Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.

Integrative modeling and analysis of signaling crosstalk reveal molecular switches coordinating Yes-associated protein transcriptional activities.

The transcriptional co-activator YAP forms complexes with distinct transcription factors, controlling cell fate decisions, such as proliferation and apoptosis. However, the mechanisms underlying its context-dependent function are poorly defined. This study explores the interplay between the TGF-ß and Hippo pathways and their influence on YAP's association with specific transcription factors. By integrating iterative mathematical modeling with experimental validation, we uncover molecular switches, predominantly controlled by RASSF1A and ITCH, which dictate the formation of YAP-SMAD (proliferative) and YAP-p73 (apoptotic) complexes. Our results show that RASSF1A enhances the formation of apoptotic complexes, whereas ITCH promotes the formation of proliferative complexes. Notably, higher levels of ITCH transform YAP-SMAD activity from a transient to a sustained state, impacting cellular behaviors. Extending these findings to various breast cancer cell lines highlights the role of cellular context in YAP regulation. Our study provides new insights into the mechanisms of YAP transcriptional activities and their therapeutic implications.

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically 'actionable' signalling axis in triple negative breast cancer.

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLCγ1/Ca 2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro . Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca 2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically 'actionable' target downstream of PEAK1.

Integrative modeling uncovers p21-driven drug resistance and prioritizes therapies for PIK3CA-mutant breast cancer.

Utility of PI3Kα inhibitors like BYL719 is limited by the acquisition of genetic and non-genetic mechanisms of resistance which cause disease recurrence. Several combination therapies based on PI3K inhibition have been proposed, but a way to systematically prioritize them for breast cancer treatment is still missing. By integrating published and in-house studies, we have developed in silico models that quantitatively capture dynamics of PI3K signaling at the network-level under a BYL719-sensitive versus BYL719 resistant-cell state. Computational predictions show that signal rewiring to alternative components of the PI3K pathway promote resistance to BYL719 and identify PDK1 as the most effective co-target with PI3Kα rescuing sensitivity of resistant cells to BYL719. To explore whether PI3K pathway-independent mechanisms further contribute to BYL719 resistance, we performed phosphoproteomics and found that selection of high levels of the cell cycle regulator p21 unexpectedly promoted drug resistance in T47D cells. Functionally, high p21 levels favored repair of BYL719-induced DNA damage and bypass of the associated cellular senescence. Importantly, targeted inhibition of the check-point inhibitor CHK1 with MK-8776 effectively caused death of p21-high T47D cells, thus establishing a new vulnerability of BYL719-resistant breast cancer cells. Together, our integrated studies uncover hidden molecular mediators causing resistance to PI3Kα inhibition and provide a framework to prioritize combination therapies for PI3K-mutant breast cancer.

SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks.

The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer.

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

The pseudokinase NRBP1 activates Rac1/Cdc42 via P-Rex1 to drive oncogenic signalling in triple-negative breast cancer.

We have determined that expression of the pseudokinase NRBP1 positively associates with poor prognosis in triple negative breast cancer (TNBC) and is required for efficient migration, invasion and proliferation of TNBC cells in culture as well as growth of TNBC orthotopic xenografts and experimental metastasis. Application of BioID/MS profiling identified P-Rex1, a known guanine nucleotide exchange factor for Rac1, as a NRBP1 binding partner. Importantly, NRBP1 overexpression enhanced levels of GTP-bound Rac1 and Cdc42 in a P-Rex1-dependent manner, while NRBP1 knockdown reduced their activation. In addition, NRBP1 associated with P-Rex1, Rac1 and Cdc42, suggesting a scaffolding function for this pseudokinase. NRBP1-mediated promotion of cell migration and invasion was P-Rex1-dependent, while constitutively-active Rac1 rescued the effect of NRBP1 knockdown on cell proliferation and invasion. Generation of reactive oxygen species via a NRBP1/P-Rex1 pathway was implicated in these oncogenic roles of NRBP1. Overall, these findings define a new function for NRBP1 and a novel oncogenic signalling pathway in TNBC that may be amenable to therapeutic intervention.

