Decreased internalisation of ErbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib

Synergistic Anti-Tumour Effect of Syk Inhibitor and Olaparib in Squamous Cell Carcinoma: Roles of Syk in EGFR Signalling and PARP1 Activation

Syk is a non-receptor tyrosine kinase involved in the signalling of immunoreceptors and growth factor receptors. Previously, we reported that Syk mediates epidermal growth factor receptor (EGFR) signalling and plays a negative role in the terminal differentiation of keratinocytes. To understand whether Syk is a potential therapeutic target of cancer cells, we further elucidated the role of Syk in disease progression of squamous cell carcinoma (SCC), which is highly associated with EGFR overactivation, and determined the combined effects of Syk and PARP1 inhibitors on SCC viability. We found that pharmacological inhibition of Syk could attenuate the EGF-induced phosphorylation of EGFR, JNK, p38 MAPK, STAT1, and STAT3 in A431, CAL27 and SAS cells. In addition, EGF could induce a Syk-dependent IL-8 gene and protein expression in SCC. Confocal microscopic data demonstrated the ability of the Syk inhibitor to change the subcellular distribution patterns of EGFR after EGF treatment in A431 and SAS cells. Moreover, according to Kaplan-Meier survival curve analysis, higher Syk expression is correlated with poorer patient survival rate and prognosis. Notably, both Syk and EGFR inhibitors could induce PARP activation, and synergistic cytotoxic actions were observed in SCC cells upon the combined treatment of the PARP1 inhibitor olaparib with Syk or the EGFR inhibitor. Collectively, we reported Syk as an important signalling molecule downstream of EGFR that plays crucial roles in SCC development. Combining Syk and PARP inhibition may represent an alternative therapeutic strategy for treating SCC.

Computational algorithms for in silico profiling of activating mutations in cancer

Methods to catalog and computationally assess the mutational landscape of proteins in human cancers are desirable. One approach is to adapt evolutionary or data-driven methods developed for predicting whether a single-nucleotide polymorphism (SNP) is deleterious to protein structure and function. In cases where understanding the mechanism of protein activation and regulation is desired, an alternative approach is to employ structure-based computational approaches to predict the effects of point mutations. Through a case study of mutations in kinase domains of three proteins, namely, the anaplastic lymphoma kinase (ALK) in pediatric neuroblastoma patients, serine/threonine-protein kinase B-Raf (BRAF) in melanoma patients, and erythroblastic oncogene B 2 (ErbB2 or HER2) in breast cancer patients, we compare the two approaches above. We find that the structure-based method is most appropriate for developing a binary classification of several different mutations, especially infrequently occurring ones, concerning the activation status of the given target protein. This approach is especially useful if the effects of mutations on the interactions of inhibitors with the target proteins are being sought. However, many patients will present with mutations spread across different target proteins, making structure-based models computationally demanding to implement and execute. In this situation, data-driven methods-including those based on machine learning techniques and evolutionary methods-are most appropriate for recognizing and illuminate mutational patterns. We show, however, that, in the present status of the field, the two methods have very different accuracies and confidence values, and hence, the optimal choice of their deployment is context-dependent.

JAK2 inhibition sensitizes resistant EGFR-mutant lung adenocarcinoma to tyrosine kinase inhibitors

Lung adenocarcinomas with mutant epidermal growth factor receptor (EGFR) respond to EGFR-targeted tyrosine kinase inhibitors (TKIs), but resistance invariably occurs. We found that the Janus kinase (JAK)/signal transduction and activator of transcription 3 (STAT3) signaling pathway was aberrantly increased in TKI-resistant EGFR-mutant non-small cell lung cancer (NSCLC) cells. JAK2 inhibition restored sensitivity to the EGFR inhibitor erlotinib in TKI-resistant cell lines and xenograft models of EGFR-mutant TKI-resistant lung cancer. JAK2 inhibition uncoupled EGFR from its negative regulator, suppressor of cytokine signaling 5 (SOCS5), consequently increasing EGFR abundance and restoring the tumor cells' dependence on EGFR signaling. Furthermore, JAK2 inhibition led to heterodimerization of mutant and wild-type EGFR subunits, the activity of which was then blocked by TKIs. Our results reveal a mechanism whereby JAK2 inhibition overcomes acquired resistance to EGFR inhibitors and support the use of combination therapy with JAK and EGFR inhibitors for the treatment of EGFR-dependent NSCLC.

