Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle

<p>Abstract</p> <p>Background</p> <p>In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.</p> <p>Results</p> <p>We introduce <it>PathwayOracle</it>, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates <it>PathwayOracle </it>from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, <it>PathwayOracle </it>includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.</p> <p>Conclusion</p> <p><it>PathwayOracle </it>provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. <it>PathwayOracle </it>is freely available for download and use.</p

Macrophages Facilitate Resistance to Anti-VEGF Therapy by Altered VEGFR Expression

Abstract Purpose: VEGF-targeted therapies have modest efficacy in cancerpatients, butacquiredresistance iscommon. Themechanisms underlying such resistance are poorly understood. Experimental Design: To evaluate the potential role of immune cells in the development of resistance to VEGF blockade, we first established a preclinical model of adaptive resistance to anti-VEGF therapy. Additional in vitro and in vivo studies were carried out to characterize the role of macrophages in such resistance. Results: Using murine cancer models of adaptive resistance to anti-VEGF antibody (AVA), we found a previously unrecognized roleofmacrophagesinsuchresistance.Macrophageswereactively recruited to the tumor microenvironment and were responsible for the emergence of AVA resistance. Depletion of macrophages following emergence of resistance halted tumor growth and prolonged survival of tumor-bearing mice. In a macrophagedeficient mouse model, resistance to AVA failed to develop, but could be induced by injection of macrophages. Downregulation of macrophage VEGFR-1 and VEGFR-3 expression accompanied upregulation of alternative angiogenic pathways, facilitating escape from anti-VEGF therapy. Conclusions: These findings provide a new understanding of the mechanisms underlying the modest efficacy of current antiangiogenesis therapies and identify new opportunities for combinationapproachesforovarianandothercancers. ClinCancerRes; 23(22); 7034–46. �2017 AACR

A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data

Hematogenous Metastasis of Ovarian Cancer: Rethinking Mode of Spread

SummaryOvarian cancer has a clear predilection for metastasis to the omentum, but the underlying mechanisms involved in ovarian cancer spread are not well understood. Here, we used a parabiosis model that demonstrates preferential hematogenous metastasis of ovarian cancer to the omentum. Our studies revealed that the ErbB3-neuregulin 1 (NRG1) axis is a dominant pathway responsible for hematogenous omental metastasis. Elevated levels of ErbB3 in ovarian cancer cells and NRG1 in the omentum allowed for tumor cell localization and growth in the omentum. Depletion of ErbB3 in ovarian cancer impaired omental metastasis. Our results highlight hematogenous metastasis as an important mode of ovarian cancer metastasis. These findings have implications for designing alternative strategies aimed at preventing and treating ovarian cancer metastasis

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods

Grey analytic hierarchy process applied to effectiveness evaluation for groundwater potential zone delineation

An attempt has been made to identify plausible groundwater potential zones (GWPZ) based on Grey Analytic Hierarchy Process Method (Grey-AHP) using integrated remote sensing and geographic information system. Grey-AHP combines the advantages of classical analytic hierarchy process and grey clustering method for accurate estimation of weight coefficients. The method also examines the effectiveness of GWPZ identification process. The proposed methodology has been applied to the Hirakud canal command area, Odisha (India). Feature layers [e.g. soil type, geology] are utilized for groundwater potential index (GWPI) calculation. The resulting GWPI map has been classified into three GWPZ namely: good, moderate and poor. Effectiveness based on grey clustering method is found to be in between ‘better’ and ‘common’ classes. Value of coefficient of determination (R2 = 0.865) supports the obtained effectiveness evaluation result. This analysis demonstrates the potential applicability of the methodology for a general aquifer system

NetWalker: a contextual network analysis tool for functional genomics

BACKGROUND: Functional analyses of genomic data within the context of a priori biomolecular networks can give valuable mechanistic insights. However, such analyses are not a trivial task, owing to the complexity of biological networks and lack of computational methods for their effective integration with experimental data. RESULTS: We developed a software application suite, NetWalker, as a one-stop platform featuring a number of novel holistic (i.e. assesses the whole data distribution without requiring data cutoffs) data integration and analysis methods for network-based comparative interpretations of genome-scale data. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data. Extensive inter-operability between the analysis components and with external applications, including R, adds to the flexibility of data analyses. Here, we present a detailed computational analysis of our microarray gene expression data from MCF7 cells treated with lethal and sublethal doses of doxorubicin. CONCLUSION: NetWalker, a detailed step-by-step tutorial containing the analyses presented in this paper and a manual are available at the web site http://netwalkersuite.org

MAP kinase phosphatase as a locus of flexibility in a Mitogen-activated protein kinase signaling network

Intracellular signaling networks receive and process information to control cellular machines. The mitogen-activated protein kinase (MAPK) 1,2/protein kinase C (PKC) system is one such network that regulates many cellular machines, including the cell cycle machinery and autocrine/paracrine factor synthesizing machinery. We used a combination of computational analysis and experiments in mouse NIH-3T3 fibroblasts to understand the design principles of this controller network. We find that the growth factor –stimulated signaling network containing MAPK 1, 2/PKC can operate with one (monostable) or two (bistable) stable states. At low concentrations of MAPK phosphatase, the system exhibits bistable behavior, such that brief stimulus results in sustained MAPK activation. The MAPK-induced increase in the amounts of MAPK phosphatase eliminates the prolonged response capability and moves the network to a monostable state, in which it behaves as a proportional response system responding acutely to stimulus. Thus, the MAPK 1, 2/PKC controller network is flexibly designed, and MAPK phosphatase may be critical for this flexible response