Dynamic modelling of the PI3K/MTOR signalling network uncovers biphasic dependence of mTORC1 activity on the mTORC2 subunit SIN1.

The PI3K/MTOR signalling network regulates a broad array of critical cellular processes, including cell growth, metabolism and autophagy. The mechanistic target of rapamycin (MTOR) kinase functions as a core catalytic subunit in two physically and functionally distinct complexes mTORC1 and mTORC2, which also share other common components including MLST8 (also known as GßL) and DEPTOR. Despite intensive research, how mTORC1 and 2 assembly and activity are coordinated, and how they are functionally linked remain to be fully characterized. This is due in part to the complex network wiring, featuring multiple feedback loops and intricate post-translational modifications. Here, we integrate predictive network modelling, in vitro experiments and -omics data analysis to elucidate the emergent dynamic behaviour of the PI3K/MTOR network. We construct new mechanistic models that encapsulate critical mechanistic details, including mTORC1/2 coordination by MLST8 (de)ubiquitination and the Akt-to-mTORC2 positive feedback loop. Model simulations validated by experimental studies revealed a previously unknown biphasic, threshold-gated dependence of mTORC1 activity on the key mTORC2 subunit SIN1, which is robust against cell-to-cell variation in protein expression. In addition, our integrative analysis demonstrates that ubiquitination of MLST8, which is reversed by OTUD7B, is regulated by IRS1/2. Our results further support the essential role of MLST8 in enabling both mTORC1 and 2's activity and suggest MLST8 as a viable therapeutic target in breast cancer. Overall, our study reports a new mechanistic model of PI3K/MTOR signalling incorporating MLST8-mediated mTORC1/2 formation and unveils a novel regulatory linkage between mTORC1 and mTORC2.

Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models.

BACKGROUND: Particular breast cancer subtypes pose a clinical challenge due to limited targeted therapeutic options and/or poor responses to the existing targeted therapies. While cell lines provide useful pre-clinical models, patient-derived xenografts (PDX) and organoids (PDO) provide significant advantages, including maintenance of genetic and phenotypic heterogeneity, 3D architecture and for PDX, tumor-stroma interactions. In this study, we applied an integrated multi-omic approach across panels of breast cancer PDXs and PDOs in order to identify candidate therapeutic targets, with a major focus on specific FGFRs. METHODS: MS-based phosphoproteomics, RNAseq, WES and Western blotting were used to characterize aberrantly activated protein kinases and effects of specific FGFR inhibitors. PDX and PDO were treated with the selective tyrosine kinase inhibitors AZD4547 (FGFR1-3) and BLU9931 (FGFR4). FGFR4 expression in cancer tissue samples and PDOs was assessed by immunohistochemistry. METABRIC and TCGA datasets were interrogated to identify specific FGFR alterations and their association with breast cancer subtype and patient survival. RESULTS: Phosphoproteomic profiling across 18 triple-negative breast cancers (TNBC) and 1 luminal B PDX revealed considerable heterogeneity in kinase activation, but 1/3 of PDX exhibited enhanced phosphorylation of FGFR1, FGFR2 or FGFR4. One TNBC PDX with high FGFR2 activation was exquisitely sensitive to AZD4547. Integrated 'omic analysis revealed a novel FGFR2-SKI fusion that comprised the majority of FGFR2 joined to the C-terminal region of SKI containing the coiled-coil domains. High FGFR4 phosphorylation characterized a luminal B PDX model and treatment with BLU9931 significantly decreased tumor growth. Phosphoproteomic and transcriptomic analyses confirmed on-target action of the two anti-FGFR drugs and also revealed novel effects on the spliceosome, metabolism and extracellular matrix (AZD4547) and RIG-I-like and NOD-like receptor signaling (BLU9931). Interrogation of public datasets revealed FGFR2 amplification, fusion or mutation in TNBC and other breast cancer subtypes, while FGFR4 overexpression and amplification occurred in all breast cancer subtypes and were associated with poor prognosis. Characterization of a PDO panel identified a luminal A PDO with high FGFR4 expression that was sensitive to BLU9931 treatment, further highlighting FGFR4 as a potential therapeutic target. CONCLUSIONS: This work highlights how patient-derived models of human breast cancer provide powerful platforms for therapeutic target identification and analysis of drug action, and also the potential of specific FGFRs, including FGFR4, as targets for precision treatment.

