2015 Oncotarget

Somatic copy number alterations detected by ultra-deep targeted sequencing predict prognosis in oral cavity squamous cell carcinoma.

, , , , , , , , ,

Oncotarget Vol. 6 (23) : 19891-906 • Aug 2015

BACKGROUND: Ultra-deep targeted sequencing (UDT-Seq) has advanced our knowledge on the incidence and functional significance of somatic mutations. However, the utility of UDT-Seq in detecting copy number alterations (CNAs) remains unclear. With the goal of improving molecular prognostication and identifying new therapeutic targets, we designed this study to assess whether UDT-Seq may be useful for detecting CNA in oral cavity squamous cell carcinoma (OSCC). METHODS: We sequenced a panel of clinically actionable cancer mutations in 310 formalin-fixed paraffin-embedded OSCC specimens. A linear model was developed to overcome uneven coverage across target regions and multiple samples. The 5-year rates of secondary primary tumors, local recurrence, neck recurrence, distant metastases, and survival served as the outcome measures. We confirmed the prognostic significance of the CNA signatures in an independent sample of 105 primary OSCC specimens. RESULTS: The CNA burden across 10 targeted genes was found to predict prognosis in two independent cohorts. FGFR1 and PIK3CAamplifications were associated with prognosis independent of clinical risk factors. Genes exhibiting CNA were clustered in the proteoglycan metabolism, the FOXO signaling, and the PI3K-AKT signaling pathways, for which targeted drugs are already available or currently under development. CONCLUSIONS: UDT-Seq is clinically useful to identify CNA, which significantly improve the prognostic information provided by traditional clinicopathological risk factors in OSCC patients.

No clinical trial protocols linked to this paper

Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.
PICO Elements

No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.

Paper Details
MeSH Terms
+10 more
Associated Data

No associated datasets or code repositories found for this paper.

Related Papers

Related paper suggestions will be available in future updates.