Quantitative modeling perspectives on the ErbB system of cell regulatory processes

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

Identifying Determinants of EGFR-Targeted Therapeutic Biochemical Efficacy Using Computational Modeling

We modeled cellular epidermal growth factor receptor (EGFR) tyrosine phosphorylation dynamics in the presence of receptor-targeting kinase inhibitors (e.g., gefitinib) or antibodies (e.g., cetuximab) to identify systematically the factors that contribute most to the ability of the therapeutics to antagonize EGFR phosphorylation, an effect we define here as biochemical efficacy. Our model identifies distinct processes as controlling gefitinib or cetuximab biochemical efficacy, suggests biochemical efficacy is favored in the presence of certain EGFR ligands, and suggests new drug design principles. For example, the model predicts that gefitinib biochemical efficacy is preferentially sensitive to perturbations in the activity of tyrosine phosphatases regulating EGFR, but that cetuximab biochemical efficacy is preferentially sensitive to perturbations in ligand binding. Our results highlight numerous other considerations that determine biochemical efficacy beyond those reflected by equilibrium affinities. By integrating these considerations, our model also predicts minimum therapeutic combination concentrations to maximally reduce receptor phosphorylation.

Multivariate signaling regulation by SHP2 differentially controls proliferation and therapeutic response in glioma cells

Information from multiple signaling axes is integrated in the determination of cellular phenotypes. Here, we demonstrate this aspect of cellular decision making in glioblastoma multiforme (GBM) cells by investigating the multivariate signaling regulatory functions of the protein tyrosine phosphatase SHP2 (also known as PTPN11). Specifically, we demonstrate that the ability of SHP2 to simultaneously drive ERK1/2 and antagonize STAT3 pathway activities produces qualitatively different effects on the phenotypes of proliferation and resistance to EGFR and c-MET co-inhibition. Whereas the ERK1/2 and STAT3 pathways independently promote proliferation and resistance to EGFR and c-MET co-inhibition, SHP2-driven ERK1/2 activity is dominant in driving cellular proliferation and SHP2-mediated antagonism of STAT3 phosphorylation prevails in the promotion of GBM cell death in response to EGFR and c-MET co-inhibition. Interestingly, the extent of these SHP2 signaling regulatory functions is diminished in glioblastoma cells that express sufficiently high levels of the EGFR variant III (EGFRvIII) mutant, which is commonly expressed in GBM. In cells and tumors that express EGFRvIII, SHP2 also antagonizes the phosphorylation of EGFRvIII and c-MET and drives expression of HIF-1α and HIF-2α, adding complexity to the evolving understanding of the regulatory functions of SHP2 in GBM.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

erbB3 is an active tyrosine kinase capable of homo- and heterointeractions

Often considered to be a "dead" kinase, erbB3 is implicated in escape from erbB-targeted cancer therapies. Here, heregulin stimulation is shown to markedly upregulate kinase activity in erbB3 immunoprecipitates. Intact, activated erbB3 phosphorylates tyrosine sites in an exogenous peptide substrate, and this activity is abolished by mutagenesis of lysine 723 in the catalytic domain. Enhanced erbB3 kinase activity is linked to heterointeractions with catalytically active erbB2, since it is largely blocked in cells pretreated with lapatinib or pertuzumab. erbB2 activation of erbB3 is not dependent on equal surface levels of these receptors, since it occurs even in erbB3-transfected CHO cells with disproportionally small amounts of erbB2. We tested a model in which transient erbB3/erbB2 heterointeractions set the stage for erbB3 homodimers to be signaling competent. erbB3 homo- and heterodimerization events were captured in real time on live cells using single-particle tracking of quantum dot probes bound to ligand or hemagglutinin tags on recombinant receptors.

Model-based analysis of HER activation in cells co-expressing EGFR, HER2 and HER3

