Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis

A simple knowledge-based mining method for exploring hidden key molecules in a human biomolecular network

BACKGROUND

In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes.

RESULTS

To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes.

CONCLUSIONS

The developed method, named NetHiKe (Network-based Hidden Key molecule miner), was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.

HER3 intracellular domains play a crucial role in HER3/HER2 dimerization and activation of downstream signaling pathways

Dimerization among the EGFR family of tyrosine kinase receptors leads to allosteric activation of the kinase domains of the partners. Unlike other members in the family, the kinase domain of HER3 lacks key amino acid residues for catalytic activity. As a result, HER3 is suggested to serve as an allosteric activator of other EGFR family members which include EGFR, HER2 and HER4. To study the role of intracellular domains in HER3 dimerization and activation of downstream signaling pathways, we constructed HER3/HER2 chimeric receptors by replacing the HER3 kinase domain (HER3-2-3) or both the kinase domain and the C-terminal tail (HER3-2-2) with the HER2 counterparts and expressed the chimeric receptors in Chinese hamster ovary (CHO) cells. While over expression of the intact human HER3 transformed CHO cells with oncogenic properties such as AKT/ERK activation and increased proliferation and migration, CHO cells expressing the HER3-2-3 chimeric receptor showed significantly reduced HER3/HER2 dimerization and decreased phosphorylation of both AKT and ERK1/2 in the presence of neuregulin-1 (NRG-1). In contrast, CHO cells expressing the HER3-2-2 chimeric receptor resulted in a total loss of downstream AKT activation in response to NRG-1, but maintained partial activation of ERK1/2. The results demonstrate that the intracellular domains play a crucial role in HER3's function as an allosteric activator and its role in downstream signaling.

ERBB3 (HER3) is a key sensor in the regulation of ERBB-mediated signaling in both low and high ERBB2 (HER2) expressing cancer cells

Aberrant expression and activation of EGFR and ERBB2 (HER2) have been successfully targeted for cancer therapeutics. Recent evidence from both basic and clinical studies suggests that ERBB3 (HER3) serves as a key activator of downstream signaling through dimerization with other ERBB proteins and plays a critical role in the widespread clinical resistance to EGFR and HER2 targeting cancer therapies. As a result, HER3 is actively pursued as an antibody therapeutic target for cancer. Ligand binding is thought to be a prerequisite for dimerization of HER3 with other ERBB proteins, which results in phosphorylation of its c-terminal tyrosine residues and activation of downstream AKT and MAPK signaling pathways. In this study, we report that an anti-HER2 monoclonal antibody (HER2Mab), which blocks HER2 dimerization with HER3, induces HER3 dimerization with EGFR in both low and high HER2 expressing cancer cells. Treatment of the low HER2 expressing MCF7 cancer cells with HER2Mab promoted cell proliferation and migration in the absence of HER3 ligand stimulation. Follow-up studies revealed that HER2Mab-induced HER3 signaling via EGFR/HER3 dimerization and activation of downstream AKT signaling pathways. These results suggest that equilibrium of dimerization among the ERBB proteins can be perturbed by HER2Mab and HER3 plays a key role in sensing the perturbation.

The evolving landscape of protein kinases in breast cancer: clinical implications

Dysfunction of protein kinases has been associated with the development of the various molecular subtypes of breast cancer. The best example is the known role of HER2 in the tumorigenesis of a proportion of breast tumors. In this article, we review the state of the art knowledge on protein kinases involved in breast cancer. Special attention is given to those that are potentially druggable and those for which targeted agents are currently under clinical evaluation. Options for targeted drug combinations will be discussed, as well as the optimal way to integrate new kinase inhibitors in the clinical armamentarium to fight breast cancer. We will review recent results from clinical studies with agents targeting different kinases involved in the pathophysiology of breast cancer. In addition, we will evaluate the clinical development of kinase inhibitors to identify areas of knowledge that could be explored in future preclinical and clinical studies.

A visual analytics approach for models of heterogeneous cell populations

In recent years, cell population models have become increasingly common. In contrast to classic single cell models, population models allow for the study of cell-to-cell variability, a crucial phenomenon in most populations of primary cells, cancer cells, and stem cells. Unfortunately, tools for in-depth analysis of population models are still missing. This problem originates from the complexity of population models. Particularly important are methods to determine the source of heterogeneity (e.g., genetics or epigenetic differences) and to select potential (bio-)markers. We propose an analysis based on visual analytics to tackle this problem. Our approach combines parallel-coordinates plots, used for a visual assessment of the high-dimensional dependencies, and nonlinear support vector machines, for the quantification of effects. The method can be employed to study qualitative and quantitative differences among cells. To illustrate the different components, we perform a case study using the proapoptotic signal transduction pathway involved in cellular apoptosis.

ErbB3 inhibitory surrobodies inhibit tumor cell proliferation in vitro and in vivo

ErbB3 is an important regulator of tumorigenesis and is implicated in development of resistance to several currently used oncology drugs. We have identified ErbB3 inhibitors based on a novel biologic scaffold termed a surrobody. Two of these inhibitors appear to work by a previously unrecognized mechanism of action. As a consequence, they not only inhibited cell proliferation and intracellular signaling driven by stimulation with the ErbB3 ligand neuregulin (NRG), but also inhibited signaling and proliferation that was driven by overexpression of ErbB2 in the absence of ligand stimulation. In addition, the surrobodies inhibited tumor growth in vivo in both ErbB2-overexpressing and nonoverexpressing cells. In ErbB2-overexpressing cells, both of the anti-ErbB3 surrobodies significantly augmented the activities of trastuzumab, lapatinib, and GDC-0941, agents that inhibit cell proliferation by different mechanisms. Moreover, although NRG diminished the efficacy of these agents, when they were combined with anti-ErbB3 surrobodies the affect of NRG was abrogated. In this capacity, the anti-ErbB3 surrobodies were more effective than the ErbB2/ErbB3 dimerization inhibitory antibody pertuzumab. Despite the fact that these surrobodies appear to engage ErbB3 differently than previously described anti-ErbB3 antibodies, they retain all of the beneficial characteristics of this class of agents, including the ability to augment drugs that inhibit EGF receptor. These anti-ErbB3 agents, therefore, show substantial promise for development as single agents or in combination with other ErbB-directed antibodies or small molecules and may provide for a broader range of therapeutic indications than previously described anti-ErbB3 antibodies.

