The article presents a high-throughput prime editing sensor strategy to evaluate the functional impact of endogenous genetic variants in cancer genomes. The method couples prime editing guide RNAs (pegRNAs) with synthetic sensor sites to quantitatively assess the effects of over 1,000 cancer-associated *TP53* variants. The study identifies alleles that impact p53 function in diverse ways, highlighting the importance of gene dosage in shaping protein stoichiometry and interactions. The authors developed a computational tool, Prime Editing Guide Generator (PEGG), to design and rank pegRNAs for thousands of genetic variants. They screened a library of pegRNAs targeting *TP53* variants in A549 lung adenocarcinoma cells, using a modified PEAR reporter and Nutlin-3 treatment to select for gain-of-function (GOF) and loss-of-function (LOF) variants. The sensor-based approach validated the utility of prime editing for identifying pathogenic *TP53* variants, including those in the p53 oligomerization domain (OD), which display opposite phenotypes in exogenous overexpression systems. The study also demonstrates the potential of prime editing sensor screens to identify variant-specific therapeutic sensitivities and to uncover new pathogenic variants.The article presents a high-throughput prime editing sensor strategy to evaluate the functional impact of endogenous genetic variants in cancer genomes. The method couples prime editing guide RNAs (pegRNAs) with synthetic sensor sites to quantitatively assess the effects of over 1,000 cancer-associated *TP53* variants. The study identifies alleles that impact p53 function in diverse ways, highlighting the importance of gene dosage in shaping protein stoichiometry and interactions. The authors developed a computational tool, Prime Editing Guide Generator (PEGG), to design and rank pegRNAs for thousands of genetic variants. They screened a library of pegRNAs targeting *TP53* variants in A549 lung adenocarcinoma cells, using a modified PEAR reporter and Nutlin-3 treatment to select for gain-of-function (GOF) and loss-of-function (LOF) variants. The sensor-based approach validated the utility of prime editing for identifying pathogenic *TP53* variants, including those in the p53 oligomerization domain (OD), which display opposite phenotypes in exogenous overexpression systems. The study also demonstrates the potential of prime editing sensor screens to identify variant-specific therapeutic sensitivities and to uncover new pathogenic variants.