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Ultra-deep targeted sequencing of advanced oral squamous cell carcinoma identifies a mutation-based prognostic gene signature.

Chen SJ, Liu H, Liao CT, Huang PJ, Huang Y, Hsu A, Tang P, Chang YS, Chen HC, Yen TC - Oncotarget (2015)

Bottom Line: Mutations in 14 genes were found to predict DFS.Multivariate analysis demonstrated that presence of a mutated gene signature was an independent predictor of poorer DFS (P = 0.005).Genetic variants identified by NGS technology in FFPE samples are clinically useful to predict prognosis in advanced OSCC patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Sciences, Chang Gung University, Taoyuan, 33302, Taiwan.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

A. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on HRAS mutation statusB. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on BRAF mutation status. C. Univariate hazard ratios and 95% confidence intervals for disease relapse according to specific genetic alterations in individual genes. The strength of statistical significance identified in Cox proportional hazard models is reported in brackets (***P < 0.001, **P < 0.01, *P < 0.05). § denotes genes selected in the genetic signature. D. Kaplan-Meier analysis of disease-free survival of the entire cohort (n = 345) according to the genetic signature mutation status.
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Figure 4: A. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on HRAS mutation statusB. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on BRAF mutation status. C. Univariate hazard ratios and 95% confidence intervals for disease relapse according to specific genetic alterations in individual genes. The strength of statistical significance identified in Cox proportional hazard models is reported in brackets (***P < 0.001, **P < 0.01, *P < 0.05). § denotes genes selected in the genetic signature. D. Kaplan-Meier analysis of disease-free survival of the entire cohort (n = 345) according to the genetic signature mutation status.

Mentions: We used Cox proportional hazard regression to identify the genetic variants independently associated with survival outcomes. Genetic mutations located in 14 genes (ABL1, AKT1, BRAF, CTNNB1, FGFR3, HRAS, KIT, MPL, NOTCH1, PTEN, PTPN11, SMAD4, STK11, and VHL) were significantly associated with DFS in univariate Cox regression analysis (Table 2); among them, mutations in 11 genes (AKT1, BRAF, CTNNB1, FGFR3, HRAS, KIT, NOTCH1, PTEN, PTPN11, SMAD4 and STK11) were found to be high-risk factors for poorer survival (hazard ratio > 2 and P < 0.01; Table 2). Kaplan-Meier analyses confirmed the presence of statistically significant differences in terms of survival according to the mutational status of individual genes (Table 2), with two genes in the EGFR signaling pathway, HRAS and BRAF, showing the most significant difference. The median DFS for patients with and without HRAS mutation was 6.5 and 94 months (p < 0.0001) (Figure 4A). The median DFS rates for patients with and without BRAF mutations were 11 months and 94 months, respectively (P = 0.0008; Figure 4B). Other variables found to be significantly associated with DFS in the entire cohort were pT3-4 tumor status, pN2 nodal status, pathological stage IV, positive ECS status, close margins (≤ 4 mm), and treatment based solely on surgery (Table 2).


Ultra-deep targeted sequencing of advanced oral squamous cell carcinoma identifies a mutation-based prognostic gene signature.

Chen SJ, Liu H, Liao CT, Huang PJ, Huang Y, Hsu A, Tang P, Chang YS, Chen HC, Yen TC - Oncotarget (2015)

A. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on HRAS mutation statusB. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on BRAF mutation status. C. Univariate hazard ratios and 95% confidence intervals for disease relapse according to specific genetic alterations in individual genes. The strength of statistical significance identified in Cox proportional hazard models is reported in brackets (***P < 0.001, **P < 0.01, *P < 0.05). § denotes genes selected in the genetic signature. D. Kaplan-Meier analysis of disease-free survival of the entire cohort (n = 345) according to the genetic signature mutation status.
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Figure 4: A. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on HRAS mutation statusB. Kaplan-Meier analysis of disease-free survival in the entire cohort (n = 345) based on BRAF mutation status. C. Univariate hazard ratios and 95% confidence intervals for disease relapse according to specific genetic alterations in individual genes. The strength of statistical significance identified in Cox proportional hazard models is reported in brackets (***P < 0.001, **P < 0.01, *P < 0.05). § denotes genes selected in the genetic signature. D. Kaplan-Meier analysis of disease-free survival of the entire cohort (n = 345) according to the genetic signature mutation status.
Mentions: We used Cox proportional hazard regression to identify the genetic variants independently associated with survival outcomes. Genetic mutations located in 14 genes (ABL1, AKT1, BRAF, CTNNB1, FGFR3, HRAS, KIT, MPL, NOTCH1, PTEN, PTPN11, SMAD4, STK11, and VHL) were significantly associated with DFS in univariate Cox regression analysis (Table 2); among them, mutations in 11 genes (AKT1, BRAF, CTNNB1, FGFR3, HRAS, KIT, NOTCH1, PTEN, PTPN11, SMAD4 and STK11) were found to be high-risk factors for poorer survival (hazard ratio > 2 and P < 0.01; Table 2). Kaplan-Meier analyses confirmed the presence of statistically significant differences in terms of survival according to the mutational status of individual genes (Table 2), with two genes in the EGFR signaling pathway, HRAS and BRAF, showing the most significant difference. The median DFS for patients with and without HRAS mutation was 6.5 and 94 months (p < 0.0001) (Figure 4A). The median DFS rates for patients with and without BRAF mutations were 11 months and 94 months, respectively (P = 0.0008; Figure 4B). Other variables found to be significantly associated with DFS in the entire cohort were pT3-4 tumor status, pN2 nodal status, pathological stage IV, positive ECS status, close margins (≤ 4 mm), and treatment based solely on surgery (Table 2).

Bottom Line: Mutations in 14 genes were found to predict DFS.Multivariate analysis demonstrated that presence of a mutated gene signature was an independent predictor of poorer DFS (P = 0.005).Genetic variants identified by NGS technology in FFPE samples are clinically useful to predict prognosis in advanced OSCC patients.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Sciences, Chang Gung University, Taoyuan, 33302, Taiwan.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus