Metabolic shifts toward glutamine regulate tumor growth, invasion and bioenergetics in ovarian cancer.

Glutamine can play a critical role in cellular growth in multiple cancers. Glutamine-addicted cancer cells are dependent on glutamine for viability, and their metabolism is reprogrammed for glutamine utilization through the tricarboxylic acid (TCA) cycle. Here, we have uncovered a missing link between cancer invasiveness and glutamine dependence. Using isotope tracer and bioenergetic analysis, we found that low-invasive ovarian cancer (OVCA) cells are glutamine independent, whereas high-invasive OVCA cells are markedly glutamine dependent. Consistent with our findings, OVCA patients' microarray data suggest that glutaminolysis correlates with poor survival. Notably, the ratio of gene expression associated with glutamine anabolism versus catabolism has emerged as a novel biomarker for patient prognosis. Significantly, we found that glutamine regulates the activation of STAT3, a mediator of signaling pathways which regulates cancer hallmarks in invasive OVCA cells. Our findings suggest that a combined approach of targeting high-invasive OVCA cells by blocking glutamine's entry into the TCA cycle, along with targeting low-invasive OVCA cells by inhibiting glutamine synthesis and STAT3 may lead to potential therapeutic approaches for treating OVCAs.

A switch in the source of ATP production and a loss in capacity to perform glycolysis are hallmarks of hepatocyte failure in advance liver disease.

BACKGROUND & AIMS: The cause of hepatic failure in the terminal stages of chronic injury is unknown. Cellular metabolic adaptations in response to the microenvironment have been implicated in cellular breakdown. METHODS: To address the role of energy metabolism in this process we studied mitochondrial number, respiration, and functional reserve, as well as cellular adenosine-5'-triphosphate (ATP) production, glycolytic flux, and expression of glycolysis related genes in isolated hepatocytes from early and terminal stages of cirrhosis using a model that produces hepatic failure from irreversible cirrhosis in rats. To study the clinical relevance of energy metabolism in terminal stages of chronic liver failure, we analyzed glycolysis and energy metabolism related gene expression in liver tissue from patients at different stages of chronic liver failure according to Child-Pugh classification. Additionally, to determine whether the expression of these genes in early-stage cirrhosis (Child-Pugh Class A) is related to patient outcome, we performed network analysis of publicly available microarray data obtained from biopsies of 216 patients with hepatitis C-related Child-Pugh A cirrhosis who were prospectively followed up for a median of 10years. RESULTS: In the early phase of cirrhosis, mitochondrial function and ATP generation are maintained by increasing energy production from glycolytic flux as production from oxidative phosphorylation falls. At the terminal stage of hepatic injury, mitochondria respiration and ATP production are significantly compromised, as the hepatocytes are unable to sustain the increased demand for high levels of ATP generation from glycolysis. This impairment corresponds to a decrease in glucose-6-phosphatase catalytic subunit and phosphoglucomutase 1. Similar decreased gene expression was observed in liver tissue from patients at different stages of chronic liver injury. Further, unbiased network analysis of microarray data revealed that expression of these genes was down regulated in the group of patients with poor outcome. CONCLUSIONS: An adaptive metabolic shift, from generating energy predominantly from oxidative phosphorylation to glycolysis, allows maintenance of energy homeostasis during early stages of liver injury, but leads to hepatocyte dysfunction during terminal stages of chronic liver disease because hepatocytes are unable to sustain high levels of energy production from glycolysis.

Platelet-derived growth factor receptor alpha (PDGFRα) targeting and relevant biomarkers in ovarian carcinoma.