Computational approaches for analyzing information flow in biological networks

The advancements in "omics" (proteomics, genomics, lipidomics, and metabolomics) technologies have yielded large inventories of genes, transcripts, proteins, and metabolites. The challenge is to find out how these entities work together to regulate the processes by which cells respond to external and internal signals. Mathematical and computational modeling of signaling networks has a key role in this task, and network analysis provides insights into biological systems and has applications for medicine. Here, we review experimental and theoretical progress and future challenges toward this goal. We focus on how networks are reconstructed from data, how these networks are structured to control the flow of biological information, and how the design features of the networks specify biological decisions.

Monoclonal antibody-induced ErbB3 receptor internalization and degradation inhibits growth and migration of human melanoma cells

Members of the ErbB receptor family are targets of a growing numbers of small molecules and monoclonal antibodies inhibitors currently under development for the treatment of cancer. Although historical efforts have been directed against ErbB1 (EGFR) and ErbB2 (HER2/neu), emerging evidences have pointed to ErbB3 as a key node in the activation of proliferation/survival pathways from the ErbB receptor family and have fueled enthusiasm toward the clinical development of anti-ErbB3 agents. In this study, we have evaluated the potential therapeutic efficacy of a set of three recently generated anti-human ErbB3 monoclonals, A2, A3 and A4, in human primary melanoma cells. We show that in melanoma cells expressing ErbB1, ErbB3 and ErbB4 but not ErbB2 receptor ligands activate the PI3K/AKT pathway, and this leads to increased cell proliferation and migration. While antibodies A3 and A4 are able to potently inhibit ligand-induced signaling, proliferation and migration, antibody A2 is unable to exert this effect. In attempt to understand the mechanism of action and the basis of this different behavior, we demonstrate, through a series of combined approaches, that antibody efficacy strongly correlates with antibody-induced receptor internalization, degradation and inhibition of receptor recycling to the cell surface. Finally, fine epitope mapping studies through a peptide array show that inhibiting vs. non-inhibiting antibodies have a dramatically different mode of binding to the to the receptor extracellular domain. Our study confirms the key role of ErbB3 and points to exploitation of novel combination therapies for treatment of malignant melanoma.

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.

Antibody therapy of cancer

The use of monoclonal antibodies (mAbs) for cancer therapy has achieved considerable success in recent years. Antibody-drug conjugates are powerful new treatment options for lymphomas and solid tumours, and immunomodulatory antibodies have also recently achieved remarkable clinical success. The development of therapeutic antibodies requires a deep understanding of cancer serology, protein-engineering techniques, mechanisms of action and resistance, and the interplay between the immune system and cancer cells. This Review outlines the fundamental strategies that are required to develop antibody therapies for cancer patients through iterative approaches to target and antibody selection, extending from preclinical studies to human trials.

Data-driven modelling of receptor tyrosine kinase signalling networks quantifies receptor-specific potencies of PI3K- and Ras-dependent ERK activation

Signal transduction networks in mammalian cells, comprising a limited set of interacting biochemical pathways, are accessed by various growth factor and cytokine receptors to elicit distinct cell responses. This raises the question as to how specificity of the stimulus-response relationship is encoded at the molecular level. It has been proposed that specificity arises not only from the activation of unique signalling pathways, but also from quantitative differences in the activation and regulation of shared receptor-proximal signalling proteins. To address such hypotheses, data sets with greater precision and coverage of experimental conditions will need to be acquired, and rigorous frameworks that codify and parameterize the inherently non-linear relationships among signalling activities will need to be developed. In the present study we apply a systematic approach combining quantitative measurements and mathematical modelling to compare the signalling networks accessed by FGF (fibroblast growth factor) and PDGF (platelet-derived growth factor) receptors in mouse fibroblasts, in which the ERK (extracellular-signal-regulated kinase) cascade is activated by Ras- and PI3K (phosphoinositide 3-kinase)-dependent pathways. We show that, whereas the FGF stimulation of PI3K signalling is relatively weak, this deficiency is compensated for by a more potent Ras-dependent activation of ERK. Thus, as the modelling would predict, the ERK pathway is activated to a greater extent in cells co-stimulated with FGF and PDGF, relative to the saturated levels achieved with either ligand alone. It is envisaged that similar approaches will prove valuable in the elucidation of quantitative differences among other closely related receptor signalling networks.

Antitumor activity of a novel bispecific antibody that targets the ErbB2/ErbB3 oncogenic unit and inhibits heregulin-induced activation of ErbB3

The prevalence of ErbB2 amplification in breast cancer has resulted in the heavy pursuit of ErbB2 as a therapeutic target. Although both the ErbB2 monoclonal antibody trastuzumab and ErbB1/ErbB2 dual kinase inhibitor lapatinib have met with success in the clinic, many patients fail to benefit. In addition, the majority of patients who initially respond will unfortunately ultimately progress on these therapies. Activation of ErbB3, the preferred dimerization partner of ErbB2, plays a key role in driving ErbB2-amplified tumor growth, but we have found that current ErbB2-directed therapies are poor inhibitors of ligand-induced activation. By simulating ErbB3 inhibition in a computational model of ErbB2/ErbB3 receptor signaling, we predicted that a bispecific antibody that docks onto ErbB2 and subsequently binds to ErbB3 and blocks ligand-induced receptor activation would be highly effective in ErbB2-amplified tumors, with superior activity to a monospecific ErbB3 inhibitor. We have developed a bispecific antibody suitable for both large scale production and systemic therapy by generating a single polypeptide fusion protein of two human scFv antibodies linked to modified human serum albumin. The resulting molecule, MM-111, forms a trimeric complex with ErbB2 and ErbB3, effectively inhibiting ErbB3 signaling and showing antitumor activity in preclinical models that is dependent on ErbB2 overexpression. MM-111 can be rationally combined with trastuzumab or lapatinib for increased antitumor activity and may in the future complement existing ErbB2-directed therapies to treat resistant tumors or deter relapse.

