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

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 u...

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Vydáno v:	Human mutation Ročník 40; číslo 6; s. 788 - 800
Hlavní autoři:	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.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States John Wiley & Sons, Inc 01.06.2019
Témata:	Algorithms Computational Biology - methods Computer Simulation Genetic Predisposition to Disease Humans Li-Fraumeni Syndrome - genetics Li‐Fraumeni syndrome Models, Genetic Mutation, Missense p53 Protein Pathogenicity quantitative Radiation therapy TP53 Tumor Suppressor Protein p53 - genetics variant classification variant classification TP53 quantitative Li-Fraumeni syndrome
ISSN:	1059-7794, 1098-1004, 1098-1004
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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. Germline pathogenic missense variants in the TP53 gene predispose individuals to a wide range of cancer types but are often difficult to interpret. The output from in silico tools and an analysis of the relationship between reported somatic and germline variants in the IARC TP53 database were used to construct a quantitative model of variant pathogenicity. The model was validated against a range of existing data and used to generate clinically interpretable classifications for 730 unique TP53 missense variants.
AbstractList	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.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. 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. Germline pathogenic missense variants in the TP53 gene predispose individuals to a wide range of cancer types but are often difficult to interpret. The output from in silico tools and an analysis of the relationship between reported somatic and germline variants in the IARC TP53 database were used to construct a quantitative model of variant pathogenicity. The model was validated against a range of existing data and used to generate clinically interpretable classifications for 730 unique TP53 missense variants. 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.
Author	Fortuno, Cristina Olivier, Magali Spurdle, Amanda B. Haupt, Ygal Haupt, Sue James, Paul A. Tavtigian, Sean V. Thomas, David M. Ruparel, Vatsal Tucker, Kathy Cipponi, Arcadi Ballinger, Mandy L. Study, International Sarcoma Kindred
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Snippet	Germline pathogenic variants in the TP53 gene cause Li‐Fraumeni syndrome, a condition that predisposes individuals to a wide range of cancer types.... Germline pathogenic variants in the TP53 gene cause Li-Fraumeni syndrome, a condition that predisposes individuals to a wide range of cancer types....
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SubjectTerms	Algorithms Computational Biology - methods Computer Simulation Genetic Predisposition to Disease Humans Li-Fraumeni Syndrome - genetics Li‐Fraumeni syndrome Models, Genetic Mutation, Missense p53 Protein Pathogenicity quantitative Radiation therapy TP53 Tumor Suppressor Protein p53 - genetics variant classification
Title	A quantitative model to predict pathogenicity of missense variants in the TP53 gene
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