Cancer is a complex, multiscale dynamical system, with interactions between tumor cells and non-cancerous systems. Therapies act on this cancer-host system, sometimes with unexpected results. Systematic investigation of mechanistic models could help identify the factors driving a treatment's success or failure, but exploring mechanistic models over highdimensional parameter spaces is computationally challenging. In this paper, we introduce a high throughput computing (HTC) framework that integrates a mechanistic 3-D multicellular simulator (PhysiCell) with an extreme-scale model exploration platform (EMEWS) to investigate high-dimensional parameter spaces. We show early results in adapting PhysiCell-EMEWS to 3-D cancer immunotherapy and show insights on therapeutic failure. We describe a PhysiCell-EMEWS workflow for high-throughput cancer hypothesis testing, where thou-sands of mechanistic simulations are compared against data-driven error metrics to perform hypothesis optimization. We close by discussing novel applications to synthetic multicellular systems for cancer therapy.