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Wodarz2001 - Viruses as antitumor weapons

ABSTRACT: This mathematical model of the dynamics between tumor, virus and virus-specific CTL populations is described by the publication: Wodarz D. "Viruses as antitumor weapons: defining conditions for tumor remission". Cancer Res. 2001 Apr 15;61(8):3501-7. PMID: 11309314 Comment: Reproduction of Fig. 3A was achieved by using the initial conditions infected_tumor_cells = 0.01, uninfected_tumor_cells = 0.0001, virus-specific_CTLs = 0, and with modified parameter sets. For Fig. 3A non-cytotoxic virus, k = 17 and beta= 0.5. For Fig. 3A cytotoxic virus, beta = 0.5. These simulation conditions yield plots similar to Fig. 3A in the manuscript. Abstract: Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactorial and nonlinear. Hence, a complete understanding of how virus and host characteristics influence the outcome of therapy requires mathematical models. In this study, such mathematical models are presented and analyzed. The study investigates three possible scenarios that could be relevant for therapy: (a) viral cytotoxicity alone kills tumor cells; (b) a virus-specific lytic CTL response contributes to killing of infected tumor cells; (c) the virus elicits immunostimulatory signals within the tumor that promote the development of tumor-specific CTL. The models precisely define conditions required for successful therapy. They identify the parameters that need to be measured and modulated to evaluate and refine the existing therapy regimes.

DISEASE(S): Cancer

SUBMITTER: Emilia Chen

PROVIDER: BIOMD0000001043 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000001043?filename=Fig3A.png	Other
	BIOMD0000001043?filename=Wodarz2001%20-%20Fig3A%20more%20cytotoxic.sedml	Other
	BIOMD0000001043?filename=Wodarz2001%20-%20Fig3A%20non-cytotoxic.sedml	Other
	BIOMD0000001043?filename=Wodarz2001-biopax2.owl	Owl
	BIOMD0000001043?filename=Wodarz2001-biopax3.owl	Owl

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Viruses as antitumor weapons: defining conditions for tumor remission.

Wodarz D D

Cancer research 20010401 8

Recent research has indicated that viruses specifically infecting tumor cells could be used as an alternative therapeutic approach in cancer patients. A particular example is the adenovirus ONYX-015, which has entered clinical trials in the context of head and neck cancer. Successful therapy crucially requires an understanding about how viral and host parameters influence tumor load. The interactions between the growing tumor, the replicating virus, and possible immune responses are multifactori ...[more]

PMID: 11309314

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Project description:This ordinary differential equation model is the Panetta-Kirschner model of tumor-immune interactions is described in the publication: Cappuccio A, Castiglione F, Piccoli B." Determination of the optimal therapeutic protocols in cancer immunotherapy." Math Biosci. 2007 Sep;209(1):1-13. doi: 10.1016/j.mbs.2007.02.009. Comment: The model represented in Equations 1-3 is the Panetta-Kirschner model and was used to reproduce Fig. 1. N.B.: Labelling of Fig. 1 graphs in manuscript mismatches with simulation results - CTL and tumor cell plots swapped. Abstract: Cancer immunotherapy aims at eliciting an immune system response against the tumor. However, it is often characterized by toxic side-effects. Limiting the tumor growth and, concurrently, avoiding the toxicity of a drug, is the problem of protocol design. We formulate this question as an optimization problem and derive an algorithm for its solution. Unlike the standard optimal control approach, the algorithm simulates impulse-like drug administrations. It relies on an exact computation of the gradient of the cost function with respect to any protocol by means of the variational equations, that can be solved in parallel with the system. In comparison with previous versions of this method [F. Castiglione, B. Piccoli, Optimal control in a model of dendritic cell transfection cancer immunotherapy, Bull. Math. Biol. 68 (2006) 255-274; B. Piccoli, F. Castiglione, Optimal vaccine scheduling in cancer immunotherapy, Physica A. 370 (2) (2007) 672-680], we optimize both the timing and the dosage of each administration and introduce a penalty term to avoid clustering of subsequent injections, a requirement consistent with the clinical practice. In addition, we implement the optimization scheme to simulate the case of multi-therapies. The procedure works for any ODE system describing the pharmacokinetics and pharmacodynamics of an arbitrary number of therapeutic agents. In this work, it was tested for a well known model of the tumor-immune system interaction [D. Kirschner, J.C. Panetta, Modeling immunotherapy of tumor-immune interaction, J. Math. Biol. 37 (1998) 235-252]. Exploring three immunotherapeutic scenarios (CTL therapy, IL-2 therapy and combined therapy), we display the stability and efficacy of the optimization method, obtaining protocols that are successful compromises between various clinical requirements.

