Article Database

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Abstract:

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

“Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling

Abstract:

Background: Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this function have not demonstrated clear clinical efficacy. This study explores strategies to enhance TSP1-induced apoptosis in endothelial cells. In particular, we focus on establishing a computational model to describe the signaling pathway, and using this model to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network.

Methods: We constructed a molecularly-detailed mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor based on literature evidence. We employed systems biology tools to train and validate the model and further expanded the model by accounting for the heterogeneity within the cell population. The initial concentrations of signaling species or kinetic rates were altered to simulate the effects of perturbations to the signaling network.

Results: Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species.

Conclusions: This new mathematical model recapitulates the intracellular dynamics of the TSP1-induced apoptosis signaling pathway. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.

The impact of tumor receptor heterogeneity on the response to anti-angiogenic cancer treatment

Abstract:

Objective: To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

Data sources: Literature review, research data, and expert opinion.

Study selection: Not applicable.

Data extraction: Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

Data synthesis: Summary of presentations and discussion supported and supplemented by relevant literature.

Conclusions: There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Constructing predictive cancer systems biology models

Abstract:

Systems biology combines computational modeling with quantitative experimental measurements to study complex biological processes. Here, we outline an approach for parameterizing and validating a systems biology model to yield predictive tool that can generate testable hypotheses and expand biological understanding.

In silico mouse study identifies tumor growth kinetics as biomarkers for the outcome of anti-angiogenic treatment

Abstract:

Angiogenesis is a crucial step in tumour progression, as this process allows tumours to recruit new blood vessels and obtain oxygen and nutrients to sustain growth. Therefore, inhibiting angiogenesis remains a viable strategy for cancer therapy. However, anti-angiogenic therapy has not proved to be effective in reducing tumour growth across a wide range of tumours, and no reliable predictive biomarkers have been found to determine the efficacy of anti-angiogenic treatment. Using our previously established computational model of tumour-bearing mice, we sought to determine whether tumour growth kinetic parameters could be used to predict the outcome of anti-angiogenic treatment. A model trained with datasets from six in vivo mice studies was used to generate a randomized in silico tumour-bearing mouse population. We analysed tumour growth in untreated mice (control) and mice treated with an anti-angiogenic agent and determined the Kaplan-Meier survival estimates based on simulated tumour volume data. We found that the ratio between two kinetic parameters, k₀ and k₁, which characterize the tumour’s exponential and linear growth rates, as well as k₁ alone, can be used as prognostic biomarkers of the population survival outcome. Our work demonstrates a robust, quantitative approach for identifying tumour growth kinetic parameters as prognostic biomarkers and serves as a template that can be used to identify other biomarkers for anti-angiogenic treatment.

Computational model of chimeric antigen receptors explains site-specific phosphorylation kinetics

Abstract:

Chimeric antigen receptors (CARs) have recently been approved for the treatment of hematological malignancies, but our lack of understanding of the basic mechanisms that activate these proteins has made it difficult to optimize and control CAR-based therapies. In this study, we use phosphoproteomic mass spectrometry and mechanistic computational modeling to quantify the in vitro kinetics of individual tyrosine phosphorylation on a variety of CARs. We show that each of the 10 tyrosine sites on the CD28-CD3ζ CAR is phosphorylated by lymphocyte-specific protein-tyrosine kinase (LCK) with distinct kinetics. The addition of CD28 at the N-terminal of CD3ζ increases the overall rate of CD3ζ phosphorylation. Our computational model identifies that LCK phosphorylates CD3ζ through a mechanism of competitive inhibition. This model agrees with previously published data in the literature and predicts that phosphatases in this system interact with CD3ζ through a similar mechanism of competitive inhibition. This quantitative modeling framework can be used to better understand CAR signaling and T cell activation.

Mechanistic insight into activation of MAPK signaling by pro-angiogenic factors

Abstract:

Background: Angiogenesis is important in physiological and pathological conditions, as blood vessels provide nutrients and oxygen needed for tissue growth and survival. Therefore, targeting angiogenesis is a prominent strategy in both tissue engineering and cancer treatment. However, not all of the approaches to promote or inhibit angiogenesis lead to successful outcomes. Angiogenesis-based therapies primarily target pro-angiogenic factors such as vascular endothelial growth factor-A (VEGF) or fibroblast growth factor (FGF) in isolation. However, pre-clinical and clinical evidence shows these therapies often have limited effects. To improve therapeutic strategies, including targeting FGF and VEGF in combination, we need a quantitative understanding of the how the promoters combine to stimulate angiogenesis.