Anti-tumor efficacy of naked siRNAs for ERBB3 or AKT2 against lung adenocarcinoma cell xenografts

The use of siRNAs against specific molecular targets has potential for cancer therapy but has been thought to be limited by the need for formulation to improve cellular uptake. Lung adenocarcinoma cells are markedly suppressed in culture by siRNAs to the receptor ERBB3 or its downstream signaling partner AKT2. We now demonstrate that naked, unformulated siRNAs to ERBB3 or AKT2, administered i.v. as saline solutions, 2 μg/g five times per week for 3 weeks (total dose 30 μg/g), were effective suppressors of growth of A549 human lung adenocarcinoma cell xenografts in athymic mice, 12 mice per group, in four different experiments. ERBB3 and AKT2 siRNAs each inhibited growth by 70-90% on average, compared to saline-treated or untreated controls; a nonsilencing siRNA was without significant effect. Lesser but significant effects were noted with a total dose of 12 μg/g. With the higher dose, effects persisted for several weeks after the end of treatment. Expected reductions of ERBB3 and AKT2 mRNAs and proteins occurred and correlated with decrease in tumor volume. There were no significant changes in serum cytokines. These results show that naked siRNAs to ERBB3 or AKT2 may have potential for lung cancer therapy.

Systems biology modeling reveals a possible mechanism of the tumor cell death upon oncogene inactivation in EGFR addicted cancers

Despite many evidences supporting the concept of “oncogene addiction” and many hypotheses rationalizing it, there is still a lack of detailed understanding to the precise molecular mechanism underlying oncogene addiction. In this account, we developed a mathematic model of epidermal growth factor receptor (EGFR) associated signaling network, which involves EGFR-driving proliferation/pro-survival signaling pathways Ras/extracellular-signal-regulated kinase (ERK) and phosphoinositol-3 kinase (PI3K)/AKT, and pro-apoptotic signaling pathway apoptosis signal-regulating kinase 1 (ASK1)/p38. In the setting of sustained EGFR activation, the simulation results show a persistent high level of proliferation/pro-survival effectors phospho-ERK and phospho-AKT, and a basal level of pro-apoptotic effector phospho-p38. The potential of p38 activation (apoptotic potential) due to the elevated level of reactive oxygen species (ROS) is largely suppressed by the negative crosstalk between PI3K/AKT and ASK1/p38 pathways. Upon acute EGFR inactivation, the survival signals decay rapidly, followed by a fast increase of the apoptotic signal due to the release of apoptotic potential. Overall, our systems biology modeling together with experimental validations reveals that inhibition of survival signals and concomitant release of apoptotic potential jointly contribute to the tumor cell death following the inhibition of addicted oncogene in EGFR addicted cancers.

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression

BACKGROUND

Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.

RESULTS

This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.

CONCLUSIONS

Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

Software for systems biology: from tools to integrated platforms

Understanding complex biological systems requires extensive support from software tools. Such tools are needed at each step of a systems biology computational workflow, which typically consists of data handling, network inference, deep curation, dynamical simulation and model analysis. In addition, there are now efforts to develop integrated software platforms, so that tools that are used at different stages of the workflow and by different researchers can easily be used together. This Review describes the types of software tools that are required at different stages of systems biology research and the current options that are available for systems biology researchers. We also discuss the challenges and prospects for modelling the effects of genetic changes on physiology and the concept of an integrated platform.

A two-in-one antibody against HER3 and EGFR has superior inhibitory activity compared with monospecific antibodies

Extensive crosstalk among ErbB/HER receptors suggests that blocking signaling from more than one family member may be essential to effectively treat cancer and limit drug resistance. We generated a conventional IgG molecule MEHD7945A with dual HER3/EGFR specificity by phage display engineering and used structural and mutational studies to understand how a single antigen recognition surface binds two epitopes with high affinity. As a human IgG1, MEHD7945A exhibited dual action by inhibiting EGFR- and HER3-mediated signaling in vitro and in vivo and the ability to engage immune effector functions. Compared with monospecific anti-HER antibodies, MEHD7945A was more broadly efficacious in multiple tumor models, showing that combined inhibition of EGFR and HER3 with a single antibody is beneficial.

Features of the reversible sensitivity-resistance transition in PI3K/PTEN/AKT signalling network after HER2 inhibition

Systems biology approaches that combine experimental data and theoretical modelling to understand cellular signalling network dynamics offer a useful platform to investigate the mechanisms of resistance to drug interventions and to identify combination drug treatments. Extending our work on modelling the PI3K/PTEN/AKT signalling network (SN), we analyse the sensitivity of the SN output signal, phospho-AKT, to inhibition of HER2 receptor. We model typical aberrations in this SN identified in cancer development and drug resistance: loss of PTEN activity, PI3K and AKT mutations, HER2 overexpression, and overproduction of GSK3β and CK2 kinases controlling PTEN phosphorylation. We show that HER2 inhibition by the monoclonal antibody pertuzumab increases SN sensitivity, both to external signals and to changes in kinetic parameters of the proteins and their expression levels induced by mutations in the SN. This increase in sensitivity arises from the transition of SN functioning from saturation to non-saturation mode in response to HER2 inhibition. PTEN loss or PIK3CA mutation causes resistance to anti-HER2 inhibitor and leads to the restoration of saturation mode in SN functioning with a consequent decrease in SN sensitivity. We suggest that a drug-induced increase in SN sensitivity to internal perturbations, and specifically mutations, causes SN fragility. In particular, the SN is vulnerable to mutations that compensate for drug action and this may result in a sensitivity-to-resistance transition. The combination of HER2 and PI3K inhibition does not sensitise the SN to internal perturbations (mutations) in the PI3K/PTEN/AKT pathway: this combination treatment provides both synergetic inhibition and may prevent the SN from acquired mutations causing drug resistance. Through combination inhibition treatments, we studied the impact of upstream and downstream interventions to suppress resistance to the HER2 inhibitor in the SN with PTEN loss. Comparison of experimental results of PI3K inhibition in the PTEN upstream pathway with PDK1 inhibition in the PTEN downstream pathway shows that upstream inhibition abrogates resistance to pertuzumab more effectively than downstream inhibition. This difference in inhibition effect arises from the compensatory mechanism of an activation loop induced in the downstream pathway by PTEN loss. We highlight that drug target identification for combination anti-cancer therapy needs to account for the mutation effects on the upstream and downstream pathways.

