Literature DB >> 26775859

Treatment strategies for combining immunostimulatory oncolytic virus therapeutics with dendritic cell injections.

Joanna R Wares1, Joseph J Crivelli, Chae-Ok Yun, Il-Kyu Choi, Jana L Gevertz, Peter S Kim.   

Abstract

Oncolytic viruses (OVs) are used to treat cancer, as they selectively replicate inside of and lyse tumor cells. The efficacy of this process is limited and new OVs are being designed to mediate tumor cell release of cytokines and co-stimulatory molecules, which attract cytotoxic T cells to target tumor cells, thus increasing the tumor-killing effects of OVs. To further promote treatment efficacy, OVs can be combined with other treatments, such as was done by Huang et al., who showed that combining OV injections with dendritic cell (DC) injections was a more effective treatment than either treatment alone. To further investigate this combination, we built a mathematical model consisting of a system of ordinary differential equations and fit the model to the hierarchical data provided from Huang et al. We used the model to determine the effect of varying doses of OV and DC injections and to test alternative treatment strategies. We found that the DC dose given in Huang et al. was near a bifurcation point and that a slightly larger dose could cause complete eradication of the tumor. Further, the model results suggest that it is more effective to treat a tumor with immunostimulatory oncolytic viruses first and then follow-up with a sequence of DCs than to alternate OV and DC injections. This protocol, which was not considered in the experiments of Huang et al., allows the infection to initially thrive before the immune response is enhanced. Taken together, our work shows how the ordering, temporal spacing, and dosage of OV and DC can be chosen to maximize efficacy and to potentially eliminate tumors altogether.

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Year:  2015        PMID: 26775859     DOI: 10.3934/mbe.2015.12.1237

Source DB:  PubMed          Journal:  Math Biosci Eng        ISSN: 1547-1063            Impact factor:   2.080


  7 in total

1.  Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy.

Authors:  Syndi Barish; Michael F Ochs; Eduardo D Sontag; Jana L Gevertz
Journal:  Proc Natl Acad Sci U S A       Date:  2017-07-17       Impact factor: 11.205

2.  From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers.

Authors:  Michael C Luo; Elpiniki Nikolopoulou; Jana L Gevertz
Journal:  Front Oncol       Date:  2022-04-28       Impact factor: 5.738

3.  Spatial Model for Oncolytic Virotherapy with Lytic Cycle Delay.

Authors:  Jiantao Zhao; Jianjun Paul Tian
Journal:  Bull Math Biol       Date:  2019-05-14       Impact factor: 3.871

Review 4.  Fighting Cancer with Mathematics and Viruses.

Authors:  Daniel N Santiago; Johannes P W Heidbuechel; Wendy M Kandell; Rachel Walker; Julie Djeu; Christine E Engeland; Daniel Abate-Daga; Heiko Enderling
Journal:  Viruses       Date:  2017-08-23       Impact factor: 5.048

5.  Developing a Minimally Structured Mathematical Model of Cancer Treatment with Oncolytic Viruses and Dendritic Cell Injections.

Authors:  Jana L Gevertz; Joanna R Wares
Journal:  Comput Math Methods Med       Date:  2018-10-30       Impact factor: 2.238

Review 6.  Mathematical Models for Immunology: Current State of the Art and Future Research Directions.

Authors:  Raluca Eftimie; Joseph J Gillard; Doreen A Cantrell
Journal:  Bull Math Biol       Date:  2016-10-06       Impact factor: 1.758

7.  Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy.

Authors:  Hang Xie; Yang Jiao; Qihui Fan; Miaomiao Hai; Jiaen Yang; Zhijian Hu; Yue Yang; Jianwei Shuai; Guo Chen; Ruchuan Liu; Liyu Liu
Journal:  PLoS One       Date:  2018-10-26       Impact factor: 3.240

  7 in total

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