Steven Nathaniel Steinway

Network modeling of TGFβ signaling in hepatocellular carcinoma epithelial-to-mesenchymal transition reveals joint Sonic hedgehog and Wnt pathway activation

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue, and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is TGFβ, which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We have constructed an EMT network of 70 nodes and 135 edges by integrating the signaling pathways involved in developmental EMT and known dysregulations in invasive HCC. We then used discrete dynamic modeling to understand the dynamics of the EMT network driven by TGFβ. Our network model recapitulates known dysregulations during the induction of EMT and predicts the activation of the Wnt and Sonic hedgehog (SHH) signaling pathways during this process. We show, across multiple murine (P2E and P2M) and human HCC cell lines (Huh7, PLC/PRF/5, HLE, and HLF), that the TGFβ signaling axis is a conserved driver of mesenchymal phenotype HCC and confirm that Wnt and SHH signaling are induced in these cell lines. Furthermore, we identify by network analysis eight regulatory feedback motifs that stabilize the EMT process and show that these motifs involve cross-talk among multiple major pathways. Our model will be useful in identifying potential therapeutic targets for the suppression of EMT, invasion, and metastasis in HCC.

Inference of Network Dynamics and Metabolic Interactions in the Gut Microbiome

We present a novel methodology to construct a Boolean dynamic model from time series metagenomic information and integrate this modeling with genome-scale metabolic network reconstructions to identify metabolic underpinnings for microbial interactions. We apply this in the context of a critical health issue: clindamycin antibiotic treatment and opportunistic Clostridium difficile infection. Our model recapitulates known dynamics of clindamycin antibiotic treatment and C. difficile infection and predicts therapeutic probiotic interventions to suppress C. difficile infection. Genome-scale metabolic network reconstructions reveal metabolic differences between community members and are used to explore the role of metabolism in the observed microbial interactions. In vitro experimental data validate a key result of our computational model, that B. intestinihominis can in fact slow C. difficile growth.

The pathogenesis and treatment of large granular lymphocyte leukemia.

Large granular lymphocyte (LGL) leukemia is a spectrum of rare lymphoproliferative diseases of T lymphocytes and natural killer cells. These diseases frequently present with splenomegaly, neutropenia, and autoimmune diseases like rheumatoid arthritis. LGL leukemia is more commonly of a chronic, indolent nature; however, rarely, they have an aggressive course. LGL leukemia is thought to arise from chronic antigen stimulation, which drives long-term cell survival through the activation of survival signaling pathways and suppression of pro-apoptotic signals. These include Jak-Stat, Mapk, Pi3k-Akt, sphingolipid, and IL-15/Pdgf signaling. Treatment traditionally includes immunosuppression with low dose methotrexate, cyclophosphamide, and other immunosuppressive agents; however, prospective and retrospective studies reveal very limited success. New studies surrounding Jak-Stat signaling suggest this may reveal new avenues for LGL leukemia therapeutics.

Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes

Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site, invade surrounding tissue and establish distant metastases. A hallmark of EMT is the loss of E-cadherin expression, and one major signal for the induction of EMT is transforming growth factor beta (TGFβ), which is dysregulated in up to 40% of hepatocellular carcinoma (HCC). We aim to identify network perturbations that suppress TGFβ-driven EMT, with the goal of suppressing invasive properties of cancer cells. We use a systems-level Boolean dynamic model of EMT to systematically screen individual and combination perturbations (inhibition or constitutive activation of up to four nodes). We use a recently developed network control approach to understand the mechanism through which the combinatorial interventions suppress EMT. We test the results of our in silico analysis using siRNA. Our model predicts that targeting key elements of feedback loops in combination with the SMAD complex is more effective than suppressing the SMAD complex alone. We demonstrate experimentally that expression of a majority of these elements is enriched in mesenchymal relative to epithelial phenotype HCC cell lines. An siRNA screen of the predicted combinations confirms that many targeting strategies suppress TGFβ-driven EMT measured by E-cadherin expression and cell migration. Our analysis reveals that some perturbations give rise to hybrid states intermediate to the epithelial and mesenchymal states. Our results indicate that EMT is driven by an interconnected signaling network and many apparently successful single interventions may lead to steady states that are in-between epithelial and mesenchymal states. As these putative hybrid or partial EMT states may retain invasive properties, our results suggest that combinatorial therapies are necessary to fully suppress invasive properties of tumor cells.

miR-200b restoration and DNA methyltransferase inhibitor block lung metastasis of mesenchymal-phenotype hepatocellular carcinoma

