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An updated quantitative model to classify missense variants in the TP53 gene: A novel multifactorial strategy
Author(s) -
Fortuno Cristina,
Pesaran Tina,
Dolinsky Jill,
Yussuf Amal,
McGoldrick Kelly,
Tavtigian Sean V.,
Goldgar David,
Spurdle Amanda B.,
James Paul A.
Publication year - 2021
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.24264
Subject(s) - missense mutation , biology , germline , genetics , rna splicing , allele , germline mutation , computational biology , gene , mutation , rna
Multigene panel testing has led to an increase in the number of variants of uncertain significance identified in the TP53 gene, associated with Li‐Fraumeni syndrome. We previously developed a quantitative model for predicting the pathogenicity of P53 missense variants based on the combination of calibrated bioinformatic information and somatic to germline ratio. Here, we extended this quantitative model for the classification of P53 predicted missense variants by adding new pieces of evidence (personal and family history parameters, loss‐of‐function results, population allele frequency, healthy individual status by age 60, and breast tumor pathology). We also annotated which missense variants might have an effect on splicing based on bioinformatic predictions. This updated model plus annotation led to the classification of 805 variants into a clinically relevant class, which correlated well with existing ClinVar classifications, and resolved a large number of conflicting and uncertain classifications. We propose this model as a reliable approach to TP53 germline variant classification and emphasize its use in contributing to optimize TP53 ‐specific ACMG/AMP guidelines.