基于ABCB1和ABCG2基因多态性建立非小细胞肺癌患者吉非替尼疗效预测模型

doi:10.3760/cma.j.cn371439-20210901-00062

摘要/Abstract

摘要： 目的探讨非小细胞肺癌（NSCLC）患者ABCB1和ABCG2基因多态性与吉非替尼治疗反应性之间的关系,建立疗效预测模型。方法选取2018年12月至2020年12月收治于河北省沧州中西医结合医院肺病科应用吉非替尼治疗的176例表皮生长因子受体（EGFR）突变NSCLC患者作为研究对象,检测ABCB1和ABCG2基因多态性。根据吉非替尼治疗3个月后的疗效分为缓解组和未缓解组,比较两组患者一般临床资料、ABCB1和ABCG2基因多态性、血清癌胚抗原（CEA）水平、糖类抗原125（CA125）水平,应用多因素logistic回归模型分析患者治疗后未缓解的影响因素,结合ABCB1和ABCG2基因多态性构建吉非替尼疗效预测模型并绘制列线图。结果随访期间5例患者失访,7例患者因出现不可耐受的不良反应而退出研究,最终缓解组纳入108例,未缓解组纳入56例。缓解组ABCB1基因rs2032582位点GG、GT、TT分型例数分别为49、50、9例,非缓解组分别为12、35、9例,差异有统计学意义（χ²=9.56,P=0.008）;缓解组ABCG2基因rs2231137位点GG、GA、AA分型例数分别为13、72、23例,非缓解组分别为11、42、3例,差异有统计学意义（χ²=7.74,P=0.021）。治疗前缓解组和未缓解组患者血清CEA水平分别为（34.28±5.11） ng/ml和（37.88±7.05） ng/ml,差异有统计学意义（t=3.74,P<0.001）;两组患者CA125水平分别为（27.24±6.50） U/ml和（33.31±6.09） U/ml,差异有统计学意义（t=-5.79,P<0.001）。多因素logistic回归模型分析显示,ABCB1基因rs2032582位点TT基因型（OR=12.99,95%CI为3.17~53.23,P<0.001）、ABCG2基因rs2231137位点GG（OR=7.75,95%CI为1.36~44.07,P=0.021）和GA基因型（OR=6.94,95%CI为1.47~32.84,P=0.015）、CA125（OR=1.18,95%CI为1.10~1.28,P<0.001）均为EGFR突变NSCLC患者治疗后未缓解的独立危险因素。列线图预测未缓解的一致性指数（C-index）为0.92（95%CI为0.86~0.94）。结论 NSCLC患者ABCB1基因rs2032582和ABCG2基因rs2231137多态性与吉非替尼疗效相关,二者结合血清CA125所构建的列线图或可预测吉非替尼疗效。

关键词: 癌,非小细胞肺, 危险因素, 吉非替尼

Abstract: ObjectiveTo explore the relationship between ABCB1 or ABCG2 gene polymorphisms and therapeutic effects of gefitinib in non-small cell lung cancer （NSCLC） patients, and establish a prediction model of efficacy. Methods A total of 176 NSCLC patients with epidermal growth factor receptor （EGFR）-sensitive mutation treated with gefitinib admitted to Department of Pulmonary Disease of Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province from December 2018 to December 2020 were employed as subjects,and all patients were detected ABCB1 and ABCG2 gene polymorphisms. Patients were divided into remission group and non-remission group according to curative effect after 3 months of gefitinib treatment. The clinical data, ABCB1 and ABCG2 gene polymorphisms, levels of serum carcinoembryonic antigen （CEA） and carbohydrate antigen 125 （CA125） were compared between the two groups. The related factors of failure to remission after treatment were analyzed by multivariate logistic regression analysis.combined with ABCB1 and ABCG2 gene polymorphisms, the prediction model for gefitinib efficacy was constructed and the nomogram was drawn. Results During the follow-up period, 5 patients were lost to follow-up and 7 patients withdrew from the trial due to intolerable adverse effects, finally 108 patients were employed as remission group, and 56 patients were employed as non-remission group. The numbers of GG, GT and TT at ABCB1 rs2032582 in the remission group were 49, 50 and 9, and those in the non-remission group were 12, 35 and 9, with a statistically significant difference （χ²=9.56, P=0.008）. The numbers of GG, GA and AA at ABCG2 rs2231137 in the remission group were 13, 72 and 23, and those in the non-remission group were 11, 42 and 3, with a statistically significant difference （χ²=7.74, P=0.021）. Before treatment, the levels of serum CEA in the remission group and the non-remission group were （34.28±5.11） ng/ml and （37.88±7.05） ng/ml, with a statistically significant difference （t=3.74, P<0.001）. The levels of CA125 of the two groups were （27.24±6.50） U/ml and （33.31±6.09） U/ml, with a statistically significant difference （t=-5.79, P<0.001）. Multivariate logistic regression analysis showed that TT at rs2032582 of ABCB1 gene （OR=12.99, 95%CI： 3.17-53.23, P<0.001）, GG at rs2231137 of ABCG2 gene （OR=7.75, 95%CI： 1.36-44.07, P=0.021） and GA（OR=6.94, 95%CI： 1.47-32.84, P=0.015）, CA125 （OR=1.18, 95%CI： 1.10-1.28, P<0.001） were independent risk factors of failure to remission in NSCLC patients with EGFR sensitive mutation after treatment. The consistency index （C-index） of nomogram for predicting failure to remission was 0.92 （95%CI： 0.86-0.94）. Conclusion ABCB1 rs2032582 and ABCG2 rs2231137 polymorphisms are related to therapeutic effects of gefitinib in the NSCLC patients, the nomogram based on the two genes combined with serum CA125 can predict efficacy of gefitinib.

