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A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

Author

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  • Mariana S Silva-Alves
  • Rodrigo Secolin
  • Benilton S Carvalho
  • Clarissa L Yasuda
  • Elizabeth Bilevicius
  • Marina K M Alvim
  • Renato O Santos
  • Claudia V Maurer-Morelli
  • Fernando Cendes
  • Iscia Lopes-Cendes

Abstract

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.

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  • Mariana S Silva-Alves & Rodrigo Secolin & Benilton S Carvalho & Clarissa L Yasuda & Elizabeth Bilevicius & Marina K M Alvim & Renato O Santos & Claudia V Maurer-Morelli & Fernando Cendes & Iscia Lopes, 2017. "A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.
    • Handle: RePEc:plo:pone00:0169214
    DOI: 10.1371/journal.pone.0169214
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    References listed on IDEAS

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    1. Jurgen Brauer, 1991. "Perspective," Challenge, Taylor & Francis Journals, vol. 34(3), pages 54-58, May.
    2. Shabeesh Balan & Sumitha Prameela Bharathan & Neetha Nanoth Vellichiramal & Sanish Sathyan & Vijai Joseph & Kurupath Radhakrishnan & Moinak Banerjee, 2014. "Genetic Association Analysis of ATP Binding Cassette Protein Family Reveals a Novel Association of ABCB1 Genetic Variants with Epilepsy Risk, but Not with Drug-Resistance," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-10, February.
    3. Hong-Lian Ruan & Hai-De Qin & Yin Yao Shugart & Jin-Xin Bei & Fu-Tian Luo & Yi-Xin Zeng & Wei-Hua Jia, 2013. "Developing Genetic Epidemiological Models to Predict Risk for Nasopharyngeal Carcinoma in High-Risk Population of China," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-8, February.
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