Results: In this study, we trained and validated a detailed mathematical model to quantitatively characterize the crosstalk of FGF and VEGF intracellular signaling. This signaling is initiated by FGF binding to the FGF receptor 1 (FGFR1) and heparan sulfate glycosaminoglycans (HSGAGs) or VEGF binding to VEGF receptor 2 (VEGFR2) to promote downstream signaling. The model focuses on FGF- and VEGF-induced mitogen-activated protein kinase (MAPK) signaling and phosphorylation of extracellular regulated kinase (ERK), which promotes cell proliferation. We apply the model to predict the dynamics of phosphorylated ERK (pERK) in response to the stimulation by FGF and VEGF individually and in combination. The model predicts that FGF and VEGF have differential effects on pERK. Additionally, since VEGFR2 upregulation has been observed in pathological conditions, we apply the model to investigate the effects of VEGFR2 density and trafficking parameters. The model predictions show that these parameters significantly influence the response to VEGF stimulation.

Conclusions: The model agrees with experimental data and is a framework to synthesize and quantitatively explain experimental studies. Ultimately, the model provides mechanistic insight into FGF and VEGF interactions needed to identify potential targets for pro- or anti-angiogenic therapies.

“Understanding the dynamics of T cell activation through the lens of computational modeling

Abstract:

T cells in the immune system are activated by binding to foreign peptides (from an external pathogen) or mutant peptide (derived from endogenous proteins) displayed on the surface of a diseased cell. This triggers a series of intracellular signaling pathways, which ultimately dictate the response of the T cell. The insights from computational models have greatly improved our understanding of the mechanisms that control T-cell activation. In this review, we focus on the use of ordinary differential equation-based mechanistic models to study T-cell activation. We highlight several examples that demonstrate the models’ utility in answering specific questions related to T-cell activation signaling, from antigen discrimination to the feedback mechanisms that initiate transcription factor activation. In addition, we describe other modeling approaches that can be combined with mechanistic models to bridge time scales and better understand how intracellular signaling events, which occur on the order of seconds to minutes, influence phenotypic responses of T-cell activation, which occur on the order of hours to days. Overall, through concrete examples, we emphasize how computational modelin

Metabolism in cancer progression

Abstract:

Whether the nom de guerre is Mathematical Oncology, Computational or Systems Biology, Theoretical Biology, Evolutionary Oncology, Bioinformatics, or simply Basic Science, there is no denying that mathematics continues to play an increasingly prominent role in cancer research. Mathematical Oncology—defined here simply as the use of mathematics in cancer research—complements and overlaps with a number of other fields that rely on mathematics as a core methodology. As a result, Mathematical Oncology has a broad scope, ranging from theoretical studies to clinical trials designed with mathematical models. This Roadmap differentiates Mathematical Oncology from related fields and demonstrates specific areas of focus within this unique field of research. The dominant theme of this Roadmap is the personalization of medicine through mathematics, modelling, and simulation. This is achieved through the use of patient-specific clinical data to: develop individualized screening strategies to detect cancer earlier; make predictions of response to therapy; design adaptive, patient-specific treatment plans to overcome therapy resistance; and establish domain-specific standards to share model predictions and to make models and simulations reproducible. The cover art for this Roadmap was chosen as an apt metaphor for the beautiful, strange, and evolving relationship between mathematics and cancer.

Metabolic reprogramming dynamics in tumor spheroids: Insights from a multicellular, multiscale model

Abstract:

Mathematical modeling provides the predictive ability to understand the metabolic reprogramming and complex pathways that mediate cancer cells’ proliferation. We present a mathematical model using a multiscale, multicellular approach to simulate avascular tumor growth, applied to pancreatic cancer. The model spans three distinct spatial and temporal scales. At the extracellular level, reaction diffusion equations describe nutrient concentrations over a span of seconds. At the cellular level, a lattice-based energy driven stochastic approach describes cellular phenomena including adhesion, proliferation, viability and cell state transitions, occurring on the timescale of hours. At the sub-cellular level, we incorporate a detailed kinetic model of intracellular metabolite dynamics on the timescale of minutes, which enables the cells to uptake and excrete metabolites and use the metabolites to generate energy and building blocks for cell growth. This is a particularly novel aspect of the model. Certain defined criteria for the concentrations of intracellular metabolites lead to cancer cell growth, proliferation or death. Overall, we model the evolution of the tumor in both time and space. Starting with a cluster of tumor cells, the model produces an avascular tumor that quantitatively and qualitatively mimics experimental measurements of multicellular tumor spheroids. Through our model simulations, we can investigate the response of individual intracellular species under a metabolic perturbation and investigate how that response contributes to the response of the tumor as a whole. The predicted response of intracellular metabolites under various targeted strategies are difficult to resolve with experimental techniques. Thus, the model can give novel predictions as to the response of the tumor as a whole, identifies potential therapies to impede tumor growth, and predicts the effects of those therapeutic strategies. In particular, the model provides quantitative insight into the dynamic reprogramming of tumor cells at the intracellular level in response to specific metabolic perturbations. Overall, the model is a useful framework to study targeted metabolic strategies for inhibiting tumor growth.