A quantitative model to predict pathogenicity of missense variants in the TP53 gene.

Hum Mutat 2019 06 18;40(6):788-800. Epub 2019 Mar 18.

Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.

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.

Download full-text PDF

Source
http://dx.doi.org/10.1002/humu.23739DOI Listing
June 2019
21 Reads

Publication Analysis

Top Keywords

missense variants
12
tp53 gene
12
pathogenicity missense
8
variants tp53
8
quantitative model
8
tp53
5
variants posterior
4
probability pathogenicity
4
posterior probability
4
benign missense
4
sets assumed
4
assumed pathogenic
4
pathogenic benign
4
pathogenicity generated
4
validated approaches
4
lrs algorithm
4
approaches total
4
algorithm outputs
4
total 730
4
reference sets
4

References

(Supplied by CrossRef)

Similar Publications