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Quantitative systems pharmacology (QSP) has been an important tool to project safety and efficacy of novel or repurposed therapies for the SARS-CoV-2 virus. Here, we present a QSP modeling framework to predict response to antiviral therapeutics with three mechanisms of action (MoA): cell entry inhibitors, anti-replicatives, and neutralizing biologics. We parameterized three distinct model structures describing virus-host interaction by fitting to published viral kinetics data of untreated COVID-19 patients. The models were used to test theoretical behaviors and map therapeutic design criteria of the different MoAs, identifying the most rapid and robust antiviral activity from neutralizing biologic and anti-replicative MoAs. We found good agreement between model predictions and clinical viral load reduction observed with anti-replicative nirmatrelvir/ritonavir (Paxlovid®) and neutralizing biologics bamlanivimab and casirivimab/imdevimab (REGEN-COV®), building confidence in the modeling framework to inform a dose selection. Finally, the model was applied to predict antiviral response with ensovibep, a novel DARPin therapeutic designed as a neutralizing biologic. We developed a new in silico measure of antiviral activity, area under the curve (AUC) of free spike protein concentration, as a metric with larger dynamic range than viral load reduction. By benchmarking to bamlanivimab predictions, we justified dose levels of 75, 225, and 600 mg ensovibep to be administered intravenously in a Phase 2 clinical investigation. Upon trial completion, we found model predictions to be in good agreement with the observed patient data. These results demonstrate the utility of this modeling framework to guide the development of novel antiviral therapeutics.
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Antivirais , Tratamento Farmacológico da COVID-19 , Descoberta de Drogas , SARS-CoV-2 , Carga Viral , Humanos , Antivirais/administração & dosagem , Antivirais/farmacologia , Antivirais/uso terapêutico , SARS-CoV-2/efeitos dos fármacos , Carga Viral/efeitos dos fármacos , Descoberta de Drogas/métodos , Anticorpos Monoclonais Humanizados/uso terapêutico , Anticorpos Monoclonais Humanizados/administração & dosagem , Modelos Biológicos , COVID-19 , Ritonavir/uso terapêutico , Ritonavir/administração & dosagem , Anticorpos NeutralizantesRESUMO
Precision medicine gives individuals tailored medical treatment, with the genotype determining the therapeutic strategy, the appropriate dosage, and the likelihood of benefit or toxicity. Cytochrome P450 (CYP) enzyme families 1, 2, and 3 play a pivotal role in eliminating most drugs. Factors that affect CYP function and expression have a major impact on treatment outcomes. Therefore, polymorphisms of these enzymes result in alleles with diverse enzymatic activity and drug metabolism phenotypes. Africa has the highest CYP genetic diversity and also the highest burden of malaria and tuberculosis, and this review presents current general information on CYP enzymes together with variation data concerning antimalarial and antituberculosis drugs, while focusing on the first three CYP families. Afrocentric alleles such as CYP2A6*17, CYP2A6*23, CYP2A6*25, CYP2A6*28, CYP2B6*6, CYP2B6*18, CYP2C8*2, CYP2C9*5, CYP2C9*8, CYP2C9*9, CYP2C19*9, CYP2C19*13, CYP2C19*15, CYP2D6*2, CYP2D6*17, CYP2D6*29, and CYP3A4*15 are implicated in diverse metabolic phenotypes of different antimalarials such as artesunate, mefloquine, quinine, primaquine, and chloroquine. Moreover, CYP3A4, CYP1A1, CYP2C8, CYP2C18, CYP2C19, CYP2J2, and CYP1B1 are implicated in the metabolism of some second-line antituberculosis drugs such as bedaquiline and linezolid. Drug-drug interactions, induction/inhibition, and enzyme polymorphisms that influence the metabolism of antituberculosis, antimalarial, and other drugs, are explored. Moreover, a mapping of Afrocentric missense mutations to CYP structures and a documentation of their known effects provided structural insights, as understanding the mechanism of action of these enzymes and how the different alleles influence enzyme function is invaluable to the advancement of precision medicine.
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Antimaláricos , Humanos , Citocromo P-450 CYP2D6/genética , Citocromo P-450 CYP2C8/genética , Citocromo P-450 CYP2C19/genética , Citocromo P-450 CYP3A/genética , Alelos , Citocromo P-450 CYP2B6/genética , Antituberculosos , Citocromo P-450 CYP2C9/genética , Sistema Enzimático do Citocromo P-450/metabolismoRESUMO
The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen).
