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

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.

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

“…For reference, Table S2 also compares the best-fit parameters found herein to those previously found using a different algorithm and a slightly different metric of fit (35). Finally, Table S2 includes the 95% credible interval for each fit parameter, as calculated from the approximated posterior distributions (Fig.…”

“…In this prior work, we used the best-fit parameters to address the question of the optimal treatment ordering when administering three doses of Ad/4-1BBL/IL-12 with three doses of the DC vaccine. We predicted that exactly one strategy, front loading the cytokine-bearing OVs (OV-OV-OV-DC-DC-DC), resulted in tumor eradication (35). However, upon further exploration, we found that the model displayed some unintuitive responses to altering the dosing order.…”

“…However, upon further exploration, we found that the model displayed some unintuitive responses to altering the dosing order. For instance, the ordering of the protocols from maximal to minimal tumor response displayed no discernible pattern (35). A striking example of this phenomenon was when the optimal protocol became the worst-case scenario by simply moving the DC dose on the last day of treatment to day one (DC-OV-OV-OV-DC-DC).…”

“…This extreme sensitivity to dosing order could be explained by our finding that the doses of Ad/4-1BBL/IL-12 and DCs used in the experiments of Huang et al (31) were near a bifurcation point in our mathematical model. As a result, slightly altering the dose or sequence drastically changed the efficacy of the protocol (35).…”

“…We have previously developed a mathematical model that describes well the data from Huang et al (31) in all experimentally explored situations ( Fig. 2): treatment with only OVs, OVs enhanced with one or more immunostimulatory molecules (4-1BBL, IL-12, or both), DC vaccines, and DC vaccines coupled with Ad/4-1BBL/IL-12 (34,35). In this prior work, we used the best-fit parameters to address the question of the optimal treatment ordering when administering three doses of Ad/4-1BBL/IL-12 with three doses of the DC vaccine.…”