Loss of MIG6 Accelerates Initiation and Progression of Mutant Epidermal Growth Factor Receptor-Driven Lung Adenocarcinoma

Somatic mutations in the EGFR kinase domain drive lung adenocarcinoma. We have previously identified MIG6, an inhibitor of ERBB signaling and a potential tumor suppressor, as a target for phosphorylation by mutant EGFRs. Here, we demonstrate that MIG6 is a tumor suppressor for the initiation and progression of mutant EGFR-driven lung adenocarcinoma in mouse models. Mutant EGFR-induced lung tumor formation was accelerated in Mig6-deficient mice, even with Mig6 haploinsufficiency. We demonstrate that constitutive phosphorylation of MIG6 at Y394/Y395 in EGFR-mutant human lung adenocarcinoma cell lines is associated with an increased interaction of MIG6 with mutant EGFR, which may stabilize EGFR protein. MIG6 also fails to promote mutant EGFR degradation. We propose a model whereby increased tyrosine phosphorylation of MIG6 decreases its capacity to inhibit mutant EGFR. Nonetheless, the residual inhibition is sufficient for MIG6 to delay mutant EGFR-driven tumor initiation and progression in mouse models.

SIGNIFICANCE

This study demonstrates that MIG6 is a potent tumor suppressor for mutant EGFR-driven lung tumor initiation and progression in mice and provides a possible mechanism by which mutant EGFR can partially circumvent this tumor suppressor in human lung adenocarcinoma.

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.

Computational delineation of tyrosyl-substrate recognition and catalytic landscapes by the epidermal growth factor receptor tyrosine kinase domain

The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase (RTK), which catalyzes protein phosphorylation reactions by transferring the γ-phosphoryl group from an ATP molecule to the hydroxyl group of tyrosine residues in protein substrates. EGFR is an important drug target in the treatment of cancers and a better understanding of the receptor function is critical to discern cancer mechanisms. We employ a suite of molecular simulation methods to explore the mechanism of substrate recognition and to delineate the catalytic landscape of the phosphoryl transfer reaction. Based on our results, we propose that a highly conserved region corresponding to Val852-Pro853-Ile854-Lys855-Trp856 in the EGFR tyrosine kinase domain (TKD) is essential for substrate binding. We also provide a possible explanation for the established experimental observation that protein tyrosine kinases (including EGFR) select substrates with a glutamic acid at the P - 1 position and a large hydrophobic amino acid at the P + 1 position. Furthermore, our mixed quantum mechanics/molecular mechanics (QM/MM) simulations show that the EGFR protein kinase favors the dissociative mechanism, although an alternative channel through the formation of an associative transition state is also possible. Our simulations establish some key molecular rules in the operation for substrate-recognition and for phosphoryl transfer in the EGFR TKD.

Harnessing system models of cell death signalling for cytotoxic chemotherapy: towards personalised medicine approaches?

Most cytotoxic chemotherapeutics are believed to kill cancer cells by inducing apoptosis. Understanding the factors that contribute to impairment of apoptosis in cancer cells is therefore critical for the development of novel therapies that circumvent the widespread chemoresistance. Apoptosis, however, is a complex and tightly controlled process that can be induced by different classes of chemotherapeutics targeting different signalling nodes and pathways. Moreover, apoptosis initiation and apoptosis execution strongly depend on patient-specific, genomic and proteomic signatures. Here, we will review recent translational studies that suggest a critical link between the sensitivity of cancer cells to initiate apoptosis and clinical outcome. Next we will discuss recent advances in the field of system modelling of apoptosis pathways for the prediction of treatment responses. We propose that initiation of mitochondrial apoptosis, defined as the process of mitochondrial outer membrane permeabilisation (MOMP), is a dose-dependent decision process that allows for a prediction of individual therapy responses and therapeutic windows. We provide evidence in contrast that apoptosis execution post-MOMP may be a binary decision that dictates whether apoptosis is executed or not. We will discuss the implications of this concept for the future use of novel adjuvant therapeutics that specifically target apoptosis signalling pathways or which may be used to reduce the impact of cell-to-cell heterogeneity on therapy responses. Finally, we will discuss the technical and regulatory requirements surrounding the use and implications of system-based patient stratification tools for the future of personalised oncology.

