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
Genomic landscape of hormone therapy-resistant HR-positive, HER2-negative breast cancer.
Chaubal R et al.
Breast Cancer Research and Treatment, 2025
40652157 (PubMed )
Supplement: 10549_2025_7759_MOESM5_ESM.xlsx › stable9
Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations.
Funk JS et al.
Nature Genetics, 2025
39774325 (PubMed )
Supplement: 41588_2024_2039_MOESM5_ESM.xlsx › SuppFig6
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
The theory of massively repeated evolution and full identifications of cancer-driving nucleotides (CDNs).
Zhang L et al.
Elife, 2024
39688960 (PubMed )
Supplement: elife-99340-supp1.xlsx › CDN.Missense.gnomad
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
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
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
Comprehensive Identification of Deleterious TP53 Missense VUS Variants Based on Their Impact on TP53 Structural Stability.
Tam B et al.
International Journal of Molecular Sciences, 2021
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_15.xlsx › Sheet1
From uncertainty to pathogenicity: clinical and functional interrogation of a rare TP53 in-frame deletion.
Quinn EA et al.
Cold Spring Harbor Molecular Case Studies, 2019
30886117 (PubMed )
Supplement: supp_mcs.a003921_Supplemental_Table_1a.xlsx › S_Table1a._TP53_in_ClinVar
The genomic landscape of the Ewing Sarcoma family of tumors reveals recurrent STAG2 mutation.
Brohl AS et al.
Plos Genetics, 2014
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