Identification of Cell Type-Specific Differences in Erythropoietin Receptor Signaling in Primary Erythroid and Lung Cancer Cells

Erythropoietin receptor is a risk factor for prognosis: A potential biomarker in lung adenocarcinoma

Lung cancer has the highest mortality rate of all cancers, and LUAD's survival rate is particularly poor. Erythropoietin receptor (EPOR) can be detected in lung adenocarcinoma (LUAD), however, the expression levels and prognostic value of EPOR in LUAD are still unclear. In our study, clinicopathological data of 92 LUAD patients between January 2008 and June 2016, multiple bioinformatics databases and immunohistochemistry were used to explore the EPOR expression, the mutant genes affecting EPOR expression, and the correlation of EPOR expression with oxidative stress - related genes, prognosis, immune microenvironment. All statistical analyses were performed in the R version 4.1.1. The study found that EPOR expression might be down-regulated at the mRNA levels and significantly up-regulated at the protein levels in LUAD, which indicates that the mRNA and protein levels of EPOR are inconsistent. The muTarget showed that the expression of EPOR was significantly different between the mutant group and the wild group of 15 genes, including DDX60L and C1orf168. Importantly, we found that EPOR was associated with VEGF and HIF family members, and had significant positive correlation with oxidative stress - related genes such as CCS, EPX and TXNRD2. This suggests that EPOR may be involved in the regulation of oxidative stress. The Kaplan-Meier Plotter and PrognoScan databases consistently concluded that EPOR was associated with prognosis in LUAD patients. Our clinicopathological data showed that high EPOR expression was associated with poorer overall survival (29.5 vs 46 months) and had a good predictive ability for 4-year and 5-year survival probability. EPOR is expected to be a potential new prognostic marker for LUAD.

Uncovering specific mechanisms across cell types in dynamical models

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistic's distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results.

The computational capabilities of many-to-many protein interaction networks

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

Dynamic modelling predicts lactate and IL-1β as interventional targets in metabolic-inflammation-clock regulatory loop in glioma

In an attempt to understand the role of dysregulated circadian rhythm in glioma, our recent findings highlighted the existence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK. To further elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this lactate dehydrogenase A (LDHA)-IL-1β-CLOCK/BMAL1 circuit and predicts potential therapeutic targets. The model was calibrated on quantitative western blotting data in two glioma cell lines in response to either lactate inhibition or IL-1β stimulation. The calibrated model described the experimental data well and most of the parameters were identifiable, thus the model was predictive. Sensitivity analysis identified IL-1β and LDHA as potential intervention points. Mathematical models described here can be useful to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in designing effective therapeutic strategies. Our findings underscore the importance of including the circadian clock when developing pharmacological approaches that target aberrant tumour metabolism and inflammation. Insight box  The complex interplay of metabolism-inflammation-circadian rhythm in tumours is not well understood. Our recent findings provided evidence of a feed-forward loop between tumour metabolite lactate, pro-inflammatory cytokine IL-1β and circadian CLOCK/BMAL1 in glioma. To elucidate the implication of this complex interplay, we developed a mathematical model that quantitatively describes this LDHA-IL-1β-CLOCK/BMAL1 circuit and integrates experimental data to predict potential therapeutic targets. The study employed a multi-start optimization strategy and profile likelihood estimations for parameter estimation and assessing identifiability. The simulations are in reasonable agreement with the experimental data. Sensitivity analysis found LDHA and IL-1β as potential therapeutic points. Mathematical models described here can provide insights to understand the complex interrelationship between metabolism, inflammation and circadian rhythm, and in identifying effective therapeutic targets.

Realistic simulation of time-course measurements in systems biology

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis

Simple Summary

Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors.

Abstract

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L₁ regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

Cell-to-cell variability in JAK2/STAT5 pathway components and cytoplasmic volumes defines survival threshold in erythroid progenitor cells

Survival or apoptosis is a binary decision in individual cells. However, at the cell-population level, a graded increase in survival of colony-forming unit-erythroid (CFU-E) cells is observed upon stimulation with erythropoietin (Epo). To identify components of Janus kinase 2/signal transducer and activator of transcription 5 (JAK2/STAT5) signal transduction that contribute to the graded population response, we extended a cell-population-level model calibrated with experimental data to study the behavior in single cells. The single-cell model shows that the high cell-to-cell variability in nuclear phosphorylated STAT5 is caused by variability in the amount of Epo receptor (EpoR):JAK2 complexes and of SHP1, as well as the extent of nuclear import because of the large variance in the cytoplasmic volume of CFU-E cells. 24-118 pSTAT5 molecules in the nucleus for 120 min are sufficient to ensure cell survival. Thus, variability in membrane-associated processes is sufficient to convert a switch-like behavior at the single-cell level to a graded population-level response.

