Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants

Human Mutation
Cristina FortunoAmanda B Spurdle

Abstract

Clinical interpretation of germline missense variants represents a major challenge, including those in the TP53 Li-Fraumeni syndrome gene. Bioinformatic prediction is a key part of variant classification strategies. We aimed to optimize the performance of the Align-GVGD tool used for p53 missense variant prediction, and compare its performance to other bioinformatic tools (SIFT, PolyPhen-2) and ensemble methods (REVEL, BayesDel). Reference sets of assumed pathogenic and assumed benign variants were defined using functional and/or clinical data. Area under the curve and Matthews correlation coefficient (MCC) values were used as objective functions to select an optimized protein multisequence alignment with best performance for Align-GVGD. MCC comparison of tools using binary categories showed optimized Align-GVGD (C15 cut-off) combined with BayesDel (0.16 cut-off), or with REVEL (0.5 cut-off), to have the best overall performance. Further, a semi-quantitative approach using multiple tiers of bioinformatic prediction, validated using an independent set of nonfunctional and functional variants, supported use of Align-GVGD and BayesDel prediction for different strength of evidence levels in ACMG/AMP rules. We provide rationale for ...Continue Reading

References

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Citations

Mar 7, 2019·Human Mutation·Cristina FortunoPaul A James
May 21, 2019·Genetics in Medicine : Official Journal of the American College of Medical Genetics·Huma Q RanaJudy E Garber
Apr 10, 2019·Journal of Medical Genetics·Amanda B SpurdleDiana M Eccles
Jul 20, 2019·Familial Cancer·Camila Matzenbacher BittarPatricia Ashton-Prolla
Dec 11, 2020·Human Mutation·Cristina FortunoUNKNOWN ClinGen TP53 Variant Curation Expert Panel
Oct 24, 2020·Clinical and Translational Gastroenterology·Federica IsidoriSandro Mattioli
Feb 27, 2021·Journal of Pediatric Hematology/oncology·David P DouglassJason E Farrar

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