Qualitative optimization of oncolytic virotherapy and immune therapy combination treatments

Oncolytic viruses (OVs) are designed to selectively target and destroy cancer cells while sparing normal, healthy tissue. Several viruses for oncolytic virotherapy are currently developed. In this paper, we will use mathematical modeling to consider key strategies that can improve the efficacy of oncolytic virotherapy. These include the integration of immunotherapy approaches with virotherapy to amplify anti-tumor immune responses, as well as optimizing the timing, dosage, and sequencing of viral administrations. Specifically, we consider strategies that increase the burst size of the virus, immunostimulation and immunosuppression, we optimize for different weekly virus injection schedules, and we consider the combination of OV therapy with chimeric antigen receptor (CAR) T-cell therapy. A limiting factor is the availability of data. We parametrize the model using several different data sets. These, however, correspond to different cancers and experimental setups. Hence our model cannot be considered to be validated. Consequently, our results are qualitative. Our results highlight the critical importance of timing for virotherapy's efficacy and overall success. They outline strong evidence for promising treatment scenarios that needs to be further tested experimentally in the future.