Associated Papers
Neoantigenic properties of TP53 variants influence cancer risk in individuals with Li-Fraumeni syndrome.
Montellier E et al.
Ebiomedicine, 2026
41370968 (PubMed )
Supplement: mmc2.xlsx › Table_S1
A quantitative, Bayesian-informed approach to gene-specific variant classification: Updated Expert Panel recommendations improve classification of TP53 germline variants for Li-Fraumeni syndrome.
Fortuno C et al.
Genome Medicine, 2025
41126324 (PubMed )
Supplement: 13073_2025_1536_MOESM2_ESM.xlsx › Table_S2
TP53 variant clusters stratify phenotypic diversity in germline carriers and reveal an osteosarcoma-prone subgroup.
Fischer NW et al.
Nature Communications, 2025
41022739 (PubMed )
Supplement: 41467_2025_63528_MOESM7_ESM.xlsx › Sheet1
Clustering of TP53 variants into functional classes correlates with cancer risk and identifies different phenotypes of Li-Fraumeni syndrome.
Montellier E et al.
Iscience, 2024
39634561 (PubMed )
Supplement: mmc2.xlsx › TableS1
Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.
Padigepati SR et al.
Human Genetics, 2024
39085601 (PubMed )
Supplement: 439_2024_2691_MOESM2_ESM.xlsx › TP53
TP53 germline pathogenic variants in modern humans were likely originated during recent human history.
Kou SH et al.
Nar Cancer, 2023
37304756 (PubMed )
Supplement: zcad025_supplemental_files.zip › Supplementary_Table_2.xlsx › BVs
Different impacts of TP53 mutations on cell cycle-related gene expression among cancer types.
Sasaki K et al.
Scientific Reports, 2023
36964217 (PubMed )
Supplement: 41598_2023_32092_MOESM1_ESM.xlsx › Suppl._table_S1
vaRHC: an R package for semi-automation of variant classification in hereditary cancer genes according to ACMG/AMP and gene-specific ClinGen guidelines.
Munté E et al.
Bioinformatics (oxford, England), 2023
36916756 (PubMed )
Supplement: btad128_supplementary_data.zip › btad128_Supplementary_Data › Supplementary_tables10022023.xlsx › ST12-TP53
Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.
Keskin Karakoyun H et al.
Frontiers in Genetics, 2023
36896237 (PubMed )
Supplement: Table1.xlsx › clinvar_breastcancer_allvariant
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53.
Ben-Cohen G et al.
Briefings in Bioinformatics, 2022
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
36561320 (PubMed )
Supplement: Table2.xlsx › TP53
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Reza MN et al.
Scientific Reports, 2021
34584144 (PubMed )
Supplement: 41598_2021_98547_MOESM1_ESM.zip › Supplementary_files-5-8-21 › Supplementary_file_3.xlsx › Sheet1
Identification and functional characterization of new missense SNPs in the coding region of the TP53 gene.
Doffe F et al.
Cell Death and Differentiation, 2021
33257846 (PubMed )
Supplement: 41418_2020_672_MOESM3_ESM.xlsx › Table_S2a
Clinicopathological and molecular characteristics of patients with hypermutant lung cancer: A retrospective cohort study.
Zhang H et al.
Oncology Letters, 2021
33692861 (PubMed )
Supplement: Supplementary_Data.pdf
Mutational processes shape the landscape of TP53 mutations in human cancer.
Giacomelli AO et al.
Nature Genetics, 2018
30224644 (PubMed )
Supplement: NIHMS1501645-supplement-4.xlsx › Giacomelli_et_al_New_Supplement
Human papillomavirus type 16 sequence variation in cervical cancers: a worldwide perspective.
Yamada T et al.
Journal of Virology, 1997
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