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目的尝试建立预测吉非替尼治疗晚期非小细胞肺癌(NSCLC)临床疗效的模型。方法对中国医学科学院肿瘤医院过去4年间(2002年7月至2007年3月)应用吉非替尼治疗的262例晚期 NSCLC 患者的临床结果进行回顾性分析,并对其中有足够肿瘤组织的55例进行了 EGFR基因突变的检测(DNA 测序)。在综合分析影响疗效主要因素的基础上,建立模型。结果吉非替尼治疗晚期 NSCLC 有效率为30.1%,疾病控制率为78.6%;中位无疾病进展时间(PFS)和中位生存期分别为6个月和16个月;1、2、3年生存率分别为60.8%、35.6%和18.3%。24例(47.3%)发生EGFR 基因突变患者的治疗有效率(50.0%)明显高于无突变者(16.1%),差异有统计学意义(x~2=7.27,P=0.009)。吉非替尼耐受性好,主要不良反应为皮疹,腹泻。单因素分析显示:女性、年龄<65岁、腺癌、不吸烟、紫杉类未耐药和有基因突变患者对占非替尼治疗更敏感。但 COX 多因素回归分析显示,只有年龄、病理类型、吸烟状况和基因突变影响吉非替尼的疗效。以这4个因素为基本元素建立疗效预测模型,以2分为界值,>2分的患者有效率(34.2%)明显高于≤2分患者(9.3%),差异有统计学意义(x~2=10.619,P=0.001);每增加1分,疗效增长1倍。结论初步建立了一个能有效预测吉非替尼治疗 NSCLC 临床疗效的模型,对选择合适患者具有一定参考价值。
Objective To establish a model for predicting the clinical efficacy of gefitinib in the treatment of advanced non-small cell lung cancer (NSCLC). Methods The clinical results of 262 patients with advanced NSCLC treated with gefitinib in the Tumor Hospital of Chinese Academy of Medical Sciences in the past 4 years (July 2002 to March 2007) were retrospectively analyzed. Among them, 55 Cases of EGFR gene mutation detection (DNA sequencing). Based on a comprehensive analysis of the main factors that influence the curative effect, a model is established. Results The effective rate of gefitinib in the treatment of advanced NSCLC was 30.1% and the disease control rate was 78.6%. The median progression-free period (PFS) and median survival time were 6 months and 16 months respectively. 1,2,3 The annual survival rates were 60.8%, 35.6% and 18.3% respectively. The response rate (50.0%) in patients with EGFR gene mutation was significantly higher than that without mutation (16.1%) in 24 patients (47.3%), the difference was statistically significant (x ~ 2 = 7.27, P = 0.009). Gefitinib is well tolerated and the main adverse reactions are rash and diarrhea. Univariate analysis showed that women, age <65 years, patients with adenocarcinoma, no smoking, non-resistance to taxane, and mutations in the gene were more susceptible to the treatment with felbide. However, multivariate Cox regression analysis showed that only age, pathological type, smoking status and gene mutation affected the efficacy of gefitinib. The curative effect prediction model was established based on these 4 factors, and the effective rate (34.2%) was significantly higher than that of patients ≤ 2 (9.3%) with 2 points as the cut-off point. The difference was statistically significant (x ~ 2 = 10.619, P = 0.001); for each additional 1 point, the efficacy increased by 1 times. Conclusion A model that can effectively predict the clinical efficacy of gefitinib in the treatment of NSCLC was established. It has some reference value for selecting appropriate patients.