Secreted ERBB3 isoforms are serum markers for early hepatoma in patients with chronic hepatitis and cirrhosis

Most hepatocellular carcinoma (HCC) is generated from chronic hepatitis and cirrhosis. To discover new markers for early HCC in patients with chronic hepatitis and cirrhosis, we initiated our search in the interstitial fluid of tumor (TIF) via differential gel electrophoresis and antibody arrays and identified secreted ERBB3 isoforms (sERBB3). The performance of serum sERBB3 in diagnosis of HCC was analyzed using receiver operating characteristic curves (ROC). The serum sERBB3 level was significantly higher in HCC than in cirrhosis (p < 0.001) and chronic hepatitis (p < 0.001). The accuracy of serum sERBB3 in detection of HCC was further validated in two independent sets of patients. In discrimination of early HCC from chronic hepatitis or cirrhosis, serum sERBB3 had a better performance than alpha-fetoprotein (AFP) (areas under ROC [AUC]: sERBB3 vs AFP = 93.1 vs 81.0% from chronic hepatitis and 70.9 vs 62.7% from cirrhosis). Combination of sERBB3 and AFP further improved the accuracy in detection of early HCC from chronic hepatitis (AUC = 97.1%) or cirrhosis (AUC = 77.5%). Higher serum sERBB3 levels were associated with portal-vein invasion and extrahepatic metastasis of HCC (p = 0.017). Therefore, sERBB3 are serum markers for early HCC in patients with chronic hepatitis and cirrhosis.

A neutralizing RNA aptamer against EGFR causes selective apoptotic cell death

Nucleic acid aptamers have been developed as high-affinity ligands that may act as antagonists of disease-associated proteins. Aptamers are non immunogenic and characterised by high specificity and low toxicity thus representing a valid alternative to antibodies or soluble ligand receptor traps/decoys to target specific cancer cell surface proteins in clinical diagnosis and therapy. The epidermal growth factor receptor (EGFR) has been implicated in the development of a wide range of human cancers including breast, glioma and lung. The observation that its inhibition can interfere with the growth of such tumors has led to the design of new drugs including monoclonal antibodies and tyrosine kinase inhibitors currently used in clinic. However, some of these molecules can result in toxicity and acquired resistance, hence the need to develop novel kinds of EGFR-targeting drugs with high specificity and low toxicity. Here we generated, by a cell-Systematic Evolution of Ligands by EXponential enrichment (SELEX) approach, a nuclease resistant RNA-aptamer that specifically binds to EGFR with a binding constant of 10 nM. When applied to EGFR-expressing cancer cells the aptamer inhibits EGFR-mediated signal pathways causing selective cell death. Furthermore, at low doses it induces apoptosis even of cells that are resistant to the most frequently used EGFR-inhibitors, such as gefitinib and cetuximab, and inhibits tumor growth in a mouse xenograft model of human non-small-cell lung cancer (NSCLC). Interestingly, combined treatment with cetuximab and the aptamer shows clear synergy in inducing apoptosis in vitro and in vivo. In conclusion, we demonstrate that this neutralizing RNA-aptamer is a promising bio-molecule that can be developed as a more effective alternative to the repertoire of already existing EGFR-inhibitors.

A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.

An ErbB-3 antibody, MP-RM-1, inhibits tumor growth by blocking ligand-dependent and independent activation of ErbB-3/Akt signaling

The ErbB receptors, such as ErbB-1 and ErbB-2, have been intensely pursued as targets for cancer therapeutics. Although initially efficacious in a subset of patients, drugs targeting these receptors led invariably to resistance, which is often associated with reactivation of the ErbB-3-PI3K-Akt signaling. This may be overcome by an ErbB-3 ligand that abrogates receptor-mediated signaling. Toward this end, we have generated a mouse monoclonal antibody, MP-RM-1, against the extracellular domain (ECD) of ErbB-3 receptor. Assessment of human tumor cell lines, as well as early passage tumor cells revealed that MP-RM-1 effectively inhibited both NRG-1β-dependent and -independent ErbB-3 activation. The antagonizing effect of MP-RM-1 was of non-competitive type, as binding of [(125)I]-labeled NRG-1β to ErbB-3 was not influenced by the antibody. MP-RM-1 treatment led, in most instances, to decreased ErbB-3 expression. In addition, MP-RM-1 was able to inhibit the colony formation ability of tumor cells and tumor growth in two human tumor xenograft nude mouse models. Treatment with the antibody was associated with a decreased ErbB-3 and Akt phosphorylation and ErbB-3 expression in the excised tumor tissue. Collectively, these results indicate that MP-RM-1 has the potential to interfere with signaling by ErbB-3 and reinforce the notion that ErbB-3 could be a key target in cancer-drug design.

Targeting ErbB3-mediated stromal-epithelial interactions in pancreatic ductal adenocarcinoma

Background:

We sought to investigate the role of ErbB3-mediated signalling on the interaction between pancreatic cancer-associated fibroblasts (CAF) and carcinoma cells in an effort to disrupt tumourigenic pancreatic ductal adenocarcinoma (PDAC) stromal–epithelial cross-communication.

Methods:

Primary CAF cultures were established from human PDAC surgical specimens. AsPC-1 pancreatic cancer cell murine subcutaneous xenografts were developed in the presence and absence of CAF and were subsequently treated with epidermal growth factor receptor (EGFR) inhibitors (erlotinib) and ErbB3 inhibitors (MM-121, monoclonal ErbB3 antibody).

