A Mathematical Model for Predicting Patient Responses to Combined Radiotherapy with CTLA-4 Immune Checkpoint Inhibitors

Cells. 2023 May 3;12(9):1305. doi: 10.3390/cells12091305.

Abstract

The purpose of this study was to develop a cell-cell interaction model that could predict a tumor's response to radiotherapy (RT) combined with CTLA-4 immune checkpoint inhibition (ICI) in patients with hepatocellular carcinoma (HCC). The previously developed model was extended by adding a new term representing tremelimumab, an inhibitor of CTLA-4. The distribution of the new immune activation term was derived from the results of a clinical trial for tremelimumab monotherapy (NCT01008358). The proposed model successfully reproduced longitudinal tumor diameter changes in HCC patients treated with tremelimumab (complete response = 0%, partial response = 17.6%, stable disease = 58.8%, and progressive disease = 23.6%). For the non-irradiated tumor control group, adding ICI to RT increased the clinical benefit rate from 8% to 32%. The simulation predicts that it is beneficial to start CTLA-4 blockade before RT in terms of treatment sequences. We developed a mathematical model that can predict the response of patients to the combined CTLA-4 blockade with radiation therapy. We anticipate that the developed model will be helpful for designing clinical trials with the ultimate aim of maximizing the efficacy of ICI-RT combination therapy.

Keywords: immune checkpoint inhibitor; mathematical modeling; radiation therapy; tremelimumab.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antibodies, Monoclonal / therapeutic use
  • CTLA-4 Antigen
  • Carcinoma, Hepatocellular* / drug therapy
  • Carcinoma, Hepatocellular* / radiotherapy
  • Humans
  • Immune Checkpoint Inhibitors
  • Liver Neoplasms* / drug therapy
  • Models, Theoretical

Substances

  • Immune Checkpoint Inhibitors
  • CTLA-4 Antigen
  • Antibodies, Monoclonal

Grants and funding

This work was supported by grants from the National Research Foundation of Korea (NRF, No. 2021R1C1C1005930) funded by the Korean Government (MSIT). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (Grant No. 2018R1A2B2005343).