Differential parsing of EGFR endocytic flux among parallel internalization pathways in lung cancer cells with EGFR-activating mutations

Due to the existence of parallel pathways for receptor endocytosis and their complexities, a quantitative understanding of receptor endocytosis in normal and pathological settings requires computational analysis. Here, we develop a mechanistic model of epidermal growth factor receptor (EGFR) endocytosis to determine the relative contributions of three parallel pathways: clathrin-dependent internalization mediated by mitogen-inducible gene 6 (MIG6), an endogenous EGFR kinase inhibitor that links EGFR to endocytic proteins; clathrin-dependent internalization mediated by the ubiquitin ligase CBL, which can be sequestered by the regulatory protein Sprouty2; or alternative pathways that may be non-clathrin mediated. We applied the model to interpret our previous measurements of EGFR endocytosis in lung cancer cells. Interestingly, our results suggest that MIG6 is responsible for at least as much wild-type EGFR internalization as CBL, indicating that a significant fraction of internalizing EGFR may be incapable of driving signaling. Model results also suggest that MIG6's endocytic function is reduced for the kinase-activated and internalization-impaired EGFR mutants found in some lung cancers. Analysis of Sprouty2 knockdown data indicates that Sprouty2 regulates EGFR endocytosis primarily by controlling EGFR expression, rather than by sequestering CBL, and supports the notion that CBL-mediated internalization is impaired for EGFR mutants. We further demonstrate that differences in internalization between wild-type and mutant EGFR cannot explain differences in EGF-mediated EGFR degradation without concomitant changes in EGFR recycling, which we previously quantified. This work provides new quantitative insights into EGFR trafficking in lung cancer and provides a framework for studying parallel endocytosis pathways for other receptors.

Regulation of EGFR trafficking and cell signaling by Sprouty2 and MIG6 in lung cancer cells

The duration and specificity of epidermal growth factor receptor (EGFR) activation and signaling are determinants of cellular decision processes and are tightly regulated by receptor dephosphorylation, internalization and degradation. In addition, regulatory proteins that are upregulated or activated post-transcriptionally upon receptor activation may initiate feedback loops that play crucial roles in spatiotemporal regulation of signaling. We examined the roles of Sprouty2 (SPRY2) and mitogen-inducible gene 6 (MIG6), two feedback regulators of EGFR trafficking and signaling, in lung cancer cells with or without EGFR-activating mutations. These mutations are of interest because they confer unusual cellular sensitivity to EGFR inhibition through a mechanism involving an impairment of EGFR endocytosis. We found that the endocytosis of wild-type and mutant EGFR was promoted by SPRY2 knockdown and antagonized by MIG6 knockdown. SPRY2 knockdown also significantly reduced extracellular signal-regulated kinase (ERK) phosphorylation, EGFR expression, and EGFR recycling. In a cell line expressing mutant EGFR, this effect on ERK led to a marked increase in cell death response to EGFR inhibition. The effects of SPRY2 knockdown on EGFR endocytosis and recycling were primarily the result of the concomitant change in EGFR expression, but this was not true for the observed changes in ERK phosphorylation. Thus, our study demonstrates that SPRY2 and MIG6 are important regulators of wild-type and mutant EGFR trafficking and points to an EGFR expression-independent function of SPRY2 in the regulation of ERK activity that may impact cellular sensitivity to EGFR inhibitors, especially in the context of EGFR mutation.

Company Profile: Selventa, Inc

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc., Selventa has undergone significant evolution from a technology-based service provider to an active partner in the development of diagnostic tests, functioning as a molecular dashboard of disease activity using a unique platform. As part of that evolution, approximately 2 years ago the company was rebranded as Selventa to reflect its new identity and mission. The contributions to biomedical research by Selventa are based on in silico, reverse-engineering methods to determine biological causality. That is, given a set of in vitro or in vivo biological observations, which biological mechanisms can explain the measured results? Facilitated by a large and carefully curated knowledge base, these in silico methods generated new insights into the mechanisms driving a disease. As Selventa's methods would enable biomarker discovery and be directly applicable to generating novel diagnostics, the scientists at Selventa have focused on the development of predictive biomarkers of response in autoimmune and oncologic diseases. Selventa is presently building a portfolio of independent, as well as partnered, biomarker projects with the intention to create diagnostic tests that predict response to therapy.

