Paper Info
Title
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53
Abstract
Correctly identifying the true driver mutations in a patient's tumor is a major challenge in precision oncology. Most efforts address frequent mutations, leaving medium- and low-frequency variants mostly unaddressed. For TP53, this identification is crucial for both somatic and germline mutations, with the latter associated with the Li-Fraumeni syndrome (LFS), a multiorgan cancer predisposition. We present TP53_PROF (prediction of functionality), a gene specific machine learning model to predict the functional consequences of every possible missense mutation in TP53, integrating human cell- and yeast-based functional assays scores along with computational scores. Variants were labeled for the training set using well-defined criteria of prevalence in four cancer genomics databases. The model's predictions provided accuracy of 96.5%. They were validated experimentally, and were compared to population data, LFS datasets, ClinVar annotations and to TCGA survival data. Very high accuracy was shown through all methods of validation. TP53_PROF allows accurate classification of TP53 missense mutations applicable for clinical practice. Our gene specific approach integrated machine learning, highly reliable features and biological knowledge, to create an unprecedented, thoroughly validated and clinically oriented classification model. This approach currently addresses TP53 mutations and will be applied in the future to other important cancer genes.
Year
DOI
Venue
2022
10.1093/bib/bbab524
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
TP53, genetic counseling, Li-Fraumeni syndrome, precision medicine, personalized oncology, machine learning
Journal
23
Issue
ISSN
Citations 
2
1467-5463
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Gil Ben-Cohen100.34
Flora Doffe200.34
Michal Devir300.34
Bernard Leroy400.34
T Soussi5219.94
Shai Rosenberg600.34