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

• Published in: Scientific reports Vol. 10; no. 1; pp. 20368 - 10
• Main Authors: Carbonnier, Vincent, Leroy, Bernard, Rosenberg, Shai, Soussi, Thierry
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.11.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/67; Alternative Splicing; Animals; Apoptosis; Apoptosis - genetics; Atlases as Topic; Cancer; Cell Cycle Checkpoints - genetics; Computational Biology - methods; Correlation analysis; Exons; Gene Expression Regulation, Neoplastic; Gene Library; Genetic Predisposition to Disease; Genomes; Humanities and Social Sciences; Humans; Introns; Life Sciences; Medicin och hälsovetenskap; Missense mutation; Multidimensional scaling; Multidimensional Scaling Analysis; multidisciplinary; Mutagenesis; Mutation; Neoplasm Proteins - genetics; Neoplasm Proteins - metabolism; Neoplasms - genetics; Neoplasms - metabolism; Neoplasms - pathology; p53 Protein; Pathogenicity; Proteomics; Ribonucleic acid; RNA; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saturation mutagenesis; Science; Science (multidisciplinary); Transcription; Transcription, Genetic; Transcriptomics; Tumor Suppressor Protein p53 - deficiency; Tumor Suppressor Protein p53 - genetics; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-74892-2

The diagnosis of somatic and germline TP53 mutations in human tumors or in individuals prone to various types of cancer has now reached the clinic. To increase the accuracy of the prediction of TP53 variant pathogenicity, we gathered functional data from three independent large-scale saturation mutagenesis screening studies with experimental data for more than 10,000 TP53 variants performed in different settings (yeast or mammalian) and with different readouts (transcription, growth arrest or apoptosis). Correlation analysis and multidimensional scaling showed excellent agreement between all these variables. Furthermore, we found that some missense mutations localized in TP53 exons led to impaired TP53 splicing as shown by an analysis of the TP53 expression data from the cancer genome atlas. With the increasing availability of genomic, transcriptomic and proteomic data, it is essential to employ both protein and RNA prediction to accurately define variant pathogenicity.