Abstract PR006: Evolution of evolvability

Abstract The purpose of this work is to better understand how the rate of phenotypic adaptation of cells in a tumor affects its ability to survive treatment. Drug resistance is an ongoing problem for maintaining a sustained response in treating advanced cancers. Regardless of their lineage, heritable phenotypic adaptions may be beneficial for the tumor to survive stepwise changes in the environment during tumor growth. There may be further benefit for cells to develop the trait of being adaptable - to respond to large shifts in the microenvironment, survive metastasis to a new organ, and thrive when treatments are applied. Genomic instability and phenotypic redundancy may aid in adaptations, but there may be limits. With too much mutation and deviation from the parental phenotype, cells lose important regulatory and maintenance functions necessary to survive. So is there an optimal rate of evolvability? We use an off-lattice agent-based model to investigate how the rate of change through phenotypic trait space affects tumor growth and response to treatment. We assign cells with different combinations of proliferation rates and migration speeds and test how the variance from the parental cell on division affects the selection of trait combinations over time. During growth, heterogeneity is gained quickly and space matters less if evolvability is forced to continually increase, but there is more selection for proliferative phenotypes and slower evolvability if its rate is allowed to drift freely. We found that tradeoffs generally imposed a greater selection force leading to faster recurrence and the coexistence of proliferation or migration specialists. We compared responses to continuous, intermittent, and adaptive treatment schedules of an anti-proliferative drug. At some point with treatment, there is an optimal evolvability, as larger increases in diversity allows for cells to fall back into a sensitive state and be killed by treatment. We aim to design better treatment strategies that work for tumors with different levels of evolvability by understanding how the rate of tumor evolution affects growth and treatment response. Citation Format: Jill A. Gallaher, Maximilian Strobl, Jeffrey West, Mark Roberston-Tessi, Alexander R. A. Anderson. Evolution of evolvability [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 PR006.