DePillis2003 - The dynamics of an optimally controlled tumor model: A case study
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ABSTRACT:
SUBMITTER: 
Mohammad Umer Sharif Shohan
PROVIDER: BIOMD0000000909 | BioModels | 2020-01-08
REPOSITORIES: BioModels
Similar Datasets
Project description:An insight into tumor dormancy equilibrium via the analysis of its domain of
attraction
A. Merola, C. Cosentino *, F. Amato
School of Computer and Biomedical Engineering, Universita` degli Studi Magna Græcia di Catanzaro, Campus ‘‘Salvatore Venuta’’, 88100 Catanzaro, Italy
A B S T R A C T
The trajectories of the dynamic system which regulates the competition between the populations of
malignant cells and immune cells may tend to an asymptotically stable equilibrium in which the sizes of
these populations do not vary, which is called tumor dormancy. Especially for lower steady-state sizes of
the population of malignant cells, this equilibrium represents a desirable clinical condition since the
tumor growth is blocked. In this context, it is of mandatory importance to analyze the robustness of this
clinical favorable state of health in the face of perturbations. To this end, the paper presents an
optimization technique to determine whether an assigned rectangular region, which surrounds an
asymptotically stable equilibrium point of a quadratic systems, is included into the domain of attraction
of the equilibrium itself. The biological relevance of the application of this technique to the analysis of
tumor growth dynamics is shown on the basis of a recent quadraticmodel of the tumor–immune system
competition dynamics. Indeed the application of the proposedmethodology allows to ensure that a given
safety region, determined on the basis of clinical considerations, belongs to the domain of attraction of
the tumor blocked equilibrium; therefore for the set of perturbed initial conditions which belong to such
region, the convergence to the healthy steady state is guaranteed. The proposed methodology can also
provide an optimal strategy for cancer treatment.
2020-01-15 | BIOMD0000000911 | BioModels
Project description:On optimal chemotherapy with a strongly targeted agent for a model of tumor-immune system interactions with generalized logistic growth.
Ledzewicz U1, Olumoye O, Schättler H.
1
Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62026-1653, USA. uledzew@siue.edu
Abstract
In this paper, a mathematical model for chemotherapy that takes tumor immune-system interactions into account is considered for a strongly targeted agent. We use a classical model originally formulated by Stepanova, but replace exponential tumor growth with a generalised logistic growth model function depending on a parameter v. This growth function interpolates between a Gompertzian model (in the limit v → 0) and an exponential model (in the limit v → ∞). The dynamics is multi-stable and equilibria and their stability will be investigated depending on the parameter v. Except for small values of v, the system has both an asymptotically stable microscopic (benign) equilibrium point and an asymptotically stable macroscopic (malignant) equilibrium point. The corresponding regions of attraction are separated by the stable manifold of a saddle. The optimal control problem of moving an initial condition that lies in the malignant region into the benign region is formulated and the structure of optimal singular controls is determined
2020-03-06 | BIOMD0000000919 | BioModels
Project description:An alternative or follow-up adjunct to conventional maximum tolerated dose (MTD) chemotherapy now in advanced phase III clinical trial assessment is metronomic chemotherapy?the close regular administration of low doses of drug with no prolonged breaks. A number of preclinical studies have shown metronomic chemotherapy can cause long term survival of mice with advanced cancer, including metastatic disease, in the absence of overt toxicity, especially when combined with targeted antiangiogenic drugs. However, similar to MTD chemotherapy acquired resistance eventually develops, the basis of which is unknown. Using a preclinical model of advanced human ovarian (SKOV-3-13) cancer in SCID mice, we show that acquired resistance can develop after terminating prolonged (over 3 months) successful therapy utilizing daily oral metronomic topotecan plus pazopanib, an oral antiangiogenic tyrosine kinase inhibitor (TKI). Two resistant sublines were isolated from a single mouse, one from a solid tumor (called KH092-7SD, referred to as 7SD) and another from ascites tumor cells (called KH092-7AS, referred to as 7AS). Using these sublines we show acquired resistance to the combination treatment is due to tumor cell alterations that confer relative refractoriness to topotecan. The resistant phenotype is heritable, associated with reduced cellular uptake of topotecan and could not be reversed by switching to MTD topotecan or to another topoisomerase-1 inhibitor, CPT-11, given either in a metronomic or MTD manner nor switching to another antiangiogenic drug, e.g. the anti-VEGFR-2 antibody, DC101, or another TKI, sunitinib. Thus, in this case cross resistance seems to exist between MTD and metronomic topotecan, the basis of which is unknown. However, gene expression profiling revealed several potential genes that are stably upregulated in the resistant lines, that previously have been implicated in resistance to various chemotherapy drugs, and which, therefore, may contribute to the drug resistant phenotype. Two-condition experiment, ovarian cell line (SKOV-3-13) sensitive to daily oral metronomic topotecan plus pazopaniband treatemnt compared to two in- vivo selected resistant sublines from a solid tumor (called KH092-7SD) and another from ascites tumor cells (called KH092-7AS). Biological replicates: 4 independently grown and harvested cell line passages. Two technical replicate per condition (including dye swap).
