Development and validation of nomograms for predicting survival probability of patients with advanced adenocarcinoma in different EGFR mutation status

Introduction
Molecular markers are important variables in the selection of treatment for cancer patients and highly associated with their survival. Therefore, a nomogram that can predict survival probability by incorporating epidermal growth factor receptor mutation status and treatments for patients with advanced adenocarcinoma would be highly valuable. The aim of the study is to develop and validate a novel nomogram, incorporating epidermal growth factor receptor mutation status and treatments, for predicting 1-year and 2-year survival probability of patients with advanced adenocarcinoma.
Material and methods
Data on 13,043 patients between June 1, 2011, and December 31, 2014 were collected. Seventy percent of them were randomly assigned to the training cohort for nomogram development, and the remaining 30% assigned to the validation cohort. The most important factors for constructing the nomogram were identified using multivariable Cox regression analysis. The discriminative ability and calibration of the nomograms were tested using C-statistics, calibration plots, and Kaplan-Meier curves.
Results
In the training cohort, 1-year and 2-year OS were 52.8% and 28.5% in EGFR(-) patients, and 73.9% and 44.1% in EGFR(+) patients, respectively. In EGFR(+) group, factors selected were age, gender, congestive heart failure, renal disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, first-line chemotherapy, ECOG performance status, malignant pleural effusion, and smoking. In EGFR(-) group, factors selected were age, gender, myocardial infarction, cerebrovascular disease, chronic pulmonary disease, number of lymph node examined, tumor stage, surgical intervention, radiotherapy, ECOG performance status, malignant pleural effusion, and a history of smoking. Two nomograms show good accuracy in predicting OS, with a concordance index of 0.83 in EGFR(+) and of 0.88 in EGFR(-).
Conclusions
The survival prediction models can be used to make individualized predictions with different EGFR mutation status and a useful tool for selecting regimens for treating advanced adenocarcinoma.

引言
分子标志物是癌症患者治疗方案选择中的重要变量，且与患者的生存结局密切相关。因此，针对晚期腺癌患者，构建一种可结合表皮生长因子受体（Epidermal Growth Factor Receptor, EGFR）突变状态与治疗方案来预测生存概率的列线图，将具有极高的临床应用价值。本研究旨在开发并验证一款新型列线图，通过纳入EGFR突变状态与治疗方案，用于预测晚期腺癌患者的1年及2年生存概率。
材料与方法
本研究收集了2011年6月1日至2014年12月31日期间的13043例患者数据。其中70%的患者被随机分配至训练队列以构建列线图，剩余30%分配至验证队列。采用多变量Cox回归分析筛选构建列线图所需的关键影响因素。通过C统计量、校准曲线以及Kaplan-Meier曲线，评估该列线图的区分能力与校准度。
结果
在训练队列中，EGFR阴性患者的1年总生存期（Overall Survival, OS）率与2年OS率分别为52.8%和28.5%，EGFR阳性患者则分别为73.9%和44.1%。在EGFR阳性组中，筛选出的影响因素包括年龄、性别、充血性心力衰竭、肾脏疾病、送检淋巴结数目、肿瘤分期、手术干预、放射治疗、一线化疗、ECOG体能状态评分、恶性胸腔积液以及吸烟史。在EGFR阴性组中，筛选出的影响因素包括年龄、性别、心肌梗死、脑血管疾病、慢性肺部疾病、送检淋巴结数目、肿瘤分期、手术干预、放射治疗、一线化疗、ECOG体能状态评分、恶性胸腔积液以及吸烟史。两款列线图在预测OS方面均表现出良好的准确性，EGFR阳性组的一致性指数为0.83，EGFR阴性组为0.88。
结论
本研究构建的生存预测模型可针对不同EGFR突变状态的患者实现个体化生存预测，是晚期腺癌患者治疗方案选择的实用工具。