Control of Glucocorticoid Receptor Levels by PTEN Establishes a Failsafe Mechanism for Tumor Suppression.

The PTEN tumor suppressor controls cell death and survival by regulating functions of various molecular targets. While the role of PTEN lipid-phosphatase activity on PtdIns(3,4,5)P3 and inhibition of PI3K pathway is well characterized, the biological relevance of PTEN protein-phosphatase activity remains undefined. Here, using knockin (KI) mice harboring cancer-associated and functionally relevant missense mutations, we show that although loss of PTEN lipid-phosphatase function cooperates with oncogenic PI3K to promote rapid mammary tumorigenesis, the additional loss of PTEN protein-phosphatase activity triggered an extensive cell death response evident in early and advanced mammary tumors. Omics and drug-targeting studies revealed that PI3Ks act to reduce glucocorticoid receptor (GR) levels, which are rescued by loss of PTEN protein-phosphatase activity to restrain cell survival. Thus, we find that the dual regulation of GR by PI3K and PTEN functions as a rheostat that can be exploited for the treatment of PTEN loss-driven cancers.

Global redox proteome and phosphoproteome analysis reveals redox switch in Akt.

Protein oxidation sits at the intersection of multiple signalling pathways, yet the magnitude and extent of crosstalk between oxidation and other post-translational modifications remains unclear. Here, we delineate global changes in adipocyte signalling networks following acute oxidative stress and reveal considerable crosstalk between cysteine oxidation and phosphorylation-based signalling. Oxidation of key regulatory kinases, including Akt, mTOR and AMPK influences the fidelity rather than their absolute activation state, highlighting an unappreciated interplay between these modifications. Mechanistic analysis of the redox regulation of Akt identified two cysteine residues in the pleckstrin homology domain (C60 and C77) to be reversibly oxidized. Oxidation at these sites affected Akt recruitment to the plasma membrane by stabilizing the PIP3 binding pocket. Our data provide insights into the interplay between oxidative stress-derived redox signalling and protein phosphorylation networks and serve as a resource for understanding the contribution of cellular oxidation to a range of diseases.

Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer.

Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.

The switching role of ß-adrenergic receptor signalling in cell survival or death decision of cardiomyocytes.

How cell fate (survival or death) is determined and whether such determination depends on the strength of stimulation has remained unclear. In this study, we discover that the cell fate of cardiomyocytes switches from survival to death with the increase of ß-adrenergic receptor (ß-AR) stimulation. Mathematical simulations combined with biochemical experimentation of ß-AR signalling pathways show that the gradual increment of isoproterenol (a non-selective ß1/ß2-AR agonist) induces the switching response of Bcl-2 expression from the initial increase followed by a decrease below its basal level. The ERK1/2 and ICER-mediated feed-forward loop is the hidden design principle underlying such cell fate switching characteristics. Moreover, we find that ß1-blocker treatment increases the survival effect of ß-AR stimuli through the regulation of Bcl-2 expression leading to the resistance to cell death, providing new insight into the mechanism of therapeutic effects. Our systems analysis further suggests a novel potential therapeutic strategy for heart disease.

The hidden switches underlying RORα-mediated circuits that critically regulate uncontrolled cell proliferation.

