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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

Association of the mutation-based genetic signature with disease-free survival in specific subgroupsA, B. Kaplan-Meier analysis of disease-free survival in subgroup A (n = 172) and subgroup B (n = 173) according to the genetic signature mutational status. C, D. Kaplan-Meier analysis of disease-free survival in the year 1996–2003 cohort (n = 143) and year 2004–2011 cohort (n = 202) according to the genetic signature mutation status.
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Figure 5: Association of the mutation-based genetic signature with disease-free survival in specific subgroupsA, B. Kaplan-Meier analysis of disease-free survival in subgroup A (n = 172) and subgroup B (n = 173) according to the genetic signature mutational status. C, D. Kaplan-Meier analysis of disease-free survival in the year 1996–2003 cohort (n = 143) and year 2004–2011 cohort (n = 202) according to the genetic signature mutation status.

Mentions: We further analyzed the reproducibility of the identified gene signature using two different resampling approaches. First, we randomly divided the cohort into two test subgroups − subgroup A (n = 172) and subgroup B (n = 173) − with a similar sample size and a comparable distribution of clinicopathological risk factors (Figure 1, Table 1). We identified 93 (54.1%) and 79 (45.7%) relapsed in subgroup A and subgroup B, respectively. We then estimated the hazard ratios associated with the presence of the prognostic gene signature for DFS in each subgroup using Cox regression. In both subgroups, patients with mutations in the gene signature showed significantly poorer DFS compared with wild-type patients (Table 3). The median DFS periods for patients with and without mutations in the gene signature for subgroup A and subgroup B were 11 vs. 94 months (P = 0.0016) and 12 vs. >180 months (P = 0.0033), respectively (Figures 5A, 5B). In the second resampling approach, we divided the entire study cohort into two different subgroups based on the time of patient enrollment. The 1996−2003 group consisted of 143 samples collected between 1996 and 2003, whereas the 2004−2011 group consisted of 202 samples collected between 2004 and 2011 (Figure 1, Table 1). In each group, we identified a total of 83 (58%) and 89 (44.1%) relapsed cases, respectively. We also analyzed the hazard ratios of the prognostic gene signature for DFS in each subgroup. In both subgroups, the prognostic gene signature was significantly associated with an increased risk of disease relapse (Table 3). Kaplan-Meier curves confirmed the presence of statistically significant differences in terms of DFS according to the prognostic gene signature in both subgroups (Table 3). The median DFS periods for patients with and without the prognostic gene signature in subgroup 1996–2003 and subgroup 2004–2011 were 10.5 vs. 56 months (P = 0.016) and 12 vs. >180 months (P = 0.0003), respectively (Figure 5C, 5D).


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)

Association of the mutation-based genetic signature with disease-free survival in specific subgroupsA, B. Kaplan-Meier analysis of disease-free survival in subgroup A (n = 172) and subgroup B (n = 173) according to the genetic signature mutational status. C, D. Kaplan-Meier analysis of disease-free survival in the year 1996–2003 cohort (n = 143) and year 2004–2011 cohort (n = 202) according to the genetic signature mutation status.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4621868&req=5

Figure 5: Association of the mutation-based genetic signature with disease-free survival in specific subgroupsA, B. Kaplan-Meier analysis of disease-free survival in subgroup A (n = 172) and subgroup B (n = 173) according to the genetic signature mutational status. C, D. Kaplan-Meier analysis of disease-free survival in the year 1996–2003 cohort (n = 143) and year 2004–2011 cohort (n = 202) according to the genetic signature mutation status.
Mentions: We further analyzed the reproducibility of the identified gene signature using two different resampling approaches. First, we randomly divided the cohort into two test subgroups − subgroup A (n = 172) and subgroup B (n = 173) − with a similar sample size and a comparable distribution of clinicopathological risk factors (Figure 1, Table 1). We identified 93 (54.1%) and 79 (45.7%) relapsed in subgroup A and subgroup B, respectively. We then estimated the hazard ratios associated with the presence of the prognostic gene signature for DFS in each subgroup using Cox regression. In both subgroups, patients with mutations in the gene signature showed significantly poorer DFS compared with wild-type patients (Table 3). The median DFS periods for patients with and without mutations in the gene signature for subgroup A and subgroup B were 11 vs. 94 months (P = 0.0016) and 12 vs. >180 months (P = 0.0033), respectively (Figures 5A, 5B). In the second resampling approach, we divided the entire study cohort into two different subgroups based on the time of patient enrollment. The 1996−2003 group consisted of 143 samples collected between 1996 and 2003, whereas the 2004−2011 group consisted of 202 samples collected between 2004 and 2011 (Figure 1, Table 1). In each group, we identified a total of 83 (58%) and 89 (44.1%) relapsed cases, respectively. We also analyzed the hazard ratios of the prognostic gene signature for DFS in each subgroup. In both subgroups, the prognostic gene signature was significantly associated with an increased risk of disease relapse (Table 3). Kaplan-Meier curves confirmed the presence of statistically significant differences in terms of DFS according to the prognostic gene signature in both subgroups (Table 3). The median DFS periods for patients with and without the prognostic gene signature in subgroup 1996–2003 and subgroup 2004–2011 were 10.5 vs. 56 months (P = 0.016) and 12 vs. >180 months (P = 0.0003), respectively (Figure 5C, 5D).

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