Silva-Alves Mariana S, Secolin Rodrigo, Carvalho Benilton S, Yasuda Clarissa L, Bilevicius Elizabeth, Alvim Marina K M, Santos Renato O, Maurer-Morelli Claudia V, Cendes Fernando, Lopes-Cendes Iscia
Department of Medical Genetics, University of Campinas-UNICAMP, and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.
Department of Statistics, Institute of Mathematics, Statistics and Scientific Computing, University of Campinas-UNICAMP, and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, São Paulo, Brazil.
PLoS One. 2017 Jan 4;12(1):e0169214. doi: 10.1371/journal.pone.0169214. eCollection 2017.
Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner.
在外科病例系列中，内侧颞叶癫痫是成人癫痫最常见的形式。目前，用于预测该综合征临床治疗反应不佳的唯一特征是海马硬化的存在。位于编码药物转运和代谢蛋白的基因中的单核苷酸多态性（SNP）可能会影响治疗反应。因此，我们旨在评估将临床变量信息以及候选基因中的SNP相结合是否能提高预测内侧颞叶癫痫患者药物治疗反应的准确性。为此，我们将237例患者分为两组：75例对抗癫痫药物治疗有反应，162例难治。我们对ABCB1、ABCC2、CYP1A1、CYP1A2、CYP1B1、CYP2C9、CYP2C19、CYP2D6、CYP2E1、CYP3A4和CYP3A5基因中的119个SNP进行了基因分型。我们使用另外98个SNP来评估群体分层。我们评估了仅使用临床变量的第一种情况和包括SNP信息的第二种情况。随机森林算法结合留一法交叉验证用于在每种情况下识别最佳预测模型，并使用曲线下面积统计量比较它们的准确性。此外，我们构建了一个变量重要性图，以展示最佳模型上最相关预测因子的集合。所选的最佳模型包括海马硬化的存在和56个SNP。此外，将SNP纳入模型可将准确性从0.4568提高到0.8177。我们的研究结果表明，添加位于药物转运和代谢基因上的SNP提供的遗传信息，可以提高预测哪些内侧颞叶癫痫患者可能对药物治疗难治的准确性，从而有可能更早地识别出可能从癫痫手术中受益的患者。
