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

BBX16 mediates the repression of seedling photomorphogenesis downstream of the GUN1/GLK1 module during retrograde signalling

Plastid-to-nucleus retrograde signalling (RS) initiated by dysfunctional chloroplasts impact photomorphogenic development. We have previously shown that the transcription factor GLK1 acts downstream of the RS regulator GUN1 in photodamaging conditions to regulate not only the well established expression of photosynthesis-associated nuclear genes (PhANGs) but also to regulate seedling morphogenesis. Specifically, the GUN1/GLK1 module inhibits the light-induced phytochrome-interacting factor (PIF)-repressed transcriptional network to suppress cotyledon development when chloroplast integrity is compromised, modulating the area exposed to potentially damaging high light. However, how the GUN1/GLK1 module inhibits photomorphogenesis upon chloroplast damage remained undefined. Here, we report the identification of BBX16 as a novel direct target of GLK1. BBX16 is induced and promotes photomorphogenesis in moderate light and is repressed via GUN1/GLK1 after chloroplast damage. Additionally, we showed that BBX16 represents a regulatory branching point downstream of GUN1/GLK1 in the regulation of PhANG expression and seedling development upon RS activation. The gun1 phenotype in lincomycin and the gun1-like phenotype of GLK1OX are markedly suppressed in gun1bbx16 and GLK1OXbbx16. This study identified BBX16 as the first member of the BBX family involved in RS, and defines a molecular bifurcation mechanism operated by GLK1/BBX16 to optimise seedling de-etiolation, and to ensure photoprotection in unfavourable light conditions.

mTOR-Rictor-EGFR axis in oncogenesis and diagnosis of glioblastoma multiforme

Glioblastoma multiforme (GBM) is one of the aggressive brain cancers with patients having less survival period upto 12-15 months. Mammalian target of rapamycin (mTOR) is a serine/threonine kinase, belongs to the phosphatidylinositol 3-kinases (PI3K) pathway and is involved in various cellular processes of cancer cells. Cancer metabolism is regulated by mTOR and its components. mTOR forms two complexes as mTORC1 and mTORC2. Studies have identified the key component of the mTORC2 complex, Rapamycin-insensitive companion of mammalian target of rapamycin (Rictor) plays a prominent role in the regulation of cancer cell proliferation and metabolism. Apart, growth factor receptor signaling such as epidermal growth factor signaling mediated by epidermal growth factor receptor (EGFR) regulates cancer-related processes. In EGFR signaling various other signaling cascades such as phosphatidyl-inositol 3-kinase (PI3K)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR pathway) and Ras/Raf/mitogen-activated protein kinase/ERK kinase (MEK)/extracellular-signal-regulated kinase (ERK) -dependent signaling cross-talk each other. From various studies about GBM, it is very well established that Rictor and EGFR mediated signaling pathways majorly playing a pivotal role in chemoresistance and tumor aggressiveness. Recent studies have shown that non-coding RNAs such as microRNAs (miRs) and long non-coding RNAs (lncRNAs) regulate the EGFR and Rictor and sensitize the cells towards chemotherapeutic agents. Thus, understanding of microRNA mediated regulation of EGFR and Rictor will help in cancer prevention and management as well as a future therapy.

Computational algorithms for in silico profiling of activating mutations in cancer

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

Weak power frequency magnetic fields induce microtubule cytoskeleton reorganization depending on the epidermal growth factor receptor and the calcium related signaling

