scholarly journals Prediction of anti-CD25 and 5-FU treatments efficacy for pancreatic cancer using a mathematical model

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sajad Shafiekhani ◽  
Hojat Dehghanbanadaki ◽  
Azam Sadat Fatemi ◽  
Sara Rahbar ◽  
Jamshid Hadjati ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Tumor Microenvironment ◽  
Kinetic Parameters ◽  
In Silico ◽  
Treatment Modalities ◽  
Ductal Adenocarcinoma ◽  
Rational Approach ◽  
Model Parameters ◽  
Tumor Escape ◽  
Uncertainty Band

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with rising incidence and with 5-years overall survival of less than 8%. PDAC creates an immune-suppressive tumor microenvironment to escape immune-mediated eradication. Regulatory T (Treg) cells and myeloid-derived suppressor cells (MDSC) are critical components of the immune-suppressive tumor microenvironment. Shifting from tumor escape or tolerance to elimination is the major challenge in the treatment of PDAC. Results In a mathematical model, we combine distinct treatment modalities for PDAC, including 5-FU chemotherapy and anti- CD25 immunotherapy to improve clinical outcome and therapeutic efficacy. To address and optimize 5-FU and anti- CD25 treatment (to suppress MDSCs and Tregs, respectively) schedule in-silico and simultaneously unravel the processes driving therapeutic responses, we designed an in vivo calibrated mathematical model of tumor-immune system (TIS) interactions. We designed a user-friendly graphical user interface (GUI) unit which is configurable for treatment timings to implement an in-silico clinical trial to test different timings of both 5-FU and anti- CD25 therapies. By optimizing combination regimens, we improved treatment efficacy. In-silico assessment of 5-FU and anti- CD25 combination therapy for PDAC significantly showed better treatment outcomes when compared to 5-FU and anti- CD25 therapies separately. Due to imprecise, missing, or incomplete experimental data, the kinetic parameters of the TIS model are uncertain that this can be captured by the fuzzy theorem. We have predicted the uncertainty band of cell/cytokines dynamics based on the parametric uncertainty, and we have shown the effect of the treatments on the displacement of the uncertainty band of the cells/cytokines. We performed global sensitivity analysis methods to identify the most influential kinetic parameters and simulate the effect of the perturbation on kinetic parameters on the dynamics of cells/cytokines. Conclusion Our findings outline a rational approach to therapy optimization with meaningful consequences for how we effectively design treatment schedules (timing) to maximize their success, and how we treat PDAC with combined 5-FU and anti- CD25 therapies. Our data revealed that a synergistic combinatorial regimen targeting the Tregs and MDSCs in both crisp and fuzzy settings of model parameters can lead to tumor eradication.

scholarly journals In Silico Assessment of 5-FU Therapy via A Mathematical Model with Fuzzy Uncertain Parameters

2020 ◽  
Author(s):  
Sajad Shafiekhani ◽  
Amir. H. Jafari ◽  
L. Jafarzadeh ◽  
N. Gheibi
Keyword(s):  
Differential Equation ◽  
Immune Cells ◽  
In Silico ◽  
Parametric Uncertainty ◽  
Testable Hypothesis ◽  
Model Parameters ◽  
Ode Model ◽  
Ode Models ◽  
Mathematical Oncology ◽  
Uncertainty Band

Abstract Background: Ordinary differential equation (ODE) models widely have been used in mathematical oncology to capture dynamics of tumor and immune cells and evaluate the efficacy of treatments. However, for dynamic models of tumor-immune system (TIS), some parameters are uncertain due to inaccurate, missing or incomplete data, which has hindered the application of ODEs that require accurate parameters. Methods: We extended an available ODE model of TIS interactions via fuzzy logic to illustrate the fuzzification procedure of an ODE model. Fuzzy ODE (FODE) models, in comparison with the stochastic differential equation (SDE) models, assigns a fuzzy number instead of a random number (from a specific probability density function) to the parameters, to capture parametric uncertainty. We used FODE model to predict tumor and immune cells dynamics and assess the efficacy of 5-FU. The present model is configurable for 5-FU chemotherapy injection timing and propose testable hypothesis in vitro/ in vivo experiments. Result: FODE model was used to explore the uncertainty of cells dynamics resulting from parametric uncertainty in presence and absence of 5-FU therapy. In silico experiments revealed that the frequent 5-FU injection created a beneficial tumor microenvironment that exerted detrimental effects on tumor cells by enhancing the infiltration of CD8+ T cells, and NK cells, and decreasing that of myeloid-derived suppressor (MDSC) cells. We investigate the effect of perturbation on model parameters on dynamics of cells through global sensitivity analysis (GSA) and compute correlation between model parameters and cell dynamics. Conclusion: ODE models with fuzzy uncertain kinetic parameters cope with insufficient experimental data in the field of mathematical oncology and can predict cells dynamics uncertainty band. In silico assessment of treatments considering parameter uncertainty and investigating the effect of the drugs on movement of cells dynamics uncertainty band may be more appropriate than in crisp setting.

