Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data

Predictive data-driven modeling of C-terminal tyrosine function in the EGFR signaling network

The epidermal growth factor receptor (EGFR) has been studied extensively because of its critical role in cellular signaling and association with disease. Previous models have elucidated interactions between EGFR and downstream adaptor proteins or showed phenotypes affected by EGFR. However, the link between specific EGFR phosphorylation sites and phenotypic outcomes is still poorly understood. Here, we employed a suite of isogenic cell lines expressing site-specific mutations at each of the EGFR C-terminal phosphorylation sites to interrogate their role in the signaling network and cell biological response to stimulation. Our results demonstrate the resilience of the EGFR network, which was largely similar even in the context of multiple Y-to-F mutations in the EGFR C-terminal tail, while also revealing nodes in the network that have not previously been linked to EGFR signaling. Our data-driven model highlights the signaling network nodes associated with distinct EGF-driven cell responses, including migration, proliferation, and receptor trafficking. Application of this same approach to less-studied RTKs should provide a plethora of novel associations that should lead to an improved understanding of these signaling networks.

Transferrin receptor 1 promotes the fibroblast growth factor receptor-mediated oncogenic potential of diffused-type gastric cancer

Diffuse-type gastric cancer (DGC) is a highly invasive subtype of gastric adenocarcinoma that frequently exhibits scattered peritoneal metastasis. Previous studies have shown that the genes of receptor tyrosine kinases (RTKs), such as fibroblast growth factor receptor 2 (FGFR2) or Met, are amplified in some DGC cell lines, leading to the constitutive activation of corresponding RTKs. In these cell lines, the survival of cancer cells appears to be dependent on the activation of RTKs. To gain novel insights into the downstream signaling pathways of RTKs specific to DGC, phosphotyrosine-containing proteins associated with activated FGFR2 were purified through two sequential rounds of immunoprecipitation from the lysates of two DGC cell lines. As a result, transferrin receptor 1 (TfR1) was identified as the binding partner of FGFR2. Biochemical analysis confirmed that TfR1 protein binds to FGFR2 and is phosphorylated at tyrosine 20 (Tyr20) in an FGFR2 kinase activity-dependent manner. The knockdown of TfR1 and treatment with an inhibitor of FGFR2 caused significant impairment in iron uptake and suppression of cellular proliferation in vitro. Moreover, the suppression of expression levels of TfR1 in the DGC cells significantly reduced their tumorigenicity and potency of peritoneal dissemination. It was indicated that TfR1, when phosphorylated by the binding partner FGFR2 in DGC cells, promotes proliferation and tumorigenicity of these cancer cells. These results suggest that the control of TfR1 function may serve as a therapeutic target in DGC with activated FGFR2.

Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells

INTRODUCTION

Many pathologies, including cancer, have been associated with aberrant phosphorylation-mediated signaling networks that drive altered cell proliferation, migration, metabolic regulation, and can lead to systemic inflammation. Phosphoproteomics, the large-scale analysis of protein phosphorylation sites, has emerged as a powerful tool to define signaling network regulation and dysregulation in normal and pathological conditions.

AREAS COVERED

We provide an overview of methodology for global phosphoproteomics as well as enrichment of specific subsets of the phosphoproteome, including phosphotyrosine and phospho-motif enrichment of kinase substrates. We review quantitative methods, advantages and limitations of different mass spectrometry acquisition formats, and computational approaches to extract biological insight from phosphoproteomics data. Throughout, we discuss various applications and their challenges in implementation.

EXPERT OPINION

Over the past 20 years the field of phosphoproteomics has advanced to enable deep biological and clinical insight through the quantitative analysis of signaling networks. Future areas of development include Clinical Laboratory Improvement Amendments (CLIA)-approved methods for analysis of clinical samples, continued improvements in sensitivity to enable analysis of small numbers of rare cells and tissue microarrays, and computational methods to integrate data resulting from multiple systems-level quantitative analytical methods.

Data-Driven Computational Modeling Identifies Determinants of Glioblastoma Response to SHP2 Inhibition

Oncogenic protein tyrosine phosphatases have long been viewed as drug targets of interest, and recently developed allosteric inhibitors of SH2 domain-containing phosphatase-2 (SHP2) have entered clinical trials. However, the ability of phosphatases to regulate many targets directly or indirectly and to both promote and antagonize oncogenic signaling may make the efficacy of phosphatase inhibition challenging to predict. Here we explore the consequences of antagonizing SHP2 in glioblastoma, a recalcitrant cancer where SHP2 has been proposed as a useful drug target. Measuring protein phosphorylation and expression in glioblastoma cells across 40 signaling pathway nodes in response to different drugs and for different oxygen tensions revealed that SHP2 antagonism has network-level, context-dependent signaling consequences that affect cell phenotypes (e.g., cell death) in unanticipated ways. To map specific signaling consequences of SHP2 antagonism to phenotypes of interest, a data-driven computational model was constructed based on the paired signaling and phenotype data. Model predictions aided in identifying three signaling processes with implications for treating glioblastoma with SHP2 inhibitors. These included PTEN-dependent DNA damage repair in response to SHP2 inhibition, AKT-mediated bypass resistance in response to chronic SHP2 inhibition, and SHP2 control of hypoxia-inducible factor expression through multiple MAPKs. Model-generated hypotheses were validated in multiple glioblastoma cell lines, in mouse tumor xenografts, and through analysis of The Cancer Genome Atlas data. Collectively, these results suggest that in glioblastoma, SHP2 inhibitors antagonize some signaling processes more effectively than existing kinase inhibitors but can also limit the efficacy of other drugs when used in combination. SIGNIFICANCE: These findings demonstrate that allosteric SHP2 inhibitors have multivariate and context-dependent effects in glioblastoma that may make them useful components of some combination therapies, but not others.

