Abstract B037: Capturing dose-response adaptation to identify evolutionarily-informed treatment strategies

Abstract Dose–response curves (DRCs) are a standard protocol in experimental biology to assess the effect of anticancer agents in vitro. Yet, therapy itself can reshape tumor composition, driving the gradual evolution of the DRC. Monitoring such changes can reveal the emergence of resistance, as well as evolutionary trade-offs that may be exploited to enhance therapeutic efficacy. Here, we investigate the evolution of DRCs in response to three different ALK inhibitors (ALKi) in ALK-positive anaplastic large cell lymphoma (ALCL) using patient-derived cell lines. Over the course of six months, ALKi concentrations were gradually escalated, allowing ALCL cells to adapt and survive at higher drug levels. Strikingly, at the same time, these resistant populations exhibited reduced fitness in the absence of the drug, indicating an evolutionary cost. This phenomenon, known as “drug addiction” or dependence, has been previously observed in melanoma following resistance to MAPK inhibitors and suggests that treatment holidays could optimize tumor control in the long term. Guided by these findings, we developed a mathematical model to capture the temporal evolution of DRCs and to test how therapy design might exploit addiction in ALCL. Calibrated with experimental data, the model simulated tumor growth under alternative dosing regimens. Simulations revealed that continuous therapy rapidly selects for resistance, leading to tumor regrowth within ∼150 days. In contrast, intermittent dosing schedules maintained long-term control for more than 300 days. Moreover, the model identified an adaptive strategy: switching from continuous to intermittent dosing precisely when tumor burden reaches its minimum. This evolutionarily informed approach prevents resistant clones from dominating while leveraging the fitness disadvantage associated with drug addiction. Preliminary experiments with naïve and resistant ALCL cell lines, cultured under intermittent dosing in both monoculture and co-culture systems, support the model’s predictions. These experiments also enable the refinement of the model's parameters to incorporate tumor heterogeneity. While additional validation is required, our findings demonstrate that integrating mathematical modeling with experimental DRCs provides a quantitative framework for predicting the evolutionary dynamics of resistance and addiction. Collectively, this study highlights the potential of adaptive and evolution-based therapy in ALCL. Optimized treatment schedules informed by evolutionary trade-offs can reduce tumor burden, delay progression, and exploit vulnerabilities generated by resistance mechanisms. Citation Format: Franco Pradelli, Lucy Hare, Jamie Matthews, Roxane Chen, Aiindrila Dhara, G. A. Amos Burke, Suzanne D. Turner, Jeffrey West. Capturing dose-response adaptation to identify evolutionarily-informed treatment strategies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr B037.