Results:

Cancer-associated fibroblasts were found to secrete neuregulin-1 (NRG-1), which promoted proliferation via phosphorylation of ErbB3 and AKT in AsPC-1 PDAC cells. This signalling cascade was effectively inhibited both in vitro and in vivo by specific ErbB3 blockade with MM-121, with greater degree of tumourigenesis inhibition when combined with erlotinib. The CAF–AsPC-1 pancreatic cancer xenografts reached significantly greater tumour volume than those xenografts lacking CAF and were resistant to the anti-tumour effects of EGFR inhibition with erlotinib.

Conclusion:

Cancer-associated fibroblasts-derived NRG-1 promote PDAC tumourigenesis via ErbB3-AKT signalling and overcomes single-agent EGFR inhibition. Disruption of this stromally mediated tumourigenic mechanism is best obtained through combined EGFR-ErbB3 inhibition with both erlotinib and MM-121. We have identified the NRG-1/ErbB3 axis as an attractive molecular target for the interruption of tumourigenic stromal–epithelial interactions within the PDAC microenvironment.

Integration of cell-free protein coexpression with an enzyme-linked immunosorbent assay enables rapid analysis of protein-protein interactions directly from DNA

Assays that integrate detection of binding with cell-free protein expression directly from DNA can dramatically increase the pace at which protein-protein interactions (PPIs) can be analyzed by mutagenesis. In this study, we present a method that combines in vitro protein production with an enzyme-linked immunosorbent assay (ELISA) to measure PPIs. This method uses readily available commodity instrumentation and generic antibody-affinity tag interactions. It is straightforward and rapid to execute, enabling many interactions to be assessed in parallel. In traditional ELISAs, reporter complexes are assembled stepwise with one layer at a time. In the method presented here, all the members of the reporter complex are present and assembled together. The signal strength is dependent on all the intercomponent interaction affinities and concentrations. Although this assay is straightforward to execute, establishing proper conditions and analysis of the results require a thorough understanding of the processes that determine the signal strength. The formation of the fully assembled reporter sandwich can be modeled as a competition between Langmuir adsorption isotherms for the immobilized components and binding equilibria of the solution components. We have shown that modeling this process provides semiquantitative understanding of the effects of affinity and concentration and can guide strategies for the development of experimental protocols. We tested the method experimentally using the interaction between a synthetic ankyrin repeat protein (Off7) and maltose-binding protein. Measurements obtained for a collection of alanine mutations in the interface between these two proteins demonstrate that a range of affinities can be analyzed.

Quantitation of protein-protein interactions by thermal stability shift analysis

Thermal stability shift analysis is a powerful method for examining binding interactions in proteins. We demonstrate that under certain circumstances, protein-protein interactions can be quantitated by monitoring shifts in thermal stability using thermodynamic models and data analysis methods presented in this work. This method relies on the determination of protein stabilities from thermal unfolding experiments using fluorescent dyes such as SYPRO Orange that report on protein denaturation. Data collection is rapid and straightforward using readily available real-time polymerase chain reaction instrumentation. We present an approach for the analysis of the unfolding transitions corresponding to each partner to extract the affinity of the interaction between the proteins. This method does not require the construction of a titration series that brackets the dissociation constant. In thermal shift experiments, protein stability data are obtained at different temperatures according to the affinity- and concentration-dependent shifts in unfolding transition midpoints. Treatment of the temperature dependence of affinity is, therefore, intrinsic to this method and is developed in this study. We used the interaction between maltose-binding protein (MBP) and a thermostable synthetic ankyrin repeat protein (Off7) as an experimental test case because their unfolding transitions overlap minimally. We found that MBP is significantly stabilized by Off7. High experimental throughput is enabled by sample parallelization, and the ability to extract quantitative binding information at a single partner concentration. In a single experiment, we were able to quantify the affinities of a series of alanine mutants, covering a wide range of affinities (∼ 100 nM to ∼ 100 μM).

A multiscale modeling approach to investigate molecular mechanisms of pseudokinase activation and drug resistance in the HER3/ErbB3 receptor tyrosine kinase signaling network

Multiscale modeling provides a powerful and quantitative platform for investigating the complexity inherent in intracellular signaling pathways and rationalizing the effects of molecular perturbations on downstream signaling events and ultimately, on the cell phenotype. Here we describe the application of a multiscale modeling scheme to the HER3/ErbB3 receptor tyrosine kinase (RTK) signaling network, which regulates critical cellular processes including proliferation, migration and differentiation. The HER3 kinase is a topic of current interest and investigation, as it has been implicated in mechanisms of resistance to tyrosine kinase inhibition (TKI) of EGFR and HER2 in the treatment of many human malignancies. Moreover, the commonly regarded status of HER3 as a catalytically inactive 'pseudokinase' has recently been challenged by our previous study, which demonstrated robust residual kinase activity for HER3. Through our multiscale model, we investigate the most significant molecular interactions that contribute to potential mechanisms of HER3 activity and the physiological relevance of this activity to mechanisms of drug resistance in an ErbB-driven tumor cell in silico. The results of our molecular-scale simulations support the characterization of HER3 as a weakly active kinase that, in contrast to its fully-active ErbB family members, depends upon a unique hydrophobic interface to coordinate the alignment of specific catalytic residues required for its activity. Translating our molecular simulation results of the uniquely active behavior of the HER3 kinase into a physiologically relevant environment, our HER3 signaling model demonstrates that even a weak level of HER3 activity may be sufficient to induce AKT signaling and TKI resistance in the context of an ErbB signaling-dependent tumor cell, and therefore therapeutic targeting of HER3 may represent a superior treatment strategy for specific ErbB-driven cancers.