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Therapeutically targeting receptor tyrosine kinases has proven to be paramount to overcoming chemotherapy resistance in several cancer indications, improving patient outcomes. Insulin-Like Growth Factor Receptor 1 (IGF-1R) and Epidermal Growth Factor Receptor 3 (ErbB3) have been implicated as two such drivers of resistance, however their simultaneous role in ovarian cancer chemotherapy resistance remains poorly elucidated. The aim of this work is to determine the effects of dual IGF-1R/ErbB3 inhibition on ovarian cancer cell signaling, growth, and in vivo efficacy. Assessment of in vitro chemotherapy response across a panel of ovarian cancer cell lines revealed that increased IGF-1R cell surface expression correlates with decreased sensitivity to chemotherapy, and that growth induced by IGF-1R and ErbB3 ligands is blocked by the tetravalent bispecific antibody targeting IGF-1R and ErbB3, istiratumab. In vitro chemotherapy treatment increased ovarian cancer cell line capacity to activate prosurvival PI3K signaling in response to ligand, which could be prevented with istiratumab treatment. Furthermore, in vivo efficacy of standard of care chemotherapies using a xenograft model of ovarian cancer was potentiated with istiratumab. Our results suggest a role for IGF-1R and ErbB3 in driving chemotherapy resistance of ovarian cancer.
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Anticorpos Monoclonais Humanizados/administração & dosagem , Antineoplásicos/administração & dosagem , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Neoplasias Ovarianas/tratamento farmacológico , Receptor ErbB-3/metabolismo , Receptor IGF Tipo 1/metabolismo , Animais , Anticorpos Monoclonais Humanizados/farmacologia , Antineoplásicos/farmacologia , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Sobrevivência Celular/efeitos dos fármacos , Cisplatino/administração & dosagem , Cisplatino/farmacologia , Doxorrubicina/administração & dosagem , Doxorrubicina/análogos & derivados , Doxorrubicina/farmacologia , Sinergismo Farmacológico , Feminino , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Humanos , Camundongos , Neoplasias Ovarianas/metabolismo , Paclitaxel/administração & dosagem , Paclitaxel/farmacologia , Polietilenoglicóis/administração & dosagem , Polietilenoglicóis/farmacologia , Receptor ErbB-3/antagonistas & inibidores , Receptor IGF Tipo 1/antagonistas & inibidores , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our clustering method is general and can be tailored to a variety of applications in science and industry. We illustrate our method on a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. Each experiment consists of a cell line-ligand combination, and contains time-course measurements of the early signalling kinases MAPK and AKT at two different ligand dose levels. By imposing appropriate structural constraints and respecting the multi-indexed structure of the data, the analysis of clusters can be optimized for biological interpretation and therapeutic understanding. We then perform a systematic, large-scale exploration of mechanistic models of MAPK-AKT crosstalk for each cluster. This analysis allows us to quantify the heterogeneity of breast cancer cell subtypes, and leads to hypotheses about the signalling mechanisms that mediate the response of the cell lines to ligands.
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Algoritmos , Neoplasias da Mama/metabolismo , Sistema de Sinalização das MAP Quinases , Modelos Biológicos , Neoplasias da Mama/patologia , Análise por Conglomerados , Feminino , Humanos , Quinases de Proteína Quinase Ativadas por Mitógeno/metabolismo , Proteínas Proto-Oncogênicas c-akt/metabolismoRESUMO
Activation of the Met receptor tyrosine kinase, either by its ligand, hepatocyte growth factor (HGF), or via ligand-independent mechanisms, such as MET amplification or receptor overexpression, has been implicated in driving tumor proliferation, metastasis, and resistance to therapy. Clinical development of Met-targeted antibodies has been challenging, however, as bivalent antibodies exhibit agonistic properties, whereas monovalent antibodies lack potency and the capacity to down-regulate Met. Through computational modeling, we found that the potency of a monovalent antibody targeting Met could be dramatically improved by introducing a second binding site that recognizes an unrelated, highly expressed antigen on the tumor cell surface. Guided by this prediction, we engineered MM-131, a bispecific antibody that is monovalent for both Met and epithelial cell adhesion molecule (EpCAM). MM-131 is a purely antagonistic antibody that blocks ligand-dependent and ligand-independent Met signaling by inhibiting HGF binding to Met and inducing receptor down-regulation. Together, these mechanisms lead to inhibition of proliferation in Met-driven cancer cells, inhibition of HGF-mediated cancer cell migration, and inhibition of tumor growth in HGF-dependent and -independent mouse xenograft models. Consistent with its design, MM-131 is more potent in EpCAM-high cells than in EpCAM-low cells, and its potency decreases when EpCAM levels are reduced by RNAi. Evaluation of Met, EpCAM, and HGF levels in human tumor samples reveals that EpCAM is expressed at high levels in a wide range of Met-positive tumor types, suggesting a broad opportunity for clinical development of MM-131.
