Associated Papers
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
Uterine lavage identifies cancer mutations and increased TP53 somatic mutation burden in individuals with ovarian cancer.
Ghezelayagh TS et al.
Cancer Research Communications, 2022
36311816 (PubMed )
Supplement: crc-22-0314-s02.xlsx › Table_S6_List_of_mutations
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
Canine osteosarcoma genome sequencing identifies recurrent mutations in DMD and the histone methyltransferase gene SETD2.
Gardner HL et al.
Communications Biology, 2019
31341965 (PubMed )
Supplement: 42003_2019_487_MOESM10_ESM.xlsx › Primary_Tumor
The relationship among p53 oligomer formation, structure and transcriptional activity using a comprehensive missense mutation library.
Kawaguchi T et al.
Oncogene, 2005
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