| Literature DB >> 32484602 |
Gal Benor1, Garold Fuks1, Suet-Feung Chin2, Oscar M Rueda2, Saptaparna Mukherjee3, Sharathchandra Arandkar3,4, Yael Aylon3, Carlos Caldas2, Eytan Domany1, Moshe Oren3.
Abstract
TP53 gene mutations are very common in human cancer. While such mutations abrogate the tumor suppressive activities of the wild-type (wt) p53 protein, some of them also endow the mutant (mut) protein with oncogenic gain of function (GOF), facilitating cancer progression. Yet, p53 may acquire altered functionality even without being mutated; in particular, experiments with cultured cells revealed that wtp53 can be rewired to adopt mut-like features in response to growth factors or cancer-mimicking genetic manipulations. To assess whether such rewiring also occurs in human tumors, we interrogated gene expression profiles and pathway deregulation patterns in the METABRIC breast cancer (BC) dataset as a function of TP53 gene mutation status. Harnessing the power of machine learning, we optimized a gene expression classifier for ER+Her2- patients that distinguishes tumors carrying TP53 mutations from those retaining wt TP53. Interestingly, a small subset of wt TP53 tumors displayed gene expression and pathway deregulation patterns markedly similar to those of TP53-mutated tumors. Moreover, similar to TP53-mutated tumors, these 'pseudomutant' cases displayed a signature for enhanced proliferation and had worse prognosis than typical wtp53 tumors. Notably, these tumors revealed upregulation of genes which, in BC cell lines, were reported to be positively regulated by p53 GOF mutants. Thus, such tumors may benefit from mut p53-associated activities without having to accrue TP53 mutations.Entities:
Keywords: METABRIC; breast cancer; machine learning; p53 gain of function; pseudomutant p53
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Year: 2020 PMID: 32484602 PMCID: PMC7400784 DOI: 10.1002/1878-0261.12736
Source DB: PubMed Journal: Mol Oncol ISSN: 1574-7891 Impact factor: 6.603