Varaico: Literature-Extracted Genetic Variants

Gene:TP53
Variant:p.P47R
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TP53

NM_000546.6:p.P47R

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

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

Comparison of TP53 mutations in myelodysplasia and acute leukemia suggests divergent roles in initiation and progression.

Jambhekar A et al.

Blood Neoplasia, 2024

40453522 (PubMed )
Supplement: BNEO_NEO-2023-000115-mmc1.xlsx › MDS

Impact of TP53 gene variants on prognosis and survival of childhood acute lymphoblastic leukemia.

Firtina S et al.

Scandinavian Journal of Clinical and Laboratory Investigation, 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

Ultrasensitive profiling of UV-induced mutations identifies thousands of subclinical facial tumors in tuberous sclerosis complex.

Klonowska K et al.

The Journal of Clinical Investigation, 2022

35358092 (PubMed )
Supplement: jci-132-155858-s188.xlsx › Supplemental_Table_5.2.

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

TP53 Combined Phenotype Score Is Associated with the Clinical Outcome of TP53-Mutated Myelodysplastic Syndromes.

Yabe M et al.

Cancers, 2021

34771665 (PubMed )
Supplement: cancers-13-05502-s001.zip › cancers-1422438-supplementary.pdf

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

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

Discrepancies in cancer genomic sequencing highlight opportunities for driver mutation discovery.

Hudson AM et al.

Cancer Research, 2014

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