Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. Here we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was validated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. The derived parameters Λ and µ were both significantly different between responding versus non-responding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within two months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology, demonstrating reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these results suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.SIGNIFICANCECheckpoint inhibitors have revolutionized cancer immunotherapy, but only a subset of patients with solid tumors responds clinically. The ability to predict tumor responses a priori or soon after starting therapy would allow for personalized and timely adaptive clinical applications of checkpoint inhibitor- based immunotherapy in patients. By applying a mechanistic mathematical model, we show that checkpoint inhibitor therapeutic effectiveness is accurately predictable in most patients within two months after treatment initiation. Our method may be implemented directly into clinical practice, as it relies on standard-of-care imaging and pathology. If successful in prospective studies, this model will improve selection of cancer patients for checkpoint inhibitor therapy, and perhaps for other forms of humoral- or cell-based immunotherapy.