OBJECTIVE: Platelet-derived growth factor receptor alpha (PDGFRα) is believed to be associated with cell survival. We examined (i) whether PDGFRα blockade enhances the antitumor activity of taxanes in ovarian carcinoma and (ii) potential biomarkers of response to anti-PDGFRα therapy. METHODS: PDGFRα expression in 176 ovarian carcinomas was evaluated with tissue microarray and correlated to survival outcome. Human-specific monoclonal antibody to PDGFRα (IMC-3G3) was used for in vitro and in vivo experiments with or without docetaxel. Gene microarrays and reverse-phase protein arrays with pathway analyses were performed to identify potential predictive biomarkers. RESULTS: When compared to low or no PDGFRα expression, increased PDGFRα expression was associated with significantly poorer overall survival of patients with ovarian cancer (P=0.014). Although treatment with IMC-3G3 alone did not affect cell viability or increase apoptosis, concurrent use of IMC-3G3 with docetaxel significantly enhanced sensitization to docetaxel and apoptosis. In an orthotopic mouse model, IMC-3G3 monotherapy had no significant antitumor effects in SKOV3-ip1 (low PDGFRα expression), but showed significant antitumor effects in HeyA8-MDR (high PDGFRα expression). Concurrent use of IMC-3G3 with docetaxel, compared with use of docetaxel alone, significantly reduced tumor weight in all tested cell lines. In protein ontology, the EGFR and AKT pathways were downregulated by IMC-3G3 therapy. MAPK and CCNB1 were downregulated only in the HeyA8-MDR model. CONCLUSION: These data identify IMC-3G3 as an attractive therapeutic strategy and identify potential predictive markers for further development.

The glucose-deprivation network counteracts lapatinib-induced toxicity in resistant ErbB2-positive breast cancer cells.

Dynamic interactions between intracellular networks regulate cellular homeostasis and responses to perturbations. Targeted therapy is aimed at perturbing oncogene addiction pathways in cancer, however, development of acquired resistance to these drugs is a significant clinical problem. A network-based computational analysis of global gene expression data from matched sensitive and acquired drug-resistant cells to lapatinib, an EGFR/ErbB2 inhibitor, revealed an increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response in the resistant cells. Importantly, the glucose deprivation response markers correlated significantly with high clinical relapse rates in ErbB2-positive breast cancer patients. Further, forcing drug-sensitive cells into glucose deprivation rendered them more resistant to lapatinib. Using a chemical genomics bioinformatics mining of the CMAP database, we identified drugs that specifically target the glucose deprivation response networks to overcome the resistant phenotype and reduced survival of resistant cells. This study implicates the chronic activation of cellular compensatory networks in response to targeted therapy and suggests novel combinations targeting signaling and metabolic networks in tumors with acquired resistance.

Core epithelial-to-mesenchymal transition interactome gene-expression signature is associated with claudin-low and metaplastic breast cancer subtypes.

The epithelial-to-mesenchymal transition (EMT) produces cancer cells that are invasive, migratory, and exhibit stem cell characteristics, hallmarks of cells that have the potential to generate metastases. Inducers of the EMT include several transcription factors (TFs), such as Goosecoid, Snail, and Twist, as well as the secreted TGF-beta1. Each of these factors is capable, on its own, of inducing an EMT in the human mammary epithelial (HMLE) cell line. However, the interactions between these regulators are poorly understood. Overexpression of each of the above EMT inducers up-regulates a subset of other EMT-inducing TFs, with Twist, Zeb1, Zeb2, TGF-beta1, and FOXC2 being commonly induced. Up-regulation of Slug and FOXC2 by either Snail or Twist does not depend on TGF-beta1 signaling. Gene expression signatures (GESs) derived by overexpressing EMT-inducing TFs reveal that the Twist GES and Snail GES are the most similar, although the Goosecoid GES is the least similar to the others. An EMT core signature was derived from the changes in gene expression shared by up-regulation of Gsc, Snail, Twist, and TGF-beta1 and by down-regulation of E-cadherin, loss of which can also trigger an EMT in certain cell types. The EMT core signature associates closely with the claudin-low and metaplastic breast cancer subtypes and correlates negatively with pathological complete response. Additionally, the expression level of FOXC1, another EMT inducer, correlates strongly with poor survival of breast cancer patients.

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.

Molecular Analysis of Short- versus Long-Term Survivors of High-Grade Serous Ovarian Carcinoma.

