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Genomic and human papillomavirus profiling of an oral cancer cohort identifies TP53 as a predictor of overall survival

BACKGROUND: The genomic landscape of head and neck cancer has been reported through The Cancer Genome Atlas project. We attempt to determine if high-risk human papillomavirus (HPV) or frequently mutated genes are correlated with survival in an oral cancer cohort. METHODS: Patient demographic data al...

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Detalles Bibliográficos
Autores principales: Mundi, Neil, Prokopec, Stephenie D., Ghasemi, Farhad, Warner, Andrew, Patel, Krupal, MacNeil, Danielle, Howlett, Christopher, Stecho, William, Plantinga, Paul, Pinto, Nicole, Ruicci, Kara M., Khan, Mohammed Imran, Han, Myung Woul, Yoo, John, Fung, Kevin, Sahovaler, Axel, Palma, David A., Winquist, Eric, Mymryk, Joe S., Barrett, John W., Boutros, Paul C., Nichols, Anthony C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6894507/
https://www.ncbi.nlm.nih.gov/pubmed/31844556
http://dx.doi.org/10.1186/s41199-019-0045-0
Descripción
Sumario:BACKGROUND: The genomic landscape of head and neck cancer has been reported through The Cancer Genome Atlas project. We attempt to determine if high-risk human papillomavirus (HPV) or frequently mutated genes are correlated with survival in an oral cancer cohort. METHODS: Patient demographic data along with data from final pathology was collected. Tumor DNA was analyzed using a custom Illumina targeted sequencing panel. Five high-risk HPV types were tested by qPCR. Statistical analyses were used to identify associations between patient outcome and mutational status. RESULTS: High-risk HPV types were identified in 7% of cases; HPV status was not associated with survival. Mutations were identified in TP53, TERT promoter, & PIK3CA. Mutations in TP53 were significantly associated with poorer overall survival on multi-variate analysis (p = 0.03). CONCLUSIONS: Mutations in TP53 were associated with poor patient survival. Expanding our sample size may identify further predictors of outcome to direct customized cancer care.