Computational Systems Biology in Cancer: Modeling Methods and Applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

Data-Modeling Identifies Conflicting Signaling Axes Governing Myoblast Proliferation and Differentiation Responses to Diverse Ligand Stimuli

Introduction

Skeletal muscle tissue development and regeneration relies on the proliferation, maturation and fusion of muscle progenitor cells (myoblasts), which arise transiently from muscle stem cells (satellite cells). Following muscle damage, myoblasts proliferate and differentiate in response to temporally-varying inflammatory cytokines, growth factors, and extracellular matrix cues, which stimulate a shared network of intracellular signaling pathways. Here we present an integrated data-modeling approach to elucidate synergies and antagonisms among proliferation and differentiation signaling axes in myoblasts stimulated by regeneration-associated ligands.

Methods

We treated mouse primary myoblasts in culture with combinations of eight regeneration-associated growth factors and cytokines in mixtures that induced additive, synergistic, and antagonistic effects on myoblast proliferation and differentiation responses. For these combinatorial stimuli, we measured the activation dynamics of seven signal transduction pathways using multiplexed phosphoprotein assays and scored proliferation and differentiation responses based on expression of myogenic commitment factors to assemble a cue-signaling-response data compendium. We interrogated the relationship between these signals and responses by partial least-squares (PLS) regression modeling.

Results

Partial least-squares data-modeling accurately predicted response outcomes in cross-validation on the training compendium (cumulative R ² = 0.96). The PLS model highlighted signaling axes that distinctly govern myoblast proliferation (MEK-ERK, Stat3) and differentiation (JNK) in response to these combinatorial cues, and we confirmed these signal-response associations with small molecule perturbations. Unexpectedly, we observed that a negative feedback circuit involving the phosphatase DUSP6/MKP-3 auto-regulates MEK-ERK signaling in myoblasts.

Conclusion

This data-modeling approach identified conflicting signaling axes that underlie muscle progenitor cell proliferation and differentiation.

Partial Least Squares Regression Models for the Analysis of Kinase Signaling

Partial least squares regression (PLSR) is a data-driven modeling approach that can be used to analyze multivariate relationships between kinase networks and cellular decisions or patient outcomes. In PLSR, a linear model relating an X matrix of dependent variables and a Y matrix of independent variables is generated by extracting the factors with the strongest covariation. While the identified relationship is correlative, PLSR models can be used to generate quantitative predictions for new conditions or perturbations to the network, allowing for mechanisms to be identified. This chapter will provide a brief explanation of PLSR and provide an instructive example to demonstrate the use of PLSR to analyze kinase signaling.

Cell signaling regulation by protein phosphorylation: a multivariate, heterogeneous, and context-dependent process

Proper spatiotemporal regulation of protein phosphorylation in cells and tissues is required for normal development and homeostasis, but aberrant protein phosphorylation regulation leads to various diseases. The study of signaling regulation by protein phosphorylation is complicated in part by the sheer scope of the kinome and phosphoproteome, dependence of signaling protein functionality on cellular localization, and the complex multivariate relationships that exist between protein phosphorylation dynamics and the cellular phenotypes they control. Additional complexities arise from the ability of microenvironmental factors to influence phosphorylation-dependent signaling and from the tendency for some signaling processes to occur heterogeneously among cells. These considerations should be taken into account when measuring cell signaling regulation by protein phosphorylation.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data

Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.

Phosphoproteomics can offer significant insight into cell signalling and how signalling is modified in response to perturbations. Here the authors develop a new tool for the analysis of high-content phosphoproteomics in the context of kinase/phosphatase-substrate knowledge, which is used to train logic models.

Toward a systems-level view of dynamic phosphorylation networks

To better understand how cells sense and respond to their environment, it is important to understand the organization and regulation of the phosphorylation networks that underlie most cellular signal transduction pathways. These networks, which are composed of protein kinases, protein phosphatases and their respective cellular targets, are highly dynamic. Importantly, to achieve signaling specificity, phosphorylation networks must be regulated at several levels, including at the level of protein expression, substrate recognition, and spatiotemporal modulation of enzymatic activity. Here, we briefly summarize some of the traditional methods used to study the phosphorylation status of cellular proteins before focusing our attention on several recent technological advances, such as protein microarrays, quantitative mass spectrometry, and genetically-targetable fluorescent biosensors, that are offering new insights into the organization and regulation of cellular phosphorylation networks. Together, these approaches promise to lead to a systems-level view of dynamic phosphorylation networks.

