Lee Eung-Sirk, Son Dae-Soon, Kim Sung-Hyun, Lee Jinseon, Jo Jisuk, Han Joungho, Kim Heesue, Lee Hyun Joo, Choi Hye Young, Jung Youngja, Park Miyeon, Lim Yu Sung, Kim Kwhanmien, Shim YoungMog, Kim Byung Chul, Lee Kyusang, Huh Nam, Ko Christopher, Park Kyunghee, Lee Jae Won, Choi Yong Soo, Kim Jhingook
Cancer Research Center, Center for Clinical Research, Samsung Biomedical Research Institute, Seoul, South Korea.
Clin Cancer Res. 2008 Nov 15;14(22):7397-404. doi: 10.1158/1078-0432.CCR-07-4937.
One of the main challenges of lung cancer research is identifying patients at high risk for recurrence after surgical resection. Simple, accurate, and reproducible methods of evaluating individual risks of recurrence are needed.
Based on a combined analysis of time-to-recurrence data, censoring information, and microarray data from a set of 138 patients, we selected statistically significant genes thought to be predictive of disease recurrence. The number of genes was further reduced by eliminating those whose expression levels were not reproducible by real-time quantitative PCR. Within these variables, a recurrence prediction model was constructed using Cox proportional hazard regression and validated via two independent cohorts (n = 56 and n = 59).
After performing a log-rank test of the microarray data and successively selecting genes based on real-time quantitative PCR analysis, the most significant 18 genes had P values of <0.05. After subsequent stepwise variable selection based on gene expression information and clinical variables, the recurrence prediction model consisted of six genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, and IFI44). Two pathologic variables, pStage and cellular differentiation, were developed. Validation by two independent cohorts confirmed that the proposed model is significantly accurate (P = 0.0314 and 0.0305, respectively). The predicted median recurrence-free survival times for each patient correlated well with the actual data.
We have developed an accurate, technically simple, and reproducible method for predicting individual recurrence risks. This model would potentially be useful in developing customized strategies for managing lung cancer.
肺癌研究的主要挑战之一是识别手术切除后复发风险高的患者。需要简单、准确且可重复的方法来评估个体复发风险。
基于对一组138例患者的复发时间数据、删失信息和微阵列数据的综合分析，我们选择了被认为可预测疾病复发的具有统计学意义的基因。通过剔除那些实时定量PCR无法重现其表达水平的基因，进一步减少了基因数量。在这些变量中，使用Cox比例风险回归构建了复发预测模型，并通过两个独立队列（n = 56和n = 59）进行验证。
在对微阵列数据进行对数秩检验并基于实时定量PCR分析相继选择基因后，最显著的18个基因的P值<0.05。基于基因表达信息和临床变量进行后续逐步变量选择后，复发预测模型由六个基因（CALB1、MMP7、SLC1A7、GSTA1、CCL19和IFI44）组成。还得出了两个病理变量，即p分期和细胞分化。两个独立队列的验证证实，所提出的模型具有显著的准确性（分别为P = 0.0314和0.0305）。每位患者预测的无复发生存时间中位数与实际数据相关性良好。
我们开发了一种准确、技术简单且可重复的方法来预测个体复发风险。该模型可能有助于制定个性化的肺癌管理策略。