Molecular Pathways Involved in the Development of Congenital Erythrocytosis

Patients with idiopathic erythrocytosis are directed to targeted genetic testing including nine genes involved in oxygen sensing pathway in kidneys, erythropoietin signal transduction in pre-erythrocytes and hemoglobin-oxygen affinity regulation in mature erythrocytes. However, in more than 60% of cases the genetic cause remains undiagnosed, suggesting that other genes and mechanisms must be involved in the disease development. This review aims to explore additional molecular mechanisms in recognized erythrocytosis pathways and propose new pathways associated with this rare hematological disorder. For this purpose, a comprehensive review of the literature was performed and different in silico tools were used. We identified genes involved in several mechanisms and molecular pathways, including mRNA transcriptional regulation, post-translational modifications, membrane transport, regulation of signal transduction, glucose metabolism and iron homeostasis, which have the potential to influence the main erythrocytosis-associated pathways. We provide valuable theoretical information for deeper insight into possible mechanisms of disease development. This information can be also helpful to improve the current diagnostic solutions for patients with idiopathic erythrocytosis.

STAT5 as a Key Protein of Erythropoietin Signalization

Erythropoietin (EPO) acts on multiple tissues through its receptor EPOR, a member of a cytokine class I receptor superfamily with pleiotropic effects. The interaction of EPO and EPOR triggers the activation of several signaling pathways that induce erythropoiesis, including JAK2/STAT5, PI3K/AKT, and MAPK. The canonical EPOR/JAK2/STAT5 pathway is a known regulator of differentiation, proliferation, and cell survival of erythroid progenitors. In addition, its role in the protection of other cells, including cancer cells, is under intense investigation. The involvement of EPOR/JAK2/STAT5 in other processes such as mRNA splicing, cytoskeleton reorganization, and cell metabolism has been recently described. The transcriptomics, proteomics, and epigenetic studies reviewed in this article provide a detailed understanding of EPO signalization. Advances in this area of research may be useful for improving the efficacy of EPO therapy in hematologic disorders, as well as in cancer treatment.

The intensification of anticancer activity of LFM-A13 by erythropoietin as a possible option for inhibition of breast cancer

Recombinant human erythropoietin (Epo) is an effective and convenient treatment for cancer-related anaemia. In our study for the first time, we evaluated the effect of simultaneous use of Epo and Bruton’s tyrosine kinase (BTK) inhibitor LFM-A13 on the viability and tumour development of breast cancer cells. The results demonstrated that Epo significantly intensifies the anticancer activity of LFM-A13 in MCF-7 and MDA-MB-231. The featured therapeutic scheme efficiently blocked the tumour development in zebrafish experimental cancer model. Epo and LFM-A13 administered together resulted in effective cell killing, accompanied by attenuation of the BTK signalling pathways, loss of mitochondrial membrane potential (MMP), accumulation of apoptotic breast cancer cells with externalised PS, a slight increase in phase G0/G1 and a reduction in cyclin D1 expression. Simultaneous use of Epo with LFM-A13 inhibited early stages of tumour progression. This therapeutic scheme may be rationale for further possible research.

Erythropoietin promoter polymorphism is associated with treatment efficacy and severe hematologic toxicity for platinum-based chemotherapy

