Abstract A031: Investigating the mechanism and impact of frequency-dependent interactions in non-small cell lung cancer

Abstract

Abstract Introduction: Pre-treatment tumor heterogeneity in cancer is a major driver of drug resistance. Given that resistance mechanisms often impose a fitness cost, the presence of drug-resistant clones prior to treatment poses a paradox—less fit drug-resistant clones should be selected out of a growing population. Recent work has identified frequency-dependent interactions (FDIs) in cancer cell lines, where different proportions of drug-sensitive and resistant cells modulate their respective fitness. FDIs may help explain pre-treatment drug resistance in cancer. Here, we sought to characterize FDIs in a drug-resistant model of non-small cell lung cancer (NSCLC). Methods: We developed a drug-resistant cell line in NSCLC by transducing PC9 cells with BRAF V600E. We then quantified the frequency-dependent interaction between sensitive and resistant cell lines across a range of gefitinib concentrations using the previously published evolutionary game assay. We also implemented an agent-based spatial model of evolution to investigate the different potential molecular mechanisms of frequency-dependent interactions. Results: We found that, despite growing slower in monoculture, low proportions of resistant cells in co-culture with sensitive cells rescued their growth rate in the absence of drug. This phenomenon may help explain pre-treatment drug-resistance and may present a future targetable pathway. Using a computational model parameterized with this data, we investigated the impact of different molecular mechanisms of FDIs on tumor evolution. We found that a mechanism driven by compound diffusion, in contrast to a cell-cell contact mechanism, facilitates drug resistance to greater extent. Through a conditioned media transfer experiment, we found that the FDI in our experimental system is most likely governed by a compound released in the cell culture media. Conclusions: Here we have identified FDIs in an engineered model of drug resistance in NSCLC. Using a computational model, we demonstrated how different mechanisms of FDIs impact tumor evolution. Our ongoing work seeks to identify the specific molecular mechanism responsible for the observed FDIs. In the future, we may leverage this approach to modulate frequency-dependent interactions, allowing us to mitigate pre-treatment drug resistance in vivo. Citation Format: Eshan S. King, Dagim S. Tadele, Maximillian A. R. Strobl, Jacob G. Scott. Investigating the mechanism and impact of frequency-dependent interactions in non-small cell lung cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A031.