Development and automation of algorithm for determining basis of nonlinear parameter functions of kinetic constants

2019 ◽  
Vol 19 (4) ◽  
pp. 252-257
Author(s):  
A. S. Ismagilova ◽  
Z. A. Khamidullina ◽  
S. I. Spivak
Keyword(s):  
Mathematical Model ◽  
Kinetic Parameters ◽  
Platinum Catalyst ◽  
Hydrogen Oxidation ◽  
Nonlinear Parameter ◽  
Kinetic Constants ◽  
Model Parameters ◽  
Functional Relations ◽  
The Mathematical Model ◽  
Theoretical Issues

Mathematical modeling of catalytic processes is necessary for the complete and accurate description, as well as for controlling the quality and physicochemical studied of catalysts. In the paper, theoretical issues of industrial catalysis are discussed. The work is devoted to theoretical graph analysis of informativity of kinetic parameters of the model of a complex chemical reaction. The aim is the development and automation of algorithm for determining basis of nonlinear parameter functions in solving inverse problems of chemical kinetics in order to define the number and form of independent combinations of rate constants of elementary stages. A program package for analysis of informativity of kinetic parameters of the mathematical model of a complex catalytic reaction is developed and described. The obtained functional relations between the kinetic parameters can be useful for experimentalists in physicochemical interpretation and analysis of mechanisms of chemical reactions. In other words, the proposed method allows independent combinations of kinetic constants to be distinguished that results in shortening the number of the model parameters and, as a consequence, enhance the accuracy of the mathematical model. The mechanism of hydrogen oxidation over a platinum catalyst is given as an example of the use of the software.

scholarly journals Tumor-Associated Macrophages in Pancreatic Ductal Adenocarcinoma: Therapeutic Opportunities and Clinical Challenges

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2860
Author(s):  
Ashleigh R. Poh ◽  
Matthias Ernst
Keyword(s):  
Tumor Microenvironment ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Pancreatic Tumor ◽  
Treatment Modalities ◽  
Ductal Adenocarcinoma ◽  
Increased Risk ◽  
Clinical Models ◽  
Risk Of Disease ◽  
Macrophage Targeting

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant disease with a 5-year survival rate of less than 10%. Macrophages are one of the earliest infiltrating cells in the pancreatic tumor microenvironment, and are associated with an increased risk of disease progression, recurrence, metastasis, and shorter overall survival. Pre-clinical studies have demonstrated an unequivocal role of macrophages in PDAC by contributing to chronic inflammation, cancer cell stemness, desmoplasia, immune suppression, angiogenesis, invasion, metastasis, and drug resistance. Several macrophage-targeting therapies have also been investigated in pre-clinical models, and include macrophage depletion, inhibiting macrophage recruitment, and macrophage reprogramming. However, the effectiveness of these drugs in pre-clinical models has not always translated into clinical trials. In this review, we discuss the molecular mechanisms that underpin macrophage heterogeneity within the pancreatic tumor microenvironment, and examine the contribution of macrophages at various stages of PDAC progression. We also provide a comprehensive update of macrophage-targeting therapies that are currently undergoing clinical evaluation, and discuss clinical challenges associated with these treatment modalities in human PDAC patients.

Usefulness of collaboration between mathematical models and cell engineering for elucidating complex disease mechanisms and discover effective drugs

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
H Kohjitani ◽  
A Kashiwa ◽  
T Makiyama ◽  
F Toyoda ◽  
Y Yamamoto ◽  
...  
Keyword(s):  
Mathematical Model ◽  
In Silico ◽  
Complex Disease ◽  
Ion Selectivity ◽  
Public Institution ◽  
Cell Engineering ◽  
Funding Source ◽  
Patch Clamp Technique ◽  
In Silico Model