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Anticorpos Biespecíficos/farmacologia , Antineoplásicos Imunológicos/farmacologia , Molécula de Adesão da Célula Epitelial/antagonistas & inibidores , Fator de Crescimento de Hepatócito/metabolismo , Neoplasias Experimentais/tratamento farmacológico , Proteínas Proto-Oncogênicas c-met/antagonistas & inibidores , Transdução de Sinais/efeitos dos fármacos , Animais , Linhagem Celular Tumoral , Molécula de Adesão da Célula Epitelial/metabolismo , Humanos , Camundongos , Neoplasias Experimentais/metabolismo , Neoplasias Experimentais/patologia , Proteínas Proto-Oncogênicas c-met/metabolismo , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
Cells respond to DNA damage by activating complex signaling networks that decide cell fate, promoting not only DNA damage repair and survival but also cell death. We have developed a multiscale computational model that quantitatively links chemotherapy-induced DNA damage response signaling to cell fate. The computational model was trained and calibrated on extensive data from U2OS osteosarcoma cells, including the cell cycle distribution of the initial cell population, signaling data measured by Western blotting, and cell fate data in response to chemotherapy treatment measured by time-lapse microscopy. The resulting mechanistic model predicted the cellular responses to chemotherapy alone and in combination with targeted inhibitors of the DNA damage response pathway, which we confirmed experimentally. Computational models such as the one presented here can be used to understand the molecular basis that defines the complex interplay between cell survival and cell death and to rationally identify chemotherapy-potentiating drug combinations.
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Antineoplásicos/farmacologia , Neoplasias Ósseas/patologia , Dano ao DNA , Osteossarcoma/patologia , Neoplasias Ovarianas/patologia , Bibliotecas de Moléculas Pequenas/farmacologia , Animais , Apoptose , Neoplasias Ósseas/tratamento farmacológico , Neoplasias Ósseas/genética , Ciclo Celular , Proliferação de Células , Reparo do DNA , Quimioterapia Combinada , Feminino , Humanos , Camundongos , Osteossarcoma/tratamento farmacológico , Osteossarcoma/genética , Neoplasias Ovarianas/tratamento farmacológico , Neoplasias Ovarianas/genética , Transdução de Sinais , Células Tumorais Cultivadas , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
Purpose: Insulin-like growth factor receptor 1 (IGF-1R) is critically involved in pancreatic cancer pathophysiology, promoting cancer cell survival and therapeutic resistance. Assessment of IGF-1R inhibitors in combination with standard-of-care chemotherapy, however, failed to demonstrate significant clinical benefit. The aim of this work is to unravel mechanisms of resistance to IGF-1R inhibition in pancreatic cancer and develop novel strategies to improve the activity of standard-of-care therapies.Experimental Design: Growth factor screening in pancreatic cancer cell lines was performed to identify activators of prosurvival PI3K/AKT signaling. The prevalence of activating growth factors and their receptors was assessed in pancreatic cancer patient samples. Effects of a bispecific IGF-1R and ErbB3 targeting antibody on receptor expression, signaling, cancer cell viability and apoptosis, spheroid growth, and in vivo chemotherapy activity in pancreatic cancer xenograft models were determined.Results: Growth factor screening in pancreatic cancer cells revealed insulin-like growth factor 1 (IGF-1) and heregulin (HRG) as the most potent AKT activators. Both growth factors reduced pancreatic cancer cell sensitivity to gemcitabine or paclitaxel in spheroid growth assays. Istiratumab (MM-141), a novel bispecific antibody that blocks IGF-1R and ErbB3, restored the activity of paclitaxel and gemcitabine in the presence of IGF-1 and HRG in vitro Dual IGF-1R/ErbB3 blocking enhanced chemosensitivity through inhibition of AKT phosphorylation and promotion of IGF-1R and ErbB3 degradation. Addition of istiratumab to gemcitabine and nab-paclitaxel improved chemotherapy activity in vivoConclusions: Our findings suggest a critical role for the HRG/ErbB3 axis and support the clinical exploration of dual IGF-1R/ErbB3 blocking in pancreatic cancer. Clin Cancer Res; 24(12); 2873-85. ©2018 AACR.
