Abstract

Functional studies of genetic variation using precision genome editing Many human diseases have a strong association with diverse types of genetic alterations. These diseases include cancer, in which tumor genomes often harbor a complex spectrum of single-nucleotide alterations and chromosomal rearrangements that can perturb gene function in ways that remain poorly understood. Some cancer-associated genes exhibit a tremendous degree of mutational heterogeneity, which may impact disease initiation, progression, and therapy responses. For example, TP53 , the most frequently mutated gene in cancer, shows extensive allelic variation that leads to the generation of altered proteins that can produce functionally distinct phenotypes. Whether distinct variants of TP53 and other genes encode proteins with loss-of-function, gain-of-function, or otherwise neomorphic phenotypes remains both controversial and technically challenging to assess, particularly at the endogenous level. Precision genome editing technologies like base editing 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 (PMID: 35165384). Using these approaches, we found that several previously uncharacterized mutant p53 alleles are bona fide drivers of cancer cell proliferation and in vivo tumor development. Building upon this work, we recently developed new prime editing guide RNA design tools and sensorbased approaches that similarly couple quantitative editing outcomes to cellular fitness, allowing us to significantly expand the breadth and complexity of cancer-associated mutations that can be interrogated using these technologies. We used this strategy to screen the largest collection of endogenous cancerassociated TP53 variants assembled to date, identifying both known and novel alleles that impact p53 function in mechanistically diverse ways. Intriguingly, we find that certain types of endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous gene overexpression systems. These include disease-relevant variants found in humans with cancer predisposition syndromes that encode altered proteins with unique molecular properties. Our results emphasize the physiological importance of gene dosage in shaping native protein stoichiometry and protein-protein interactions, highlight the limitations of using exogenous overexpression systems to interpret pathogenic alleles, and establish a computational and experimental framework for studying diverse types of genetic variants in their endogenous context, providing insight into variant-function relationships that could be leveraged to develop more precise therapies.