2014-02-04 | E-GEOD-54621 | biostudies-arrayexpress
Project description:Reliable clinical tests for predicting cancer chemotherapy response are not available and individual markers failed to correctly predict resistance against anticancer agents. We hypothesized that gene expression patterns attributable to chemotherapy-resistant cells can be used as a classification tool for chemoresistance and provide novel candidate genes involved in anthracycline resistance mechanisms. We contrasted the expression profiles of 4 different human tumor cell lines of gastric, pancreatic, colon and breast origin and of their counterparts resistant to the topoisomerase inhibitors daunorubicin or doxorubicin. We also profiled the sensitive parental cells treated with doxorubicin for 24h. We interrogated Affymetrix HGU133A and U95A arrays independently. We applied two independent methods for data normalization and used Prediction Analysis of Microarrays (PAM) for feature selection. In addition, we established data sets related to drug resistance by using a “virtual array” composed of features represented on both types of oligonucleotide arrays. We identified 71 candidate genes associated with doxorubicine/daunorubicine resistance. To validate the microarray data, we also analyzed the expression of 12 selected genes by quantitative RT-PCR or immunocytochemistry, respectively. While the comparison of drug-sensitive versus drug-resistant cells yields candidates associated with drug resistance, the 24h treatment of sensitive parental cells produced a distinct transcriptional profile related to short-term drug effects. Keywords: cell type comparison See Summary. Note: this series includes Affymetrix HGU95A chips (difference between resistant and parental cell lines).
2010-04-15 | E-GEOD-3927 | biostudies-arrayexpress
Project description:Mathematical Modeling, Analysis, and Simulation of Tumor Dynamics with Drug Interventions
Pranav Unni 1 and Padmanabhan Seshaiyer
Abstract
Over the last few decades, there have been significant developments in theoretical, experimental, and clinical approaches to understand the dynamics of cancer cells and their interactions with the immune system. These have led to the development of important methods for cancer therapy including virotherapy, immunotherapy, chemotherapy, targeted drug therapy, and many others. Along with this, there have also been some developments on analytical and computational models to help provide insights into clinical observations. This work develops a new mathematical model that combines important interactions between tumor cells and cells in the immune systems including natural killer cells, dendritic cells, and cytotoxic CD8+ T cells combined with drug delivery to these cell sites. These interactions are described via a system of ordinary differential equations that are solved numerically. A stability analysis of this model is also performed to determine conditions for tumor-free equilibrium to be stable. We also study the influence of proliferation rates and drug interventions in the dynamics of all the cells involved. Another contribution is the development of a novel parameter estimation methodology to determine optimal parameters in the model that can reproduce a given dataset. Our results seem to suggest that the model employed is a robust candidate for studying the dynamics of tumor cells and it helps to provide the dynamic interactions between the tumor cells, immune system, and drug-response systems.
2019-12-10 | BIOMD0000000888 | BioModels
Project description:Role of vascular normalization in benefit from metronomic chemotherapy.
Mpekris F1, Baish JW2, Stylianopoulos T3, Jain RK4.
Author information
Abstract
Metronomic dosing of chemotherapy-defined as frequent administration at lower doses-has been shown to be more efficacious than maximum tolerated dose treatment in preclinical studies, and is currently being tested in the clinic. Although multiple mechanisms of benefit from metronomic chemotherapy have been proposed, how these mechanisms are related to one another and which one is dominant for a given tumor-drug combination is not known. To this end, we have developed a mathematical model that incorporates various proposed mechanisms, and report here that improved function of tumor vessels is a key determinant of benefit from metronomic chemotherapy. In our analysis, we used multiple dosage schedules and incorporated interactions among cancer cells, stem-like cancer cells, immune cells, and the tumor vasculature. We found that metronomic chemotherapy induces functional normalization of tumor blood vessels, resulting in improved tumor perfusion. Improved perfusion alleviates hypoxia, which reprograms the immunosuppressive tumor microenvironment toward immunostimulation and improves drug delivery and therapeutic outcomes. Indeed, in our model, improved vessel function enhanced the delivery of oxygen and drugs, increased the number of effector immune cells, and decreased the number of regulatory T cells, which in turn killed a larger number of cancer cells, including cancer stem-like cells. Vessel function was further improved owing to decompression of intratumoral vessels as a result of increased killing of cancer cells, setting up a positive feedback loop. Our model enables evaluation of the relative importance of these mechanisms, and suggests guidelines for the optimal use of metronomic therapy.
KEYWORDS:
drug delivery; immune response; oxygenation; thrompospondin-1; tumor perfusion
2020-01-20 | MODEL2001200002 | BioModels
Project description:A minimally parameterized mathematical model for low-dose metronomic
chemotherapy is formulated that takes into account angiogenic signaling between
the tumor and its vasculature and tumor inhibiting effects of tumor-immune system
interactions. The dynamical equations combine a model for tumor development under
angiogenic signaling formulated by Hahnfeldt et al. with a model for tumor-immune
system interactions by Stepanova. The dynamical properties of the model are analyzed.