Diminished functional role and altered localization of SHP2 in non-small cell lung cancer cells with EGFR-activating mutations

Non-small cell lung cancer (NSCLC) cells harboring activating mutations of the epidermal growth factor receptor (EGFR) tend to display elevated activity of several survival signaling pathways. Surprisingly, these mutations also correlate with reduced phosphorylation of ERK and SHP2, a protein tyrosine phosphatase required for complete ERK activation downstream of most receptor tyrosine kinases. As ERK activity influences cellular response to EGFR inhibition, altered SHP2 function could have a role in the striking response to gefitinib witnessed with EGFR mutation. Here, we demonstrate that impaired SHP2 phosphorylation correlates with diminished SHP2 function in NSCLC cells expressing mutant, versus wild-type, EGFR. In NSCLC cells expressing wild-type EGFR, SHP2 knockdown decreased ERK phosphorylation, basally and in response to gefitinib, and increased cellular sensitivity to gefitinib. In cells expressing EGFR mutants, these effects of SHP2 knockdown were less substantial, but the expression of constitutively active SHP2 reduced cellular sensitivity to gefitinib. In cells expressing EGFR mutants, which do not undergo efficient ligand-mediated endocytosis, SHP2 was basally associated with GRB2-associated binder 1 (GAB1) and EGFR, and SHP2's presence in membrane fractions was dependent on EGFR activity. Whereas EGF promoted a more uniform intracellular distribution of initially centrally localized SHP2 in cells expressing wild-type EGFR, SHP2 was basally evenly distributed and did not redistribute in response to EGF in cells with EGFR mutation. Thus, EGFR mutation may promote association of a fraction of SHP2 at the plasma membrane with adapters that promote SHP2 activity. Consistent with this, SHP2 immunoprecipitated from cells with EGFR mutation was active, and EGF treatment did not change this activity. Overall, our data suggest that a fraction of SHP2 is sequestered at the plasma membrane in cells with EGFR mutation in a way that impedes SHP2's ability to promote ERK activity and identify SHP2 as a potential target for co-inhibition with EGFR in NSCLC.

Comparing the epidermal growth factor interaction with four different cell lines: intriguing effects imply strong dependency of cellular context

The interaction of the epidermal growth factor (EGF) with its receptor (EGFR) is known to be complex, and the common over-expression of EGF receptor family members in a multitude of tumors makes it important to decipher this interaction and the following signaling pathways. We have investigated the affinity and kinetics of (125)I-EGF binding to EGFR in four human tumor cell lines, each using four culturing conditions, in real time by use of LigandTracer®.Highly repeatable and precise measurements show that the overall apparent affinity of the (125)I-EGF - EGFR interaction is greatly dependent on cell line at normal culturing conditions, ranging from K(D) ≈ 200 pM on SKBR3 cells to K(D)≈8 nM on A431 cells. The (125)I-EGF - EGFR binding curves (irrespective of cell line) have strong signs of multiple simultaneous interactions. Furthermore, for the cell lines A431 and SKOV3, gefitinib treatment increases the (125)I-EGF - EGFR affinity, in particular when the cells are starved. The (125)I-EGF - EGFR interaction on cell line U343 is sensitive to starvation while as on SKBR3 it is insensitive to gefitinib and starvation.The intriguing pattern of the binding characteristics proves that the cellular context is important when deciphering how EGF interacts with EGFR. From a general perspective, care is advisable when generalizing ligand-receptor interaction results across multiple cell-lines.

Cellular level models as tools for cytokine design

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency.

Impaired SHP2-mediated extracellular signal-regulated kinase activation contributes to gefitinib sensitivity of lung cancer cells with epidermal growth factor receptor-activating mutations

Most non-small cell lung cancers (NSCLC) display elevated expression of epidermal growth factor receptor (EGFR), but response to EGFR kinase inhibitors is predominantly limited to NSCLC harboring EGFR-activating mutations. These mutations are associated with increased activity of survival pathways, including phosphatidylinositol 3-kinase/AKT and signal transducer and activator of transcription 3/5. We report that EGFR-activating mutations also surprisingly lead to decreased ability to activate extracellular signal-regulated kinase (ERK) compared with wild-type EGFR. In NSCLC cells and mouse embryonic fibroblasts expressing mutant EGFR, this effect on ERK correlates with decreased EGFR internalization and reduced phosphorylation of SHP2, a tyrosine phosphatase required for the full activation of ERK. We further show that ERK activation levels affect cellular response to gefitinib. NSCLC cells with EGFR mutation display reduced gefitinib sensitivity when ERK activation is augmented by expression of constitutively active mutants of mitogen-activated protein kinase/ERK kinase (MEK). Conversely, in a NSCLC cell line expressing wild-type EGFR, gefitinib treatment along with or following MEK inhibition increases death response compared with treatment with gefitinib alone. Our results show that EGFR-activating mutations may promote some survival pathways but simultaneously impair others. This multivariate alteration of the network governing cellular response to gefitinib, which we term "oncogene imbalance," portends a potentially broader ability to treat gefitinib-resistant NSCLC.

