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

Abstract

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.

Suggested application of HER2+ breast tumor phenotype for germline TP53 variant classification within ACMG/AMP guidelines

“…The findings from this study, based on formal analysis of multiple datasets, provide a strategy for use of breast tumor HER2+ status for future TP53 variant interpretation within ACMG/AMP guidelines, for cases diagnosed <40 years that are not selected for testing on the basis of HER2+ tumor phenotype. Finally, another application of this study is to include the LRs toward pathogenicity calculated as an additional component in quantitative statistical modeling to predict the pathogenicity of p53 missense variants (Fortuno et al, 2019).…”

“…A recent study has reported that TP53 pathogenic variants in cancer cells induce HER2 overexpression through transcriptional activation of the HER2 protein (Roman-Rosales, Garcia-Villa, Herrera, Gariglio, & Diaz-Chavez, 2018), providing a biological explanation for previously mentioned associations. Of note, it has also been suggested that breast tumors from carriers of the NM_000546.5(TP53):c.1010G>A (p.R337H) Brazilian founder variant are less likely to be HER2+ compared to carriers of other TP53 pathogenic variants (Fitarelli-Kiehl et al, 2015). A number of other published studies have assessed the population-based proportion of HER2+ breast tumors, with estimates of 19.9% when diagnosed <40 years and <13% when diagnosed after age 40 (Lund et al, 2010), and 19% when diagnosed before 50 years and 15% after age 50 (Cronin, Harlan, Dodd, Abrams, & Ballard-Barbash, 2010), illustrating how the proportion of HER2+ breast tumors decreases with increasing age at diagnosis.…”

Classification of variants of uncertain significance in BRCA1 and BRCA2 using personal and family history of cancer from individuals in a large hereditary cancer multigene panel testing cohort

“…The evidence presented in this paper should be integrated into ongoing efforts to provide large-scale multifactorial classification 7 as well as translated directly into components of qualitative classifications such as the ACMG criteria used by many clinical testing laboratories, 26,28,29 and further should be integrated into public resources displaying variant data (BRCA Challenge). These efforts will reduce the prevalence of VUS classifications that are so problematic from both provider and patient perspectives.…”

Specifications of the ACMG/AMP variant interpretation guidelines for germline TP53 variants

“…More definitive studies are warranted to calibrate the strength of predictions of splicing algorithms, and so future iterations of these specifications may provide further details on the use of a specific splicing predictor. The specifications may also consider additional evidence types to improve TP53 variant classification, such as the relationship between somatic and germline counts reported to be positively correlated only for pathogenic variants (Fortuno et al, 2019) or HER2+ breast tumor pathology as a positive predictor of TP53 variant pathogenicity (Fortuno et al, 2020). The most up to date version of the ClinGen TP53 VCEP specifications is available at https://clinicalgenome.org/affiliation/50013/.…”