High-throughput functional evaluation of human cancer-associated mutations using base editors

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Characterizing cancer mutations is crucial for understanding cancer genomics.
• Many cancer-related mutations result in protein variants with unknown functional significance.
• Developing high-throughput methods to assess these variants is a major challenge.

Purpose of the Study:

• To functionally characterize the effects of 29,060 cancer-related transition mutations on cell survival and proliferation.
• To establish a method for linking specific protein variants to cellular phenotypes using base editing.
• To investigate the functional impact of a large set of protein variants in non-tumorigenic lung cells.

Main Methods:

• Utilized cytosine and adenine base editors with single guide RNA (sgRNA) libraries for targeted mutagenesis.
• Employed lentiviral delivery constructs with surrogate target sequences to monitor base editing efficiency.
• Developed a system to link individual sgRNAs to specific protein variants and their resulting cellular phenotypes.

Main Results:

• Evaluated the functional impact of 29,060 cancer-related transition mutations.
• Identified sgRNAs capable of inducing single primary protein variants for phenotype analysis.
• Classified the vast majority of variants (98%) as neutral or likely neutral.
• Observed outgrowing phenotypes in only a small fraction of variants (0.06% caused outgrowing, 0.5% likely outgrowing).

Conclusions:

• The base editing approach effectively links specific mutations to cellular phenotypes.
• Most cancer-related protein variants studied exhibit neutral functional effects.
• This methodology can be extended to assess variants of unknown significance across various cancer types.