Varaico: Literature-Extracted Genetic Variants

Gene:TP53
Variant:p.F113Y
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TP53

NM_000546.6:p.F113Y

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

Functional profiling of serine, threonine and tyrosine sites.

Li Y et al.

Nature Chemical Biology, 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 correlation between clinical outcomes and genomic analysis with high risk factors for the progression of osteosarcoma.

Liu W et al.

Molecular Oncology, 2024

37727135 (PubMed )
Supplement: MOL2-18-939-s001.pdf

Clinical, Pathological and Molecular Insights on KRAS, NRAS, BRAF, PIK3CA and TP53 Mutations in Metastatic Colorectal Cancer Patients from Northeastern Romania.

Afrăsânie VA et al.

International Journal of Molecular Sciences, 2023

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

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

Comprehensive Molecular Characterization of Young Chinese Patients with Lung Adenocarcinoma Identified a Distinctive Genetic Profile.

Hou H et al.

The Oncologist, 2018

Isolation of temperature-sensitive p53 mutations from a comprehensive missense mutation library.

Shiraishi K et al.

The Journal of Biological Chemistry, 2004

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Regarding the variants extracted from papers and shown text snippets from research papers, the following holds: "Some rights reserved. This work permits non-commercial use, distribution and reproduction in any medium, provided the original author and source are credited."