Background: Erythropoietin (EPO) plays a substantial role in cancer development and probably affects clinical outcomes. A functional polymorphism (rs1617640, G > T) in the promoter region of the EPO increases protein expression. This study investigated the association of EPO rs1617640 with treatment efficacy and severe toxicity in non-small cell lung cancer (NSCLC) patients undergoing platinum-based regimens.Methods: 437 Chinese NSCLC patients treated with platinum-based chemotherapy were recruited. Association between EPO rs1617640 and clinical outcomes was calculated by multivariable logistic regression.Results: The TT genotype of EPO rs1617640 was significantly correlated with a higher response rate to platinum-based treatment than the other genotypes (OR, 0.507; 95% CI: 0.305-0.842; P = 0.009), particularly in elderly patients (>55 years), male gender, smokers, IV stage, cisplatin-based chemotherapies, and platinum-gemcitabine regimen subgroups. As for toxicity, EPO rs1617640 TT genotype demonstrated poorer tolerance to grade 3-4 hematologic toxicity (OR, 1.783; 95% CI: 1.098-2.898; P = 0.019), particularly in subgroups of elderly patients (>55 years), male gender, smokers, IIIA+IIIB stage, and cisplatin-based chemotherapies.Conclusion: Our results demonstrated the role of EPO rs1617640 as a possible predictive marker of treatment efficacy and severe toxicity for platinum-based chemotherapy.

Disentangling molecular mechanisms regulating sensitization of interferon alpha signal transduction

Tightly interlinked feedback regulators control the dynamics of intracellular responses elicited by the activation of signal transduction pathways. Interferon alpha (IFNα) orchestrates antiviral responses in hepatocytes, yet mechanisms that define pathway sensitization in response to prestimulation with different IFNα doses remained unresolved. We establish, based on quantitative measurements obtained for the hepatoma cell line Huh7.5, an ordinary differential equation model for IFNα signal transduction that comprises the feedback regulators STAT1, STAT2, IRF9, USP18, SOCS1, SOCS3, and IRF2. The model-based analysis shows that, mediated by the signaling proteins STAT2 and IRF9, prestimulation with a low IFNα dose hypersensitizes the pathway. In contrast, prestimulation with a high dose of IFNα leads to a dose-dependent desensitization, mediated by the negative regulators USP18 and SOCS1 that act at the receptor. The analysis of basal protein abundance in primary human hepatocytes reveals high heterogeneity in patient-specific amounts of STAT1, STAT2, IRF9, and USP18. The mathematical modeling approach shows that the basal amount of USP18 determines patient-specific pathway desensitization, while the abundance of STAT2 predicts the patient-specific IFNα signal response.

Benchmark problems for dynamic modeling of intracellular processes

Motivation

Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing.

Results

We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection.

Availability and implementation

The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models.

Supplementary information

Supplementary data are available at Bioinformatics online.

Erythropoietin and its derivatives: from tissue protection to immune regulation

Erythropoietin (EPO) is an evolutionarily conserved hormone well documented for its erythropoietic role via binding the homodimeric EPO receptor (EPOR)₂. In past decades, evidence has proved that EPO acts far beyond erythropoiesis. By binding the tissue-protective receptor (TPR), EPO suppresses proinflammatory cytokines, protects cells from apoptosis and promotes wound healing. Very recently, new data revealed that TPR is widely expressed on a variety of immune cells, and EPO could directly modulate their activation, differentiation and function. Notably, nonerythropoietic EPO derivatives, which mimic the structure of helix B within EPO, specifically bind TPR and show great potency in tissue protection and immune regulation. These small peptides prevent the cardiovascular side effects of EPO and are promising as clinical drugs. This review briefly introduces the receptors and tissue-protective effects of EPO and its derivatives and highlights their immunomodulatory functions and application prospects.

Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

Mechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Extensions of ℓ1 regularization increase detection specificity for cell-type specific parameters in dynamic models

BACKGROUND

Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to which extent malign cells differ from healthy ones to provide a specific treatment with least side effects. As these cell-type specific properties may stem from any part of biochemical cell processes, systematic quantitative approaches are necessary to identify the relevant potential drug targets. An ℓ₁ regularization for the maximum likelihood parameter estimation proved to be successful, but falsely predicted cell-type dependent behaviour had to be corrected manually by using a Profile Likelihood approach.

RESULTS

The choice of extended ℓ₁ penalty functions significantly decreased the number of falsely detected cell-type specific parameters. Thus, the total accuracy of the prediction could be increased. This was tested on a realistic dynamical benchmark model used for the DREAM6 challenge. Among Elastic Net, Adaptive Lasso and a non-convex ℓ_q penalty, the latter one showed the best predictions whilst also requiring least computation time. All extended methods include a hyper-parameter in the regularization function. For an Erythropoietin (EPO) induced signalling pathway, the extended methods ℓ_q and Adaptive Lasso revealed an unpublished alternative parsimonious model when varying the respective hyper-parameters.

