Radiotherapy and Immunotherapy Combinations: Optimize the Synergistic Effect Via a Quantitative System
Y Xing1,2*, C Timmerman3, D Saha4, B Chen4, X Jia2,4, M Story4, R Timmerman4, D Nguyen2,4, S Jiang2,4, (1) Department of Advanced Data Analytics, University of North Texas, Denton, TX (2) Medical Artificial Intelligence and Automation (MAIA) lab, University of Texas Southwestern Medical Center, Dallas, TX (3) Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX (4) Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
Presentations
SU-H330-IePD-F9-1 (Sunday, 7/10/2022) 3:30 PM - 4:00 PM [Eastern Time (GMT-4)]
Exhibit Hall | Forum 9
Purpose: Radiation therapy and immunotherapy have each played an important role in tumor control and improving patient outcomes and quality of life. The success of combining radiation with immunotherapy in clinical practice encourages a better understanding of the synergistic anti-tumor effect and optimal radiation timing in the presence of immune checkpoint inhibitors. Early pre-clinical investigations of a new paradigm, personalized ultrafractionated stereotactic adaptive radiation therapy (PULSAR), have further shown that radiation scheduling highly impacts combined therapeutic efficacy. However, little is known about the underlying mechanism. Our work aims to model the cellular and molecular interactions in the tumor during radioimmunotherapy that can potentially guide further experiments and optimize the PULSAR effects.
Methods: We constructed biological hypotheses for the PULSAR effect based on the radio-resistant tissue-resident memory T-cells and radio-sensitive lymphoid tissue T-cells. Accordingly, a discrete-time mathematical framework has been proposed to describe the combination of fractionated irradiation regimes and immune checkpoint blockers. In this mathematically simplified framework, we capture key effects observed in the experiments using three major populations including two types of T-cells and tumor cells, and three factors including radiation therapy (RT), PDL1, and anti-PDL1. The data from in vivo studies in mice were used to validate the ability of the model to predict the tumor growth.
Results: The estimated parameters of the model support the major hypothesis that the T-cell reprogramming leads to the PULSAR effect. Also, the model achieved high accuracy in both the fitting and testing results.
Conclusion: A quantitative model was successfully developed to describe the treatment results observed in various scenarios of Lewis lung cell line including single and multiple fraction irradiation schemes. Integrating therapeutic modalities by mathematical equations, the model can be used to simulate clinical outcomes, predict synergistic effects, and potentially provide optimal clinical solutions to tumor control.
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