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.

Should we be surprised at the paucity of response to EGFR inhibitors?

Data suggest that neither our current understanding of the function and signalling of epidermal growth factor receptor (EGFR), nor measurements of receptor expression are reliably predictive of therapeutic responses to EGFR inhibitors. The time has now come to consider whether such poor correlation between receptor expression and clinical response is caused by poor assays or by more fundamental issues relating to the in-vivo function of EGFR. Revisiting some of the early findings of the biology of EGFR function and understanding the limitations of immunohistochemistry as a quantitative technique might provide some clues. However, we still have a lot to learn about this receptor, its many ligands, and its binding partners in normal physiology and disease.

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Mathematical models are increasingly used to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling generic properties of biological processes to be specified independently of specific instances. These, in turn, require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created, which can be instantiated and reused repeatedly in different contexts with different components. We have developed a computational infrastructure that accomplishes this. We show here why such capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously.

Molecular systems biology of ErbB1 signaling: bridging the gap through multiscale modeling and high-performance computing

The complexity in intracellular signaling mechanisms relevant for the conquest of many diseases resides at different levels of organization with scales ranging from the subatomic realm relevant to catalytic functions of enzymes to the mesoscopic realm relevant to the cooperative association of molecular assemblies and membrane processes. Consequently, the challenge of representing and quantifying functional or dysfunctional modules within the networks remains due to the current limitations in our understanding of mesoscopic biology, i.e., how the components assemble into functional molecular ensembles. A multiscale approach is necessary to treat a hierarchy of interactions ranging from molecular (nm, ns) to signaling (microm, ms) length and time scales, which necessitates the development and application of specialized modeling tools. Complementary to multiscale experimentation (encompassing structural biology, mechanistic enzymology, cell biology, and single molecule studies) multiscale modeling offers a powerful and quantitative alternative for the study of functional intracellular signaling modules. Here, we describe the application of a multiscale approach to signaling mediated by the ErbB1 receptor which constitutes a network hub for the cell's proliferative, migratory, and survival programs. Through our multiscale model, we mechanistically describe how point-mutations in the ErbB1 receptor can profoundly alter signaling characteristics leading to the onset of oncogenic transformations. Specifically, we describe how the point mutations induce cascading fragility mechanisms at the molecular scale as well as at the scale of the signaling network to preferentially activate the survival factor Akt. We provide a quantitative explanation for how the hallmark of preferential Akt activation in cell-lines harboring the constitutively active mutant ErbB1 receptors causes these cell-lines to be addicted to ErbB1-mediated generation of survival signals. Consequently, inhibition of ErbB1 activity leads to a remarkable therapeutic response in the addicted cell lines.

Quantitative modeling perspectives on the ErbB system of cell regulatory processes

The complexities of the processes involved in ErbB-mediated regulation of cellular phenotype are broadly appreciated, so much so that it might be reasonably argued that this highly studied system provided significant impetus for the systems perspective on cell signaling processes in general. Recent years have seen major advances in the level of characterization of the ErbB system as well as our ability to make measurements of the system. This new data provides significant new insight, while at the same time creating new challenges for making quantitative statements and predictions with certainty. Here, we discuss recent advances in each of these directions and the interplay between them, with a particular focus on quantitative modeling approaches to interpret data and provide predictive power. Our discussion follows the sequential order of ErbB pathway activation, beginning with considerations of receptor/ligand interactions and dynamics, proceeding to the generation of intracellular signals, and ending with determination of cellular phenotype. As discussed herein, these processes become increasingly difficult to describe or interpret in terms of traditional models, and we review emerging methodologies to address this complexity.

