A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information
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 encodi...
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| creator | 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 |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0169214 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0169214</identifier><identifier>PMID: 28052106</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Anticonvulsants - therapeutic use ; Antiepileptic agents ; ATP Binding Cassette Transporter, Sub-Family B - genetics ; Biology and Life Sciences ; Breast cancer ; Control ; CYP1A2 protein ; CYP2D6 protein ; Cytochrome P-450 CYP1A1 - genetics ; Cytochrome P-450 CYP1A2 - genetics ; Cytochrome P-450 CYP1B1 - genetics ; Cytochrome P-450 CYP2C19 - genetics ; Cytochrome P-450 CYP2C9 - genetics ; Cytochrome P-450 CYP2D6 - genetics ; Cytochrome P-450 CYP2E1 - genetics ; Cytochrome P-450 CYP3A - genetics ; Cytochrome P450 ; Drug abuse treatment ; Drug metabolism ; Drugs ; Epilepsy ; Epilepsy, Temporal Lobe - drug therapy ; Epilepsy, Temporal Lobe - genetics ; Evaluation ; Gene expression ; Genes ; Genetics ; Genomics ; Genotype ; Health aspects ; Hippocampus ; Hippocampus - metabolism ; Hippocampus - pathology ; Humans ; Laboratories ; Laws, regulations and rules ; