Abstract A09: Computational modeling analysis of the tumor response to anti-PD-1 immunotherapy
Authors: Valentinuzzi Damijan , Ursic Katja , Simoncic Urban , Maruna Matea , Turk Marusa , Vrankar Martina , Cemazar Maja , Sersa Gregor , Jeraj Robert 
Published: June 2021

Immune checkpoint inhibitors have shown impressive benefits for patients with various types of cancer. However, patients who respond are still a minority. To improve the response rates, combinations of various immunotherapies, as well as combinations with other conventional therapies, have been extensively studied. Due to an unmanageably high number of all possible combinations and dosing regimens, alternatives to the costly and time-consuming trial-and-error approach are of utmost importance. Our main goal was to develop a verifiable computational model that would analyze the tumor response to anti-PD-1 antibodies and provide suggestions about the possible biomarkers of response to anti-PD-1 immunotherapy. Our model was built with validation in mind, and so contains minimum number of parameters. Moreover, all parameters can be measured experimentally. The model was tuned and validated in vivo using 3 murine tumor cell lines (B16-F10, CT26, 4T1) in 3 different settings: (1) growth of tumors in NUDE mice to assess intrinsic tumor growth in the absence of T-cells, (2) growth of tumors in wild-type (WT) mice to assess the effect of the immune system on untreated tumor growth, and (3) growth of tumors in WT mice receiving anti-PD-1 antibodies to assess the therapeutic effect. MHC Class I and PD-L1 expression on tumor cells was measured in vitro using flow cytometry to assess the parameters associated with immunogenicity of selected tumor cell lines. Single nucleotide variations (SNV) data, indicative of the mutational load, were taken from literature. Finally, we performed a sensitivity study of key model parameters to identify possible biomarkers of tumor response to anti-PD-1 therapy. In vitro results showed comparable PD-L1 expression in all 3 cell lines (11%-21%), while MHC class I expression varied significantly between B16-F10 (2.8%), 4T1 (99.5%), and CT26 (99.9%). Additionally, SNV data indicated an order of magnitude higher CT26 SNV (3023) compared to 4T1 (293), and more than 3 times higher compared to B16-F10 (908). Using the above-measured parameters our computational model was able to reproduce all in vivo experiments. The model suggests that the average occupancy of PD-1 receptors on tumor-infiltrating T cells by anti-PD-1 antibodies is much higher in CT26 (74%) compared to 4T1 (30%) or B16-F10 (8%). It indicates that the ability of antibodies to penetrate the tumor might vary depending on the tumor type. The results of the sensitivity study suggested that a combination of MHC class I, PD-L1 and SNV might be superior for predicting tumor response to anti-PD-1 compared to either of the biomarkers alone. Namely, CT26, the cell line with high MHC class I, high SNV and moderate PD-L1 expression, was the only cell line where complete responses to anti-PD-1 antibodies were observed experimentally. Despite simplified description of the reality, our model generates meaningful hypotheses to be tested in future (pre)clinical trials. Such models show promise to support, guide and accelerate immunotherapy research.

Citation Format: Damijan Valentinuzzi, Katja Ursic, Urban Simoncic, Matea Maruna, Marusa Turk, Martina Vrankar, Maja Cemazar, Gregor Sersa, Robert Jeraj. Computational modeling analysis of the tumor response to anti-PD-1 immunotherapy [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr A09.