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A quantitative model to predict pathogenicity of missense variants in the TP53 gene
Author(s) -
Fortuno Cristina,
Cipponi Arcadi,
Ballinger Mandy L.,
Tavtigian Sean V.,
Olivier Magali,
Ruparel Vatsal,
Haupt Ygal,
Haupt Sue,
Study International Sarcoma Kindred,
Tucker Kathy,
Spurdle Amanda B.,
Thomas David M.,
James Paul A.
Publication year - 2019
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.23739
Subject(s) - library science , publishing , biology , political science , law , computer science
Germline pathogenic variants in the TP53 gene cause Li‐Fraumeni syndrome, a condition that predisposes individuals to a wide range of cancer types. Identification of individuals carrying a TP53 pathogenic variant is linked to clinical management decisions, such as the avoidance of radiotherapy and use of high‐intensity screening programs. The aim of this study was to develop an evidence‐based quantitative model that integrates independent in silico data (Align‐GVGD and BayesDel) and somatic to germline ratio (SGR), to assign pathogenicity to every possible missense variant in the TP53 gene. To do this, a likelihood ratio for pathogenicity (LR) was derived from each component calibrated using reference sets of assumed pathogenic and benign missense variants. A posterior probability of pathogenicity was generated by combining LRs, and algorithm outputs were validated using different approaches. A total of 730 TP53 missense variants could be assigned to a clinically interpretable class. The outputs of the model correlated well with existing clinical information, functional data, and ClinVar classifications. In conclusion, these quantitative outputs provide the basis for individualized assessment of cancer risk useful for clinical interpretation. In addition, we propose the value of the novel SGR approach for use within the ACMG/AMP guidelines for variant classification.