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
Quantitative functional profiling of ERCC2 mutations deciphers cisplatin sensitivity in bladder cancer.
Börcsök J et al.
The Journal of Clinical Investigation, 2025
40829180 (PubMed )
Supplement: jci-135-186688-s236.xlsx › SFigure5E-G
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
Using multiplexed functional data to reduce variant classification inequities in underrepresented populations.
Dawood M et al.
Genome Medicine, 2024
39627863 (PubMed )
Supplement: 13073_2024_1392_MOESM1_ESM.xlsx › Table_S19
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
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
Whole exome sequencing identified a novel POT1 variant as a candidate pathogenic allele underlying a Li-Fraumeni-like family.
Li Y et al.
Frontiers in Oncology, 2022
36387164 (PubMed )
Supplement: Table_1.xlsx › sheet1
Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.
G V et al.
Frontiers in Genetics, 2022
36561320 (PubMed )
Supplement: Table2.xlsx › TP53
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
TP53 germline mutations in the context of families with hereditary breast and ovarian cancer: a clinical challenge.
Grill S et al.
Archives of Gynecology and Obstetrics, 2021
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
Variable population prevalence estimates of germline TP53 variants: A gnomAD-based analysis.
de Andrade KC et al.
Human Mutation, 2019
30352134 (PubMed )
Supplement: supplement_1.xlsx › Supplementary_Table_1
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
Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma.
Schwartzentruber J et al.
Nature, 2012
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