The HER/ErbB family of receptor tyrosine kinases drives critical responses in normal physiology and cancer, and the expression levels of the various HER receptors are critical determinants of clinical outcomes. HER activation is driven by the formation of various dimer complexes between members of this receptor family. The HER dimer types can have differential effects on downstream signaling and phenotypic outcomes. We constructed an integrated mathematical model of HER activation, and trafficking to quantitatively link receptor expression levels to dimerization and activation. We parameterized the model with a comprehensive set of HER phosphorylation and abundance data collected in a panel of human mammary epithelial cells expressing varying levels of EGFR/HER1, HER2 and HER3. Although parameter estimation yielded multiple solutions, predictions for dimer phosphorylation were in agreement with each other. We validated the model using experiments where pertuzumab was used to block HER2 dimerization. We used the model to predict HER dimerization and activation patterns in a panel of human mammary epithelial cells lines with known HER expression levels in response to stimulations with ligands EGF and HRG. Simulations over the range of expression levels seen in various cell lines indicate that: i) EGFR phosphorylation is driven by HER1-HER1 and HER1-HER2 dimers, and not HER1-HER3 dimers, ii) HER1-HER2 and HER2-HER3 dimers both contribute significantly to HER2 activation with the EGFR expression level determining the relative importance of these species, and iii) the HER2-HER3 dimer is largely responsible for HER3 activation. The model can be used to predict phosphorylated dimer levels for any given HER expression profile. This information in turn can be used to quantify the potencies of the various HER dimers, and can potentially inform personalized therapeutic approaches.

A family of cell surface molecules called the HER receptor family plays important roles in normal physiology and cancer. This family has four members, HER1-4. These receptors convert signals received from the extracellular environment into cell decisions such as growth and survival – a process termed signal transduction. In particular, HER2 and HER3 are over-expressed in a number of tumors, and their expression levels are associated with abnormal growth and poor clinical prognosis. A key step in HER-mediated signal transduction is the formation of dimer complexes between members of this family. Different dimer types have different potencies for activating normal and aberrant responses. Prediction of the dimerization pattern for a given HER expression level may pave the way for personalized therapeutic approaches targeting specific dimers. Towards this end, we constructed a mathematical model for HER dimerization and activation. We determined unknown model parameters by analyzing HER activation data collected in a panel of human mammary epithelial cells that express different levels of the HER molecules. The model enables us to quantitatively link HER expression levels to receptor dimerization and activation. Further, the model can be used to support additional quantitative investigations into the basic biology of HER-mediated signal transduction.

Receptors, endocytosis, and trafficking: the biological basis of targeted delivery of antisense and siRNA oligonucleotides

The problem of targeted delivery of antisense and siRNA oligonucleotides can be resolved into two distinct aspects. The first concerns devising ligand-oligonucleotide or ligand-carrier moieties that bind with high selectivity to receptors on the cell type of interest and that are efficiently internalized by endocytosis. The second concerns releasing oligonucleotides from pharmacologically inert endomembrane compartments so that they can access RNA in the cytosol or nucleus. In this review, we will address both of these aspects. Thus, we present information on three important receptor families, the integrins, the receptor tyrosine kinases, and the G protein-coupled receptors in terms of their suitability for targeted delivery of oligonucleotides. This includes discussion of receptor abundance, internalization and trafficking pathways, and the availability of suitable high affinity ligands. We also consider the process of oligonucleotide uptake and intracellular trafficking and discuss approaches to modulating these processes in a pharmacologically productive manner. Hopefully, the basic information presented in this review will be of value to investigators involved in designing delivery approaches for oligonucleotides.

Multiscale models of breast cancer progression

Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models to determine the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: (1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, (2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and (3) receptor phosphorylation kinetics at the cell surface and early endosomes are comparable. We validated the last finding by measuring the EGFR dephosphorylation rates at various times following ligand addition both in whole cells and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.

Multi-pathway network analysis of mammalian epithelial cell responses in inflammatory environments

Inflammation is a key physiological response to infection and injury and, although usually beneficial, it can also be damaging to the host. The liver is a prototypical example in this regard because inflammation helps to resolve liver injury, but it also underlies the aetiology of pathologies such as fibrosis and hepatocellular carcinoma. Liver cells sense their environment, including the inflammatory environment, through the activities of receptor-mediated signal transduction pathways. These pathways are organized in a complex interconnected network, and it is becoming increasingly recognized that cellular adaptations result from the quantitative integration of multi-pathway network activities, rather than isolated pathways causing particular phenotypes. Therefore comprehending liver cell signalling in inflammation requires a scientific approach that is appropriate for studying complex networks. In the present paper, we review our application of systems analyses of liver cell signalling in response to inflammatory environments. Our studies feature broad measurements of cell signalling and phenotypes in response to numerous experimental perturbations reflective of inflammatory environments, the data from which are analysed using Boolean and fuzzy logic models and regression-based methods in order to quantitatively relate the phenotypic responses to cell signalling network states. Our principal biological insight from these studies is that hepatocellular carcinoma cells feature uncoupled inflammatory and growth factor signalling, which may underlie their immune evasion and hyperproliferative properties.

