期刊
CANCER RESEARCH
卷 75, 期 7, 页码 1527-1536出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/0008-5472.CAN-14-2735
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资金
- UTMDACC PANTHEON program
- UTMDACC Sisters Institution Network Fund
- National Institute of Health Specialized Program of Research Excellence Grant [P50CA097007]
- NIH [R01 DE14613, R01 GM079656, R01 GM066099]
- Cancer Prevention and Research Institute of Texas [RP120258]
- National Research Science Award Institutional Research Training Grant [T32CA60374]
- National Institute of Health Program Project Grant [CA06294]
- Cancer Center Support Grant [CA016672]
- NSF [DBI 1356569, DBI 0851393]
- Pharmacoinformatics Training Program of the Neck Center of the Gulf Coast Consortia (NIH) [5 R90 DKO71505]
- Grants-in-Aid for Scientific Research [26861406] Funding Source: KAKEN
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1062455] Funding Source: National Science Foundation
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1356569] Funding Source: National Science Foundation
TP53 is the most frequently altered gene in head and neck squamous cell carcinoma, with mutations occurring in over two-thirds of cases, but the prognostic significance of these mutations remains elusive. In the current study, we evaluated a novel computational approach termed evolutionary action (EAp53) to stratify patients with tumors harboring TP53 mutations as high or low risk, and validated this system in both in vivo and in vitro models. Patients with high-risk TP53 mutations had the poorest survival outcomes and the shortest time to the development of distant metastases. Tumor cells expressing high-risk TP53 mutations were more invasive and tumorigenic and they exhibited a higher incidence of lung metastases. We also documented an association between the presence of high-risk mutations and decreased expression of TP53 target genes, highlighting key cellular pathways that are likely to be dysregulated by this subset of p53 mutations that confer particularly aggressive tumor behavior. Overall, our work validated EAp53 as a novel computational tool that may be useful in clinical prognosis of tumors harboring p53 mutations. (C)2015 AACR.
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