Abstract

Abstract Tumor genomes often harbor a complex spectrum of single nucleotide mutations, small indels, and large chromosome rearrangements that can perturb coding and non-coding regions of the genome in ways that remain poorly understood. The mutational processes responsible for the genesis of these events are also not static; instead, they continue to operate throughout disease evolution and further diversify the genetic and phenotypic landscape of cancer cells. Mounting evidence suggests that tumor genotype can be an important determinant of disease progression and therapy response, such as by modulating the sensitivity or resistance of cancer cells to mutant-specific drugs. These types of observations have motivated efforts to treat cancers with genome-informed therapies, highlighting the need to understand how different genetic variants precisely affect gene function and overall tumor phenotypes. Variant-function maps that provide a mechanistic understanding of the biology driven by cancer-associated mutations are needed to design these types of treatment strategies. Precision genome editing technologies like base and prime editing are uniquely suited to tackle this problem. Nevertheless, deploying these methods for systematic variant-function studies and disease modeling in vivo has not been straightforward due to lack of robust and scalable platforms capable of assessing editing efficiency and precision, particularly at endogenous loci. With this goal in mind, we previously developed and applied high-throughput base editing ‘sensor’ approaches that link endogenous genome editing outcomes with synthetic DNA-based readouts and cellular fitness measurements. Using these approaches, we showed that several uncharacterized mutant p53 alleles drive cancer cell proliferation and in vivo tumor development. Building upon this work, we recently developed new prime editing guide RNA design tools and sensor-based approaches that similarly couple quantitative editing outcomes to cellular fitness, allowing us to significantly expand the breadth of cancer-associated mutations that can be interrogated using these precision genome editing technologies. In this talk, I will describe ongoing work using base and prime editing sensor libraries to probe the biological impact of thousands of functionally-distinct genetic variants in diverse types of protein-coding genes and families to learn how these influence various cancer phenotypes. I will also discuss how the implementation of these modular technologies will allow researchers to functionally disentangle the impact of endogenous and exogenous mutational processes on functional selection and tumor evolution with close to single base pair resolution. This generalizable precision genome editing framework will facilitate the functional interrogation of genetic variants across diverse biological contexts, providing much needed insight into cancer variant-function relationships that could be leveraged to develop more precise cancer treatment paradigms, including synthetic lethal therapies that exploit tumor genotype. Citation Format: Francisco J. Sánchez-Rivera, Samuel I. Gould, Alexandra N. Wuest, Kexin Dong, Grace A. Johnson, Alvin Hsu, Varun K. Narendra, Ondine Atwa, Stuart S. Levine, David R. Liu. Functional studies of genetic variation using precision genome editing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA020.