Yuxiang Ma1, Shuang Xin2,3, Qingguang Lin1, Wei Zhuang2, Yuanyuan Zhao1, Xia Zhu2, Hongyun Zhao1, Min Huang2, Xu Xun3, Yunpeng Yang1, Wenfeng Fang1, Li Zhang1, Xueding Wang2. 1. Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China. 2. Institute of Clinical Pharmacology, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510060, China. 3. BGI-Shenzhen, Shenzhen 518083, China.
Abstract
BACKGROUND: The current study is aimed to examine the impact of pharmacokinetics and gene polymorphisms of enzymes involving in absorption, distribution, metabolism and excretion (ADME) on the efficacy of gefitinib in non-small cell lung cancer (NSCLC) patients. METHODS: Eligible patients with indication of gefitinib treatment were prospectively enrolled in this study. Two peripheral blood samples at baseline and before cycle 2 day 1 were collected for the detection of single nucleotide polymorphisms (SNPs) of drug ADME enzymes and trough drug concentration (Ctrough) at steady state. Thirteen SNPs were genotyped using the Sequenom Massarray system. Ctrough was determined by validated high-performance liquid chromatographic method with tandem mass spectrometric (LC-MS/MS). RESULTS: Fifty-eight NSCLC patients were enrolled in this study. The median of Ctrough was 175ng/mL (range from 47.8 to 470 ng/mL). The trough concentration was not associated with either objective response or progression free survival (PFS). Ctrough was significantly lower in CYP3A4 rs2242480 CC + CT genotype than in TT genotype (P=0.019) and in ABCG2 rs2231142 AA genotype than in AC + CC genotype (P=0.031). ABCB1 rs2032582 dominant model was significantly correlated with overall response rate (ORR) and patients with GG phenotype respond better than patients with GT + TT phenotypes (84.6% vs. 51.2%, P=0.032). ABCB1 rs10256836 recessive model was significantly correlated with PFS and patients with GG phenotype achieved longer PFS than patients with GC + CC phenotypes (17.40 vs. 10.33 months, P=0.040). CONCLUSIONS: The Ctrough of gefitinib was significantly different between CYP3A4 and ABCG2 genotypes, but not with the efficacy of gefitinib treatment. ABCB1 rs2032582 and rs10256836 polymorphisms were correlated treatment outcome. Polymorphisms analysis of ABCB1 could be a predictive biomarker for gefitinib treatment. 2019 Annals of Translational Medicine. All rights reserved.
BACKGROUND: The current study is aimed to examine the impact of pharmacokinetics and gene polymorphisms of enzymes involving in absorption, distribution, metabolism and excretion (ADME) on the efficacy of gefitinib in non-small cell lung cancer (NSCLC) patients. METHODS: Eligible patients with indication of gefitinib treatment were prospectively enrolled in this study. Two peripheral blood samples at baseline and before cycle 2 day 1 were collected for the detection of single nucleotide polymorphisms (SNPs) of drug ADME enzymes and trough drug concentration (Ctrough) at steady state. Thirteen SNPs were genotyped using the Sequenom Massarray system. Ctrough was determined by validated high-performance liquid chromatographic method with tandem mass spectrometric (LC-MS/MS). RESULTS: Fifty-eight NSCLC patients were enrolled in this study. The median of Ctrough was 175ng/mL (range from 47.8 to 470 ng/mL). The trough concentration was not associated with either objective response or progression free survival (PFS). Ctrough was significantly lower in CYP3A4 rs2242480 CC + CT genotype than in TT genotype (P=0.019) and in ABCG2 rs2231142 AA genotype than in AC + CC genotype (P=0.031). ABCB1 rs2032582 dominant model was significantly correlated with overall response rate (ORR) and patients with GG phenotype respond better than patients with GT + TT phenotypes (84.6% vs. 51.2%, P=0.032). ABCB1 rs10256836 recessive model was significantly correlated with PFS and patients with GG phenotype achieved longer PFS than patients with GC + CC phenotypes (17.40 vs. 10.33 months, P=0.040). CONCLUSIONS: The Ctrough of gefitinib was significantly different between CYP3A4 and ABCG2 genotypes, but not with the efficacy of gefitinib treatment. ABCB1 rs2032582 and rs10256836 polymorphisms were correlated treatment outcome. Polymorphisms analysis of ABCB1 could be a predictive biomarker for gefitinib treatment. 2019 Annals of Translational Medicine. All rights reserved.
Entities:
Keywords:
ABCB1; Non-small cell lung cancer (NSCLC); gefitinib; pharmacokinetic; single nucleotide polymorphisms (SNPs)
Authors: György Marko-Varga; Thomas E Fehniger; Melinda Rezeli; Balázs Döme; Thomas Laurell; Akos Végvári Journal: J Proteomics Date: 2011-04-01 Impact factor: 4.044
Authors: David McKillop; Elizabeth A Partridge; John V Kemp; Mike P Spence; Jane Kendrew; Sharon Barnett; Phillippa G Wood; Petrina B Giles; Andrew B Patterson; Francis Bichat; Nicolas Guilbaud; Trevor C Stephens Journal: Mol Cancer Ther Date: 2005-04 Impact factor: 6.261
Authors: Charles M Rudin; Wanqing Liu; Apurva Desai; Theodore Karrison; Xuemin Jiang; Linda Janisch; Soma Das; Jacqueline Ramirez; Balasubramanian Poonkuzhali; Erin Schuetz; Donna Lee Fackenthal; Peixian Chen; Deborah K Armstrong; Julie R Brahmer; Gini F Fleming; Everett E Vokes; Michael A Carducci; Mark J Ratain Journal: J Clin Oncol Date: 2008-03-01 Impact factor: 44.544
Authors: Jean-Yves Douillard; Frances A Shepherd; Vera Hirsh; Tony Mok; Mark A Socinski; Radj Gervais; Mei-Lin Liao; Helge Bischoff; Martin Reck; Mark V Sellers; Claire L Watkins; Georgina Speake; Alison A Armour; Edward S Kim Journal: J Clin Oncol Date: 2009-12-28 Impact factor: 44.544