Journal
NATURE BIOTECHNOLOGY
Volume 40, Issue 6, Pages 862-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41587-021-01172-3
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Funding
- Integrated Genomics Operation and Bioinformatics Core [P30 CA008748]
- NIH/NCI [R01CA229773-01A1, P01 CA087497]
- MSKCC Functional Genomics Initiative (FGI) grant
- Agilent Technologies Thought Leader Award
- MSKCC TROT program [5T32CA160001]
- GMTEC Postdoctoral Researcher Innovation Grant
- F31 Ruth L. Kirschstein Predoctoral Individual National Research Service Award [F31-CA261061-01, F31-CA192835, F31-CA247351-02]
- NCI [R35CA197588]
- German Research Foundation (DFG)
- Shulamit Katzman Endowed Postdoctoral Research Fellowship
- German Academic Scholarship Foundation
- GMTEC Postdoctoral Fellowship
- MSKCC's Translational Research Oncology Training Fellowship [5T32CA160001-08]
- Edward P. Evans Foundation
- Jane Coffin Childs Memorial Fund for Medical Research
- MSKCC Marie-Josee and Henry R. Kravis Center for Molecular Oncology
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Improved base editing libraries enable high-throughput functional analysis of single-nucleotide variants in cancer. A modular base-editing-activity sensor was developed to measure the editing efficiency and precision of thousands of sgRNAs paired with functionally distinct base editors. This comprehensive resource of sgRNAs can be used to study cancer-associated single nucleotide variants in cell and animal models.
Improved base editing libraries enable high-throughput functional analysis of single-nucleotide variants in cancer. 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.
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