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
Mutations in the TP53, VEGFA, and CTH Genes as Key Molecular Markers for the Diagnosis of Glioblastoma.
Khalil SS et al.
Cureus, 2024
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
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
35043155 (PubMed )
Supplement: supplementary_table_s1_bbab524.xlsx › S1B._Negative_Set
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_S2c
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
Pan-urologic cancer genomic subtypes that transcend tissue of origin.
Chen F et al.
Nature Communications, 2017
28775315 (PubMed )
Supplement: 41467_2017_289_MOESM7_ESM.xlsx › mutation_data
Role of charged and hydrophobic residues in the oligomerization of the PYRIN domain of ASC.
Moriya M et al.
Biochemistry, 2005
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