Epithelial-to-mesenchymal transition (EMT) is associated with poor prognosis and metastasis in hepatocellular carcinoma. We have previously demonstrated an in vivo model of liver cancer in which mesenchymal cells post-EMT demonstrate a high rate of invasive growth and metastasis. Here, we investigate the role of microRNA 200 (miR-200) family members and epigenetic modifications on the maintenance of mesenchymal/metastatic phenotype after EMT. Mesenchymal cells post-EMT demonstrates high levels of E-box repressors Zeb1 and Zeb2 and downregulation of four miR-200 family members (miR-200a, miR-200b, miR-200c and miR-429). In addition, DNA sequencing after bisulfite modification demonstrates that several CpG sites within the E-cadherin promoter are methylated in mesenchymal cells. In mesenchymal cells, forced expression of miR-200b results in a significant increase in E-cadherin and a reduction in cell migration/invasion. Despite these mesenchymal-to-epithelial transition (MET) changes in vitro, there is no significant change in metastatic potential after miR-200b upregulation in vivo. After the mesenchymal cells were treated with combination of DNA methyltransferase (DNMT) inhibitor and upregulation of miR-200b, invasive phenotype was significantly reduced and metastatic potential was eliminated. Direct targeting of E-cadherin with short hairpin RNA does not restore metastatic potential after DNMT inhibition and miR-200b re-expression. In addition, restoration of E-cadherin alone was unable to block metastatic potential in primary mesenchymal cells. In conclusion, targeting mesenchymal liver cancer cells with miR-200b and DNMT inhibitor reduces metastatic potential irrespective of E-cadherin expression. Thus, the broader differentiation and MET effects of DNMT inhibition and miR-200b must be considered in terms of rescuing metastatic potential.

Induction of tumor initiation is dependent on CD44s in c-Met + hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) patients with active hepatocyte growth factor (HGF)/c-Met signaling have a significantly worse prognosis. c-Met, a high affinity receptor for HGF, plays a critical role in cancer growth, invasion and metastasis. c-Met and CD44 have been utilized as cell surface markers to identify mesenchymal tumor-initiating stem-like cells (TISC) in several cancers including HCC. In this work, we examine the complex relationship between c-Met and CD44s (standard form), and investigate the specific role of CD44s as a tumor initiator and stemness marker in HCC.MethodsGene and protein expression assays were utilized to investigate the relationship between CD44s and c-Met in HCC cell lines. Tumor-sphere assays and in vivo tumor assays were performed to investigate the role of CD44+ cells as TISCs. Student’s t-test or one-way ANOVA with Tukeys post-hoc test was performed to test for differences amongst groups with a p < .05 as significant.ResultsIn an immunohistochemical and immunoblot analysis of human HCC samples, we observed that more than 39% of human HCC samples express c-Met and CD44. To study the relationship between c-Met and CD44, we used MHCC97-H cells, which are CD44+/c-Met+. The knockdown of c-Met in MHCC97-H cells decreased CD44s, reduced TISC characteristics and decreased tumorsphere formation. Furthermore, we demonstrate that the inhibition of PI3K/AKT signaling decreased CD44s expression and subsequently decreased tumorsphere formation. The down-regulation of CD44s leads to a significant loss of a TISC and mesenchymal phenotype. Finally, the down-regulation of CD44s in MHCC97-H cells decreased tumor initiation in vivo compared with the scrambled control.ConclusionsIn summary, our data suggest that CD44s is modulated by the c-Met-PI3K-AKT signaling cascade to support a mesenchymal and TISC phenotype in HCC cells. Moreover, c-Met could be a potential therapeutic drug for targeting HCC cells with TISC and mesenchymal phenotypes.

The EGFR/ErbB3 Pathway Acts as a Compensatory Survival Mechanism upon c-Met Inhibition in Human c-Met+ Hepatocellular Carcinoma.

Background c-Met, a high-affinity receptor for Hepatocyte Growth Factor (HGF), plays a critical role in tumor growth, invasion, and metastasis. Hepatocellular carcinoma (HCC) patients with activated HGF/c-Met signaling have a significantly worse prognosis. Targeted therapies using c-Met tyrosine kinase inhibitors are currently in clinical trials for HCC, although receptor tyrosine kinase inhibition in other cancers has demonstrated early success. Unfortunately, therapeutic effect is frequently not durable due to acquired resistance. Methods We utilized the human MHCC97-H c-Met positive (c-Met+) HCC cell line to explore the compensatory survival mechanisms that are acquired after c-Met inhibition. MHCC97-H cells with stable c-Met knockdown (MHCC97-H c-Met KD cells) were generated using a c-Met shRNA vector with puromycin selection and stably transfected scrambled shRNA as a control. Gene expression profiling was conducted, and protein expression was analyzed to characterize MHCC97-H cells after blockade of the c-Met oncogene. A high-throughput siRNA screen was performed to find putative compensatory survival proteins, which could drive HCC growth in the absence of c-Met. Findings from this screen were validated through subsequent analyses. Results We have previously demonstrated that treatment of MHCC97-H cells with a c-Met inhibitor, PHA665752, results in stasis of tumor growth in vivo. MHCC97-H c-Met KD cells demonstrate slower growth kinetics, similar to c-Met inhibitor treated tumors. Using gene expression profiling and siRNA screening against 873 kinases and phosphatases, we identified ErbB3 and TGF-α as compensatory survival factors that are upregulated after c-Met inhibition. Suppressing these factors in c-Met KD MHCC97-H cells suppresses tumor growth in vitro. In addition, we found that the PI3K/Akt signaling pathway serves as a negative feedback signal responsible for the ErbB3 upregulation after c-Met inhibition. Furthermore, in vitro studies demonstrate that combination therapy with PHA665752 and Gefitinib (an EGFR inhibitor) significantly reduced cell viability and increased apoptosis compared with either PHA665752 or Gefitinib treatment alone. Conclusion c-Met inhibition monotherapy is not sufficient to eliminate c-Met+ HCC tumor growth. Inhibition of both c-Met and EGFR oncogenic pathways provides superior suppression of HCC tumor growth. Thus, combination of c-Met and EGFR inhibition may represent a superior therapeutic regimen for c-Met+ HCC.

