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
Deep CRISPR mutagenesis characterizes the functional diversity of TP53 mutations.
Funk JS et al.
Nature Genetics, 2025
39774325 (PubMed )
Supplement: 41588_2024_2039_MOESM12_ESM.xlsx › Fig7b_d
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
Comprehensive classification of TP53 somatic missense variants based on their impact on p53 structural stability.
Tam B et al.
Briefings in Bioinformatics, 2024
39140857 (PubMed )
Supplement: table_s1_bbae400.xlsx › Sheet1
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
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
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
Predictive Biomarkers for Immune Checkpoint Inhibitors in Metastatic Breast Cancer.
Sivapiragasam A et al.
Cancer Medicine, 2021
33314633 (PubMed )
Supplement: CAM4-10-53-s003.xlsx › Sheet1
Finding driver mutations in cancer: Elucidating the role of background mutational processes.
Brown AL et al.
Plos Computational Biology, 2019
31034466 (PubMed )
Supplement: pcbi.1006981.s010.xlsx › final_benchmark
Assessment of computational methods for predicting the effects of missense mutations in human cancers.
Gnad F et al.
Bmc Genomics, 2013
23819521 (PubMed )
Supplement: 1471-2164-14-S3-S7-S1.xlsx › COSMIC
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