Prostaglandin E2 (PGE2) is known to have a key role in the development of colorectal cancer, but previous experiments showed its contrasting (i.e. tumor-promoting or tumor-suppressive) roles depending on experimental conditions. To elucidate the mechanisms underlying such contrasting roles of PGE2 in tumorigenesis, we investigated all the previous experiments and found a new signal transduction pathway mediated by retinoic acid receptor-related orphan receptor (ROR)α, in which PGE2/PKCα-dependent phosphorylation of RORα attenuates Wnt target gene expression in colon cancer cells. From mathematical simulations combined with biochemical experimentation, we revealed that RORα induces a biphasic response of Wnt target genes to PGE2 stimulation through a regulatory switch formed by an incoherent feedforward loop, which provides a mechanistic explanation on the contrasting roles of PGE2 observed in previous experiments. More interestingly, we found that RORα constitutes another regulatory switch formed by coupled positive and negative feedback loops, which regulates the hysteretic response of Wnt signaling and eventually converts a proliferative cellular state into an anti-proliferative state in a very delicate way. Our results indicate that RORα is the key regulator at the center of these hidden switches that critically regulate cancer cell proliferation and thereby being a promising anti-cancer therapeutic target.

ELECANS--an integrated model development environment for multiscale cancer systems biology.

MOTIVATION: Computational multiscale models help cancer biologists to study the spatiotemporal dynamics of complex biological systems and to reveal the underlying mechanism of emergent properties. RESULTS: To facilitate the construction of such models, we have developed a next generation modelling platform for cancer systems biology, termed 'ELECANS' (electronic cancer system). It is equipped with a graphical user interface-based development environment for multiscale modelling along with a software development kit such that hierarchically complex biological systems can be conveniently modelled and simulated by using the graphical user interface/software development kit combination. Associated software accessories can also help users to perform post-processing of the simulation data for visualization and further analysis. In summary, ELECANS is a new modelling platform for cancer systems biology and provides a convenient and flexible modelling and simulation environment that is particularly useful for those without an intensive programming background. AVAILABILITY AND IMPLEMENTATION: ELECANS, its associated software accessories, demo examples, documentation and issues database are freely available at http://sbie.kaist.ac.kr/sub_0204.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The crossregulation between ERK and PI3K signaling pathways determines the tumoricidal efficacy of MEK inhibitor.

MEK inhibitor has been highlighted as a promising anti-tumor drug but its effect has been reported as varying over a wide range depending on patho-physiological conditions. In this study, we employed a systems approach by combining biochemical experimentation with in silico simulations to investigate the resistance mechanism and functional consequences of MEK inhibitor. To this end, we have developed an extended integrative model of ERK and PI3K signaling pathways by considering the crosstalk between Ras and PI3K, and analyzed the resistance mechanism to the MEK inhibitor under various mutational conditions. We found that the phospho-Akt level under the Raf mutation was remarkably augmented by MEK inhibitor, while the phospho-ERK level was almost completely repressed. These results suggest that bypassing of the ERK signal to the PI3K signal causes the resistance to the MEK inhibitor in a complex oncogenic signaling network. We further investigated the underlying mechanism of the drug resistance and revealed that the MEK inhibitor disrupts the negative feedback loops from ERK to SOS and GAB1, but activates the positive feedback loop composed of GAB1, Ras, and PI3K, which induces the bypass of the ERK signal to the PI3K signal. Based on these core feedback circuits, we suggested promising candidates for combination therapy and examined the improved inhibitory effects.

Multiscale modeling of tumorigenesis induced by mitochondrial incapacitation in cell death.

There is a renewed interest in tumorigenesis provoked by glycolysis and prosurvival autophagy following the mitochondrial permeability transition during cell death. To investigate such mitochondrial dysfunction, we have developed a multiscale model by integrating the dynamic behaviors of essential oncogenic proteins, cells, and their microenvironment. We found that 1) the concentration of cellular ATP (adenosine triphosphate) available during the autophagy-related processes is a critical factor in determining tumorigenesis; 2) mitochondrial aging rate has a significant influence on this tumorigenic effect, 3) specific hypoxic and oxidative stresses work cooperatively for tumorigenesis during cell death. We conclude that the cellular mitochondrial status is critical in triggering tumorigenesis during the cell death process, particularly under harsh microenvironments.

