Abstract 3696: Evolutionary immunotherapy in NSCLC: identifying optimal dosing strategies in TIL therapy using agent-based modeling
Authors: Sandhya Prabhakaran, Mark Robertson-Tessi, Kimberly Luddy, Rafael R. Bravo, Taylor M. Bursell, Julian Pineiro, Jeffrey West, Megan Johnson, Jhanelle A. Gray, Amer A. Beg, Scott J. Antonia, Robert A. Gatenby, Alexander R. Anderson
Journal: Cancer Research (2025)

Purpose: Our study examines tumor growth dynamics and immunosuppression under the presence of immune attack to identify optimal immune pulsing treatment strategies. Motivation: While immunotherapy has shown remarkable success in treating many cancers like non-small cell lung cancer (NSCLC), not all patients respond well to immunotherapy. This mixed response can be attributed to the complexity of the tumor-immune system dynamics. TIL therapy is an emerging immunotherapy where activated T cells are injected into the patient. This therapy can fail due to tumor-induced immunosuppression, for example via the PDL1/PD1 axis. PDL1 expression has been studied and can increase under IFN-γ, released by activated T cells, but PD-L1 relaxation is not understood. We hypothesize that a) the regimen of TIL therapy administration should impact the dynamics of PDL1, and that b) there may be better strategies of therapy delivery that maximize tumor kill while minimizing immune suppression. We investigate these complex PD-L1 driven dynamics through a unique combination of in vitro studies and mathematical modeling. Methods: We have collected in vitro and temporal confocal microscopy images of PD-L1 expression on NSCLC cells, which were either untreated or treated for 48 hours with high dose IFN-γ followed by chronic low dose IFN-γ for 21 days. To reinforce our in vitro experiments and findings, we developed a hybrid agent-based model (ABM) to study PD-L1 dynamics. The agents in the ABM are tumor cells having variable PD-L1 expression, and immune cells that secrete IFN-γ and can kill tumor cells. Tumor cells can be randomly- or cluster-seeded. IFN-γ is modeled as either a binary well-mixed pulse (to simulate the in vitro experimental setup), or as a diffusible via a reaction-diffusion process (to simulate the tumor-immune cell interactions in an in vivo spatial manner). We model the TIL therapy as a pulse of immune cells, given at regular or irregular intervals and with different quantities of cells. Results: Our ABM is calibrated to capture in vitro data dynamics. Under certain combinations of spatial configurations of the tumor and intermittent immune dosing schedules, we observe the presence of ‘sweet spots’ where tumor extinction is possible and immune exhaustion is avoided. This indicates that an appropriate pulsing of TIL therapy may lead to better overall immune efficacy than a bolus injection or continuous immunotherapy, by preventing sustained suppression that increases immune cell exhaustion, despite the potential for tumor regrowth between pulses. Conclusion: We have built an ABM to study tumor-immune evolution under various pulsing strategies of TIL therapy. Our novel findings can potentially benefit clinical cancer research by giving multiple insights related to the tumor extinction, equilibrium and escape phenomena, and lead to improved schedules in patients receiving immunotherapy. Citation Format: Sandhya Prabhakaran,Mark Robertson-Tessi,Kimberly Luddy,Rafael R. Bravo,Taylor M. Bursell,ulian Pineiro,Jeffrey West,Megan Johnson,Jhanelle A. Gray,Amer A. Beg,Scott J. Antonia,Robert A. Gatenby,Alexander R. Anderson. Evolutionary immunotherapy in NSCLC: identifying optimal dosing strategies in TIL therapy using agent-based modeling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3696.