Modular mechanistic mathematical models for improved biomarker identification and treatment scheduling in oncology

The continued development of cancer immunotherapies necessitates complementary approaches to identify target patient populations and improve outcomes. Mechanistic mathematical models are one such approach. In response to treatment development needs, mechanistic mathematical models are increasingly integrated into experimental and clinical research in oncology to identify biomarkers and therapeutic schedules that better treatment responses. Here we describe general strategies to modelling tumour immune interactions and incorporating heterogeneity into mechanistic mathematical models. We then highlight recent work in our group as case studies. We focus particularly on immunotherapies, drug repurposing, and virtual clinical subject generation with applications to glioblastoma, acute myeloid leukemia, immune checkpoint inhibitors, and oncolytic virotherapy.