CONCLUSIONS

Using ℓ_q or Adaptive Lasso with an a-priori choice for the hyper-parameter can lead to a more specific and accurate result than ℓ₁. Scanning different hyper-parameters can yield additional pieces of information about the system.

Systemic network analysis identifies XIAP and IκBα as potential drug targets in TRAIL resistant BRAF mutated melanoma

Metastatic melanoma remains a life-threatening disease because most tumors develop resistance to targeted kinase inhibitors thereby regaining tumorigenic capacity. We show the 2nd generation hexavalent TRAIL receptor-targeted agonist IZI1551 to induce pronounced apoptotic cell death in mutBRAF melanoma cells. Aiming to identify molecular changes that may confer IZI1551 resistance we combined Dynamic Bayesian Network modelling with a sophisticated regularization strategy resulting in sparse and context-sensitive networks and show the performance of this strategy in the detection of cell line-specific deregulations of a signalling network. Comparing IZI1551-sensitive to IZI1551-resistant melanoma cells the model accurately and correctly predicted activation of NFκB in concert with upregulation of the anti-apoptotic protein XIAP as the key mediator of IZI1551 resistance. Thus, the incorporation of multiple regularization functions in logical network optimization may provide a promising avenue to assess the effects of drug combinations and to identify responders to selected combination therapies.

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.

Investigation of the role of ion channels in human pancreatic β-cell hubs: A mathematical modeling study

In many cellular networks, the structure of the network follows a scale-free organization, where a limited number of cells are strongly coupled to other cells. These cells are called hub cells and their critical roles are well accepted. Despite their importance, there have been only a few studies investigating the characteristic features of these cells. In this paper, a computational approach is proposed to study the possible role of different ion channels in distinguishing between the hub and non-hub cells. The results show that the P/Q-type and T-type calcium channels may have an especial role in the β-cell hubs because the high-level expressions of these channels make a pancreatic β-cell more potent to force other coupled cells to follow it. In addition, in order to consider the variation of the coupling strength with voltage, a novel mathematical model is proposed for the gap junction coupling between the pancreatic β-cells. The proposed approach is validated based on the data from the literature.

Erythropoietin drives breast cancer progression by activation of its receptor EPOR

Breast cancer is a leading cause of cancer-related deaths. Anemia is common in breast cancer patients and can be treated with blood transfusions or with recombinant erythropoietin (EPO) to stimulate red blood cell production. Clinical studies have indicated decreased survival in some groups of cancer patients treated with EPO. Numerous tumor cells express the EPO receptor (EPOR), posing a risk that EPO treatment would enhance tumor growth, but the mechanisms involved in breast tumor progression are poorly understood.

Here, we have examined the functional role of the EPO-EPOR axis in pre-clinical models of breast cancer. EPO induced the activation of PI3K/AKT and MAPK pathways in human breast cancer cell lines. EPOR knockdown abrogated human tumor cell growth, induced apoptosis through Bim, reduced invasiveness, and caused downregulation of MYC expression. EPO-induced MYC expression is mediated through the PI3K/AKT and MAPK pathways, and overexpression of MYC partially rescued loss of cell proliferation caused by EPOR downregulation. In a xenotransplantation model, designed to simulate recombinant EPO therapy in breast cancer patients, knockdown of EPOR markedly reduced tumor growth.

Thus, our experiments in vitro and in vivo demonstrate that functional EPOR signaling is essential for the tumor-promoting effects of EPO and underline the importance of the EPO-EPOR axis in breast tumor progression.

Correlated receptor transport processes buffer single-cell heterogeneity

Cells typically vary in their response to extracellular ligands. Receptor transport processes modulate ligand-receptor induced signal transduction and impact the variability in cellular responses. Here, we quantitatively characterized cellular variability in erythropoietin receptor (EpoR) trafficking at the single-cell level based on live-cell imaging and mathematical modeling. Using ensembles of single-cell mathematical models reduced parameter uncertainties and showed that rapid EpoR turnover, transport of internalized EpoR back to the plasma membrane, and degradation of Epo-EpoR complexes were essential for receptor trafficking. EpoR trafficking dynamics in adherent H838 lung cancer cells closely resembled the dynamics previously characterized by mathematical modeling in suspension cells, indicating that dynamic properties of the EpoR system are widely conserved. Receptor transport processes differed by one order of magnitude between individual cells. However, the concentration of activated Epo-EpoR complexes was less variable due to the correlated kinetics of opposing transport processes acting as a buffering system.