Project description:Chromosome substitution strains (CSS or consomic strains) are useful for mapping phenotypes to chromosomes. However, huge efforts are needed to identify the gene(s) responsible for the phenotype in the complex context of the chromosome. Here, we report the identification of candidate disease genes from a CSS using a combination of genetic and genomic approaches as well as by using knowledge about the germ cell tumor disease etiology. We utilized the CSS, 129.MOLF-Chr 19 chromosome substitution strain (or M19), in which males develop germ cell tumors of the testes at an extremely high rate. We are able to identify 3 protein-coding genes and 1 microRNA on chromosome 19 that have previously not been implicated to be testicular tumor susceptibility genes. Our findings suggest that changes in gene expression levels in the gonadal tissues of multiple genes from Chr 19 likely contribute to the high TGCT incidence of the M19 strain. Our data advances the use of CSS to identify disease susceptibility genes and demonstrates that the 129.MOLF-Chr 19 strain serves as a useful model to elucidate the genetics and biology of germ cell transformation and tumor development. For gene expression profiling, we used male gonads from different developmental stages, E13.5 and PN1, from the M19 and 129 strains. The idea was to detect the common gene expression changes in the gonads at stages when germ cells are known to transform to embryonal carcinoma (EC) cells. E13.5 stage was chosen because tumor development is reported to start around E13.5 in 129 strains (Stevens 1973b; Stevens and Hummel 1957). PN1 stage gonads were chosen because our studies with M19;Oct4-GFP mice, as described above, showed that both germ cells and EC cells are present at this stage suggesting that germ cell transformation may also be ongoing at this stage in the testes of M19 strain. We also compared gene expression differences in gonads to that of embryos to determine whether gene expression differences are specific to the gonads. Our goal was to identify gene(s) whose expression is consistently changed in the gonads at E13.5 and PN1 in the M19 as these would likely be candidate TGCT susceptibility gene(s). Fig. 3A indicates the 3 different tissue samples collected from the M19 and 129 strains: gonads (genital ridges) dissected from male E13.5 embryos and from PN1 mice and male embryos at E13. Because tumor incidence in M19 is approximately 80% we anticipated that gene expression changes could be masked because ~20% of cells/tissues within a sample may have normal levels of gene expression or 20% of the testes may have normal gene expression. Moreover, the amount of RNA extracted from a single genital ridge or PN1 testis is insufficient for a microarray experiment. To minimize the effect of individual samples and to isolate sufficient RNA for microarray analysis from these small sized gonads, we pooled RNA from multiple samples (Table 1). Six RNA pooled samples were prepared (Fig.3A and Supplementary Table 2): E13.5 gonad RNA from males of 129 (129-GR) and M19 (M1-GR); PN1 testes RNA from 129 (129-NBT) and M19 (M1-NBT); E13.5 embryo RNA from males of 129 (129-E) and M19 (M1-E). The six RNA samples were hybridized to six Affymetrix mouse genome genechips individually. Hierarchical clustering analysis revealed a dendrogram in which gene expression profiles of the same tissue type clustered together (Fig. 3B). Profiles of the gonads, E13.5 and PN1, were related more closely than to those of embryos. Comparison of the expression profiles of E13.5 gonads from M19 and 129 revealed 10 genes with more than a 2-fold difference in expression levels (Fig. 3A and Supplementary Table 3A). Six of the 10 genes mapped to Chr 19. For the PN1 testes, 266 genes showed significant fold changes between the two strains and of these 13 genes mapped to Chr 19 (Fig. 3A and Supplementary Table 3C). For the E13.5 embryos, 35 genes showed greater than a 2-fold change in expression between the M19 and 129 and 5 out of 35 genes mapped to Chr 19 (Fig. 3A and Supplementary Table 3B). By analyzing the data of differentially expressed genes present in the 3 samples, as well as selecting those that map to Chr 19, we were able to exclude a majority of the genes but found 3 genes in common. The 3 genes map to Chr 19 and were found to be downregulated in the M19 strain E13.5 and PN1 gonads as well as E13.5 embryos. These are Zfp162, D19Bwg1357e and Cox15. These 3 genes have not been previously implicated in testicular tumorigenesis and are novel TGCT candidate susceptibility genes.

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