Conformational transition and energy landscape of ErbB4 activated by neuregulin1β: one microsecond molecular dynamics simulations

ErbB4, a receptor tyrosine kinase of the ErbB family, plays crucial roles in cell growth and differentiation, especially in the development of the heart and nervous system. Ligand binding to its extracellular region could modulate the activation process. To understand the mechanism of ErbB4 activation induced by ligand binding, we performed one microsecond molecular dynamics (MD) simulations on the ErbB4 extracellular region (ECR) with and without its endogenous ligand neuregulin1β (NRG1β). The conformational transition of the ECR-ErbB4/NRG1β complex from a tethered inactive conformation to an extended active-like form has been observed, while such large and function-related conformational change has not been seen in the simulation on the ECR-ErbB4, suggesting that ligand binding is indeed the active inducing force for the conformational transition and further dimerization. On the basis of MD simulations and principal component analysis, we constructed a rough energy landscape for the conformational transition of ECR-ErbB4/NRG1β complex, suggesting that the conformational change from the inactive state to active-like state involves a stable conformation. The energy barrier for the tether opening was estimated as ~2.7 kcal/mol, which is very close to the experimental value (1-2 kcal/mol) reported for ErbB1. On the basis of the simulation results, an atomic mechanism for the ligand-induced activation of ErbB4 was postulated. The present MD simulations provide a new insight into the conformational changes underlying the activation of ErbB4.

Discovery of improved EGF agonists using a novel in vitro screening platform

Directed evolution is a powerful strategy for protein engineering; however, evolution of pharmaceutical proteins has been limited by the reliance of current screens on binding interactions. Here, we present a method that identifies protein mutants with improved overall cellular efficacy, an objective not feasible with previous approaches. Mutated protein libraries were produced in soluble, active form by means of cell-free protein synthesis. The efficacy of each individual protein was determined at a uniform dosage with a high-throughput protein product assay followed by a cell-based functional assay without requiring protein purification. We validated our platform by first screening mock libraries of epidermal growth factor (EGF) for stimulation of cell proliferation. We then demonstrated its effectiveness by identifying EGF mutants with significantly enhanced mitogenic activity at low concentrations compared to that of wild-type EGF. This is the first report of EGF mutants with improved biological efficacy despite much previous effort. Our platform can be extended to engineer a broad range of proteins, offering a general method to evolve proteins for improved biological efficacy.

Engineered epidermal growth factor mutants with faster binding on-rates correlate with enhanced receptor activation

Receptor tyrosine kinases (RTKs) regulate critical cell signaling pathways, yet the properties of their cognate ligands that influence receptor activation are not fully understood. There is great interest in parsing these complex ligand-receptor relationships using engineered proteins with altered binding properties. Here we focus on the interaction between two engineered epidermal growth factor (EGF) mutants and the EGF receptor (EGFR), a model member of the RTK superfamily. We found that EGF mutants with faster kinetic on-rates stimulate increased EGFR activation compared to wild-type EGF. These findings support previous predictions that faster association rates correlate with enhanced receptor activity.

Quantification of three-dimensional cell-mediated collagen remodeling using graph theory

Background

Cell cooperation is a critical event during tissue development. We present the first precise metrics to quantify the interaction between mesenchymal stem cells (MSCs) and extra cellular matrix (ECM). In particular, we describe cooperative collagen alignment process with respect to the spatio-temporal organization and function of mesenchymal stem cells in three dimensions.

Methodology/Principal Findings

We defined two precise metrics: Collagen Alignment Index and Cell Dissatisfaction Level, for quantitatively tracking type I collagen and fibrillogenesis remodeling by mesenchymal stem cells over time. Computation of these metrics was based on graph theory and vector calculus. The cells and their three dimensional type I collagen microenvironment were modeled by three dimensional cell-graphs and collagen fiber organization was calculated from gradient vectors. With the enhancement of mesenchymal stem cell differentiation, acceleration through different phases was quantitatively demonstrated. The phases were clustered in a statistically significant manner based on collagen organization, with late phases of remodeling by untreated cells clustering strongly with early phases of remodeling by differentiating cells. The experiments were repeated three times to conclude that the metrics could successfully identify critical phases of collagen remodeling that were dependent upon cooperativity within the cell population.

Conclusions/Significance

Definition of early metrics that are able to predict long-term functionality by linking engineered tissue structure to function is an important step toward optimizing biomaterials for the purposes of regenerative medicine.