Cell surface receptors translate extracellular ligand concentrations to intracellular responses. Receptor transport between the plasma membrane and other cellular compartments regulates the number of accessible receptors at the plasma membrane that determines the strength of downstream pathway activation at a given ligand concentration. In cell populations, pathway activation strength and cellular responses vary between cells. Understanding origins of cell-to-cell variability is highly relevant for cancer research, motivated by the problem of fractional killing by chemotherapies and development of resistance in subpopulations of tumor cells. The erythropoietin receptor (EpoR) is a characteristic example of a receptor system that strongly depends on receptor transport processes. It is involved in several cellular processes, such as differentiation or proliferation, regulates the renewal of erythrocytes, and is expressed in several tumors. To investigate the involvement of receptor transport processes in cell-to-cell variability, we quantitatively characterized trafficking of EpoR in individual cells by combining live-cell imaging with mathematical modeling. Thereby, we found that EpoR dynamics was strongly dependent on rapid receptor transport and turnover. Interestingly, although transport processes largely differed between individual cells, receptor concentrations in cellular compartments were robust to variability in trafficking processes due to the correlated kinetics of opposing transport processes.

Logic Modeling in Quantitative Systems Pharmacology

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

Local blockage of self-sustainable erythropoietin signaling suppresses tumor progression in non-small cell lung cancer

Functional significance of co-expressed erythropoietin (EPO) and its receptor (EPOR) in non-small cell lung cancer (NSCLC) had been under debate. In this study, co-overexpression of EPO/EPOR was confirmed to be positively associated with poor survival in NSCLC. The serum EPO in 14 of 35 enrolled NSCLC patients were found elevated significantly and decreased to normal level after tumor resection. With primary tumor cell culture and patient-derived tumor xenograft (PDX) mouse model, the EPO secretion from the tumors of these 14 patients was verified. Then, we proved the patient derived serum EPO was functionally active and had growth promotion effect in EPO/EPOR overexpressed but not in EPO/EPOR under-expressed NSCLC cells. We also illustrated EPO promoted NSCLC cell proliferation through an EPOR/Jak2/Stat5a/cyclinD1 pathway. In xenograft mouse model, we proved local application of EPO neutralizing antibody and short hairpin RNA (shRNA) against EPOR effectively inhibited the growth of EPO/EPOR overexpressed NSCLC cells and prolonged survivals of the mice. Finally, EPO/EPOR/Jak2/Stat5a/cyclinD1 signaling was found to be a mediator of hypoxia induced growth in EPO/EPOR overexpressed NSCLC. Our results illustrated a subgroup of NSCLC adapt to hypoxia through self-sustainable EPO/EPOR signaling and suggest local blockage of EPO/EPOR as potential therapeutic method in this distinct NSCLC population.

Drug Resistance Mechanisms in Colorectal Cancer Dissected with Cell Type-Specific Dynamic Logic Models

Genomic features are used as biomarkers of sensitivity to kinase inhibitors used widely to treat human cancer, but effective patient stratification based on these principles remains limited in impact. Insofar as kinase inhibitors interfere with signaling dynamics, and, in turn, signaling dynamics affects inhibitor responses, we investigated associations in this study between cell-specific dynamic signaling pathways and drug sensitivity. Specifically, we measured 14 phosphoproteins under 43 different perturbed conditions (combinations of 5 stimuli and 7 inhibitors) in 14 colorectal cancer cell lines, building cell line-specific dynamic logic models of underlying signaling networks. Model parameters representing pathway dynamics were used as features to predict sensitivity to a panel of 27 drugs. Specific parameters of signaling dynamics correlated strongly with drug sensitivity for 14 of the drugs, 9 of which had no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by coblockade of GSK3, which was not found based on associations with genomic data. These results suggest that to better understand the cancer resistance and move toward personalized medicine, it is essential to consider signaling network dynamics that cannot be inferred from static genotypes. Cancer Res; 77(12); 3364-75. ©2017 AACR.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.