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

Vincent Carbonnier,Bernard Leroy,Thierry Soussi,Shai Rosenberg

doi:10.1038/s41598-020-74892-2

Abstract

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.

Highlights

The diagnosis of somatic and germline TP53 mutations in human tumors or in individuals prone to various types of cancer has reached the clinic
We focused on missense variants as they are the most frequent modifications observed in the TP53 gene and the most difficult to predict
We showed that TP53 variants resulting from dinucleotide (DNS) or trinucleotide substitutions (TNS) at hotspot codons 175, 248 and 273 could be highly deleterious for TP53 activity but were never observed in human cancer due to the very low probability of such e vents[8]

Summary

Introduction

The diagnosis of somatic and germline TP53 mutations in human tumors or in individuals prone to various types of cancer has reached the clinic. One of the most unusual aspects of the TP53 gene is the high frequency of somatic and germline missense mutations that occur in it, which is unusual for a tumor suppressor g ene[4]. This specific selection is believed to be linked to the antimorphic and/or neomorphic activities of the variants that transform the tumorsuppressive wild-type TP53 into a mutant oncogene. Machine learning has been used to develop algorithms that improve variant classification Many of these methods have been used for the prediction of TP53 variants, none of them have reached sufficient specificity and sensitivity for routine use.

Methods

Results

Conclusion