Base editing sensor libraries for high-throughput engineering and functional analysis of cancer-associated single nucleotide variants

Abstract

Base editing (BE) can be applied to characterize single nucleotide variants (SNVs) of unknown function, yet defining effective combinations of single guide RNAs (sgRNAs) and base editors remains challenging. Here, we describe modular BE-activity ‘sensors’ that link sgRNAs and cognate target sites in cis and use them to systematically measure the editing efficiency and precision of thousands of sgRNAs paired with functionally distinct base editors. By quantifying sensor editing across >200,000 editor–sgRNA combinations, we provide a comprehensive resource of sgRNAs for introducing and interrogating cancer-associated SNVs in multiple model systems. We demonstrate that sensor-validated tools streamline production of in vivo cancer models, and that integrating sensor modules in pooled sgRNA libraries can aid interpretation of high-throughput BE screens. Using this approach, we identify several previously uncharacterized mutant TP53 alleles as drivers of cancer cell proliferation and in vivo tumor development. We anticipate that the framework described here will facilitate the functional interrogation of cancer variants in cell and animal models.

Efficient multitool/multiplex gene engineering with TALE-BE

“…While extremely precise introduction of the intended mutation (high purity of the final product) is a prerequisite for application such as gene correction, bystander and byproduct edits might be of a lesser concern for gene disruption applications. However, generation of DSBs by base editors may raise greater concerns as CRISPR/Cas base nucleases have been recently associated with major on-target genome instability or chromosomal abnormalities ( Weisheit et al, 2020 ; Alanis-Lobato et al, 2021 ; Boutin et al, 2021 ; Papathanasiou et al, 2021 ; Boutin et al, 2022 ; Geng et al, 2022 ; Nahmad et al, 2022 ; Sánchez-Rivera et al, 2022 ). In this study we only found marginal byproduct mutation (C-to-A/G), and more importantly low Indel creation, by TALE-BE looking at dozens of these molecular tools, even at high editing frequencies (>80% in bulk population), occurring at levels similar to, or lower than, those reported by other groups ( Mok et al, 2020 ; Lim et al, 2022 ).…”

CRISPR in cancer biology and therapy

“…The latter example is a clear case in which CRISPR tools have enabled interrogation of direct genotype-phenotype relationships in noncoding genomic elements, which has traditionally been very difficult. We recently described cancer mutationfocused BE 'sensor' libraries that enable the simultaneous induction of mis sense mutations and measurement of BE efficiency in a pooled format 129 . BE is a powerful tool for engineer ing cancerassociated mutations but with the current enzymes, it cannot capture all alterations.…”

“…Cas9 and a library of pooled sgRNAs can be infected into cells or organoids, and, after selective pressure is applied by proliferation over time or drug treatments, sgRNA enrichment and depletion are measured to determine targets of interest [87][88][89][90]93,94 . In addition, infection of a cytosine base editor or adenine base editor and a pooled sgRNA library and addition of drug has led to the identification of missense mutations that confer resistance or sensitivity to poly(ADP-ribose) polymerase (PARP) inhibitors, BH3 mimetics or response to DNA damaging agents 124,125,129 . BE, base editing; Cas9n, Cas9 nickase; LSL, lox-stop-lox; TRE, tetracycline-responsive.…”

Rapid generation of precision preclinical cancer models using regulatable in vivo base editing

“…In contrast to plasmid-based delivery, nucleofection of dox-treated organoids with chemically stabilized synthetic sgRNAs led to significantly higher editing, up to 43-fold higher in the case of CR8 OS2 (Figure 3c, Supplementary Figure 2e; p = 0.0005). Using the more efficient synthetic sgRNA approach, we next tested a range of additional BE sgRNAs predicted by BE-SCAN (https://dowlab.shinyapps.io/ BEscan/) 38 . In unselected populations, 7d post-transfection, C>T editing efficiencies ranged from ~20%-90% (Figure 3d).…”