Comprehensive assessment of TP53 loss of function using multiple combinatorial mutagenesis libraries

Area of Science:

• Genetics and Genomics
• Cancer Biology
• Molecular Diagnostics

Background:

• TP53 mutations are critical in human tumors and cancer predisposition, with clinical diagnostic implications.
• Accurate prediction of TP53 variant pathogenicity is essential for effective cancer management and genetic counseling.

Purpose of the Study:

• To enhance the accuracy of predicting TP53 variant pathogenicity by integrating diverse functional data.
• To investigate the impact of TP53 missense mutations on splicing and their contribution to pathogenicity.

Main Methods:

• Compiled functional data from three large-scale saturation mutagenesis screening studies (>10,000 TP53 variants).
• Utilized correlation analysis and multidimensional scaling to assess agreement between different experimental settings and readouts (yeast/mammalian, transcription/growth arrest/apoptosis).
• Analyzed TP53 expression data from The Cancer Genome Atlas to identify splicing defects associated with missense mutations.

Main Results:

• Demonstrated excellent agreement between functional data from independent studies and various experimental readouts.
• Identified missense mutations in TP53 exons that impair RNA splicing.
• Highlighted the necessity of combining protein and RNA prediction for accurate variant pathogenicity assessment.

Conclusions:

• Integrating large-scale functional data significantly improves TP53 variant pathogenicity prediction.
• Splicing alterations represent an important mechanism by which TP53 variants can impact pathogenicity.
• A multi-modal approach combining protein and RNA analysis is vital for precise variant classification in the genomic era.