Abstract Background A missense mutation, CACNA1C-E1115K, located in the cardiac L-type calcium channel (LTCC), was recently reported to be associated with diverse arrhythmias. Several studies reported in-vivo and in-vitro modeling of this mutation, but actual mechanism and target drug of this disease has not been clarified due to its complex ion-mechanisms. Objective To reveal the mechanism of this diverse arrhythmogenic phenotype using combination of in-vitro and in-silico model. Methods and results Cell-Engineering Phase: We generated human induced pluripotent stem cell (hiPSC) from a patient carrying heterozygous CACNA1C-E1115K and differentiated into cardiomyocytes. Spontaneous APs were recorded from spontaneously beating single cardiomyocytes by using the perforated patch-clamp technique. Mathematical-Modeling Phase: We newly developed ICaL-mutation mathematical model, fitted into experimental data, including its impaired ion selectivity. Furthermore, we installed this mathematical model into hiPSC-CM simulation model. Collaboration Phase: Mutant in-silico model showed APD prolongation and frequent early afterdepolarization (EAD), which are same as in-vitro model. In-silico model revealed this EAD was mostly related to robust late-mode of sodium current occurred by Na+ overload and suggested that mexiletine is capable of reducing arrhythmia. Afterward, we applicated mexiletine onto hiPSC-CMs mutant model and found mexiletine suppress EADs. Conclusions Precise in-silico disease model can elucidate complicated ion currents and contribute predicting result of drug-testing. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Japan Society for the Promotion of Science, Grant-in-Aid for Young Scientists

scholarly journals CARTmath—A Mathematical Model of CAR-T Immunotherapy in Preclinical Studies of Hematological Cancers

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2941
Author(s):  
Luciana R. C. Barros ◽  
Emanuelle A. Paixão ◽  
Andrea M. P. Valli ◽  
Gustavo T. Naozuka ◽  
Artur C. Fassoni ◽  
...  
Keyword(s):  
Mathematical Model ◽  
T Cells ◽  
T Cell ◽  
Mouse Models ◽  
In Silico ◽  
Car T Cells ◽  
Car T Cell ◽  
Cell Immunotherapy ◽  
Car T ◽  
T Cell Immunotherapy

Immunotherapy has gained great momentum with chimeric antigen receptor T cell (CAR-T) therapy, in which patient’s T lymphocytes are genetically manipulated to recognize tumor-specific antigens, increasing tumor elimination efficiency. In recent years, CAR-T cell immunotherapy for hematological malignancies achieved a great response rate in patients and is a very promising therapy for several other malignancies. Each new CAR design requires a preclinical proof-of-concept experiment using immunodeficient mouse models. The absence of a functional immune system in these mice makes them simple and suitable for use as mathematical models. In this work, we develop a three-population mathematical model to describe tumor response to CAR-T cell immunotherapy in immunodeficient mouse models, encompassing interactions between a non-solid tumor and CAR-T cells (effector and long-term memory). We account for several phenomena, such as tumor-induced immunosuppression, memory pool formation, and conversion of memory into effector CAR-T cells in the presence of new tumor cells. Individual donor and tumor specificities are considered uncertainties in the model parameters. Our model is able to reproduce several CAR-T cell immunotherapy scenarios, with different CAR receptors and tumor targets reported in the literature. We found that therapy effectiveness mostly depends on specific parameters such as the differentiation of effector to memory CAR-T cells, CAR-T cytotoxic capacity, tumor growth rate, and tumor-induced immunosuppression. In summary, our model can contribute to reducing and optimizing the number of in vivo experiments with in silico tests to select specific scenarios that could be tested in experimental research. Such an in silico laboratory is an easy-to-run open-source simulator, built on a Shiny R-based platform called CARTmath. It contains the results of this manuscript as examples and documentation. The developed model together with the CARTmath platform have potential use in assessing different CAR-T cell immunotherapy protocols and its associated efficacy, becoming an accessory for in silico trials.

scholarly journals Differentiated glioblastoma cells accelerate tumor progression by shaping the tumor microenvironment via CCN1-mediated macrophage infiltration

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Atsuhito Uneda ◽  
Kazuhiko Kurozumi ◽  
Atsushi Fujimura ◽  
Kentaro Fujii ◽  
Joji Ishida ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Tumor Progression ◽  
Tumor Cells ◽  
In Silico ◽  
Tumor Tissue ◽  
The Cancer Genome Atlas ◽  
Macrophage Infiltration ◽  
Macrophage Migration