Reduction of complex signaling networks to a representative kernel

The network of biomolecular interactions that occurs within cells is large and complex. When such a network is analyzed, it can be helpful to reduce the complexity of the network to a "kernel" that maintains the essential regulatory functions for the output under consideration. We developed an algorithm to identify such a kernel and showed that the resultant kernel preserves the network dynamics. Using an integrated network of all of the human signaling pathways retrieved from the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, we identified this network's kernel and compared the properties of the kernel to those of the original network. We found that the percentage of essential genes to the genes encoding nodes outside of the kernel was about 10%, whereas ~32% of the genes encoding nodes within the kernel were essential. In addition, we found that 95% of the kernel nodes corresponded to Mendelian disease genes and that 93% of synthetic lethal pairs associated with the network were contained in the kernel. Genes corresponding to nodes in the kernel had low evolutionary rates, were ubiquitously expressed in various tissues, and were well conserved between species. Furthermore, kernel genes included many drug targets, suggesting that other kernel nodes may be potential drug targets. Owing to the simplification of the entire network, the efficient modeling of a large-scale signaling network and an understanding of the core structure within a complex framework become possible.

The challenges of integrating molecular imaging into the optimization of cancer therapy

We review novel, in vivo and tissue-based imaging technologies that monitor and optimize cancer therapeutics. Recent advances in cancer treatment centre around the development of targeted therapies and personalisation of treatment regimes to individual tumour characteristics. However, clinical outcomes have not improved as expected. Further development of the use of molecular imaging to predict or assess treatment response must address spatial heterogeneity of cancer within the body. A combination of different imaging modalities should be used to relate the effect of the drug to dosing regimen or effective drug concentration at the local site of action. Molecular imaging provides a functional and dynamic read-out of cancer therapeutics, from nanometre to whole body scale. At the whole body scale, an increase in the sensitivity and specificity of the imaging probe is required to localise (micro)metastatic foci and/or residual disease that are currently below the limit of detection. The use of image-guided endoscopic biopsy can produce tumour cells or tissues for nanoscopic analysis in a relatively patient-compliant manner, thereby linking clinical imaging to a more precise assessment of molecular mechanisms. This multimodality imaging approach (in combination with genetics/genomic information) could be used to bridge the gap between our knowledge of mechanisms underlying the processes of metastasis, tumour dormancy and routine clinical practice. Treatment regimes could therefore be individually tailored both at diagnosis and throughout treatment, through monitoring of drug pharmacodynamics providing an early read-out of response or resistance.

Rational engineering of antibody therapeutics targeting multiple oncogene pathways

Monoclonal antibodies have significantly advanced our ability to treat cancer, yet clinical studies have shown that many patients do not adequately respond to monospecific therapy. This is in part due to the multifactorial nature of the disease, where tumors rely on multiple and often redundant pathways for proliferation. Bi- or multi- specific antibodies capable of blocking multiple growth and survival pathways at once have a potential to better meet the challenge of blocking cancer growth, and indeed many of them are advancing in clinical development. ( 1) However, bispecific antibodies present significant design challenges mostly due to the increased number of variables to consider. In this perspective we describe an innovative integrated approach to the discovery of bispecific antibodies with optimal molecular properties, such as affinity, avidity, molecular format and stability. This approach combines simulations of potential inhibitors using mechanistic models of the disease-relevant biological system to reveal optimal inhibitor characteristics with antibody engineering techniques that yield manufacturable therapeutics with robust pharmaceutical properties. We illustrate how challenges of meeting the optimal design criteria and chemistry, manufacturing and control concerns can be addressed simultaneously in the context of an accelerated therapeutic design cycle. Finally, to demonstrate how this rational approach can be applied, we present a case study where the insights from mechanistic modeling were used to guide the engineering of an IgG-like bispecific antibody.

Antibodies in oncology

Monoclonal antibodies (mAbs) have become one of the largest classes of new therapeutic agents approved for use in oncology, and have revolutionised the treatment of many human malignancies. Clinically useful mAbs can function through several different mechanisms, including inhibition of tumour-related signalling, induction of apoptosis, inhibition of angiogenesis, enhancing host immune response against cancer and targeted delivery of payloads (such as toxins, cytotoxic agents or radioisotopes) to the tumour site. The increasing knowledge of key molecules and cellular pathways involved in tumour induction and progression has led to a rise in the proportion of therapeutic mAbs entering clinical trials. These mAbs consist of various conventional or recombinant, murine, humanised, chimeric or fully human and fusion constructs. In this review, we provide an overview of mAbs approved for use in clinical oncology and those currently in clinical development. We also discuss the mechanisms of action of anti-cancer mAbs, as well as the antigen targets recognised by these antibodies.

Analysis of Somatic Mutations in Cancer: Molecular Mechanisms of Activation in the ErbB Family of Receptor Tyrosine Kinases

The ErbB/EGFR/HER family of kinases consists of four homologous receptor tyrosine kinases which are important regulatory elements in many cellular processes, including cell proliferation, differentiation, and migration. Somatic mutations in, or over-expression of, the ErbB family is found in many cancers and is correlated with a poor prognosis; particularly, clinically identified mutations found in non-small-cell lung cancer (NSCLC) of ErbB1 have been shown to increase its basal kinase activity and patients carrying these mutations respond remarkably to the small tyrosine kinase inhibitor gefitinib. Here, we analyze the potential effects of the currently catalogued clinically identified mutations in the ErbB family kinase domains on the molecular mechanisms of kinase activation. Recently, we identified conserved networks of hydrophilic and hydrophobic interactions characteristic to the active and inactive conformation, respectively. Here, we show that the clinically identified mutants influence the kinase activity in distinctive fashion by affecting the characteristic interaction networks.