A hidden incoherent switch regulates RCAN1 in the calcineurin-NFAT signaling network.

Regulator of calcineurin 1 (RCAN1) is a key regulator of the calcineurin-NFAT signaling network in organisms ranging from yeast to human, but its functional role is still under debate because different roles of RCAN1 have been suggested under various experimental conditions. To elucidate the mechanisms underlying the RCAN1 regulatory system, we used a systems approach by combining single-cell experimentation with in silico simulations. In particular, we found that the nuclear export of GSK3ß, which switches on the facilitative role of RCAN1 in the calcineurin-NFAT signaling pathway, is promoted by PI3K signaling. Based on this, along with integrated information from previous experiments, we developed a mathematical model in which the functional role of RCAN1 changes in a dose-dependent manner: RCAN1 functions as an inhibitor when its levels are low, but as a facilitator when its levels are high. Furthermore, we identified a hidden incoherent regulation switch that mediates this role change, which entails negative regulation through RCAN1 binding to calcineurin and positive regulation through sequential phosphorylation of RCAN1.

Functional roles of multiple feedback loops in extracellular signal-regulated kinase and Wnt signaling pathways that regulate epithelial-mesenchymal transition.

Epithelial-mesenchymal transition (EMT) is a key event in the generation of invasive tumor cells. A hallmark of EMT is the repression of E-cadherin expression, which is regulated by various signal transduction pathways including extracellular signal-regulated kinase (ERK) and Wnt. These pathways are highly interconnected via multiple coupled feedback loops (CFL). As the function of such coupled feedback regulations is difficult to analyze experimentally, we used a systems biology approach where computational models were designed to predict biological effects that result from the complex interplay of CFLs. Using epidermal growth factor (EGF) and Wnt as input and E-cadherin transcriptional regulation as output, we established an ordinary differential equation model of the ERK and Wnt signaling network containing six feedback links and used extensive computer simulations to analyze the effects of these feedback links in isolation and different combinations. The results show that the feedbacks can generate a rich dynamic behavior leading to various dose-response patterns and have a decisive role in determining network responses to EGF and Wnt. In particular, we made two important findings: first, that coupled positive feedback loops composed of phosphorylation of Raf kinase inhibitor RKIP by ERK and transcriptional repression of RKIP by Snail have an essential role in causing a switch-like behavior of E-cadherin expression; and second, that RKIP expression inhibits EMT progression by preventing E-cadherin suppression. Taken together, our findings provide us with a system-level understanding of how RKIP can regulate EMT progression and may explain why RKIP is downregulated in so many metastatic cancer cells.

Positive- and negative-feedback regulations coordinate the dynamic behavior of the Ras-Raf-MEK-ERK signal transduction pathway.

The Ras-Raf-MEK-ERK pathway (or ERK pathway) is an important signal transduction system involved in the control of cell proliferation, survival and differentiation. However, the dynamic regulation of the pathway by positive- and negative-feedback mechanisms, in particular the functional role of Raf kinase inhibitor protein (RKIP) are still incompletely understood. RKIP is a physiological endogenous inhibitor of MEK phosphorylation by Raf kinases, but also participates in a positive-feedback loop in which ERK can inactivate RKIP. The aim of this study was to elucidate the hidden dynamics of these feedback mechanisms and to identify the functional role of RKIP through combined efforts of biochemical experiments and in silico simulations based on an experimentally validated mathematical model. We show that the negative-feedback loop from ERK to SOS plays a crucial role in generating an oscillatory behavior of ERK activity. The positive-feedback loop in which ERK functionally inactivates RKIP also enhances the oscillatory activation pattern of ERK. However, RKIP itself has an important role in inducing a switch-like behavior of MEK activity. When overexpressed, RKIP also causes delayed and reduced responses of ERK. Thus, positive- and negative-feedback loops and RKIP work together to shape the response pattern and dynamical characteristics of the ERK pathway.