Establishment and application of a predictive model for gefitinib-induced severe rash based on pharmacometabolomic profiling and polymorphisms of transporters in non-small cell lung cancer.
Establishment and application of a predictive model for gefitinib-induced severe rash based on pharmacometabolomic profiling and polymorphisms of transporters in non-small cell lung cancer.
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基于药物代谢组学分析和转运蛋白多态性的非小细胞肺癌吉非替尼所致严重皮疹预测模型的建立及应用
DOI：
10.1016/j.tranon.2020.100951
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发表时间：
2021-01
影响因子：
5
通讯作者：
Zhang L
中科院分区：
文献类型：
--
作者：
Guan S;Chen X;Xin S;Liu S;Yang Y;Fang W;Huang Y;Zhao H;Zhu X;Zhuang W;Wang F;Feng W;Zhang X;Huang M;Wang X;Zhang L
共有346名患者参加了这项研究。与1级和2级皮疹相比，严重皮疹（3级和4级）没有获得更多的骨化。在患者血浆中检测吉非替尼及其四种代谢物。基于药物代谢组学分析和药物基因组学方法，建立了特异性和敏感性的预测模型。众所周知，皮疹是吉非替尼治疗非小细胞肺癌（NSCLC）患者生存的预测因素。然而，是否越严重的皮疹患者从吉非替尼中获得更多的生存获益仍然是未知的，并且需要严重皮疹的预测模型。吉非替尼诱导的皮疹与无进展生存期（PFS）之间的关系主要在回顾性队列中进行探讨。在探索性队列中，通过药物代谢组学和药物基因组学方法确定皮疹与吉非替尼/代谢物浓度和遗传多态性之间的关系，并在外部队列中进行验证。皮疹患者的生存率显著高于无皮疹患者（p = 0.0002,p = 0.0089），但1/2级和3/4级之间无差异。只有吉非替尼的浓度与严重皮疹相关，而其代谢物与严重皮疹无关，ROC曲线分析吉非替尼的临界值为204.6 ng/mL （AUC=0.685）。一个预测模型建立了严重皮疹:吉非替尼的浓度(或 = 11.523,95% CI = 2.898 - -64.016,p = 0.0016),SLC22A8 rs4149179 (CT对CC或 = 3.156,95% CI = 0.958 - -11.164,p = 0.0629),SLC22A1 rs4709400 (CG vs CC或 = 10.267,95% CI = 2.067 - -72.465,p = 0.0087;GG vs CC,或 = 5.103,95% CI = 1.032 - -33.938,p = 0.061)。该模型在验证队列中得到证实，具有良好的预测能力（AUC = 0.749,95% CI = 0.710-0.951）。我们的研究结果表明，吉非替尼引起的皮疹的发生率，而不是严重程度，预示着生存率的提高，吉非替尼的浓度和SLC22A8和SLC22A1的多态性被推荐用于治疗严重皮疹。
A total of 346 patients were enrolled in this study. Severe rash (grade 3&4) did not gain more bonification compare to grade 1&2 rash. Gefitinib and its four metabolites were detected in patients’ plasma. A specific and sensitive predictive model were established based on pharmacometabolomic profiling and pharmacogenomics approach. Rash is a well-known predictor of survival for patients with gefitinib therapy with non-small cell lung cancer (NSCLC). However, whether patients with more severe rash obtain the more survival benefits from gefitinib is still unknown, and predicted model for severe rash is needed. The relationship between gefitinib-induced rash and progression free survival (PFS) was primarily explored in the retrospective cohort. The association between rash and gefitinib/metabolites concentration and genetic polymorphisms were determined by pharmacometabolomic and pharmacogenomics methods in the exploratory cohort and validated in an external cohort. The survival for patients with rash was significantly higher than that of patients without rash (p = 0.0002, p = 0.0089), but no difference was found between grade 1/2 or grade 3/4. Only the concentration of gefitinib, but not its metabolites, was found to be associated with severe rash, and the cutoff value of gefitinib was 204.6 ng/mL conducted by ROC curve analysis (AUC=0.685). A predictive model for severe rash was established: gefitinib concentration (OR = 11.523, 95% CI = 2.898-64.016, p = 0.0016), SLC22A8 rs4149179(CT vs CC, OR = 3.156, 95% CI = 0.958–11.164, p = 0.0629), SLC22A1 rs4709400(CG vs CC, OR = 10.267, 95% CI = 2.067–72.465, p = 0.0087; GG vs CC, OR = 5.103, 95% CI = 1.032–33.938, p = 0.061). This model was confirmed in the validation cohort with an excellent predictive ability (AUC = 0.749, 95% CI = 0.710–0.951). Our finding demonstrated that the incidence, not the severity, of gefitinib-induced rash predicted improved survival, the gefitinib concentration and polymorphisms of SLC22A8 and SLC22A1 were recommended to manage severe rash.
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影响因子：
51.1
作者：
Mitsudomi, Tetsuya;Morita, Satoshi;Fukuoka, Masahiro
通讯作者：
Fukuoka, Masahiro
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Lacouture, Mario E.
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作者：
Galetti, Maricla;Alfieri, Roberta R.;Petronini, Pier Giorgio
通讯作者：
Petronini, Pier Giorgio
影响因子：
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作者：
Maag JLV
通讯作者：
Maag JLV