Review

. 2025 Oct 22;17(1):128.

doi: 10.1186/s13073-025-01536-3.

A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome

Cristina Fortuno^#¹, Megan N Frone^#², Jessica Mester³, Miguel de la Hoya⁴, Phuong L Mai⁵, Tina Pesaran⁶, Maria Isabel Achatz⁷, Rebecca Bassett⁸, Carolina Bustamante⁹, Stephanie Crowley¹⁰, Kelvin Cesar de Andrade¹¹, D Gareth Evans¹², Bingjian Feng¹³, Laura Fuqua¹⁴, Maria Isabel Harrell¹⁵, Jessica N Hatton¹¹, Robert Huether¹⁶, Chimene Kesserwan¹⁷, Kristy Lee¹⁸, Suzanne P MacFarland¹⁹, Jamie L Maciaszek²⁰, Kara Maxwell¹⁹, Kelly McGoldrick⁶, Maureen Murphy²¹, Bita Nehoray²², Judith Penkert^{23

24}, Emilia Modolo Pinto²⁰, Sharon E Plon²⁵, Alison Schwartz-Levine²⁶, Ashley S Thompson²⁷, Wenyi Wang²⁸, Gerard P Zambetti²⁰, Kristin Zelley¹⁹, Paul A James²⁹, Sharon A Savage¹¹, Christian P Kratz²⁴, Amanda B Spurdle³⁰

Affiliations

¹ Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. cristina.fortuno@qimrberghofer.edu.au.
² Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. megan.frone@nih.gov.
³ GeneDx, Gaithersburg, MD, USA.
⁴ Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
⁵ Center for Clinical Genetics and Genomics, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
⁶ Ambry Genetics, Aliso Viejo, CA, USA.
⁷ Centro de Oncologia, Hospital Sírio-Libanês, São Paulo, Brazil.
⁸ Moanalua Medical Center, Hawaii Permanente Medical Group, Kaiser Permanente, Honolulu, HI, USA.
⁹ OC Precision Medicine, São Paulo, Brazil.
¹⁰ Invitae Corporation, San Francisco, CA, USA.
¹¹ Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
¹² Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
¹³ University of Utah, Salt Lake City, UT, USA.
¹⁴ Baylor Genetics, Houston, TX, USA.
¹⁵ Labcorp Genetics (Formerly Invitae Corp), Burlington, NC, USA.
¹⁶ Tempus AI, Chicago, IL, USA.
¹⁷ Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁹ Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
²⁰ Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA.
²¹ Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, USA.
²² Division of Clinical Cancer Genomics, Departments of Medical Oncology and Therapeutics Research and Population Sciences, City of Hope, Duarte, CA, USA.
²³ Department of Human Genetics, Hannover Medical School, Hannover, Germany.
²⁴ Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
²⁵ Dan L. Duncan Cancer Center and the Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
²⁶ Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA, USA.
²⁷ Department of Pathology, Microbiology, and Immunology, Molecular Diagnostics Section, Vanderbilt Medical Laboratories, Nashville, TN, USA.
²⁸ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
²⁹ Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
³⁰ Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

^# Contributed equally.

PMID: 41126324
PMCID: PMC12548217
DOI: 10.1186/s13073-025-01536-3

Review

A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome

Cristina Fortuno et al. Genome Med. 2025.

. 2025 Oct 22;17(1):128.

doi: 10.1186/s13073-025-01536-3.

Authors

Affiliations

¹ Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia. cristina.fortuno@qimrberghofer.edu.au.
² Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA. megan.frone@nih.gov.
³ GeneDx, Gaithersburg, MD, USA.
⁴ Molecular Oncology Laboratory, Hospital Clinico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
⁵ Center for Clinical Genetics and Genomics, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
⁶ Ambry Genetics, Aliso Viejo, CA, USA.
⁷ Centro de Oncologia, Hospital Sírio-Libanês, São Paulo, Brazil.
⁸ Moanalua Medical Center, Hawaii Permanente Medical Group, Kaiser Permanente, Honolulu, HI, USA.
⁹ OC Precision Medicine, São Paulo, Brazil.
¹⁰ Invitae Corporation, San Francisco, CA, USA.
¹¹ Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA.
¹² Division of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
¹³ University of Utah, Salt Lake City, UT, USA.
¹⁴ Baylor Genetics, Houston, TX, USA.
¹⁵ Labcorp Genetics (Formerly Invitae Corp), Burlington, NC, USA.
¹⁶ Tempus AI, Chicago, IL, USA.
¹⁷ Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁹ Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
²⁰ Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA.
²¹ Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, USA.
²² Division of Clinical Cancer Genomics, Departments of Medical Oncology and Therapeutics Research and Population Sciences, City of Hope, Duarte, CA, USA.
²³ Department of Human Genetics, Hannover Medical School, Hannover, Germany.
²⁴ Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany.
²⁵ Dan L. Duncan Cancer Center and the Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
²⁶ Division of Cancer Genetics and Prevention, Dana-Farber Cancer Institute, Boston, MA, USA.
²⁷ Department of Pathology, Microbiology, and Immunology, Molecular Diagnostics Section, Vanderbilt Medical Laboratories, Nashville, TN, USA.
²⁸ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
²⁹ Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
³⁰ Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

^# Contributed equally.