Neuregulin/erythroblastic leukemia viral oncogene homolog 3 autocrine loop contributes to invasion and early recurrence of human hepatoma

Intrahepatic metastasis is the primary cause of the high recurrence and poor prognosis of human hepatocellular carcinoma (HCC). However, neither its molecular mechanisms nor markers for its prediction before hepatectomy have been identified. We recently revealed up-regulation of erythroblastic leukemia viral oncogene homolog 3 (ERBB3) in human HCC. Here we examined the clinical and biological significance of ERBB3 in HCC. Up-regulation of ERBB3 in HCC was strongly associated with male gender (P < 0.001), chronic hepatitis B (P = 0.002), microscopic vascular invasion (P = 0.034), early recurrence (P = 0.003), and worse prognosis (P = 0.004). Phosphorylated ERBB3 and its ligands [neuregulins (NRGs)] were detected in both HCC tissues and cells. Phosphorylation of ERBB3 could be induced by conditioned media of HCC cells and abolished by the pretreatment of conditioned media with anti-NRG antibodies or by the silencing of the endogenous NRG expression of the donor HCC cells. Human epidermal growth factor receptor 2 was required for ERBB3 phosphorylation. The downstream phosphoinositide 3-kinase/v-akt murine thymoma viral oncogene homolog pathways were primarily elicited by NRG1/ERBB3 signaling, whereas the mitogen-activated protein kinase/extracellular signal-regulated kinase pathways were elicited by both epidermal growth factor/epidermal growth factor receptor and NRG1/ERBB3 signaling. The activation and silencing of ERBB3-dependent signaling had potent effects on both the migration and invasion of HCC cells, but neither had significant effects on the proliferation of HCC cells, tumor formation, or tumor growth in vitro and in vivo.

CONCLUSION

The constitutive activation of ERBB3-dependent signaling via the NRG1/ERBB3 autocrine loop plays a crucial role in the regulation of cell motility and invasion, which contribute to intrahepatic metastasis and early recurrence of HCC. ERBB3 is a marker for the prediction of intrahepatic metastasis and early recurrence. ERBB3-dependent signaling is a candidate target for the treatment of microscopic vascular invasion and for the prevention of HCC recurrence.

Profiling the HER3/PI3K pathway in breast tumors using proximity-directed assays identifies correlations between protein complexes and phosphoproteins

Background

The identification of patients for targeted antineoplastic therapies requires accurate measurement of therapeutic targets and associated signaling complexes. HER3 signaling through heterodimerization is an important growth-promoting mechanism in several tumor types and may be a principal resistance mechanism by which EGFR and HER2 expressing tumors elude targeted therapies. Current methods that can study these interactions are inadequate for formalin-fixed, paraffin-embedded (FFPE) tumor samples.

Methodology and Principal Findings

Herein, we describe a panel of proximity-directed assays capable of measuring protein-interactions and phosphorylation in FFPE samples in the HER3/PI3K/Akt pathway and examine the capability of these assays to inform on the functional state of the pathway. We used FFPE breast cancer cell line and tumor models for this study. In breast cancer cell lines we observe both ligand-dependent and independent activation of the pathway and strong correlations between measured activation of key analytes. When selected cell lines are treated with HER2 inhibitors, we not only observe the expected molecular effects based on mechanism of action knowledge, but also novel effects of HER2 inhibition on key targets in the HER receptor pathway. Significantly, in a xenograft model of delayed tumor fixation, HER3 phosphorylation is unstable, while alternate measures of pathway activation, such as formation of the HER3PI3K complex is preserved. Measurements in breast tumor samples showed correlations between HER3 phosphorylation and receptor interactions, obviating the need to use phosphorylation as a surrogate for HER3 activation.

Significance

This assay system is capable of quantitatively measuring therapeutically relevant responses and enables molecular profiling of receptor networks in both preclinical and tumor models.

Using small molecules and chemical genetics to interrogate signaling networks

The limited clinical success of therapeutics targeting cellular signaling processes is due to multiple factors, including off-target effects and complex feedback regulation encoded within the signaling network. To understand these effects, chemical proteomics and chemical genetics tools have been developed to map the direct targets of kinase inhibitors, determine the network-level response to inhibitor treatment, and to infer network topology. Here we provide an overview of chemical phosphoproteomic and chemical genetic methods, including specific examples where these methods have been applied to yield biological insight regarding network structure and the system-wide effects of targeted therapeutics. The challenges and caveats associated with each method are described, along with approaches being used to resolve some of these issues. With the broad array of available techniques the next decade should see a rapid improvement in our understanding of signaling networks regulation and response to targeted perturbations, leading to more efficacious therapeutic strategies.

Integrated computational model of cell cycle and checkpoint reveals different essential roles of Aurora-A and Plk1 in mitotic entry

Understanding the regulation of mitotic entry is one of the most important goals of modern cell biology, and computational modeling of mitotic entry has been a subject of several recent studies. However, there are still many regulation mechanisms that remain poorly characterized. Two crucial aspects are how mitotic entry is controlled by its upstream regulators Aurora-A and Plk1, and how mitotic entry is coordinated with other biological events, especially G2/M checkpoint. In this context, we reconstructed a comprehensive computational model that integrates the mitotic entry network and the G2/M checkpoint system. Computational simulation of this model and subsequent experimental verification revealed that Aurora-A and Plk1 are redundant to the activation of cyclin B/Cdk1 during normal mitotic entry, but become especially important for cyclin B/Cdk1 activation during G2/M checkpoint recovery. Further analysis indicated that, in response to DNA damage, Chk1-mediated network rewiring makes cyclin B/Cdk1 more sensitive to the down-regulation of Aurora-A and Plk1. In addition, we demonstrated that concurrently targeting Aurora-A and Plk1 during G2/M checkpoint recovery achieves a synergistic effect, which suggests the combinational use of Aurora-A and Plk1 inhibitors after chemotherapy or radiotherapy. Thus, the results presented here provide novel insights into the regulation mechanism of mitotic entry and have potential value in cancer therapy.

