals. Then, using a novel measure of bimodality, the coexpression of each petal is evaluated against proteomic (2 D differential In Gel Electrophoresis, 2D-DIGE) measurements from the Apc^1638N+/-mouse to test the network-based hypotheses.

Conclusions

The predicted pathways linking Apc and Hapln1 exhibited the highest amount of bimodal coexpression with the proteomic targets, prioritizing the Apc-Hapln1 petal over other CAN-gene pairs and suggesting that this petal may be involved in regulating the observed proteome-level effects. These results not only demonstrate how functional 'omics data can be employed to test in silico predictions of CAN-gene pathways, but also reveal an approach to integrate models of upstream genetic interference with measured, downstream effects.