Discrete Dynamic Modeling: A Network Approach for Systems Pharmacology

Systems pharmacology is an interdisciplinary field that aims to apply the theoretical and experimental tools of systems biology to drug development. The goal is to go beyond the interaction between a drug and the target to which it binds to explore drug effects on the cellular networks affected by disease. Over the years, vast amounts of information about the regulatory relationships among genes, proteins, and small molecules have been acquired. Similarly, there is much known about the deregulation of these systems during disease. However, many knowledge gaps still exist. There is an abundance of qualitative or relative information related to the activation of signaling pathways, but a paucity of kinetic and temporal information. Discrete dynamic modeling provides a means to create predictive models of signal transduction pathways by integrating fragmentary and qualitative interaction information. Using discrete dynamic modeling, a structural (static) network of biological regulatory relationships can be translated into a mathematical model without the use of kinetic parameters. This model can describe the dynamics of a biological system over time, both in normal and in perturbation scenarios. In this chapter, we discuss the fundamentals of discrete dynamic modeling as it pertains to systems pharmacology. As an example, we apply this methodology to a previously constructed pharmacodynamic model of epidermal derived growth factor receptor (EGFR) signaling. We (1) translate this model into two types of discrete models, a Boolean model and a three-state model, (2) show how the effects of an EGFR inhibitor (such as gefitinib) can suppress tumor growth, and (3) model how genomic variants can augment the effect of EGFR inhibition in tumor growth. We argue that discrete dynamic models can be used to facilitate many of the goals of systems pharmacology. These include understanding how individual differences contribute to variability in drug response and determining which drugs would be best depending on individual genetic differences.

Abstract PR8: Network modeling of epithelial-to-mesenchymal transition in liver cancer metastasis

Hepatocellular carcinoma (HCC) is the third most common cause of cancer death in the world and its incidence is rising in the United States. Over 90% of cancer deaths occur due to tumor metastasis. Epithelial-to-mesenchymal transition (EMT) is a developmental process hijacked by cancer cells to leave the primary tumor site and establish distant metastases. Cells undergoing EMT lose cell-cell adhesion properties and change cell morphology in order to migrate into the blood stream. Loss of expression of E-cadherin, a cell adhesion protein, is considered the hallmark of EMT, and molecular analyses have revealed complex signaling pathways regulating E-cadherin expression. The hepatocyte growth factor (HGF)/C-met receptor axis is a major EMT inducer and is dysregulated in 40% of HCC. TGF-beta signaling, another important EMT inducer, is dysregulated in 20% of HCC. To systematically understand signaling components that regulate HCC metastasis, we constructed an EMT network (74 nodes, 143 edges) by integrating the signaling pathways involved in developmental EMT and known dysregulations in metastatic HCC. This network was subsequently translated into a predictive, discrete, dynamic model. Using HGF and TGF-beta1 as our EMT inducers and E-cadherin expression as our EMT marker, we reveal that the EMT process, although robust, appears to be targetable through inhibition of a small subset of critical (EMT “driver”) nodes. Our model suggests the following: 1) A critical eight-node transcriptional network that is downstream of major growth signals is necessary for EMT induction. 2) Cross-talk with TGF-beta and Notch signaling pathways exists and coordinated inhibition of both pathways will inhibit HGF-induced EMT. 3) Of the 135 nodes that did not block EMT transmission (termed “passenger” nodes), when combined with other EMT “passengers,” 11 combination knockouts were able to inhibit transmission of the EMT signal. 5) Analysis of the states in the basin of attraction of the “EMT negative” steady states has revealed a critical subnetwork (a strongly connected component of the larger EMT network) for EMT transmission. Initial testing has revealed that Snail1 inhibition blocks TGF-Beta1 induced EMT. Further predictions for nodes that inhibit EMT are currently being tested with a novel in vitro EMT screen. These results reveal network modeling as an important tool for identifying critical mediators in biological processes. Furthermore, we propose network modeling as a tool for rational drug targeting of disease pathways, specifically in liver cancer metastasis. This proffered talk is also presented as Poster A12. Citation Format: Steven N. Steinway, Hien Dang, Wei Ding, Carl B. Rountree, Reka Albert. Network modeling of epithelial-to-mesenchymal transition in liver cancer metastasis [abstract]. In: Proceedings of the AACR Special Conference on Chemical Systems Biology: Assembling and Interrogating Computational Models of the Cancer Cell by Chemical Perturbations; 2012 Jun 27-30; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2012;72(13 Suppl):Abstract nr PR8.