Combining phage and staphylococcal surface display for generation of ErbB3-specific Affibody molecules

Emerging evidence suggests that the catalytically inactive ErbB3 (HER3) protein plays a fundamental role in normal tyrosine kinase receptor signaling as well as in aberrant functioning of these signaling pathways, resulting in several forms of human cancers. ErbB3 has recently also been implicated in resistance to ErbB2-targeting therapies. Here we report the generation of high-affinity ErbB3-specific Affibody molecules intended for future molecular imaging and biotherapeutic applications. Using a high-complexity phage-displayed Affibody library, a number of ErbB3 binders were isolated and specific cell-binding activity was demonstrated in immunofluorescence microscopic studies. Subsequently, a second-generation library was constructed based on sequences of the candidates from the phage display selection. By exploiting the sensitive affinity discrimination capacity of a novel bacterial surface display technology, the affinity of candidate Affibody molecules was further increased down to subnanomolar affinity. In summary, the demonstrated specific targeting of native ErbB3 receptor on human cancer cell lines as well as competition with the heregulin/ErbB3 interaction indicates that these novel biological agents may become useful tools for diagnostic and therapeutic targeting of ErbB3-expressing cancers. Our studies also highlight the powerful approach of combining the advantages of different display technologies for generation of functional high-affinity protein-based binders. Potential future applications, such as radionuclide-based diagnosis and treatment of human cancers are discussed.

Systems analysis of small signaling modules relevant to eight human diseases

Using eight newly generated models relevant to addiction, Alzheimer’s disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4–25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV’s viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.

Combining theoretical analysis and experimental data generation reveals IRF9 as a crucial factor for accelerating interferon α-induced early antiviral signalling

Type I interferons (IFN) are important components of the innate antiviral response. A key signalling pathway activated by IFNα is the Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway. Major components of the pathway have been identified. However, critical kinetic properties that facilitate accelerated initiation of intracellular antiviral signalling and thereby promote virus elimination remain to be determined. By combining mathematical modelling with experimental analysis, we show that control of dynamic behaviour is not distributed among several pathway components but can be primarily attributed to interferon regulatory factor 9 (IRF9), constituting a positive feedback loop. Model simulations revealed that increasing the initial IRF9 concentration reduced the time to peak, increased the amplitude and enhanced termination of pathway activation. These model predictions were experimentally verified by IRF9 over-expression studies. Furthermore, acceleration of signal processing was linked to more rapid and enhanced expression of IFNα target genes. Thus, the amount of cellular IRF9 is a crucial determinant for amplification of early dynamics of IFNα-mediated signal transduction.

Compensatory effects in the PI3K/PTEN/AKT signaling network following receptor tyrosine kinase inhibition

Overcoming de novo and acquired resistance to anticancer drugs that target signaling networks is a formidable challenge for drug design and effective cancer therapy. Understanding the mechanisms by which this resistance arises may offer a route to addressing the insensitivity of signaling networks to drug intervention and restore the efficacy of anticancer therapy. Extending our recent work identifying PTEN as a key regulator of Herceptin sensitivity, we present an integrated theoretical and experimental approach to study the compensatory mechanisms within the PI3K/PTEN/AKT signaling network that afford resistance to receptor tyrosine kinase (RTK) inhibition by anti-HER2 monoclonal antibodies. In a computational model representing the dynamics of the signaling network, we define a single control parameter that encapsulates the balance of activities of the enzymes involved in the PI3K/PTEN/AKT cycle. By varying this control parameter we are able to demonstrate both distinct dynamic regimes of behavior of the signaling network and the transitions between those regimes. We demonstrate resistance, sensitivity, and suppression of RTK signals by the signaling network. Through model analysis we link the sensitivity-to-resistance transition to specific compensatory mechanisms within the signaling network. We study this transition in detail theoretically by variation of activities of PTEN, PI3K, AKT enzymes, and use the results to inform experiments that perturb the signaling network using combinatorial inhibition of RTK, PTEN, and PI3K enzymes in human ovarian carcinoma cell lines. We find good alignment between theoretical predictions and experimental results. We discuss the application of the results to the challenges of hypersensitivity of the signaling network to RTK signals, suppression of drug resistance, and efficacy of drug combinations in anticancer therapy.

Cause-effect relationships in medicine: a protein network perspective

Current target-based drug discovery platforms are not able to predict drug efficacy and the full spectrum of drug effects in organisms. Hence, many experimental drugs do not survive the lengthy and costly process of drug development. Understanding how drugs affect cellular network structures and how the resulting signals are translated into drug effects is extremely important for the discovery of new medicines. This requires a greater understanding of cause-effect relationships at the organism, organ, tissue, cellular, and molecular level. There is a growing recognition that this information must be integrated into discovery paradigms, but a 'road map' for obtaining and integrating information about heterogeneous networks into drug-discovery platforms currently does not exist. This review explores recent network-centered approaches developed to investigate the genesis of medicine and disease effects, specifically highlighting protein-protein interaction network models and their use in cause-effect analyses in medicine.

Dissecting variability in responses to cancer chemotherapy through systems pharmacology

Variability in patient responses to even the most potent and targeted therapeutics is now the primary challenge facing drug discovery and patient care, particularly in oncology and immune therapy. Variability with respect to mechanisms of induced resistance is observed both in drug-naive patients and among those who are initially responsive. Genomics has developed powerful tools for systematic interrogation of disease genotype and transcriptional states (particularly in cancer) and for correlation of these measures with parameters of disease such as histological diagnosis and outcome. In contrast, mechanistic preclinical studies remain relatively narrowly focused, leading to many apparent contradictions and poor understanding of the determinants of response. We describe the emergence of a systems pharmacology approach that is mechanistic, quantitative, probabilistic, and postgenomic and promises to do for mechanistic pharmacology what genomics is doing for correlative studies. We focus on studies in cell lines (which currently dominate mechanism-oriented analysis), but our arguments are equally valid for real tumors studied in short-term culture as xenografts and, perhaps some time in the future, in humans.

Triple-negative breast cancer: present challenges and new perspectives

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Receptor tyrosine kinase coactivation networks in cancer

Cancer cells employ multiple mechanisms to evade tightly regulated cellular processes such as proliferation, apoptosis, and senescence. Systems-wide analyses of tumors have recently identified receptor tyrosine kinase (RTK) coactivation as an important mechanism by which cancer cells achieve chemoresistance. This mini-review discusses our current understanding of the complex and dynamic process of RTK coactivation. We highlight how systems biology and computational modeling have been employed to predict integrated signaling outcomes and cancer phenotypes downstream of RTK coactivation. We conclude by providing an outlook on the feasibility of targeting RTK networks to overcome chemoresistance in cancer.