Improving immunovirotherapies: the intersection of mathematical modelling and experiments

Combined oncolytic virotherapy and immunotherapy (immunovirotherapy) protocols represent a promising treatment strategy for a range of cancers and offer many advantages over conventional anti-cancer therapies. Nevertheless, there are considerable challenges for this therapeutic modality, and clinical treatment failures remain prevalent. Determining which combination regimens to investigate given the burgeoning number of virotherapy and immunotherapy derivatives remains a tremendous challenge for the field. Fortunately, mathematical modelling is well placed to assist in identifying optimal combination regimens and improving these treatments. However, translation of modelling predictions to actionable changes is severely lacking. Here, two mathematicians and two experimentalists discuss their respective viewpoints concerning the current state of immunovirotherapy, the challenges facing this promising field and how contributions from this modelling and experimental research can be better integrated in the future. By initiating this dialogue, we arrive at the conclusion that the translational process can be improved by first conducting extensive mathematical investigations using relevant data before proceeding to pre-clinical and finally clinical trials. By exploiting mathematical approaches such as virtual clinical trials, we may be able to limit the number of virotherapy and immunotherapy combinations that should be tested clinically. Overall, the current integration of efforts by modellers and experimentalists is insufficient to support major translational advances in this field, and it is only with cross-disciplinary efforts that immunovirotherapy can be a robustly effective cancer treatment.