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
Multi-gene panel sequencing reveals the relationship between driver gene mutation and clinical characteristics in lung adenocarcinoma.
Li J et al.
Discover Oncology, 2025
40053265 (PubMed )
Supplement: 12672_2025_2008_MOESM1_ESM.xlsx › Table_S2
Exploring the role of splicing in TP53 variant pathogenicity through predictions and minigene assays.
Fortuno C et al.
Human Genomics, 2025
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 TP53-activated E3 ligase RNF144B is a tumour suppressor that prevents genomic instability.
Abad E et al.
Journal of Experimental & Clinical Cancer Research : Cr, 2024
38685100 (PubMed )
Supplement: 13046_2024_3045_MOESM2_ESM.xlsx › Patient_p53_mutated_samples
Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity.
Shah SM et al.
Cancer Research Communications, 2024
38282550 (PubMed )
Supplement: crc-23-0312-s02.xlsx › Supplemental_Figure_3_OC_data
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
Somatic mutation distribution across tumour cohorts provides a signal for positive selection in cancer.
Boström M et al.
Nature Communications, 2022
36396655 (PubMed )
Supplement: 41467_2022_34746_MOESM10_ESM.xlsx › Supplementary_Data_7
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
Identification of relevant genetic alterations in cancer using topological data analysis.
Rabadán R et al.
Nature Communications, 2020
32732999 (PubMed )
Supplement: 41467_2020_17659_MOESM3_ESM.xls › TableS1
Immunogenicity and Immune Silence in Human Cancer.
Yarmarkovich M et al.
Frontiers in Immunology, 2020
32256484 (PubMed )
Supplement: Table_4.XLSX › TCGA_HLAs_and_mutations
Identification of p53-46F as a super p53 with an enhanced ability to induce p53-dependent apoptosis.
Nakamura Y et al.
Cancer Science, 2006
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