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

  • Fortuno, Cristina1
  • James, Paul A2, 3, 4
  • Young, Erin L5
  • Feng, Bing6
  • Olivier, Magali7
  • Pesaran, Tina8
  • Tavtigian, Sean V5
  • Spurdle, Amanda B1
  • 1 QIMR Berghofer Medical Research Institute, Genetics and Computational Division, Herston, Queensland, Australia. , (Australia)
  • 2 Parkville Familial Cancer Centre, Parkville, Melbourne, Victoria, Australia. , (Australia)
  • 3 Peter MacCallum Cancer Centre, Parkville, Melbourne, Victoria, Australia. , (Australia)
  • 4 Royal Melbourne Hospital, Parkville, Melbourne, Victoria, Australia. , (Australia)
  • 5 Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah.
  • 6 Department of Dermatology and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah.
  • 7 Molecular Mechanisms and Biomarkers Group, International Agency for Research on Cancer, Lyon, France. , (France)
  • 8 Ambry Genetics, Aliso Viejo, California.
Published Article
Human Mutation
Wiley (John Wiley & Sons)
Publication Date
May 18, 2018
DOI: 10.1002/humu.23553
PMID: 29775997


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 bioinformatic tool selection for TP53 variant classification, and have also computed relevant bioinformatic predictions for every possible p53 missense variant to facilitate their use by the scientific and medical community.

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