Autocrine signaling is a key regulatory element during osteoclastogenesis

Osteoclasts are responsible for bone destruction in degenerative, inflammatory and metastatic bone disorders. Although osteoclastogenesis has been well-characterized in mouse models, many questions remain regarding the regulation of osteoclast formation in human diseases. We examined the regulation of human precursors induced to differentiate and fuse into multinucleated osteoclasts by receptor activator of nuclear factor kappa-B ligand (RANKL). High-content single cell microscopy enabled the time-resolved quantification of both the population of monocytic precursors and the emerging osteoclasts. We observed that prior to induction of osteoclast fusion, RANKL stimulated precursor proliferation, acting in part through an autocrine mediator. Cytokines secreted during osteoclastogenesis were resolved using multiplexed quantification combined with a Partial Least Squares Regression model to identify the relative importance of specific cytokines for the osteoclastogenesis outcome. Interleukin 8 (IL-8) was identified as one of RANKL-induced cytokines and validated for its role in osteoclast formation using inhibitors of the IL-8 cognate receptors CXCR1 and CXCR2 or an IL-8 blocking antibody. These insights demonstrate that autocrine signaling induced by RANKL represents a key regulatory component of human osteoclastogenesis.

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

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

Proteogenomic convergence for understanding cancer pathways and networks

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

Quantitative analysis of castration resistant prostate cancer progression through phosphoproteome signaling

Background

Although recent progress has been made in treating castration resistant prostate cancer, the interplay of signaling pathways which enable castration resistant growth is incompletely understood. A data driven, multivariate approach, was used in this study to predict prostate cancer cell survival based on the phosphorylation levels of key proteins in PC3, LNCaP, and MDA-PCa-2b cell lines in response to EGF, IGF1, IL6, TNFα, dihydrotestosterone, and docetaxel treatment.

Methods

The prostate cancer cell lines were treated with ligands or inhibitors, cell lyates were collected, and the amount of phosphoprotein quantified using 384 well ELISA assays. In separate experiments, relative cell viability was determined using an MTT assay. Normalized data was imported into Matlab where regression analysis was performed.

Results

Based on a linear model developed using partial least squares regression, p-Erk1/2 was found to correlate with castration resistant survival along with p-RPS6, and this model was determined to have a leave-one-out cross validated R² value of 0.61. The effect of androgen on the phosphoproteome was examined, and increases in PI3K related phosphoproteins (p-Akt, p-RPS6, and p-GSK3) were observed which accounted for the majority of the significant increase in androgen-mediated cell survival. Simultaneous inhibition of the PI3K pathway and treatment with androgen resulted in a non-significant increase in survival. Given the strong effect of PI3K related signaling in enabling castration resistant survival, the specific effect of mTor versus complete inhibition was examined using targeted inhibitors. It was determine that mTor inhibition accounts for 52% of the effect of complete PI3K inhibition on cell survival. The differences in signaling between the cell lines were explored it was observed that MDA-PCa-2b exhibited far less activation of p-Erk in response to varying treatments, explaining one of the reasons for the lack of castration resistance.

Conclusion

In this work, regression analysis to the phosphoproteome was used to illustrate the sources of castration resistance between the cell lines including reduced p-Erk signaling in MDA-PCa-2b and variations in p-JNK across the cell lines, as well as studying the signaling pathways which androgen acts through, and determining the response to treatment with targeted inhibitors.

Mesenchymal morphogenesis of embryonic stem cells dynamically modulates the biophysical microtissue niche

Stem cell fate and function are dynamically modulated by the interdependent relationships between biochemical and biophysical signals constituting the local 3D microenvironment. While approaches to recapitulate the stem cell niche have been explored for directing stem cell differentiation, a quantitative relationship between embryonic stem cell (ESC) morphogenesis and intrinsic biophysical cues within three-dimensional microtissues has not been established. In this study, we demonstrate that mesenchymal embryonic microtissues induced by BMP4 exhibited increased stiffness and viscosity accompanying differentiation, with cytoskeletal tension significantly contributing to multicellular stiffness. Perturbation of the cytoskeleton during ESC differentiation led to modulation of the biomechanical and gene expression profiles, with the resulting cell phenotype and biophysical properties being highly correlated by multivariate analyses. Together, this study elucidates the dynamics of biophysical and biochemical signatures within embryonic microenvironments, with broad implications for monitoring tissue dynamics, modeling pathophysiological and embryonic morphogenesis and directing stem cell patterning and differentiation.