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Albuminas/farmacologia , Desoxicitidina/análogos & derivados , Paclitaxel/farmacologia , Neoplasias Pancreáticas/metabolismo , Receptor ErbB-3/antagonistas & inibidores , Receptores de Somatomedina/antagonistas & inibidores , Animais , Caspases/metabolismo , Linhagem Celular Tumoral , Desoxicitidina/farmacologia , Modelos Animais de Doenças , Avaliação Pré-Clínica de Medicamentos , Humanos , Camundongos , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/patologia , Receptor ErbB-3/metabolismo , Receptor IGF Tipo 1 , Receptores de Somatomedina/metabolismo , Transdução de Sinais/efeitos dos fármacos , Ensaios Antitumorais Modelo de Xenoenxerto , GencitabinaRESUMO
As interactions between the immune system and tumour cells are governed by a complex network of cell-cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient's response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.
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Perfilação da Expressão Gênica/métodos , Sistema Imunitário/metabolismo , Neoplasias/genética , Análise de Célula Única/métodos , Algoritmos , Células Cultivadas , Humanos , Sistema Imunitário/imunologia , Sistema Imunitário/patologia , Neoplasias/imunologia , Neoplasias/patologia , Células Estromais/metabolismo , Microambiente Tumoral/genéticaRESUMO
Targeted therapies have shown significant patient benefit in about 5-10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo.
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The ErbB family of receptor tyrosine kinases comprises four members: epidermal growth factor receptor (EGFR/ErbB1), human EGFR 2 (HER2/ErbB2), ErbB3/HER3, and ErbB4/HER4. The first two members of this family, EGFR and HER2, have been implicated in tumorigenesis and cancer progression for several decades, and numerous drugs have now been approved that target these two proteins. Less attention, however, has been paid to the role of this family in mediating cancer cell survival and drug tolerance. To better understand the complex signal transduction network triggered by the ErbB receptor family, we built a computational model that quantitatively captures the dynamics of ErbB signaling. Sensitivity analysis identified ErbB3 as the most critical activator of phosphoinositide 3-kinase (PI3K) and Akt signaling, a key pro-survival pathway in cancer cells. Based on this insight, we designed a fully human monoclonal antibody, seribantumab (MM-121), that binds to ErbB3 and blocks signaling induced by the extracellular growth factors heregulin (HRG) and betacellulin (BTC). In this article, we present some of the key preclinical simulations and experimental data that formed the scientific foundation for three Phase 2 clinical trials in metastatic cancer. These trials were designed to determine if patients with advanced malignancies would derive benefit from the addition of seribantumab to standard-of-care drugs in platinum-resistant/refractory ovarian cancer, hormone receptor-positive HER2-negative breast cancer, and EGFR wild-type non-small cell lung cancer (NSCLC). From preclinical studies we learned that basal levels of ErbB3 phosphorylation correlate with response to seribantumab monotherapy in mouse xenograft models. As ErbB3 is rapidly dephosphorylated and hence difficult to measure clinically, we used the computational model to identify a set of five surrogate biomarkers that most directly affect the levels of p-ErbB3: HRG, BTC, EGFR, HER2, and ErbB3. Preclinically, the combined information from these five markers was sufficient to accurately predict which xenograft models would respond to seribantumab, and the single-most accurate predictor was HRG. When tested clinically in ovarian, breast and lung cancer, HRG mRNA expression was found to be both potentially prognostic of insensitivity to standard therapy and potentially predictive of benefit from the addition of seribantumab to standard of care therapy in all three indications. In addition, it was found that seribantumab was most active in cancers with low levels of HER2, consistent with preclinical predictions. Overall, our clinical studies and studies of others suggest that HRG expression defines a drug-tolerant cancer cell phenotype that persists in most solid tumor indications and may contribute to rapid clinical progression. To our knowledge, this is the first example of a drug designed and clinically tested using the principles of Systems Biology.