Despite having similar histologic features, patients with high-grade serous ovarian carcinoma (HGSC) often experience highly variable outcomes. The underlying determinants for long-term survival (LTS, ≥10 years) versus short-term survival (STS, <3 years) are largely unknown. The present study sought to identify molecular predictors of LTS for women with HGSC. A cohort of 24 frozen HGSC samples was collected (12 LTS and 12 STS) and analyzed at DNA, RNA, and protein levels. OVCAR5 and OVCAR8 cell lines were used for in vitro validation studies. For in vivo studies, we injected OVCAR8 cells into the peritoneal cavity of female athymic nude mice. From RNAseq analysis, 11 genes were found to be differentially expressed between the STS and LTS groups (fold change > 2; false discovery rate < 0.01). In the subsequent validation cohort, transmembrane protein 62 (TMEM62) was found to be related to LTS. CIBERSORT analysis showed that T cells (follicular helper) were found at higher levels in tumors from LTS than STS groups. In vitro data using OVCAR5 and OVCAR8 cells showed decreased proliferation with TMEM62 overexpression and positive correlation with a longevity-regulating pathway (KEGG HSA04213) at the RNA level. In vivo analysis using the OVCAR8-TMEM62-TetON model showed decreased tumor burden in mice with high- vs. low-expressing TMEM62 tumors. Our results demonstrate that restoring TMEM62 may be a novel approach for treatment of HGSC. These findings may have implications for biomarker and intervention strategies to help improve patient outcomes

Classification of High-Grade Serous Ovarian Cancer Using Tumor Morphologic Characteristics.

Importance: Despite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics. Objective: To develop and characterize a gross morphologic classification system for HGSOC. Design, Setting, and Participants: This cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021. Exposures: Gross tumor morphologic characteristics. Main Outcomes and Measures: Clinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared. Results: Of 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10-24), hypoxia (FDR q-value, 1.52 × 10-5), and angiogenesis pathways (FDR q-value, 2.11 × 10-2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10-9) and cell cycle progression (FDR q-value, 1.10 × 10-5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes. Conclusions and Relevance: This study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

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.

Rational Combination of CRM1 Inhibitor Selinexor and Olaparib Shows Synergy in Ovarian Cancer Cell Lines and Mouse Models.

CRM1 inhibitors have demonstrated antitumor effects in ovarian and other cancers; however, rational combinations are largely unexplored. We performed a high-throughput drug library screen to identify drugs that might combine well with selinexor in ovarian cancer. Next, we tested the combination of selinexor with the top hit from the drug screen in vitro and in vivo Finally, we assessed for mechanisms underlying the identified synergy using reverse phase protein arrays (RPPA). The drug library screen assessing 688 drugs identified olaparib (a PARP inhibitor) as the most synergistic combination with selinexor. Synergy was further demonstrated by MTT assays. In the A2780luc ip1 mouse model, the combination of selinexor and olaparib yielded significantly lower tumor weight and fewer tumor nodules compared with the control group (P < 0.04 and P < 0.03). In the OVCAR5 mouse model, the combination yielded significantly fewer nodules (P = 0.006) and markedly lower tumor weight compared with the control group (P = 0.059). RPPA analysis indicated decreased expression of DNA damage repair proteins and increased expression of tumor suppressor proteins in the combination treatment group. Collectively, our preclinical findings indicate that combination with selinexor to expand the utility and efficacy of PARP inhibitors in ovarian cancer warrants further exploration.

CD63-mediated cloaking of VEGF in small extracellular vesicles contributes to anti-VEGF therapy resistance.

Despite wide use of anti-vascular endothelial growth factor (VEGF) therapy for many solid cancers, most individuals become resistant to this therapy, leading to disease progression. Therefore, new biomarkers and strategies for blocking adaptive resistance of cancer to anti-VEGF therapy are needed. As described here, we demonstrate that cancer-derived small extracellular vesicles package increasing quantities of VEGF and other factors in response to anti-VEGF therapy. The packaging process of VEGF into small extracellular vesicles (EVs) is mediated by the tetraspanin CD63. Furthermore, small EV-VEGF (eVEGF) is not accessible to anti-VEGF antibodies and can trigger intracrine VEGF signaling in endothelial cells. eVEGF promotes angiogenesis and enhances tumor growth despite bevacizumab treatment. These data demonstrate a mechanism where VEGF is partitioned into small EVs and promotes tumor angiogenesis and progression. These findings have clinical implications for biomarkers and therapeutic strategies for ovarian cancer.

Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling.