AbstractGlioblastoma (GBM) is the most lethal primary brain tumor characterized by significant cellular heterogeneity, namely tumor cells, including GBM stem-like cells (GSCs) and differentiated GBM cells (DGCs), and non-tumor cells such as endothelial cells, vascular pericytes, macrophages, and other types of immune cells. GSCs are essential to drive tumor progression, whereas the biological roles of DGCs are largely unknown. In this study, we focused on the roles of DGCs in the tumor microenvironment. To this end, we extracted DGC-specific signature genes from transcriptomic profiles of matched pairs of in vitro GSC and DGC models. By evaluating the DGC signature using single cell data, we confirmed the presence of cell subpopulations emulated by in vitro culture models within a primary tumor. The DGC signature was correlated with the mesenchymal subtype and a poor prognosis in large GBM cohorts such as The Cancer Genome Atlas and Ivy Glioblastoma Atlas Project. In silico signaling pathway analysis suggested a role of DGCs in macrophage infiltration. Consistent with in silico findings, in vitro DGC models promoted macrophage migration. In vivo, coimplantation of DGCs and GSCs reduced the survival of tumor xenograft-bearing mice and increased macrophage infiltration into tumor tissue compared with transplantation of GSCs alone. DGCs exhibited a significant increase in YAP/TAZ/TEAD activity compared with GSCs. CCN1, a transcriptional target of YAP/TAZ, was selected from the DGC signature as a candidate secreted protein involved in macrophage recruitment. In fact, CCN1 was secreted abundantly from DGCs, but not GSCs. DGCs promoted macrophage migration in vitro and macrophage infiltration into tumor tissue in vivo through secretion of CCN1. Collectively, these results demonstrate that DGCs contribute to GSC-dependent tumor progression by shaping a mesenchymal microenvironment via CCN1-mediated macrophage infiltration. This study provides new insight into the complex GBM microenvironment consisting of heterogeneous cells.

scholarly journals Interactions between Cancer-Associated Fibroblasts and T Cells in the Pancreatic Tumor Microenvironment and the Role of Chemokines

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2995
Author(s):  
Laia Gorchs ◽  
Helen Kaipe
Keyword(s):  
T Cells ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Current Knowledge ◽  
Ductal Adenocarcinoma ◽  
Pancreatic Tumors ◽  
Cell Trafficking ◽  
Cancer Associated Fibroblasts ◽  
Cell Axis ◽  
Combinational Treatment

Less than 10% of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC) survive 5 years or more, making it one of the most fatal cancers. Accumulation of T cells in pancreatic tumors is associated with better prognosis, but immunotherapies to enhance the anti-tumor activity of infiltrating T cells are failing in this devastating disease. Pancreatic tumors are characterized by a desmoplastic stroma, which mainly consists of activated cancer-associated fibroblasts (CAFs). Pancreatic CAFs have emerged as important regulators of the tumor microenvironment by contributing to immune evasion through the release of chemokines, cytokines, and growth factors, which alters T-cell migration, differentiation and cytotoxic activity. However, recent discoveries have also revealed that subsets of CAFs with diverse functions can either restrain or promote tumor progression. Here, we discuss our current knowledge about the interactions between CAFs and T cells in PDAC and summarize different therapy strategies targeting the CAF–T cell axis with focus on CAF-derived soluble immunosuppressive factors and chemokines. Identifying the functions of different CAF subsets and understanding their roles in T-cell trafficking within the tumor may be fundamental for the development of an effective combinational treatment for PDAC.

scholarly journals The Roles of Frequently Mutated Genes of Pancreatic Cancer in Regulation of Tumor Microenvironment

2020 ◽  
Vol 19 ◽  
pp. 153303382092096
Author(s):  
Hongzhi Sun ◽  
Bo Zhang ◽  
Haijun Li
Keyword(s):  
Tumor Microenvironment ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Stromal Cells ◽  
Genomic Analysis ◽  
Ductal Adenocarcinoma ◽  
Genetic Mutations ◽  
Cellular Processes ◽  
Dismal Prognosis ◽  
Mutant Genes

Pancreatic ductal adenocarcinoma has extremely high malignancy and patients with pancreatic ductal adenocarcinoma have dismal prognosis. The failure of pancreatic ductal adenocarcinoma treatment is largely due to the tumor microenvironment, which is featured by ample stromal cells and complicated extracellular matrix. Recent genomic analysis revealed that pancreatic ductal adenocarcinoma harbors frequently mutated genes including KRAS, TP53, CDKN2A, and SMAD4, which can widely alter cellular processes and behaviors. As shown by accumulating studies, these mutant genes may also change tumor microenvironment, which in turn affects pancreatic ductal adenocarcinoma progression. In this review, we summarize the role of such genetic mutations in tumor microenvironment regulation and potential mechanisms.

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