*Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; and Departments of †Computer Science, ‡Medical Biophysics, §Medicine, and ‖Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
J Thorac Oncol. 2014 Jan;9(1):59-64. doi: 10.1097/JTO.0000000000000042.
Patients with early-stage non-small-cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. In this study, we assessed its value in an independent set of cases.
Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate 5-year overall survival probabilities, and the prognostic effect of the classifier was assessed using the log-rank test. A Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors.
Expression data of the 15-gene classifier stratified UHN181 cases into high- and low-risk subgroups with significantly different overall survival (hazard ratio [HR] = 1.92; 95% confidence interval [CI], 1.15-3.23; p = 0.012). In a subgroup analysis, this classifier predicted survival in 127 stage I patients (HR = 2.17; 95% CI, 1.12-4.20; p = 0.018) and the smaller subgroup of 48 stage IA patients (HR = 5.61; 95% CI, 1.19-26.45; p = 0.014). The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR = 1.76, p = 0.058; HR = 4.19, p = 0.045, respectively).
The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early-stage NSCLC samples including stage IA cases and in different NSCLC histologic subtypes.
早期非小细胞肺癌(NSCLC)患者可能受益于基于更准确预后的治疗方法。NCIC CTG JBR.10 试验从肿瘤样本的 mRNA 表达谱中鉴定出了一种用于 NSCLC 的 15 基因预后分类器。在这项研究中,我们在一组独立的病例中评估了它的价值。
对来自大学健康网络(UHN181)的 181 例 I 期和 II 期 NSCLC 病例的冷冻、切除肿瘤组织的 RNA 进行了表达谱分析。Kaplan-Meier 方法用于估计 5 年总生存率,使用对数秩检验评估分类器的预后作用。Cox 比例风险模型用于调整临床预后因素后评估该特征的效果。
15 基因分类器的表达数据将 UHN181 病例分为高风险和低风险亚组,总生存率有显著差异(风险比[HR] = 1.92;95%置信区间[CI],1.15-3.23;p = 0.012)。在亚组分析中,该分类器预测了 127 例 I 期患者(HR = 2.17;95% CI,1.12-4.20;p = 0.018)和 48 例 IA 期患者的生存情况(HR = 5.61;95% CI,1.19-26.45;p = 0.014)。该特征对腺癌和鳞状细胞癌病例均具有预后意义(HR = 1.76,p = 0.058;HR = 4.19,p = 0.045)。
在包括 IA 期病例和不同 NSCLC 组织学亚型的 181 例早期 NSCLC 样本的独立队列中验证了 15 基因分类器的预后准确性。