We have shown previously that a weak 50 Hz magnetic field (MF) invoked the actin-cytoskeleton, and provoked cell migration at the cell level, probably through activating the epidermal growth factor receptor (EGFR) related motility pathways. However, whether the MF also affects the microtubule (MT)-cytoskeleton is still unknown. In this article, we continuously investigate the effects of 0.4 mT, 50 Hz MF on the MT, and try to understand if the MT effects are also associated with the EGFR pathway as the actin-cytoskeleton effects were. Our results strongly suggest that the MF effects are similar to that of EGF stimulation on the MT cytoskeleton, showing that 1) the MF suppressed MT in multiple cell types including PC12 and FL; 2) the MF promoted the clustering of the EGFR at the protein and the cell levels, in a similar way of that EGF did but with higher sensitivity to PD153035 inhibition, and triggered EGFR phosphorylation on sites of Y1173 and S1046/1047; 3) these effects were strongly depending on the Ca²⁺ signaling through the L-type calcium channel (LTCC) phosphorylation and elevation of the intracellular Ca²⁺ level. Strong associations were observed between EGFR and the Ca²⁺ signaling to regulate the MF-induced-reorganization of the cytoskeleton network, via phosphorylating the signaling proteins in the two pathways, including a significant MT protein, tau. These results strongly suggest that the MF activates the overall cytoskeleton in the absence of EGF, through a mechanism related to both the EGFR and the LTCC/Ca²⁺ signaling pathways.

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

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

Personalized medicine approaches for colon cancer driven by genomics and systems biology: OncoTrack

The post-genomic era promises to pave the way to a personalized understanding of disease processes, with technological and analytical advances helping to solve some of the world's health challenges. Despite extraordinary progress in our understanding of cancer pathogenesis, the disease remains one of the world's major medical problems. New therapies and diagnostic procedures to guide their clinical application are urgently required. OncoTrack, a consortium between industry and academia, supported by the Innovative Medicines Initiative, signifies a new era in personalized medicine, which synthesizes current technological advances in omics techniques, systems biology approaches, and mathematical modeling. A truly personalized molecular imprint of the tumor micro-environment and subsequent diagnostic and therapeutic insight is gained, with the ultimate goal of matching the "right" patient to the "right" drug and identifying predictive biomarkers for clinical application. This comprehensive mapping of the colon cancer molecular landscape in tandem with crucial, clinical functional annotation for systems biology analysis provides unprecedented insight and predictive power for colon cancer management. Overall, we show that major biotechnological developments in tandem with changes in clinical thinking have laid the foundations for the OncoTrack approach and the future clinical application of a truly personalized approach to colon cancer theranostics.

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

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

Are erlotinib and gefitinib interchangeable, opposite or complementary for non-small cell lung cancer treatment? Biological, pharmacological and clinical aspects

Gefitinib and erlotinib are the two anti-epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) approved for treatment of advanced NSCLC patients. These drugs target one of the most important pathways in lung carcinogenesis and are able to exploit the phenomenon of 'oncogene addiction', with different efficacy according to EGFR gene mutational status in tumor samples. Gefitinib has been approved only for EGFR mutation bearing patients regardless the line of treatment, while erlotinib is also indicated in patients without EGFR mutation who undergo second- or third-line treatment. Some studies evaluated the main differences between these drugs both for direct comparison and to improve their sequential use. In particular, toxicity profile resulted partially different, and these observations may be explained by several molecular and pharmacokinetic features. Therefore, this review integrates preclinical data with clinical evidences of TKIs to guide the optimization of currently available treatments in advanced NSCLC patients.

Computational analysis of the regulation of EGFR by protein tyrosine phosphatases

The tyrosine phosphorylated epidermal growth factor receptor (EGFR) initiates numerous cell signaling pathways. Although EGFR phosphorylation levels are ultimately determined by the balance of receptor kinase and protein tyrosine phosphatase (PTP) activities, the kinetics of EGFR dephosphorylation are not well understood. Previous models of EGFR signaling have generally neglected PTP activity or computed PTP activity by considering data that do not fully reveal the kinetics and compartmentalization of EGFR dephosphorylation. We developed a compartmentalized, mechanistic model to elucidate the kinetics of EGFR dephosphorylation and the coupling of this process to phosphorylation-dependent EGFR endocytosis. Model regression against data from HeLa cells for EGFR phosphorylation response to EGFR activation, PTP inhibition, and EGFR kinase inhibition led to the conclusion that EGFR dephosphorylation occurs at the plasma membrane and in the cell interior with a timescale that is smaller than that for ligand-mediated EGFR endocytosis. The model further predicted that sufficiently rapid dephosphorylation of EGFR at the plasma membrane could potentially impede EGFR endocytosis, consistent with recent experimental findings. Overall, our results suggest that PTPs regulate multiple receptor-level phenomena via their action at the plasma membrane and cell interior and point to new possibilities for targeting PTPs for modulation of EGFR dynamics.