Peritoneal spread is the primary mechanism of metastasis of ovarian cancer, and survival of ovarian cancer cells in the peritoneal cavity as nonadherent spheroids and their adherence to the mesothelium of distant organs lead to cancer progression, metastasis, and mortality. However, the mechanisms that govern this metastatic process in ovarian cancer cells remain poorly understood. In this study, we cultured ovarian cancer cell lines in adherent and nonadherent conditions in vitro and analyzed changes in mRNA and protein levels to identify mechanisms of tumor cell survival and proliferation in adherent and nonadherent cells. EGFR or ERBB2 upregulated ZEB1 in nonadherent cells, which caused resistance to cell death and increased tumor-initiating capacity. Conversely, Forkhead box M1 (FOXM1) was required for the induction of integrin ß1, integrin-α V, and integrin-α 5 for adhesion of cancer cells. FOXM1 also upregulated ZEB1, which could act as a feedback inhibitor of FOXM1, and caused the transition of adherent cells to nonadherent cells. Strikingly, the combinatorial treatment with lapatinib [dual kinase inhibitor of EGFR (ERBB1) and ERBB2] and thiostrepton (FOXM1 inhibitor) reduced growth and peritoneal spread of ovarian cancer cells more effectively than either single-agent treatment in vivo. In conclusion, these results demonstrate that FOXM1 and EGFR/ERBB2 pathways are key points of vulnerability for therapy to disrupt peritoneal spread and adhesion of ovarian cancer cells. SIGNIFICANCE: This study describes the mechanism exhibited by ovarian cancer cells required for adherent cell transition to nonadherent form during peritoneal spread and metastasis. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/24/5554/F1.large.jpg.

Network topology determines dynamics of the mammalian MAPK1,2 signaling network: bifan motif regulation of C-Raf and B-Raf isoforms by FGFR and MC1R.

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.

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. 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.

GnRH-R-Targeted Lytic Peptide Sensitizes BRCA Wild-type Ovarian Cancer to PARP Inhibition.

EP-100 is a synthetic lytic peptide that specifically targets the gonadotropin-releasing hormone receptor on cancer cells. To extend the utility of EP-100, we aimed to identify effective combination therapies with EP-100 for ovarian cancer and explore potential mechanisms of this combination. A series of in vitro (MTT assay, immunoblot analysis, reverse-phase protein array, comet assay, and immunofluorescence staining) and in vivo experiments were carried out to determine the biological effects of EP-100 alone and in combination with standard-of-care drugs. EP-100 decreased the viability of ovarian cancer cells and reduced tumor growth in orthotopic mouse models. Of five standard drugs tested (cisplatin, paclitaxel, doxorubicin, topotecan, and olaparib), we found that the combination of EP-100 and olaparib was synergistic in ovarian cancer cell lines. Further experiments revealed that combined treatment of EP-100 and olaparib significantly increased the number of nuclear foci of phosphorylated histone H2AX. In addition, the extent of DNA damage was significantly increased after treatment with EP-100 and olaparib in comet assay. We performed reverse-phase protein array analyses and identified that the PI3K/AKT pathway was inhibited by EP-100, which we validated with in vitro experiments. In vivo experiment using the HeyA8 mouse model demonstrated that mice treated with EP-100 and olaparib had lower tumor weights (0.06 ± 0.13 g) than those treated with a vehicle (1.19 ± 1.09 g), EP-100 alone (0.62 ± 0.78 g), or olaparib alone (0.50 ± 0.63 g). Our findings indicate that combining EP-100 with olaparib is a promising therapeutic strategy for ovarian cancer.

Predicting Novel Therapies and Targets: Regulation of Notch3 by the Bromodomain Protein BRD4.

Systematic approaches for accurate repurposing of targeted therapies are needed. We developed and aimed to biologically validate our therapy predicting tool (TPT) for the repurposing of targeted therapies for specific tumor types by testing the role of Bromodomain and Extra-Terminal motif inhibitors (BETi) in inhibiting BRD4 function and downregulating Notch3 signaling in ovarian cancer.Utilizing established ovarian cancer preclinical models, we carried out in vitro and in vivo studies with clinically relevant BETis to determine their therapeutic effect and impact on Notch3 signaling.Treatment with BETis or siRNA-mediated BRD4 knockdown resulted in decreased cell viability, reduced cell proliferation, and increased cell apoptosis in vitro. In vivo studies with orthotopic mouse models demonstrated that treatment with BETi decreased tumor growth. In addition, knockdown of BRD4 with doxycycline-inducible shRNA increased survival up to 50% (P < 0.001). Treatment with either BETis or BRD4 siRNA decreased Notch3 expression both in vitro and in vivo BRD4 inhibition also decreased the expression of NOTCH3 targets, including HES1 Chromatin immunoprecipitation revealed that BRD4 was present at the NOTCH3 promoter.Our findings provide biological validation for the TPT by demonstrating that BETis can be an effective therapeutic agent for ovarian cancer by downregulating Notch3 expression.The TPT could rapidly identify candidate drugs for ovarian or other cancers along with novel companion biomarkers.

