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
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
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
Investigating the genomic alteration improved the clinical outcome of aged patients with lung carcinoma.
Chen S et al.
Bmc Genomics, 2022
35031014 (PubMed )
Supplement: 12864_2021_8289_MOESM1_ESM.xlsx › Table_S1
Somatic gene mutation signatures predict cancer type and prognosis in multiple cancers with pan-cancer 1000 gene panel.
Wang HL et al.
Cancer Letters, 2020
Identification of predictors of drug sensitivity using patient-derived models of esophageal squamous cell carcinoma.
Su D et al.
Nature Communications, 2019
31700061 (PubMed )
Supplement: 41467_2019_12846_MOESM21_ESM.xlsx › PDX_Mutations
Laying the foundation for genomically-based risk assessment in chronic myeloid leukemia.
Branford S et al.
Leukemia, 2019
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