Purpose: Immunotherapy with checkpoint inhibitors has an enormous potential in therapy of metastatic cancers. Immunotherapy is generally combined with local treatments, such as radiation therapy. The time schedule of drug-radiation combination is largely based on empirical observations, and a comprehensive predictive model would be needed to optimize treatments. We present a biophysical model predicting the combined interaction and apply it to describe preclinical experimental data.
Methods and materials: The model considers the dependences of primary and distal tumor masses, immune cell kinetics targeting tumor cells, and signals causing immune cell replenishment after radiation mechanistic interpretation of the low frequency of abscopal responses. It is benchmarked against 16 experiments with synthetic tumors in murine models.
Results: The model predicts that immune response is stronger for checkpoint inhibitor administration at the time of irradiation or shortly after. The model discriminates correctly between tumor remission and continued growth in all considered experimental cases, including radiation and checkpoint delivery alone or in combination. It identifies a radiation dose window maximizing immune response and avoiding on one side the understimulation of the immune system and radiation-induced depletion of the immune cell pool on the other. Consequently, abscopal effects can be established in certain circumstances only.
Conclusions: The model allows a quantitative mechanistic interpretation of the interaction of radiation with checkpoint blockers and will be helpful for optimizing clinical trials.
Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.