Correlating phosphoproteomic signaling with castration resistant prostate cancer survival through regression analysis

Prostate cancer most commonly presents as initially castration dependent, however in a minority of patients the disease will progress to a state of castration resistance. Here, approaches for correlating alterations in the phosphoproteome with androgen independent cell survival in the LNCaP, PC3, and MDa-PCa-2b cell lines are discussed. The performance of the regression techniques multiple linear, ridge, principal component, and partial least squares regression is compared. The predictive performance of these algorithms over randomized data sets and using the Akaike Information Criterion is explored, and principal component and partial least squares regression are found to outperform other regression approaches. The effect of altering the number of features versus observations on the R(2) value and predictive performance is also examined using the partial least squares regression model. Utilizing these approaches "drivers" of castration resistant disease can be identified whose modulation alters phenotypic outcomes. These data provide an empirical comparison of the various considerations when statistically analyzing phosphorylation data with the aim of correlating with phenotypic outcomes.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Survival of HER2-Positive Breast Cancer Cells: Receptor Signaling to Apoptotic Control Centers

HER2 is overexpressed in a subset of breast cancers and controls an oncogenic signaling network that inhibits tumor cell death through the specific biochemical regulation of apoptotic pathways. In particular, the mitochondrial pathway for apoptosis is important for death induced by inhibitors of HER2. This review focuses on the connections between this oncogenic signaling network and individual components of the mitochondrial pathway. A comprehensive view of this signaling network is crucial for developing novel drugs in this area and to gain an understanding of how these regulatory interactions are altered in drug-refractory cancers.

Computational Model of Gab1/2-Dependent VEGFR2 Pathway to Akt Activation

Vascular endothelial growth factor (VEGF) signal transduction is central to angiogenesis in development and in pathological conditions such as cancer, retinopathy and ischemic diseases. However, no detailed mass-action models of VEGF receptor signaling have been developed. We constructed and validated the first computational model of VEGFR2 trafficking and signaling, to study the opposing roles of Gab1 and Gab2 in regulation of Akt phosphorylation in VEGF-stimulated endothelial cells. Trafficking parameters were optimized against 5 previously published in vitro experiments, and the model was validated against six independent published datasets. The model showed agreement at several key nodes, involving scaffolding proteins Gab1, Gab2 and their complexes with Shp2. VEGFR2 recruitment of Gab1 is greater in magnitude, slower, and more sustained than that of Gab2. As Gab2 binds VEGFR2 complexes more transiently than Gab1, VEGFR2 complexes can recycle and continue to participate in other signaling pathways. Correspondingly, the simulation results show a log-linear relationship between a decrease in Akt phosphorylation and Gab1 knockdown while a linear relationship was observed between an increase in Akt phosphorylation and Gab2 knockdown. Global sensitivity analysis demonstrated the importance of initial-concentration ratios of antagonistic molecular species (Gab1/Gab2 and PI3K/Shp2) in determining Akt phosphorylation profiles. It also showed that kinetic parameters responsible for transient Gab2 binding affect the system at specific nodes. This model can be expanded to study multiple signaling contexts and receptor crosstalk and can form a basis for investigation of therapeutic approaches, such as tyrosine kinase inhibitors (TKIs), overexpression of key signaling proteins or knockdown experiments.

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

Automated and high confidence protein phosphorylation site localization using complementary collision-activated dissociation and electron transfer dissociation tandem mass spectrometry

Reversible protein phosphorylation plays a critical role in cell signaling and is responsible for the regulation of many biological processes in most living organisms. The low stoichiometry of protein phosphorylation requires sensitive analysis by tandem mass spectrometry. However, incomplete peptide fragmentation and the loss of labile phosphate groups complicate identification of the site of the phosphate motif. Here, we have implemented and evaluated a novel approach for phospho-site localization by the combined use of peptide tandem mass spectrometry data obtained using both collision-activated dissociation and electron transfer dissociation, an approach termed the Cscore. The scoring algorithm used in the Cscore was adapted from the widely used Ascore method. The analytical benefit of integrating the product ion information of both ETD and CAD data are evident by increased confidence in phospho-site localization and the number of assigned phospho-sites at a fixed false-localization rate. The average calculated Cscore from a large data set (>7000 phosphopeptide MS/MS spectra) was ∼32 compared to ∼23 and ∼17 for the Ascore using collision-activated dissociation (CAD) or electron transfer dissociation (ETD), respectively. Compared with the Ascore using either CAD or ETD, the Cscore identified up to 88% more phosphorylation sites. Using a phosphopeptide library revealed that the score threshold for obtaining a false-localization rate of 0.5% was lower for the Cscore than either the Ascore (CAD) or the Ascore (ETD).

Quantitative proteomics with siRNA screening identifies novel mechanisms of trastuzumab resistance in HER2 amplified breast cancers

HER2 is a receptor tyrosine kinase that is overexpressed in 20% to 30% of human breast cancers and which affects patient prognosis and survival. Treatment of HER2-positive breast cancer with the monoclonal antibody trastuzumab (Herceptin) has improved patient survival, but the development of trastuzumab resistance is a major medical problem. Many of the known mechanisms of trastuzumab resistance cause changes in protein phosphorylation patterns, and therefore quantitative proteomics was used to examine phosphotyrosine signaling networks in trastuzumab-resistant cells. The model system used in this study was two pairs of trastuzumab-sensitive and -resistant breast cancer cell lines. Using stable isotope labeling, phosphotyrosine immunoprecipitations, and online TiO(2) chromatography utilizing a dual trap configuration, ~1700 proteins were quantified. Comparing quantified proteins between the two cell line pairs showed only a small number of common protein ratio changes, demonstrating heterogeneity in phosphotyrosine signaling networks across different trastuzumab-resistant cancers. Proteins showing significant increases in resistant versus sensitive cells were subjected to a focused siRNA screen to evaluate their functional relevance to trastuzumab resistance. The screen revealed proteins related to the Src kinase pathway, such as CDCP1/Trask, embryonal Fyn substrate, and Paxillin. We also identify several novel proteins that increased trastuzumab sensitivity in resistant cells when targeted by siRNAs, including FAM83A and MAPK1. These proteins may present targets for the development of clinical diagnostics or therapeutic strategies to guide the treatment of HER2+ breast cancer patients who develop trastuzumab resistance.