Key words: Carcinoma, non-small-cell lung, Risk factors, Gefitinib

张艳, 潘磊, 刘树亭. 基于ABCB1和ABCG2基因多态性建立非小细胞肺癌患者吉非替尼疗效预测模型[J]. 国际肿瘤学杂志, 2022, 49(6): 327-333.

Zhang Yan, Pan Lei, Liu Shuting. Establishment of an efficacy prediction model for gefitinib in non-small cell lung cancer patients based on ABCB1 and ABCG2 gene polymorphisms[J]. Journal of International Oncology, 2022, 49(6): 327-333.

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正向引物	反向引物
5’-AGGTGAGTTTTCAGAAAATA-3’	5’-TTTAGTTTGACTCACCTTCCCG-3’
5’-CACTTCAGTTACCCATCTCG-3’	5’-TTCCCGTAGAAACCTTACAT-3’
5’-TGCTGGTCCTGAAGTTGATCTGTGAAC-3’	5’-ACATTAGGCAGTGACTCGATGAAGGCA-3’
5’-ACGTTGGATGTCAGGTCATTGGAAGCTGTC-3’	5’-ACGTTGGATGGATGTCTTCCAGTAATGTCG-3’
5’-ACGTTGGATGTGATGTTGTGATGGGCACTC-3’	5’-ACGTTGGATGGTCATAGTTGTTGCAAGCCG-3’
5’-ACGTTGGATGACTCTGAAAGCACTGTTTTG-3’	5’-ACGTTGGATGCATTTGAATGTCAGCTAGTC-3’

临床特征	缓解组（n=108）	未缓解组（n=56）	t/χ²/Z值	P值
年龄（岁）	57.96±7.41	60.33±9.08	-1.80	0.074
性别
男	62	30	0.22	0.639
女	46	26	0.22	0.639
病理分型
鳞状细胞癌	6	10	6.52	0.038
腺癌	99	44
其他	3	2
吸烟史
有	68	37	0.16	0.694
无	40	19	0.16	0.694
骨转移
有	20	15	1.50	0.220
无	88	41	1.50	0.220
ECOG评分（分）
0	23	13	-0.11	0.915
1	77	37
2	8	6
肿瘤家族史
有	9	6	0.25	0.616
无	99	50	0.25	0.616
化疗方案
铂类联合培美曲塞	62	27	1.94	0.584
铂类联合吉西他滨	23	13
铂类联合紫杉醇	14	8
其他	9	8
吉非替尼应用方案
一线治疗	67	37	0.26	0.611
二线治疗	41	19	0.26	0.611
放疗
有	47	25	0.02	0.891
无	61	31	0.02	0.891
EGFR突变类型
19外显子缺失突变	49	26	0.02	0.897
21外显子p.L858R点突变	59	30	0.02	0.897

因素	β值	SE值	Wald值	OR值	95%CI	P值
病理分型
其他				1.00
鳞状细胞癌	0.61	1.22	0.25	1.83	0.17~19.99	0.619
腺癌	-0.07	1.08	0.00	0.94	0.11~7.75	0.951
ABCB1基因rs2032582位点
GG				1.00
GT	0.06	0.45	0.02	1.06	0.44~2.53	0.900
TT	2.56	0.72	12.70	12.99	3.17~53.23	<0.001
ABCG2基因rs2231137位点
AA				1.00
GG	2.05	0.89	5.34	7.75	1.36~44.07	0.021
GA	1.94	0.79	5.96	6.94	1.47~32.84	0.015
CEA	0.05	0.03	2.11	1.05	0.99~1.11	0.146
CA125	0.17	0.04	20.00	1.18	1.10~1.28	<0.001