Dataset Information

Kim2014 - Tumor model under immune suppression

ABSTRACT: its a mathematical model explaining the impact of chemotherapy and immunotherpay together on Tumor cells involving cytokines like Il2, IL10 and TGFbeta, immune cells like CD4+ T cells, CD8+ T cells, Memory T cells, NK cells, dendrtic cells. This model is based on the previous model published by Robertson-Tessi M 2012 (PMID:22051568).

SUBMITTER: Krishna Kumar Tiwari

PROVIDER: MODEL1909240002 | BioModels | 2019-09-25

REPOSITORIES: BioModels

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Publications

Optimal treatment strategy for a tumor model under immune suppression.

Kim Kwang Su KS Cho Giphil G Jung Il Hyo IH

Computational and mathematical methods in medicine 20140723

We propose a mathematical model describing tumor-immune interactions under immune suppression. These days evidences indicate that the immune suppression related to cancer contributes to its progression. The mathematical model for tumor-immune interactions would provide a new methodology for more sophisticated treatment options of cancer. To do this we have developed a system of 11 ordinary differential equations including the movement, interaction, and activation of NK cells, CD8(+)T-cells, CD4( ...[more]

PMID: 25140193

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Project description:Immune traits (ITs) are potentially relevant criteria to characterize individualM-bM-^@M-^Ys immunocompetence. Thus, porcine ITs related to innate and adaptive immunity were studied by functional genomics approaches, with no initial focus on resistance to specific pathogens. Peripheral blood transcriptome was analysed in 60 days old Large White pigs (N=445) 3 weeks after vaccination against Mycoplasma hyopneumoniae. Groups of 4 to 10 animals classified in the extreme tails of CD4-/CD8+and TCRM-NM-3M-NM-4+ cell counts, phagocytosis, in vitro production of IL2, IL10, TNF and IFNG, and anti-Mycoplasma antibodies distributions were selected for transcriptome studies with a porcine generic array enriched with immunity-related genes (SLA-RI/NRSP8-13K). Among the ITs studied, transcriptome analysis revealed differentially expressed genes for CD4-/CD8+cell counts, phagocytosis, and in vitro production of IL2 and IL10. A subset of these genes was confirmed by real time qPCR. Gene set enrichment analysis showed a significant over-representation of immune response functions. An independent set of 74 no overlapping animals was employed for validation and a total of 5 potential gene biomarkers were found for prediction of immunocompetence. These biomarkers performed with 79% sensitivity (95% CI 61% to 97%) and 86% specificity (95% CI 72% to 100%). Considering the observed transcriptome differences in animals with extreme ITs levels, we conclude that gene expression profiling appears promising as a tool for biological monitoring of genetic variance in pigs. Test set: Two-condition experiment (High vs Low) for a total of 8 ITS. Three ITs were measured from total blood and referred to as in vivo ITs. They included the seric levels of IgG directed against Mycoplasma hyopneumoniae (IgG-Mh), and the cell counts for M-NM-1M-NM-2 T lymphocytes CD4- CD8+ (CD4- CD8+, or M-NM-3M-NM-4 T lymphocytes (TCRM-NM-3M-NM-4+)) . Additionally, five ITs were measured after in vitro tests, and were further referred to as in vitro ITs. They include the phagocytosis capacity (PHAG), and the in vitro production of TNFM-NM-1, IFNM-NM-3, IL2 and IL10 after in vitro stimulation of total blood diluted five times. For each IT included in the present report, pigs were selected at the higher and lower 10% of the Gaussian distribution in order to generate two extreme groups defined as High (H) and Low (L). Groups of 4 to 10 animals classified in the extreme tails of different ITs were selected. A two-channel microarray experiments were carried out using a common reference hybridization design. The standard reference RNA sample was prepared by pooling of total RNAs from different porcine tissues. As the reference is common to all the arrays, this design allowed an indirect comparison between the conditions of interest (H vs L groups). Additionally, in order to provide potential gene biomarkers to produce new insight into pig immunocompetence, the top 50 genes most differentially expressed genes when comparing H vs L groups in CD4-/CD8+ cell counts, phagocytosis, and in vitro production of IL2 and IL10 phenotypes were assessed across 74 animals (experimental animals non included) that were analyzed with the same SLA-RI/NRSP8-13K microarray. Validation set: A total of 74 animals were analyzed in order to validate potential gene biomarkers found in the following immune traits: CD4- CD8+ lymphocyte counts, phagocytosis capacity, IL2 and IL10 production. As no differentially gene expressions were detected between animals with High and Low levels of gamma delta T lymphocyte counts, IgG-Mh, and in vitro production of TNF-alpha or IFN-gamma immune traits, some of them were used in the validation set (n=50). None of experimental animals with extreme CD4- CD8+ lymphocyte counts, phagocytosis capacity, IL2 and IL10 production levels were included were included in the validation set.

Project description:This ordinary differential equation model simulating the interactions between tumor and immune cells is detailed in the publication: Ahmed M. Makhlouf, Lamiaa El-Shennawy, Hesham A. Elkaranshawy, "Mathematical Modelling for the Role of CD4+T Cells in Tumor-Immune Interactions", Comput Math Methods Med. 2020 Feb 19;2020:7187602. doi: 10.1155/2020/7187602 Comment: This no treatment model is described by equations 1-7 of the publication manuscript. Abstract: Mathematical modelling has been used to study tumor-immune cell interaction. Some models were proposed to examine the effect of circulating lymphocytes, natural killer cells, and CD8+T cells, but they neglected the role of CD4+T cells. Other models were constructed to study the role of CD4+T cells but did not consider the role of other immune cells. In this study, we propose a mathematical model, in the form of a system of nonlinear ordinary differential equations, that predicts the interaction between tumor cells and natural killer cells, CD4+T cells, CD8+T cells, and circulating lymphocytes with or without immunotherapy and/or chemotherapy. This system is stiff, and the Runge–Kutta method failed to solve it. Consequently, the “Adams predictor-corrector” method is used. The results reveal that the patient’s immune system can overcome small tumors; however, if the tumor is large, adoptive therapy with CD4+T cells can be an alternative to both CD8+T cell therapy and cytokines in some cases. Moreover, CD4+T cell therapy could replace chemotherapy depending upon tumor size. Even if a combination of chemotherapy and immunotherapy is necessary, using CD4+T cell therapy can better reduce the dose of the associated chemotherapy compared to using combined CD8+T cells and cytokine therapy. Stability analysis is performed for the studied patients. It has been found that all equilibrium points are unstable, and a condition for preventing tumor recurrence after treatment has been deduced. Finally, a bifurcation analysis is performed to study the effect of varying system parameters on the stability, and bifurcation points are specified. New equilibrium points are created or demolished at some bifurcation points, and stability is changed at some others. Hence, for systems turning to be stable, tumors can be eradicated without the possibility of recurrence. The proposed mathematical model provides a valuable tool for designing patients’ treatment intervention strategies.

Dataset Information

Kim2014 - Tumor model under immune suppression

Publications

Optimal treatment strategy for a tumor model under immune suppression.

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