This study implicates the glucose deprivation response in breast cancer cell resistance to lapatinib and high relapse rates in Her2-positive patients. Identification of these compensatory networks suggests novel strategies to target cancer signaling and metabolism.

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.Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity.We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

To identify regulators of intracellular signaling we targeted 541 kinases and kinase-related molecules with siRNAs and determined their effects on signaling with a functional proteomics reverse phase protein array (RPPA) platform assessing 42 phospho and total proteins. The kinome wide screen demonstrated a strong inverse correlation between phosphorylation of AKT and MAPK with 115 genes that when targeted by siRNAs demonstrated opposite effects on MAPK and AKT phosphorylation. Network based analysis identified the MAPK subnetwork of genes along with p70S6K and FRAP1 as the most prominent targets that increased phosphorylation of AKT, a key regulator of cell survival. The regulatory loops induced by the MAPK pathway are dependent on TSC2 but demonstrate a lesser dependence on p70S6K than the previously identified FRAP1 feedback loop. The siRNA screen also revealed novel bi-directionality in the AKT and GSK3 interaction, whereby genetic ablation of GSK3 significantly blocks AKT phosphorylation, an unexpected observation as GSK3 has only been predicted to be downstream of AKT. This method uncovered novel modulators of AKT phosphorylation and facilitated the mapping of regulatory loops.

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E-series prostanoid (EP)4 receptor is up-regulated in numerous cancers, including cervical carcinomas, and has been implicated in mediating the effects of prostaglandin (PG)E(2) in tumorigenesis. In addition to regulation by endogenously biosynthesized PGE(2), neoplastic cervical epithelial cells in sexually active women may also be regulated by PGs present in seminal plasma. In this study, we investigated the signal transduction pathways mediating the role of seminal plasma and PGE(2) in the regulation of tumorigenic and angiogenic genes via the EP4 receptor in cervical adenocarcinoma (HeLa) cells. HeLa cells were stably transfected with EP4 receptor in the sense orientation. Seminal plasma and PGE(2) signaling via the EP4 receptor induced the activation of cyclooxygenase (COX)-2 and vascular endothelial growth factor (VEGF) promoters, expression of COX-2 and VEGF mRNA and protein, and secretion of VEGF protein into the culture medium. Treatment of HeLa cells with seminal plasma or PGE(2) also rapidly induced the phosphorylation of ERK1/2 via the EP4 receptor. Preincubation of cells with a specific EP4 receptor antagonist (ONO-AE2-227) or chemical inhibitors of epidermal growth factor receptor (EGFR) tyrosine kinase or MAPK kinase or cotransfection of cells with dominant-negative mutant cDNA targeted against the EGFR, serine/threonine kinase Raf, or MAPK kinase abolished the EP4-induced activation of COX-2, VEGF, and ERK1/2. Therefore, we have demonstrated that seminal plasma and PGE(2) can promote the expression of tumorigenic and angiogenic factors, in cervical adenocarcinoma cells via the EP4 receptor, EGFR, and ERK1/2 signaling pathways.

Activation of the fibroblast growth factor (FGFR) and melanocyte stimulating hormone (MC1R) receptors stimulates B-Raf and C-Raf isoforms that regulate the dynamics of MAPK1,2 signaling. Network topology motifs in mammalian cells include feed-forward and feedback loops and bifans where signals from two upstream molecules integrate to modulate the activity of two downstream molecules. We computationally modeled and experimentally tested signal processing in the FGFR/MC1R/B-Raf/C-Raf/MAPK1,2 network in human melanoma cells; identifying 7 regulatory loops and a bifan motif. Signaling from FGFR leads to sustained activation of MAPK1,2, whereas signaling from MC1R results in transient activation of MAPK1,2. The dynamics of MAPK activation depends critically on the expression level and connectivity to C-Raf, which is critical for a sustained MAPK1,2 response. A partially incoherent bifan motif with a feedback loop acts as a logic gate to integrate signals and regulate duration of activation of the MAPK signaling cascade. Further reducing a 106-node ordinary differential equations network encompassing the complete network to a 6-node network encompassing rate-limiting processes sustains the feedback loops and the bifan, providing sufficient information to predict biological responses.

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