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Understanding the molecular pathways by which oncogenes drive cancerous cell growth, and how dependence on such pathways varies between tumors could be highly valuable for the design of anti-cancer treatment strategies. In this work we study how dependence upon the canonical PI3K and MAPK cascades varies across HER2+ cancers, and define biomarkers predictive of pathway dependencies. A panel of 18 HER2+ (ERBB2-amplified) cell lines representing a variety of indications was used to characterize the functional and molecular diversity within this oncogene-defined cancer. PI3K and MAPK-pathway dependencies were quantified by measuring in vitro cell growth responses to combinations of AKT (MK2206) and MEK (GSK1120212; trametinib) inhibitors, in the presence and absence of the ERBB3 ligand heregulin (NRG1). A combination of three protein measurements comprising the receptors EGFR, ERBB3 (HER3), and the cyclin-dependent kinase inhibitor p27 (CDKN1B) was found to accurately predict dependence on PI3K/AKT vs. MAPK/ERK signaling axes. Notably, this multivariate classifier outperformed the more intuitive and clinically employed metrics, such as expression of phospho-AKT and phospho-ERK, and PI3K pathway mutations (PIK3CA, PTEN, and PIK3R1). In both cell lines and primary patient samples, we observed consistent expression patterns of these biomarkers varies by cancer indication, such that ERBB3 and CDKN1B expression are relatively high in breast tumors while EGFR expression is relatively high in other indications. The predictability of the three protein biomarkers for differentiating PI3K/AKT vs. MAPK dependence in HER2+ cancers was confirmed using external datasets (Project Achilles and GDSC), again out-performing clinically used genetic markers. Measurement of this minimal set of three protein biomarkers could thus inform treatment, and predict mechanisms of drug resistance in HER2+ cancers. More generally, our results show a single oncogenic transformation can have differing effects on cell signaling and growth, contingent upon the molecular and cellular context.
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Sistema de Sinalização das MAP Quinases , Neoplasias/genética , Neoplasias/metabolismo , Fosfatidilinositol 3-Quinases/metabolismo , Receptor ErbB-2/metabolismo , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Linhagem Celular Tumoral , Biologia Computacional , Inibidor de Quinase Dependente de Ciclina p27/genética , Inibidor de Quinase Dependente de Ciclina p27/metabolismo , Receptores ErbB/genética , Receptores ErbB/metabolismo , Feminino , Técnicas de Silenciamento de Genes , Genes erbB-2 , Humanos , Sistema de Sinalização das MAP Quinases/genética , Mutação , Neoplasias/patologia , Fosfatidilinositol 3-Quinases/genética , Inibidores de Fosfoinositídeo-3 Quinase , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , RNA Neoplásico/genética , RNA Neoplásico/metabolismo , Receptor ErbB-3/genética , Receptor ErbB-3/metabolismoRESUMO
In this week's issue of Science Signaling, Fey et al. introduce a new type of biomarker. Using the example of neuroblastoma, the authors demonstrate that patient-specific differences in the computed property (the Hill coefficient) of the dynamics of a pathway involved in cell death signaling outperformed the prognostic capability of any single static biomarker alone or in combination.
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Biomarcadores , Transdução de Sinais , Humanos , Neuroblastoma , PrognósticoRESUMO
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.