Modeling the latent dimensions of multivariate signaling datasets

Cellular signal transduction is coordinated by modifications of many proteins within cells. Protein modifications are not independent, because some are connected through shared signaling cascades and others jointly converge upon common cellular functions. This coupling creates a hidden structure within a signaling network that can point to higher level organizing principles of interest to systems biology. One can identify important covariations within large-scale datasets by using mathematical models that extract latent dimensions-the key structural elements of a measurement set. In this paper, we introduce two principal component-based methods for identifying and interpreting latent dimensions. Principal component analysis provides a starting point for unbiased inspection of the major sources of variation within a dataset. Partial least-squares regression reorients these dimensions toward a specific hypothesis of interest. Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate.

Identifying biological network structure, predicting network behavior, and classifying network state with High Dimensional Model Representation (HDMR)

This work presents an adapted Random Sampling - High Dimensional Model Representation (RS-HDMR) algorithm for synergistically addressing three key problems in network biology: (1) identifying the structure of biological networks from multivariate data, (2) predicting network response under previously unsampled conditions, and (3) inferring experimental perturbations based on the observed network state. RS-HDMR is a multivariate regression method that decomposes network interactions into a hierarchy of non-linear component functions. Sensitivity analysis based on these functions provides a clear physical and statistical interpretation of the underlying network structure. The advantages of RS-HDMR include efficient extraction of nonlinear and cooperative network relationships without resorting to discretization, prediction of network behavior without mechanistic modeling, robustness to data noise, and favorable scalability of the sampling requirement with respect to network size. As a proof-of-principle study, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various experimental perturbations. A comparison to network structure identified in the literature and through other inference methods, including Bayesian and mutual-information based algorithms, suggests that RS-HDMR can successfully reveal a network structure with a low false positive rate while still capturing non-linear and cooperative interactions. RS-HDMR identified several higher-order network interactions that correspond to known feedback regulations among multiple network species and that were unidentified by other network inference methods. Furthermore, RS-HDMR has a better ability to predict network response under unsampled conditions in this application than the best statistical inference algorithm presented in the recent DREAM3 signaling-prediction competition. RS-HDMR can discern and predict differences in network state that arise from sources ranging from intrinsic cell-cell variability to altered experimental conditions, such as when drug perturbations are introduced. This ability ultimately allows RS-HDMR to accurately classify the experimental conditions of a given sample based on its observed network state.

Toward quantitative phosphotyrosine profiling in vivo

Tyrosine phosphorylation is a dynamic reversible post-translational modification that regulates many aspects of cell biology. To understand how this modification controls biological function, it is necessary to not only identify the specific sites of phosphorylation, but also to quantify how phosphorylation levels on these sites may be altered under specific physiological conditions. Due to its sensitivity and accuracy, mass spectrometry (MS) has widely been applied to the identification and characterization of phosphotyrosine signaling across biological systems. In this review we highlight the advances in both MS and phosphotyrosine enrichment methods that have been developed to enable the identification of low level tyrosine phosphorylation events. Computational and manual approaches to ensure confident identification of phosphopeptide sequence and determination of phosphorylation site localization are discussed along with methods that have been applied to the relative quantification of large numbers of phosphorylation sites. Finally, we provide an overview of the challenges ahead as we extend these technologies to the characterization of tyrosine phosphorylation signaling in vivo. With these latest developments in analytical and computational techniques, it is now possible to derive biological insight from quantitative MS-based analysis of signaling networks in vitro and in vivo. Application of these approaches to a wide variety of biological systems will define how signal transduction regulates cellular physiology in health and disease.

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.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

Mass spectrometry-based proteomics: qualitative identification to activity-based protein profiling

Mass spectrometry has become the method of choice for proteome characterization, including multicomponent protein complexes (typically tens to hundreds of proteins) and total protein expression (up to tens of thousands of proteins), in biological samples. Qualitative sequence assignment based on MS/MS spectra is relatively well-defined, while statistical metrics for relative quantification have not completely stabilized. Nonetheless, proteomics studies have progressed to the point whereby various gene-, pathway-, or network-oriented computational frameworks may be used to place mass spectrometry data into biological context. Despite this progress, the dynamic range of protein expression remains a significant hurdle, and impedes comprehensive proteome analysis. Methods designed to enrich specific protein classes have emerged as an effective means to characterize enzymes or other catalytically active proteins that are otherwise difficult to detect in typical discovery mode proteomics experiments. Collectively, these approaches will facilitate identification of biomarkers and pathways relevant to diagnosis and treatment of human disease.

