Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Biotechnol. 2022 Jun;40(6):862-873. doi: 10.1038/s41587-021-01172-3. Epub 2022 Feb 14.
Base editing can be applied to characterize single nucleotide variants of unknown function, yet defining effective combinations of single guide RNAs (sgRNAs) and base editors remains challenging. Here, we describe modular base-editing-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 single nucleotide variants 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 base editing 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.
碱基编辑可用于研究未知功能的单核苷酸变异体，但定义有效的单指导 RNA（sgRNA）和碱基编辑器组合仍然具有挑战性。在这里，我们描述了模块化的碱基编辑活性“传感器”，它们将 sgRNA 和同源靶位点在顺式连接，并利用它们系统地测量数千个 sgRNA 与功能不同的碱基编辑器配对的编辑效率和精度。通过对超过 200,000 个编辑器-sgRNA 组合进行定量传感器编辑，我们提供了一个全面的 sgRNA 资源，用于在多个模型系统中引入和研究与癌症相关的单核苷酸变异体。我们证明，传感器验证的工具简化了体内癌症模型的产生，并且将传感器模块整合到 pooled sgRNA 文库中可以帮助解释高通量碱基编辑筛选。使用这种方法，我们鉴定了几个以前未被表征的突变 TP53 等位基因，它们是癌细胞增殖和体内肿瘤发展的驱动因素。我们预计，这里描述的框架将有助于在细胞和动物模型中对癌症变异进行功能研究。