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BACKGROUND: The Fibroblast Growth Factor (FGF) pathway is driving various aspects of cellular responses in both normal and malignant cells. One interesting characteristic of this pathway is the biphasic nature of the cellular response to some FGF ligands like FGF2. Specifically, it has been shown that phenotypic behaviors controlled by FGF signaling, like migration and growth, reach maximal levels in response to intermediate concentrations, while high levels of FGF2 elicit weak responses. The mechanisms leading to the observed biphasic response remains unexplained. RESULTS: A combination of experiments and computational modeling was used to understand the mechanism behind the observed biphasic signaling responses. FGF signaling involves a tertiary surface interaction that we captured with a computational model based on Ordinary Differential Equations (ODEs). It accounts for FGF2 binding to FGF receptors (FGFRs) and heparan sulfate glycosaminoglycans (HSGAGs), followed by receptor-phosphorylation, activation of the FRS2 adapter protein and the Ras-Raf signaling cascade. Quantitative protein assays were used to measure the dynamics of phosphorylated ERK (pERK) in response to a wide range of FGF2 ligand concentrations on a fine-grained time scale for the squamous cell lung cancer cell line H1703. We developed a novel approach combining Particle Swarm Optimization (PSO) and feature-based constraints in the objective function to calibrate the computational model to the experimental data. The model is validated using a series of extracellular and intracellular perturbation experiments. We demonstrate that in silico model predictions are in accordance with the observed in vitro results. CONCLUSIONS: Using a combined approach of computational modeling and experiments we found that competition between binding of the ligand FGF2 to HSGAG and FGF receptor leads to the biphasic response. At low to intermediate concentrations of FGF2 there are sufficient free FGF receptors available for the FGF2-HSGAG complex to enable the formation of the trimeric signaling unit. At high ligand concentrations the ligand binding sites of the receptor become saturated and the trimeric signaling unit cannot be formed. This insight into the pathway is an important consideration for the pharmacological inhibition of this pathway.
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Fatores de Crescimento de Fibroblastos/metabolismo , Sistema de Sinalização das MAP Quinases , Modelos Biológicos , Linhagem Celular Tumoral , HumanosRESUMO
BACKGROUND: Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines. RESULTS: We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways. CONCLUSIONS: Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an "indirect negative regulation" by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.
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Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Peptídeos e Proteínas de Sinalização Intercelular/metabolismo , Transdução de Sinais , Neoplasias da Mama/enzimologia , Linhagem Celular Tumoral , Análise por Conglomerados , Relação Dose-Resposta a Droga , MAP Quinases Reguladas por Sinal Extracelular/metabolismo , Feminino , Humanos , Fator de Crescimento Insulin-Like I/metabolismo , Cinética , Ligantes , Fosfatidilinositol 3-Quinases/metabolismo , Fosforilação , Proteínas Proto-Oncogênicas c-akt/metabolismo , Receptor ErbB-2/metabolismo , Fatores de TempoRESUMO
Although inhibition of the insulin-like growth factor (IGF) signaling pathway was expected to eliminate a key resistance mechanism for EGF receptor (EGFR)-driven cancers, the effectiveness of IGF-I receptor (IGF-IR) inhibitors in clinical trials has been limited. A multiplicity of survival mechanisms are available to cancer cells. Both IGF-IR and the ErbB3 receptor activate the PI3K/AKT/mTOR axis, but ErbB3 has only recently been pursued as a therapeutic target. We show that coactivation of the ErbB3 pathway is prevalent in a majority of cell lines responsive to IGF ligands and antagonizes IGF-IR-mediated growth inhibition. Blockade of the redundant IGF-IR and ErbB3 survival pathways and downstream resistance mechanisms was achieved with MM-141, a tetravalent bispecific antibody antagonist of IGF-IR and ErbB3. MM-141 potency was superior to monospecific and combination antibody therapies and was insensitive to variation in the ratio of IGF-IR and ErbB3 receptors. MM-141 enhanced the biologic impact of receptor inhibition in vivo as a monotherapy and in combination with the mTOR inhibitor everolimus, gemcitabine, or docetaxel, through blockade of IGF-IR and ErbB3 signaling and prevention of PI3K/AKT/mTOR network adaptation.