HER-2 signaling, acquisition of growth factor independence, and regulation of biological networks associated with cell transformation

Activated oncogenes are the dominant drivers of malignant progression in human cancer, yet little is known about how the transformation from proto-oncogene to activated oncogene drives the expression of transformed phenotypes. An isogenic model of HER-2-mediated transformation of human mammary epithelial cells was used along with HER-2-amplified human breast cancers to investigate how HER-2 activation alters its properties as a signaling molecule and changes the networks of HER-2-regulated genes. Our results show that full oncogenic activation of HER-2 is the result of a transition in which activated HER-2 acquires dominant signaling properties that qualitatively alter the network of genes regulated by the activated oncogene compared with the proto-oncogene. Consequently, gene expression programs related to invasion, cell stress, and stemness become regulated by HER-2 in a manner not observed in nontransformed cells, even when HER-2 is overexpressed. Our results offer novel insights into biological processes that come under the control of HER-2 after it acquires full oncogenic potential.

Cell signaling by receptor tyrosine kinases

Recent structural studies of receptor tyrosine kinases (RTKs) have revealed unexpected diversity in the mechanisms of their activation by growth factor ligands. Strategies for inducing dimerization by ligand binding are surprisingly diverse, as are mechanisms that couple this event to activation of the intracellular tyrosine kinase domains. As our understanding of these details becomes increasingly sophisticated, it provides an important context for therapeutically countering the effects of pathogenic RTK mutations in cancer and other diseases. Much remains to be learned, however, about the complex signaling networks downstream from RTKs and how alterations in these networks are translated into cellular responses.

EGF decreases the abundance of microRNAs that restrain oncogenic transcription factors

Epidermal growth factor (EGF) stimulates cells by launching gene expression programs that are frequently deregulated in cancer. MicroRNAs, which attenuate gene expression by binding complementary regions in messenger RNAs, are broadly implicated in cancer. Using genome-wide approaches, we showed that EGF stimulation initiates a coordinated transcriptional program of microRNAs and transcription factors. The earliest event involved a decrease in the abundance of a subset of 23 microRNAs. This step permitted rapid induction of oncogenic transcription factors, such as c-FOS, encoded by immediate early genes. In line with roles as suppressors of EGF receptor (EGFR) signaling, we report that the abundance of this early subset of microRNAs is decreased in breast and in brain tumors driven by the EGFR or the closely related HER2. These findings identify specific microRNAs as attenuators of growth factor signaling and oncogenesis.

Cancer systems biology: a network modeling perspective

Cancer is now appreciated as not only a highly heterogenous pathology with respect to cell type and tissue origin but also as a disease involving dysregulation of multiple pathways governing fundamental cell processes such as death, proliferation, differentiation and migration. Thus, the activities of molecular networks that execute metabolic or cytoskeletal processes, or regulate these by signal transduction, are altered in a complex manner by diverse genetic mutations in concert with the environmental context. A major challenge therefore is how to develop actionable understanding of this multivariate dysregulation, with respect both to how it arises from diverse genetic mutations and to how it may be ameliorated by prospective treatments. While high-throughput experimental platform technologies ranging from genomic sequencing to transcriptomic, proteomic and metabolomic profiling are now commonly used for molecular-level characterization of tumor cells and surrounding tissues, the resulting data sets defy straightforward intuitive interpretation with respect to potential therapeutic targets or the effects of perturbation. In this review article, we will discuss how significant advances can be obtained by applying computational modeling approaches to elucidate the pathways most critically involved in tumor formation and progression, impact of particular mutations on pathway operation, consequences of altered cell behavior in tissue environments and effects of molecular therapeutics.

The natural and engineered 3D microenvironment as a regulatory cue during stem cell fate determination

The concept of using stem cells as self-renewing sources of healthy cells in regenerative medicine has existed for decades, but most applications have yet to achieve clinical success. A main reason for the lack of successful stem cell therapies is the difficulty in fully recreating the maintenance and control of the native stem cell niche. Improving the performance of transplanted stem cells therefore requires a better understanding of the cellular mechanisms guiding stem cell behavior in both native and engineered three-dimensional (3D) microenvironments. Most techniques, however, for uncovering mechanisms controlling cell behavior in vitro have been developed using 2D cell cultures and are of limited use in 3D environments such as engineered tissue constructs. Deciphering the mechanisms controlling stem cell fate in native and engineered 3D environments, therefore, requires rigorous quantitative techniques that permit mechanistic, hypothesis-driven studies of cell-microenvironment interactions. Here, we review the current understanding of 2D and 3D stem cell control mechanisms and propose an approach to uncovering the mechanisms that govern stem cell behavior in 3D.