Modeling of tumor progression in NSCLC and intrinsic resistance to TKI in loss of PTEN expression

EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.

Phosphorylated EGFR expression may predict outcome of EGFR-TKIs therapy for the advanced NSCLC patients with wild-type EGFR

Background

EGFR mutation is a strong predictive factor of EGFR-TKIs therapy. However, at least 10% of patients with EGFR wild-type are responsive to TKIs, suggesting that other determinants of outcome besides EGFR mutation might exist. We hypothesized that activation of phosphorylated EGFR could be a potential predictive biomarker to EGFR-TKIs treatment among patients in wild-type EGFR.

Method

Total of 205 stage IIIb and IV NSCLC patients, tissue samples of whom were available for molecular analysis, were enrolled in this study. The phosphorylation of EGFR at tyrosine 1068 (pTyr1068) and 1173 (pTyr1173) were assessed by immunohistochemistry, and EGFR mutations were detected by denaturing high performance liquid chromatograph (DHPLC).

Results

Among 205 patients assessable for EGFR mutation and phosphorylation analysis, 92 (44.9%) were EGFR mutant and 165 patients (57.6%) had pTyr1173 expression. Superior progression-free survival (PFS) was seen after EGFR-TKIs therapy in patients with pTyr1068 expression compared to pTyr1068 negative ones (median PFS 7.0 months vs. 1.2 months, P < 0.001). Inversely, patients with pTyr1173 had a shorter PFS (4.8 months VS. 7.7 months, P = 0.016). In subgroup of patients with wild-type EGFR, pTyr1068 expression positive ones had a significantly prolonged PFS (4.2 months vs.1.2 months P < 0.001) compared with those without pTyr1068 expression. Sixteen patients with both wild-type EGFR and pTyr1068 who responded to EGFR-TKIs had median PFS of 15.6 months (95%CI: 7.28-23.9).

Conclusion

pTyr1068 may be a predictive biomarker for screening the population for clinical response to EGFR-TKIs treatment; especially for patients with wild-type EGFR.

Structural systems biology and multiscale signaling models

We review current advances in experimental as well as computational modeling and simulation approaches to structural systems biology, whose overall aim is to build quantitative models of signaling networks while retaining the crucial elements of molecular specificity. We briefly discuss the current and emerging experimental and computational methods, particularly focusing on hybrid and multiscale methods, and highlight several applications in cell signaling with quantitative and predictive capabilities. The scope of such models range from delineating protein-protein interactions to describing clinical implications.

Multiscale cancer modeling

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insights in the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community.

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.

Cancer Cell: Linking Oncogenic Signaling to Molecular Structure

A multiscale strategy is presented for constructing models of intracellular signaling networks in which the oncogenic behavior of the network is encoded through alternate parameterization of the kinetic and structural properties of mutant oncoproteins. The approach uses molecular dynamics and docking simulations to quantify altered topologies of interactions as well as to provide the missing parameters for network models of both wild-type and oncogenic signaling. Through simulation of the resulting signaling networks, the global behavior of these networks may then be compared and functional roles may be assigned to the mutant oncoproteins. An example of this approach is presented in which structural alterations found in a mutant form of the epidermal growth factor receptor are represented as kinetic perturbations in a model of growth factor signaling. Based on network parameters estimated from molecular-level simulations, simulations at the network level show that small perturbations in molecular structure can lead to profoundly altered cellular phenotype.

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

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