Quantitative analysis of gradient sensing: towards building predictive models of chemotaxis in cancer

Chemotaxis of tumor cells in response to a gradient of extracellular ligand is an important step in cancer metastasis. The heterogeneity of chemotactic responses in cancer has not been widely addressed by experimental or mathematical modeling techniques. However, recent advancements in chemoattractant presentation, fluorescent-based signaling probes, and phenotypic analysis paradigms provide rich sources for building data-driven relational models that describe tumor cell chemotaxis in response to a wide variety of stimuli. Here we present gradient sensing, and the resulting chemotactic behavior, in a 'cue-signal-response' framework and suggest methods for utilizing recently reported experimental methods in data-driven modeling ventures.

Phosphoproteomics-based systems analysis of signal transduction networks

Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

Modeling and analysis of retinoic acid induced differentiation of uncommitted precursor cells

Manipulation of differentiation programs has therapeutic potential in a spectrum of human cancers and neurodegenerative disorders. In this study, we integrated computational and experimental methods to unravel the response of a lineage uncommitted precursor cell-line, HL-60, to Retinoic Acid (RA). HL-60 is a human myeloblastic leukemia cell-line used extensively to study human differentiation programs. Initially, we focused on the role of the BLR1 receptor in RA-induced differentiation and G1/0-arrest in HL-60. BLR1, a putative G protein-coupled receptor expressed following RA exposure, is required for RA-induced cell-cycle arrest and differentiation and causes persistent MAPK signaling. A mathematical model of RA-induced cell-cycle arrest and differentiation was formulated and tested against BLR1 wild-type (wt) knock-out and knock-in HL-60 cell-lines with and without RA. The current model described the dynamics of 729 proteins and protein complexes interconnected by 1356 interactions. An ensemble strategy was used to compensate for uncertain model parameters. The ensemble of HL-60 models recapitulated the positive feedback between BLR1 and MAPK signaling. The ensemble of models also correctly predicted Rb and p47phox regulation and the correlation between p21-CDK4-cyclin D formation and G1/0-arrest following exposure to RA. Finally, we investigated the robustness of the HL-60 network architecture to structural perturbations and generated experimentally testable hypotheses for future study. Taken together, the model presented here was a first step toward a systematic framework for analysis of programmed differentiation. These studies also demonstrated that mechanistic network modeling can help prioritize experimental directions by generating falsifiable hypotheses despite uncertainty.

Prestroke proteomic changes in cerebral microvessels in stroke-prone, transgenic[hCETP]-Hyperlipidemic, Dahl salt-sensitive hypertensive rats

Stroke is the third leading cause of death in the United States with high rates of morbidity among survivors. The search to fill the unequivocal need for new therapeutic approaches would benefit from unbiased proteomic analyses of animal models of spontaneous stroke in the prestroke stage. Since brain microvessels play key roles in neurovascular coupling, we investigated prestroke microvascular proteome changes. Proteomic analysis of cerebral cortical microvessels (cMVs) was done by tandem mass spectrometry comparing two prestroke time points. Metaprotein-pathway analyses of proteomic spectral count data were done to identify risk factor-induced changes, followed by QSPEC-analyses of individual protein changes associated with increased stroke susceptibility. We report 26 cMV proteome profiles from male and female stroke-prone and non-stroke-prone rats at 2 months and 4.5 months of age prior to overt stroke events. We identified 1,934 proteins by two or more peptides. Metaprotein pathway analysis detected age-associated changes in energy metabolism and cell-to-microenvironment interactions, as well as sex-specific changes in energy metabolism and endothelial leukocyte transmigration pathways. Stroke susceptibility was associated independently with multiple protein changes associated with ischemia, angiogenesis or involved in blood brain barrier (BBB) integrity. Immunohistochemical analysis confirmed aquaporin-4 and laminin-α1 induction in cMVs, representative of proteomic changes with >65 Bayes factor (BF), associated with stroke susceptibility. Altogether, proteomic analysis demonstrates significant molecular changes in ischemic cerebral microvasculature in the prestroke stage, which could contribute to the observed model phenotype of microhemorrhages and postischemic hemorrhagic transformation. These pathways comprise putative targets for translational research of much needed novel diagnostic and therapeutic approaches for stroke.

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks.

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.