PMID: 41126324
PMCID: PMC12548217
DOI: 10.1186/s13073-025-01536-3

Abstract

Background: Germline pathogenic variants in TP53 cause Li-Fraumeni syndrome, with significantly elevated cancer risk from infancy. Accurate classification of TP53 variants is essential to guide clinical management and surveillance, yet many variants remain classified as variants of uncertain significance (VUS). To improve classification accuracy and reduce the proportion of VUS, the ClinGen TP53 Variant Curation Expert Panel (VCEP) has updated its specifications.

Methods: The updated specifications incorporate the latest ClinGen recommendations and methodological advances, providing greater granularity for multiple evidence types, and also introduce the novel use of variant allele fraction as evidence of pathogenicity, particularly in the context of clonal hematopoiesis. Whenever feasible, the VCEP followed a data-driven approach using likelihood ratio-based quantitative analyses to guide code application and determine strength modifications, while also factoring in expert judgment. Proposed modifications were first discussed in working group meetings and then subjected to comprehensive review during monthly general VCEP meetings to reach consensus.

Results: The performance of new specifications was compared to that of the old specifications for 43 pilot variants, and led to both decreased VUS and increased certainty, with clinically meaningful classifications for 93% of variants.

Conclusions: The updated TP53 specfications are expected to reduce VUS rates, increase inter-laboratory concordance, and improve medical management for individuals with germline TP53 variants. The most current version is available at the ClinGen Criteria Specifications Registry (CSpec): https://cspec.genome.network/cspec/ui/svi/svi/GN009 .

Keywords: TP53; ACMG guidelines; ClinGen Variant Curation Expert Panel (VCEP); Li-Fraumeni syndrome.

PubMed Disclaimer

Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The following authors work for laboratories that offer fee-for-service testing: RH, CB, SC, LF, KM, MIH, JM, TP. The following authors have made substantial contributions to the TP53 gene-disease literature: CF, PAJ, ABS, MNF, SAS, EMP, GPZ, DGE. MNF receives royalties for product development of CancerGene Connect from Ommdom, Inc. DGE has received consultancy fees from Everything genetic Ltd. All other authors declare that they have no competing interests.

Figures

**Fig. 1**
Flowchart for the application of PVS1 to *TP53* null variants in relation to the transcript NM_000546.6. Splicing predictions for GT-AG sites are based on SpliceAI and available experimental data. PVS1_Variable Weight (RNA) may be applicable for variants with RNA-based assay data demonstrating aberration (see Additional file 1: Table S1). Δ = exon skipping, ▼ = intron retention

**Fig. 2**
Flowchart for application of in silico codes PP3, BP4, and BP7 ^*AND no predicted differences in splicing (SpliceAI < 0.2) ^☨Including silent, and apparent “missense” or “single amino acid in-frame deletions” for which there is a predicted splice effect ^☨☨Excluding ± 1,2 positions **Core splice motif includes last three nucleotides and first nucleotide of the exon

**Fig. 3**
Flowchart for application of functional codes PS3 and BS3 via protein function for missense variants (left) and small deletions (right) *Eligible functional studies include Kato [32], Funk [21], Giacomelli [33], Kotler [38], and Kawaguchi [42], as well as other assays considered to provide relevant functional information after expert interpretation, as previously reported [13]

**Fig. 4**
Summary of results for the 43 *TP53* pilot variants undergoing reclassification using the updated *TP53* VCEP’s specifications (v2) in comparison to the v1 VCEP specifications (v1). Abbreviations: B = Benign, LB = Likely Benign; LP = Likely Pathogenic P = Pathogenic; VUS = Variant

See this image and copyright information in PMC

References

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1. de Andrade KC, Khincha PP, Hatton JN, Frone MN, Wegman-Ostrosky T, Mai PL, et al. Cancer incidence, patterns, and genotype-phenotype associations in individuals with pathogenic or likely pathogenic germline TP53 variants: an observational cohort study. Lancet Oncol. 2021;22(12):1787–98. - PMC - PubMed
1. Kratz CP, Freycon C, Maxwell KN, Nichols KE, Schiffman JD, Evans DG, et al. Analysis of the Li-Fraumeni spectrum based on an international germline TP53 variant data set: an International Agency for Research on Cancer TP53 database analysis. JAMA Oncol. 2021;7(12):1800–5. - PMC - PubMed

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A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome

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A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome

Authors

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Conflict of interest statement

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