Role of network branching in eliciting differential short-term signaling responses in the hypersensitive epidermal growth factor receptor mutants implicated in lung cancer

We study the effects of EGFR inhibition in wild-type and mutant cell lines upon tyrosine kinase inhibitor TKI treatment through a systems level deterministic and spatially homogeneous model to help characterize the hypersensitive response of the cancer cell lines harboring constitutively active mutant kinases to inhibitor treatment. By introducing a molecularly resolved branched network systems model (the molecular resolution is introduced for EGFR reactions and interactions in order to distinguish differences in activation between wild-type and mutants), we are able to quantify differences in (1) short-term signaling in downstream ERK and Akt activation, (2) the changes in the cellular inhibition EC50 associated with receptor phosphorylation (i.e., 50% inhibition of receptor phosphorylation in the cellular context), and (3) EC50 for the inhibition of activated downstream markers ERK-(p) and Akt-(p), where (p) denotes phosphorylated, upon treatment with the inhibitors in cell lines carrying both wild-type and mutant forms of the receptor. Using the branched signaling model, we illustrate a possible mechanism for preferential Akt activation in the cell lines harboring the oncogenic mutants of EGFR implicated in non-small-cell lung cancer and the enhanced efficacy of the inhibitor erlotinib especially in ablating the cellular Akt-(p) response. Using a simple phenomenological model to describe the effect of Akt activation on cellular decisions, we discuss how this preferential Akt activation is conducive to cellular oncogene addiction and how its disruption can lead to dramatic apoptotic response and hence remarkable inhibitor efficacies. We also identify key network nodes of our branched signaling model through sensitivity analysis as those rendering the network hypersensitive to enhanced ERK-(p) and Akt-(p); intriguingly, the identified nodes have a strong correlation with species implicated in oncogenic transformations in human cancers as well as in drug resistance mechanisms identified for the inhibitors in non-small-cell lung cancer therapy.

Simulating non-small cell lung cancer with a multiscale agent-based model

BACKGROUND

The epidermal growth factor receptor (EGFR) is frequently overexpressed in many cancers, including non-small cell lung cancer (NSCLC). In silico modeling is considered to be an increasingly promising tool to add useful insights into the dynamics of the EGFR signal transduction pathway. However, most of the previous modeling work focused on the molecular or the cellular level only, neglecting the crucial feedback between these scales as well as the interaction with the heterogeneous biochemical microenvironment.

RESULTS

We developed a multiscale model for investigating expansion dynamics of NSCLC within a two-dimensional in silico microenvironment. At the molecular level, a specific EGFR-ERK intracellular signal transduction pathway was implemented. Dynamical alterations of these molecules were used to trigger phenotypic changes at the cellular level. Examining the relationship between extrinsic ligand concentrations, intrinsic molecular profiles and microscopic patterns, the results confirmed that increasing the amount of available growth factor leads to a spatially more aggressive cancer system. Moreover, for the cell closest to nutrient abundance, a phase-transition emerges where a minimal increase in extrinsic ligand abolishes the proliferative phenotype altogether.

CONCLUSION

Our in silico results indicate that in NSCLC, in the presence of a strong extrinsic chemotactic stimulus (and depending on the cell's location) downstream EGFR-ERK signaling may be processed more efficiently, thereby yielding a migration-dominant cell phenotype and overall, an accelerated spatio-temporal expansion rate.

The impact of the regulatory design on the response of epidermal growth factor receptor-mediated signal transduction towards oncogenic mutations

Epidermal growth factor receptor (EGFR)-mediated signal transduction is often hyperactivated in tumour cells and therefore considered a promising target for cancer therapy. A number of computational models have been developed which describe the pathway in great detail. These models are similar in their description of the activation events. The deactivation of the EGFR signalling seems to be cell type-specific and is less understood. Deactivation via receptor internalization, feedback inhibition of son of sevenless (SOS) by double phosphorylated, extracellular signal-regulated kinase (ERKPP) or transiently activated Ras-GTPase activating protein (Ras-GAP) proteins is discussed to play a role. In this study we address the question of to what extent the effect of oncogenic perturbations on EGFR signalling depend on the specific regulation structure. This is investigated using a detailed pathway model under two regulatory modes: the negative feedback via ERKPP to SOS and feed-forward deactivation via transiently activated Ras-GAP proteins. We show that the effect of receptor overexpression differs qualitatively under both regulations. In the system with transiently activated Ras-GAP it may result in an attenuation of the ERK activation. Such a nonintuitive effect was also observed experimentally. In general we find the model with transiently activated Ras-GAP to have a higher robustness towards receptor overexpression and Ras mutations. In particular, we demonstrate that this model can compensate for these oncogenic perturbations if the regulation is strong. The negative feedback can not protect the system against Ras mutations. A general sensitivity analysis, however, shows a higher robustness of the model under negative feedback, indicating the limited significance of such analyses for the prediction of specific oncogenic perturbations.