Critical role of Shp2 in tumor growth involving regulation of c-Myc

Activating mutants of Shp2 protein tyrosine phosphatase, encoded by the PTPN11 gene, are linked to leukemia. In solid tumors, however, PTPN11 mutations occur at low frequencies while the wildtype Shp2 is activated by protein tyrosine kinases (PTKs) in cancer cells and mediates PTK signaling. Therefore, it is important to address whether the wildtype Shp2 plays a functional role critical for tumor growth. Using shRNAs and a PTP-inactive mutant to inhibit Shp2, we find here that tumor growth of DU145 prostate cancer and H292 lung cancer cells depends on Shp2. Suppression of Shp2 inhibited cell proliferation, decreased c-Myc and increased p27 expression in cell cultures. In H292 tumor tissues, c-Myc-positive cells coincided with Ki67-positive cells and smaller tumors from Shp2 knockdown cells had less c-Myc-positive cells and more nuclear p27. Shp2-regulated c-Myc expression was mediated by Src and Erk1/2. Down-regulation of c-Myc reduced cell proliferation while up-regulation of c-Myc in Shp2 knockdown H292 cells partially rescued the inhibitory effect of Shp2 suppression on cell proliferation. Tyrosine phosphoproteomic analysis of H292 tumor tissues showed that Shp2 could both up- and down-regulate tyrosine phosphorylation on cellular proteins. Among other changes, Shp2 inhibition increased phosphorylation of Src Tyr-530 and Cdk1 Thr-14/Tyr-15 and decreased phosphorylation of Erk1 and Erk2 activating sites in the tumors. Significantly, we found that Shp2 positively regulated Gab1 Tyr-627/Tyr-659 phosphorylation. This finding reveals that Shp2 can auto-regulate its own activating signal. Shp2 Tyr-62/Tyr-63 phosphorylation was observed in tumor tissues, indicating that Shp2 is activated in the tumors.

Epithelial and mesenchymal phenotypic switchings modulate cell motility in metastasis

The most ominous stage of cancer progression is metastasis, or the dissemination of carcinoma cells from the primary site into distant organs. Metastases are often resistant to current extirpative therapies and even the newest biological agents cure only a small subset of patients. Therefore a greater understanding of tumor biology that integrates properties intrinsic to carcinomas with tissue environmental modulators of behavior is needed. In no aspect of tumor progression is this more evident than the acquisition of cell motility that is critical for both escape from the primary tumor and colonization. In this overview, we discuss how this behavior is modified by carcinoma cell phenotypic plasticity that is evidenced by reversible switching between epithelial and mesenchymal phenotypes. The presence or absence of intercellular adhesions mediate these switches and dictate the receptivity towards signals from the extracellular milieu. These signals, which include soluble growth factors, cytokines, and extracellular matrix embedded with matrikines and matricryptines will be discussed in depth. Finally, we will describe a new mode of discerning the balance between epithelioid and mesenchymal movement.

Functional proteomics to dissect tyrosine kinase signalling pathways in cancer

Advances in the generation and interpretation of proteomics data have spurred a transition from focusing on protein identification to functional analysis. Here we review recent proteomics results that have elucidated new aspects of the roles and regulation of signal transduction pathways in cancer using the epidermal growth factor receptor (EGFR), ERK and breakpoint cluster region (BCR)-ABL1 networks as examples. The emerging theme is to understand cancer signalling as networks of multiprotein machines which process information in a highly dynamic environment that is shaped by changing protein interactions and post-translational modifications (PTMs). Cancerous genetic mutations derange these protein networks in complex ways that are tractable by proteomics.

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

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

Cytokine-associated drug toxicity in human hepatocytes is associated with signaling network dysregulation

Idiosyncratic drug hepatotoxicity is a major problem in pharmaceutical development due to poor prediction capability of standard preclinical toxicity assessments and limited knowledge of its underlying mechanisms. Findings in animal models have shown that adverse effects of numerous drugs with idiosyncratic hepatotoxicity in humans can be reproduced in the presence of coincident inflammatory cytokine signaling. Following these observations, we have recently developed an in vitro drug/inflammatory cytokine co-treatment approach that can reproduce clinical drug hepatotoxicity signatures-particularly for idiosyncratic drugs-in cultured primary human hepatocytes. These observations have suggested that drug-induced stresses may interact with cytokine signaling to induce hepatic cytotoxicity, but the hepatocyte signaling mechanisms governing these interactions are poorly understood. Here, we collect high-throughput phosphoprotein signaling and cytotoxicity measurements in cultured hepatocytes, from multiple human donors, treated with combinations of hepatotoxic drugs (e.g. trovafloxacin, clarithromycin) and cytokines (tumor necrosis factor-alpha, interferon-gamma, interleukin-1 alpha, and interleukin-6). We demonstrate, through orthogonal partial least-squares regression (OPLSR) modeling of these signal-response data, that drug/cytokine hepatic cytotoxicity is integratively controlled by four key signaling pathways: Akt, p70 S6 kinase, MEK-ERK, and p38-HSP27. This modeling predicted, and experimental studies confirmed, that the MEK-ERK and p38-HSP27 pathways contribute pro-death signaling influences in drug/cytokine hepatic cytotoxicity synergy. Further, our four-pathway OPLSR model produced successful prediction of drug/cytokine hepatic cytotoxicities across different human donors, even though signaling and cytotoxicity responses were both highly donor-specific. Our findings highlight the critical role of kinase signaling in drug/cytokine hepatic cytotoxicity synergies and reveal that hepatic cytotoxicity responses are governed by multi-pathway signaling network balance.

Decoding signalling networks by mass spectrometry-based proteomics

Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system-wide characterization of signalling events at the levels of post-translational modifications, protein-protein interactions and changes in protein expression. This technology delivers accurate and unbiased information about the quantitative changes of thousands of proteins and their modifications in response to any perturbation. Current studies focus on phosphorylation, but acetylation, methylation, glycosylation and ubiquitylation are also becoming amenable to investigation. Large-scale proteomics-based signalling research will fundamentally change our understanding of signalling networks.

