TP53 mutations, expression and interaction networks in human cancers

• Published in: Oncotarget 2017-01, Vol.8 (1), p.624-643
• Main Authors: Wang, Xiaosheng, Sun, Qingrong
• Format: Article
• Language: English
• Subjects: Cell Line, Tumor; Computational Biology - methods; Databases, Nucleic Acid; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genetic Association Studies; Genotype; Humans; Kaplan-Meier Estimate; Mutation; Neoplasm Grading; Neoplasm Metastasis; Neoplasm Staging; Neoplasms - genetics; Neoplasms - metabolism; Neoplasms - mortality; Neoplasms - pathology; Phenotype; Prognosis; Research Paper; Synthetic Lethal Mutations; Tumor Suppressor Protein p53 - genetics; Tumor Suppressor Protein p53 - metabolism
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.13483

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.