Ultra-deep targeted sequencing of advanced oral squamous cell carcinoma identifies a mutation-based prognostic gene signature
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Shu-Jen Chen1,2,3,*, Hsuan Liu2,3,*, Chun-Ta Liao4,*, Po-Jung Huang5, Yi Huang2, An Hsu3, Petrus Tang5, Yu-Sun Chang2, Hua-Chien Chen1,2,3, Tzu-Chen Yen6
1Department of Biomedical Sciences, Chang Gung University, Taoyuan, 33302, Taiwan
2Genomic Core Laboratory, Chang Gung University, Taoyuan, 33302, Taiwan
3Graduate Institute of Biomedical Sciences, Chang Gung University, Taoyuan, 33302, Taiwan
4Department of Otorhinolaryngology, Chang Gung Memorial Hospital, Taoyuan, 33305, Taiwan
5Bioinformatics Core Laboratory, Chang Gung University, Taoyuan, 33305, Taiwan
6Department of Nuclear Medicine, Chang Gung Memorial Hospital, Taoyuan, 33305, Taiwan
*These authors have contributed equally to this work
Tzu-Chen Yen, e-mail: [email protected]
Hua-Chien Chen, e-mail: [email protected]
Keywords: next-generation sequencing, oral squamous cell carcinoma, mutation, prognosis
Received: February 08, 2015 Accepted: April 13, 2015 Published: April 25, 2015
Background: Patients with advanced oral squamous cell carcinoma (OSCC) have heterogeneous outcomes that limit the implementation of tailored treatment options. Genetic markers for improved prognostic stratification are eagerly awaited.
Methods: Herein, next-generation sequencing (NGS) was performed in 345 formalin-fixed paraffin-embedded (FFPE) samples obtained from advanced OSCC patients. Genetic mutations on the hotspot regions of 45 cancer-related genes were detected using an ultra-deep (>1000×) sequencing approach. Kaplan-Meier plots and Cox regression analyses were used to investigate the associations between the mutation status and disease-free survival (DFS).
Results: We identified 1269 non-synonymous mutations in 276 OSCC samples. TP53, PIK3CA, CDKN2A, HRAS and BRAF were the most frequently mutated genes. Mutations in 14 genes were found to predict DFS. A mutation-based signature affecting ten genes (HRAS, BRAF, FGFR3, SMAD4, KIT, PTEN, NOTCH1, AKT1, CTNNB1, and PTPN11) was devised to predict DFS. Two different resampling methods were used to validate the prognostic value of the identified gene signature. Multivariate analysis demonstrated that presence of a mutated gene signature was an independent predictor of poorer DFS (P = 0.005).
Conclusions: Genetic variants identified by NGS technology in FFPE samples are clinically useful to predict prognosis in advanced OSCC patients.
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