Phosphotyrosine signaling analysis of site-specific mutations on EGFRvIII identifies determinants governing glioblastoma cell growth

To evaluate the role of individual EGFR phosphorylation sites in activating components of the cellular signaling network we have performed a mass spectrometry-based analysis of the phosphotyrosine network downstream of site-specific EGFRvIII mutants, enabling quantification of network-level effects of site-specific point mutations. Mutation at Y845, Y1068 or Y1148 resulted in diminished receptor phosphorylation, while mutation at Y1173 led to increased phosphorylation on multiple EGFRvIII residues. Altered phosphorylation at the receptor was recapitulated in downstream signaling network activation levels, with Y1173F mutation leading to increased phosphorylation throughout the network. Computational modeling of GBM cell growth as a function of network phosphorylation levels highlights the Erk pathway as crucial for regulating EGFRvIII-driven U87MG GBM cell behavior, with the unexpected finding that Erk1/2 is negatively correlated to GBM cell growth. Genetic manipulation of this pathway supports the model, demonstrating that EGFRvIII-expressing U87MG GBM cells are sensitive to Erk activation levels. Additionally, we developed a model describing glioblastoma cell growth based on a reduced set of phosphoproteins, which represent potential candidates for future development as therapeutic targets for EGFRvIII-positive glioblastoma patients.

Integration of multiple signaling pathway activities resolves K-RAS/N-RAS mutation paradox in colon epithelial cell response to inflammatory cytokine stimulation

Colon tumors frequently harbor mutation in K-RAS and/or N-RAS, members of a GTPase family operating as a central hub for multiple key signaling pathways. While these proteins are strongly homologous, they exhibit diverse downstream effects on cell behavior. Utilizing an isogenic panel of human colon carcinoma cells bearing oncogenic mutations in K-RAS and/or N-RAS, we observed that K-RAS and double mutants similarly yield elevated apoptosis in response to treatment with TNFalpha compared to N-RAS mutants. Regardless, and in surprising contrast, key phospho-protein signals were more similar between N-RAS and dual mutants. This apparent contradiction could not be explained by any of the key signals individually, but a multi-pathway model constructed from the single-mutant cell line data was able to predict the behavior of the dual-mutant cell line. This success arises from a quantitative integration of multiple pro-apoptotic (pIkappaBalpha, pERK2) and pro-survival (pJNK, pHSP27) signals in manner not easily discerned from intuitive inspection.

Multipathway kinase signatures of multipotent stromal cells are predictive for osteogenic differentiation: tissue-specific stem cells

Bone marrow-derived multipotent stromal cells (MSCs) offer great promise for regenerating tissue. Although certain transcription factors have been identified in association with tendency toward particular MSC differentiation phenotypes, the regulatory network of key receptor-mediated signaling pathways activated by extracellular ligands that induce various differentiation responses remains poorly understood. Attempts to predict differentiation fate tendencies from individual pathways in isolation are problematic due to the complex pathway interactions inherent in signaling networks. Accordingly, we have undertaken a multivariate systems approach integrating experimental measurement of multiple kinase pathway activities and osteogenic differentiation in MSCs, together with computational analysis to elucidate quantitative combinations of kinase signals predictive of cell behavior across diverse contexts. In particular, for culture on polymeric biomaterial surfaces presenting tethered epidermal growth factor, type I collagen, neither, or both, we have found that a partial least-squares regression model yields successful prediction of phenotypic behavior on the basis of two principal components comprising the weighted sums of eight intracellular phosphoproteins: phospho-epidermal growth factor receptor, phospho-Akt, phospho-extracellular signal-related kinase 1/2, phospho-heat shock protein 27, phospho-c-Jun, phospho-glycogen synthase kinase 3alpha/beta, phospho-p38, and phospho-signal transducer and activator of transcription 3. This combination provides the strongest predictive capability for 21-day differentiated phenotype status when calculated from day-7 signal measurements; day-4 and day-14 signal measurements are also significantly predictive, indicating a broad time frame during MSC osteogenesis wherein multiple pathways and states of the kinase signaling network are quantitatively integrated to regulate gene expression, cell processes, and ultimately, cell fate.

Quantifying oncogenic phosphotyrosine signaling networks through systems biology

Pathways linking oncogenic mutations to increased proliferative or migratory capacity are poorly characterized, yet provide potential targets for therapeutic intervention. As tyrosine phosphorylation signaling networks are known to mediate proliferation and migration, and frequently go awry in cancers, a comprehensive understanding of these networks in normal and diseased states is warranted. To this end, recent advances in mass spectrometry, protein microarrays, and computational algorithms provide insight into various aspects of the network including phosphotyrosine identification, analysis of kinase/phosphatase substrates, and phosphorylation-mediated protein-protein interactions. Here we detail technological advances underlying these system-level approaches and give examples of their applications. By combining multiple approaches, it is now possible to quantify changes in the phosphotyrosine signaling network with various oncogenic mutations, thereby unveiling novel therapeutic targets.

Cancer systems biology: a network modeling perspective

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