A high-throughput prime editing sensor strategy is introduced to quantitatively assess the functional impact of endogenous genetic variants. The method couples prime editing guide RNAs (pegRNAs) with synthetic versions of their target sites to evaluate the effects of genetic variants in their native genomic context. The approach was applied to screen over 1,000 endogenous cancer-associated variants of TP53, the most frequently mutated gene in cancer, to identify alleles that impact p53 function. The results show that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. This highlights the physiological importance of gene dosage in shaping native protein stoichiometry and protein–protein interactions. The study establishes a framework for studying genetic variants in their endogenous sequence context at scale. The method involves generating a library of over 28,000 pegRNAs targeting over 1,000 TP53 variants observed in more than 40,000 cancer patients. The library includes SNVs, insertions, deletions, and silent substitutions. The pegRNAs are paired with synthetic 'sensor' sites that recapitulate the native architecture of the endogenous target locus. This sensor-based approach links pegRNA identity to editing outcomes for simultaneous high-throughput quantification of pegRNA editing activity and empirical calibration of screening data. The study demonstrates that the sensor-based approach can accurately identify pathogenic TP53 variants. It shows that variants in the DNA-binding domain and oligomerization domain of p53 are strongly enriched, while variants in the DBD that may retain WT p53 activity or fail to exert a dominant negative effect are strongly depleted. The results validate the utility of the approach in accurately identifying functionally diverse pathogenic TP53 variants. The study also shows that the sensor-based approach can be used to identify variant-specific therapeutic sensitivities. It demonstrates that small molecules targeting specific mutant proteins, including those produced by oncogenic point mutant KRAS alleles, can have therapeutic potential. The approach can be used to systematically identify variant-specific vulnerabilities to diverse therapies, augmenting cDNA-based approaches for performing similar screens. The study highlights the importance of integrating quantitative, sensor-like approaches to accurately extract true signal from the high levels of noise inherent in large-scale prime editing screens. The results emphasize the need for such approaches to understand the functional impact of genetic variants in their native genomic context. The study also demonstrates that the sensor-based approach can be used to identify new pathogenic variants, including those that may not be detected by traditional cDNA screening methods. The results suggest that the spectrum of cancer-associated TP53 mutations is mechanistically diverse and likely arises through the contextual combination of disproportionate mutagenesis rates and phenotypic selection of functionally important codons and their cognate residues.A high-throughput prime editing sensor strategy is introduced to quantitatively assess the functional impact of endogenous genetic variants. The method couples prime editing guide RNAs (pegRNAs) with synthetic versions of their target sites to evaluate the effects of genetic variants in their native genomic context. The approach was applied to screen over 1,000 endogenous cancer-associated variants of TP53, the most frequently mutated gene in cancer, to identify alleles that impact p53 function. The results show that certain endogenous TP53 variants, particularly those in the p53 oligomerization domain, display opposite phenotypes in exogenous overexpression systems. This highlights the physiological importance of gene dosage in shaping native protein stoichiometry and protein–protein interactions. The study establishes a framework for studying genetic variants in their endogenous sequence context at scale. The method involves generating a library of over 28,000 pegRNAs targeting over 1,000 TP53 variants observed in more than 40,000 cancer patients. The library includes SNVs, insertions, deletions, and silent substitutions. The pegRNAs are paired with synthetic 'sensor' sites that recapitulate the native architecture of the endogenous target locus. This sensor-based approach links pegRNA identity to editing outcomes for simultaneous high-throughput quantification of pegRNA editing activity and empirical calibration of screening data. The study demonstrates that the sensor-based approach can accurately identify pathogenic TP53 variants. It shows that variants in the DNA-binding domain and oligomerization domain of p53 are strongly enriched, while variants in the DBD that may retain WT p53 activity or fail to exert a dominant negative effect are strongly depleted. The results validate the utility of the approach in accurately identifying functionally diverse pathogenic TP53 variants. The study also shows that the sensor-based approach can be used to identify variant-specific therapeutic sensitivities. It demonstrates that small molecules targeting specific mutant proteins, including those produced by oncogenic point mutant KRAS alleles, can have therapeutic potential. The approach can be used to systematically identify variant-specific vulnerabilities to diverse therapies, augmenting cDNA-based approaches for performing similar screens. The study highlights the importance of integrating quantitative, sensor-like approaches to accurately extract true signal from the high levels of noise inherent in large-scale prime editing screens. The results emphasize the need for such approaches to understand the functional impact of genetic variants in their native genomic context. The study also demonstrates that the sensor-based approach can be used to identify new pathogenic variants, including those that may not be detected by traditional cDNA screening methods. The results suggest that the spectrum of cancer-associated TP53 mutations is mechanistically diverse and likely arises through the contextual combination of disproportionate mutagenesis rates and phenotypic selection of functionally important codons and their cognate residues.