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

  • Received: 01 October 2014 Accepted: 29 June 2018 Published: 01 August 2015
  • MSC : Primary: 92C50, 92B05; Secondary: 37N25.

  • 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.

    Citation: Joanna R. Wares, Joseph J. Crivelli, Chae-Ok Yun, Il-Kyu Choi, Jana L. Gevertz, Peter S. Kim. Treatment strategies for combining immunostimulatory oncolytic virus therapeutics with dendritic cell injections[J]. Mathematical Biosciences and Engineering, 2015, 12(6): 1237-1256. doi: 10.3934/mbe.2015.12.1237

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  • 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.


    [1] Surgery, 156 (2014), 263-269.
    [2] J. Theor. Biol., 225 (2003), 257-274.
    [3] PLoS Comput. Biol., 7 (2011), e1001085.
    [4] J. Theor. Biol., 252 (2008), 109-122.
    [5] Bull. Math. Biol., 72 (2010), 469-489.
    [6] Immunity, 21 (2004), 341-347.
    [7] Cancer Res., 61 (2001), 5453-5460.
    [8] J. Virol., 75 (2001), 10663-10669.
    [9] Front Oncol, 3 (2013), p56.
    [10] Cancer Res., 67 (2007), p8420.
    [11] Clin. Cancer Res., 13 (2007), 4677-4685.
    [12] Cancer Gene Ther., 16 (2009), 873-882.
    [13] Cancer Gene Ther., 18 (2011), 305-317.
    [14] Bull. Math. Biol., 73 (2011), 2-32.
    [15] Expert Rev Vaccines, 12 (2013), 1155-1172.
    [16] Cancer Res., 66 (2006), 2314-2319.
    [17] J. Virol., 74 (2000), 2895-2899.
    [18] Mol. Ther., 18 (2010), 264-274.
    [19] J. Virol., 80 (2006), 3549-3558.
    [20] Biomaterials, 31 (2010), 1865-1874.
    [21] Biomaterials, 32 (2011), 2314-2326.
    [22] (submitted).
    [23] Nat. Med., 7 (2001), 781-787.
    [24] J. Theor. Biol., 263 (2010), 530-543.
    [25] PLoS ONE, 5 (2010), e15482.
    [26] Nat Commun, 4 (2013), p1974.
    [27] Mol. Ther., 18 (2010), 888-895.
    [28] Gene Ther., 15 (2008), 247-256.
    [29] J. Theor. Biol., 239 (2006), 334-350.
    [30] Mol. Ther., 19 (2011), 1008-1016.
    [31] Nat. Rev. Microbiol., 12 (2014), 23-34.
    [32] Clin. Cancer Res., 15 (2009), 2352-2360.
    [33] Bioinformatics, 30 (2014), 1884-1891.
    [34] J. Theor. Biol., 294 (2012), 56-73.
    [35] Gene Ther., 19 (2012), 543-549.
    [36] Nat. Biotechnol., 30 (2012), 658-670.
    [37] Clin. Cancer Res., 18 (2012), 6679-6689.
    [38] Nat Immunol., 2 (2001), 423-429.
    [39] Nat Immunol., 1 (2000), 47-53.
    [40] Mol. Cancer Ther., 5 (2006), 362-366.
    [41] Cancer Res., 61 (2001), 3501-3507.
    [42] Math Biosci Eng, 10 (2013), 939-957.
    [43] PLoS ONE, 4 (2009), e4271.
    [44] Hum. Gene Ther., 8 (1997), 37-44.
    [45] Bull. Math. Biol., 66 (2004), 605-625.
    [46] Pathol. Oncol. Res., 18 (2012), 771-781.
    [47] Neoplasia, 15 (2013), 591-599.
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