A Gene Expression Signature to Predict Metastasis and Survival in Non-small Cell Lung Cancer. Homo sapiens

Background: Current histopathological methods are inadequate for predicting outcome and recurrence in patients with non-small cell lung carcinoma (NSCLC) after surgery. In this study, we investigated the use of gene expression signatures to predict outcome and metastasis in lung cancer patients. Methods: Gene expression was studied by microarray and the real-time reverse transcriptase polymerase chain reaction (RT-PCR) in normal and lung tumor tissue of 188 NSCLC patients who underwent surgical resection. The 5 cancer-related genes and 1 reference gene expression levels measureed by real-time RT-PCR were used in a prospectively defined algorithm to determine the risk for each patient. Finally, we used an independent cohort to verify the 5 gene-based predictive model derived from decision tree analysis. Results: The 5 gene-based decision tree model was able to predict the prognosis. The recurrence rate at 36 months was 53% in the low-risk group versus 83% in the high-risk group (P=0.002). The 5 gene-based model could also predict overall survival (P<0.001). In multivariate analysis, the decision tree model predicted that high-low dichotomy and stage were both significant for recurrence. In addition, it could also predict metastasis and survival of NSCLC patients within the stage I-II subgroups. A similar result was found using an independent cohort of NSCLC patients. The high-risk patients had a significantly poorer overall survival than the low-risk patients (P=0.005). We also found distinct gene signatures which could distinguish between NSCLC, and normal tissue and histology subtypes. Conclusions: A gene expression signature can predict metastasis and survival of NSCLC patients. Keywords: Survival and metastasis analysis Overall design: In this study, we investigated the use of gene expression signatures to predict outcome and metastasis in lung cancer patients. Lung cancer tissue specimens from 125 patients who underwent surgical resection of their primary NSCLC at the Taichung Veterans General Hospital in Taiwan between December 1999 and December 2003 were included in this study. Specimens were stored frozen. Of these patients, 60 had adenocarcinoma, 52 squamous cell carcinoma, and 13 other types, all histologically defined.

背景：当前的组织病理学方法无法满足非小细胞肺癌（non-small cell lung carcinoma, NSCLC）患者术后预后与复发预测的临床需求。本研究旨在探究基因表达特征在肺癌患者预后与转移预测中的应用价值。
方法：本研究通过基因芯片（microarray）与实时逆转录聚合酶链反应（real-time reverse transcriptase polymerase chain reaction, RT-PCR），对188例接受手术切除的非小细胞肺癌患者的正常肺组织与肿瘤组织开展基因表达分析。研究采用预先定义的算法，通过实时RT-PCR检测5个癌相关基因与1个内参基因的表达水平，以此为每位患者制定风险分级。最后，本研究利用独立队列验证了基于决策树分析得到的5基因预测模型。
结果：基于5基因的决策树模型可有效预测患者预后。低风险组患者术后36个月复发率为53%，高风险组则为83%（P=0.002）。该5基因模型同样可预测患者总生存期（P<0.001）。多因素分析显示，决策树模型的高低风险二分法与肿瘤分期均为复发的独立影响因素。此外，该模型还可预测Ⅰ~Ⅱ期亚组非小细胞肺癌患者的转移情况与生存期。在另一组独立非小细胞肺癌患者队列中得到了一致结果：高风险组患者总生存期显著差于低风险组（P=0.005）。本研究还发现了可区分非小细胞肺癌与正常肺组织、以及不同组织学亚型的特征性基因表达谱。
结论：基因表达特征可有效预测非小细胞肺癌患者的转移情况与生存期。
关键词：生存与转移分析
整体实验设计：本研究旨在探究基因表达特征在肺癌患者预后与转移预测中的应用价值。本研究纳入了1999年12月至2003年12月期间，在中国台湾台中荣民总医院接受原发性非小细胞肺癌手术切除的125例患者的肺癌组织标本。所有标本均以冻存方式保存。经组织病理学确诊，其中60例为腺癌、52例为鳞状细胞癌，剩余13例为其他组织学类型。