Assuntos
Anticorpos Biespecíficos/farmacologia , Proliferação de Células/efeitos dos fármacos , Receptor ErbB-3/antagonistas & inibidores , Receptor IGF Tipo 1/antagonistas & inibidores , Transdução de Sinais/efeitos dos fármacos , Animais , Anticorpos Biespecíficos/administração & dosagem , Anticorpos Biespecíficos/imunologia , Anticorpos Monoclonais Humanizados , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Western Blotting , Linhagem Celular Tumoral , Desoxicitidina/administração & dosagem , Desoxicitidina/análogos & derivados , Docetaxel , Everolimo , Feminino , Humanos , Camundongos Endogâmicos NOD , Camundongos Nus , Camundongos SCID , Neoplasias/tratamento farmacológico , Neoplasias/metabolismo , Neoplasias/patologia , Fosfatidilinositol 3-Quinases/metabolismo , Proteínas Proto-Oncogênicas c-akt/metabolismo , Receptor ErbB-3/imunologia , Receptor IGF Tipo 1/imunologia , Sirolimo/administração & dosagem , Sirolimo/análogos & derivados , Serina-Treonina Quinases TOR/metabolismo , Taxoides/administração & dosagem , Carga Tumoral/efeitos dos fármacos , Ensaios Antitumorais Modelo de Xenoenxerto , GencitabinaRESUMO
Identifying factors responsible for variation in drug response is essential for the effective use of targeted therapeutics. We profiled signaling pathway activity in a collection of breast cancer cell lines before and after stimulation with physiologically relevant ligands, which revealed the variability in network activity among cells of known genotype and molecular subtype. Despite the receptor-based classification of breast cancer subtypes, we found that the abundance and activity of signaling proteins in unstimulated cells (basal profile), as well as the activity of proteins in stimulated cells (signaling profile), varied within each subtype. Using a partial least-squares regression approach, we constructed models that significantly predicted sensitivity to 23 targeted therapeutics. For example, one model showed that the response to the growth factor receptor ligand heregulin effectively predicted the sensitivity of cells to drugs targeting the cell survival pathway mediated by PI3K (phosphoinositide 3-kinase) and Akt, whereas the abundance of Akt or the mutational status of the enzymes in the pathway did not. Thus, basal and signaling protein profiles may yield new biomarkers of drug sensitivity and enable the identification of appropriate therapies in cancers characterized by similar functional dysregulation of signaling networks.
Assuntos
Antineoplásicos/uso terapêutico , Biomarcadores Tumorais , Neoplasias da Mama , Regulação Neoplásica da Expressão Gênica , Receptores de Fatores de Crescimento , Transdução de Sinais , Biomarcadores Tumorais/biossíntese , Biomarcadores Tumorais/genética , Neoplasias da Mama/tratamento farmacológico , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Neoplasias da Mama/patologia , Linhagem Celular Tumoral , Feminino , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica/efeitos dos fármacos , Regulação Neoplásica da Expressão Gênica/genética , Humanos , Mutação , Neuregulina-1/genética , Neuregulina-1/metabolismo , Fosfatidilinositol 3-Quinases/genética , Fosfatidilinositol 3-Quinases/metabolismo , Proteínas Proto-Oncogênicas c-akt/genética , Proteínas Proto-Oncogênicas c-akt/metabolismo , Receptores de Fatores de Crescimento/genética , Receptores de Fatores de Crescimento/metabolismo , Transdução de Sinais/efeitos dos fármacos , Transdução de Sinais/genéticaRESUMO
Multispecific antibody-like molecules have the potential to advance the standard-of-care in many human diseases. The design of therapeutic molecules in this class, however, has proven to be difficult and, despite significant successes in preclinical research, only one trivalent antibody, catumaxomab, has demonstrated clinical utility. The challenge originates from the complexity of the design space where multiple parameters such as affinity, avidity, effector functions, and pharmaceutical properties need to be engineered in concurrent fashion to achieve the desired therapeutic efficacy. Here, we present a rapid prototyping approach that allows us to successfully optimize these parameters within one campaign cycle that includes modular design, yeast display of structure focused antibody libraries and high throughput biophysical profiling. We delineate this approach by presenting a design case study of MM-141, a tetravalent bispecific antibody targeting two compensatory signaling growth factor receptors: insulin-like growth factor 1 receptor (IGF-1R) and v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (ErbB3). A MM-141 proof-of-concept (POC) parent molecule did not meet initial design criteria due to modest bioactivity and poor stability properties. Using a combination of yeast display, structured-guided antibody design and library-scale thermal challenge assay, we discovered a diverse set of stable and active anti-IGF-1R and anti-ErbB3 single-chain variable fragments (scFvs). These optimized modules were reformatted to create a diverse set of full-length tetravalent bispecific antibodies. These re-engineered molecules achieved complete blockade of growth factor induced pro-survival signaling, were stable in serum, and had adequate activity and pharmaceutical properties for clinical development. We believe this approach can be readily applied to the optimization of other classes of bispecific or even multispecific antibody-like molecules.