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JOURNAL

Cancer Informatics

Significant Prognostic Features and Patterns of Somatic Mutations in Human Cancers

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Cancer Informatics 2017:16 1176935117691267

Original Research

Published on 20 Feb 2017

DOI: 10.1177/1176935117691267


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Abstract

TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 (p53) status has been a controversial issue. In this work, we aimed to characterize TP53 somatic mutations in tumor cells across multiple cancer types, primarily focusing on several less investigated features of the mutation spectra, and determine their prognostic implications. We performed an integrative study on the clinically annotated genomic data released by The Cancer Genome Atlas. Standard statistical methods, such as the Cox proportional hazards model and logistic regression, were used. This study resulted in several novel findings. They include the following: (1) similar to previously reported cases in breast cancer, the mutations in exons 1 to 4 of TP53 were more lethal than those in exons 5 to 9 for the patients with lung adenocarcinomas; (2) TP53 mutants tended to be negatively selected in mammalian evolution, but the evolutionary conservation had various clinical implications for different cancers; (3) conserved correlation patterns (ie, consistent co-occurrence or consistent mutual exclusivity) between TP53 mutations and the alterations in several other cancer genes (ie, PIK3CA, PTEN, KRAS, APC, CDKN2A, and ATM) were present in several cancers in which prognosis was associated with TP53 status and/or the mutational characteristics; (4) among TP53-mutated tumors, the total mutation burden in other driver genes was a predictive signature (P < .05, false discovery rate <0.11) for better patient survival outcome in several cancer types, including glioblastoma multiforme. Among these findings, the fourth is of special significance as it suggested the potential existence of epistatic interaction effects among the mutations in different cancer driver genes on clinical outcomes.



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