Exploiting the PI3K/AKT pathway for cancer drug discovery.

Evolving studies with several different targeted therapeutic agents are demonstrating that patients with genomic alterations of the target, including amplification, translocation and mutation, are more likely to respond to the therapy. Recent studies indicate that numerous components of the phosphatidylinositol-3-kinase (PI3K)/AKT pathway are targeted by amplification, mutation and translocation more frequently than any other pathway in cancer patients, with resultant activation of the pathway. This warrants exploiting the PI3K/AKT pathway for cancer drug discovery.

Sustained Adrenergic Signaling Promotes Intratumoral Innervation through BDNF Induction.

Mounting clinical and preclinical evidence supports a key role for sustained adrenergic signaling in the tumor microenvironment as a driver of tumor growth and progression. However, the mechanisms by which adrenergic neurotransmitters are delivered to the tumor microenvironment are not well understood. Here we present evidence for a feed-forward loop whereby adrenergic signaling leads to increased tumoral innervation. In response to catecholamines, tumor cells produced brain-derived neurotrophic factor (BDNF) in an ADRB3/cAMP/Epac/JNK-dependent manner. Elevated BDNF levels in the tumor microenvironment increased innervation by signaling through host neurotrophic receptor tyrosine kinase 2 receptors. In patients with cancer, high tumor nerve counts were significantly associated with increased BDNF and norepinephrine levels and decreased overall survival. Collectively, these data describe a novel pathway for tumor innervation, with resultant biological and clinical implications.Significance: Sustained adrenergic signaling promotes tumor growth and metastasis through BDNF-mediated tumoral innervation. Cancer Res; 78(12); 3233-42. ©2018 AACR.

An androgen-IL-6-Stat3 autocrine loop re-routes EGF signal in prostate cancer cells.

Dynamic modulation of information flow within signaling networks allows the cell to respond to micro-environmental changes. This property of the cell, while being essential to survival and eliciting appropriate responses, can also be detrimental to the organism by allowing cancerous cells to evade regulation and proliferate. We determined if changes in expression levels of transcriptional regulators and their interactions could alter routing within signaling networks in prostate cancer cells. Increasing the protein levels of the signal transducer and activator of transcription 3 (Stat3) led to Stat3-androgen receptor (AR) complex formation in response to epidermal growth factor (EGF) and interleukin-6 (IL-6) stimulation. Increasing the protein levels of Stat3 increased the EGF induced transcriptional activation of the androgen receptor. Androgen pre-treatment increased Stat3 protein levels in an IL-6 autocrine/paracrine dependent manner in the cells suggesting a feedback loop within cells. Increased Stat3-AR complex leads to a change in the routing of the epidermal growth factor signal allowing the androgen receptor to become activated in a Stat3 dependent manner. Understanding interactions and changes in signal flow within the cell is important to our understanding of signaling networks as well as our ability to identify cellular targets for novel therapies to inhibit cancer progression.

Hypothesis generation in signaling networks.

Biological signaling networks comprise the chemical processes by which cells detect and respond to changes in their environment. Such networks have been implicated in the regulation of important cellular activities, including cellular reproduction, mobility, and death. Though technological and scientific advances have facilitated the rapid accumulation of information about signaling networks, utilizing these massive information resources has become infeasible except through computational methods and computer-based tools. To date, visualization and simulation tools have received significant emphasis. In this paper, we present a graph-theoretic formalization of biological signaling network models that are in wide but informal use, and formulate two problems on the graph: the Constrained Downstream and Minimum Knockout Problems. Solutions to these problems yield qualitative tools for generating hypotheses about the networks, which can then be experimentally tested in a laboratory setting. Using established graph algorithms, we provide a solution to the Constrained Downstream Problem. We also show that the Minimum Knockout Problem is NP-Hard, propose a heuristic, and assess its performance. In tests on the Epidermal Growth Factor Receptor (EGFR) network, we find that our heuristic reports the correct solution to the problem in seconds. Source code for the implementations of both solutions is available from the authors upon request.