Depending on the parameter values, the system encompasses a variety of medically
realistic scenarios that range from cases when (i) low-dose metronomic chemotherapy
is able to eradicate the tumor (all trajectories converge to a tumor-free equilibrium
point) to situations when (ii) tumor dormancy is induced (a unique, globally asymp-
totically stable benign equilibrium point exists) to (iii) multi-stable situations that have
both persistent benign and malignant behaviors separated by the stable manifold of
an unstable equilibrium point and finally to (iv) situations when tumor growth can-
not be overcome by low-dose metronomic chemotherapy. The model forms a basis
for a more general study of chemotherapy when the main components of a tumor’s
microenvironment are taken into account
2020-02-03 | MODEL2002030001 | BioModels
Project description:Reliable clinical tests for predicting cancer chemotherapy response are not available and individual markers failed to correctly predict resistance against anticancer agents. We hypothesized that gene expression patterns attributable to chemotherapy-resistant cells can be used as a classification tool for chemoresistance and provide novel candidate genes involved in anthracycline resistance mechanisms. We contrasted the expression profiles of 4 different human tumor cell lines of gastric, pancreatic, colon and breast origin and of their counterparts resistant to the topoisomerase inhibitors daunorubicin or doxorubicin. We also profiled the sensitive parental cells treated with doxorubicin for 24h. We interrogated Affymetrix HGU133A and U95A arrays independently. We applied two independent methods for data normalization and used Prediction Analysis of Microarrays (PAM) for feature selection. In addition, we established data sets related to drug resistance by using a âvirtual arrayâ composed of features represented on both types of oligonucleotide arrays. We identified 71 candidate genes associated with doxorubicine/daunorubicine resistance. To validate the microarray data, we also analyzed the expression of 12 selected genes by quantitative RT-PCR or immunocytochemistry, respectively. While the comparison of drug-sensitive versus drug-resistant cells yields candidates associated with drug resistance, the 24h treatment of sensitive parental cells produced a distinct transcriptional profile related to short-term drug effects. Keywords: cell type and treatment comparison See Summary. Note: this series includes Affymetrix HGU133A chips (difference between resistance and treatment response).
2010-04-27 | E-GEOD-3926 | biostudies-arrayexpress
Project description:Reliable clinical tests for predicting cancer chemotherapy response are not available and individual markers failed to correctly predict resistance against anticancer agents. We hypothesized that gene expression patterns attributable to chemotherapy-resistant cells can be used as a classification tool for chemoresistance and provide novel candidate genes involved in anthracycline resistance mechanisms. We contrasted the expression profiles of 4 different human tumor cell lines of gastric, pancreatic, colon and breast origin and of their counterparts resistant to the topoisomerase inhibitors daunorubicin or doxorubicin. We also profiled the sensitive parental cells treated with doxorubicin for 24h. We interrogated Affymetrix HGU133A and U95A arrays independently. We applied two independent methods for data normalization and used Prediction Analysis of Microarrays (PAM) for feature selection. In addition, we established data sets related to drug resistance by using a “virtual array” composed of features represented on both types of oligonucleotide arrays. We identified 71 candidate genes associated with doxorubicine/daunorubicine resistance. To validate the microarray data, we also analyzed the expression of 12 selected genes by quantitative RT-PCR or immunocytochemistry, respectively. While the comparison of drug-sensitive versus drug-resistant cells yields candidates associated with drug resistance, the 24h treatment of sensitive parental cells produced a distinct transcriptional profile related to short-term drug effects. Keywords: superSeries This reference Series links data in the following related Series: GSE3926 Anthracycline treatment and resistance in four human cancer cell lines (HGU133A) GSE3927 Anthracycline resistance in four human cancer cell lines (HGU95A)
2010-04-14 | E-GEOD-3929 | biostudies-arrayexpress
Project description:Although acute lymphoblastic leukemia (ALL) is highly responsive to chemotherapy, it is unknown how and which host immune factors influence the long-term remission of this cancer. To this end, we systematically evaluated the effects of T cell immunity on ALL chemotherapy outcomes. Using murine BCR-ABL1 ALL model, we showed that loss of total T cells, CD4 cells, or CD8 cells in the host drastically increased leukemia relapse following dasatinib or cytotoxic chemotherapy. Although ABL1 mutations emerged early during dasatinib treatment in both immunocompetent and immunocompromised hosts, T cell immunity was essential for suppressing the outgrowth of drug-resistant leukemia. Bulk and single-cell transcriptome profiling of T cells during therapy pointed to the activation Type I immunity-related cytokine signaling linked to long-term leukemia remission in mice. Consistent with these observations, IFNG and IL12 directly modulated dasatinib anti-leukemia efficacy in vivo. Finally, we evaluated peripheral blood immune cell composition in 102 children with ALL during chemotherapy and observed a significant association of T cell abundance with treatment outcomes. Together, these results suggest that T cell immunity plays pivotal roles in maintaining long-term remission of ALL, highlighting that the interplay between host immunity and drug resistance can be harnessed to improve ALL chemotherapy outcomes.
2022-08-03 | GSE201765 | GEO