A Multiscale Mathematical Model for Oncolytic Virotherapy

Abstract

One of the most promising strategies to treat cancer is attacking it with viruses. Oncolytic viruses can kill tumor cells specifically or induce anticancer immune response. A multiscale model for virotherapy of cancer is investigated through simulations. It was found that, for intratumoral virus administration, a solid tumor can be completely eradicated or keep growing after a transient remission. Furthermore, the model reveals undamped oscillatory dynamics of tumor cells and virus populations, which demands new in vivo and in vitro quantitative experiments aiming to detect this oscillatory response. The conditions for which each one of the different tumor responses dominates, as well as the occurrence probabilities for the other nondominant therapeutic outcomes, were determined. From a clinical point of view, our findings indicate that a successful, single agent virotherapy requires a strong inhibition of the host immune response and the use of potent virus species with a high intratumoral mobility. Moreover, due to the discrete and stochastic nature of cells and their responses, an optimal range for viral cytotoxicity is predicted because the virotherapy fails if the oncolytic virus demands either a too short or a very large time to kill the tumor cell. This result suggests that the search for viruses able to destroy tumor cells very fast does not necessarily lead to a more effective control of tumor growth.

Questing for an optimal, universal viral agent for oncolytic virotherapy

“…In turn, a very long T l generates successive waves of infection at a low frequency unable to destroy the tumor. These results are consistent with those reported on reference [8], supporting the robustness of both models.…”

“…However, for virotherapies based on low virus entries and M OIs there is, fixed the viral diffusivity, an optimal range for its lytic cycle period because the virotherapy fails if the cytolysis is either too short or too long. This result is in complete agreement with the findings of Paiva et al [8]. Furthermore, we point out that the virotherapeutic efficacy is much more sensitive to other parameters, for instance viral entry and burst size, than to M OI.…”

“…Figure 3 illustrates our typical results: after a single intratumoral virus administration, these tumors can either be completely eradicated or keep growing with time despite a transient remission, as previously obtained for compact tumors [8]. Furthermore, the last behavior can be either a monotonic or an oscillating growth.…”

“…Such coexistence can exhibit oscillations in cancer cells and virus populations due to successive rounds of infection unable to eliminate the tumor. As previously reported [8], it is worth to emphasize that any of these behaviors can randomly emerge as the response of a given tumor to the virotherapy. Hence, it becomes imperative to determine for each tumor morphology the most probable prognosis after the treatment as well as the chances for the occurrence of the other tumor responses as functions of the virotherapeutic parameters.…”

“…Several mathematical models have been proposed to study virotherapy [7][8][9][10]. They emerge as valuable tools to provide quantitative understanding of the major mechanisms that affect anti-cancer treatment based on viruses * leticia.paiva@ufv.br † mmartins@ufv.br; National Institute of Science and Technology for Complex Systems, Brazil ‡ On leave at Departament de Física y Enginyeria Nuclear, Universitat Politécnica de Catalunya, Spain and to select parameter ranges that enhance its therapeutic success.…”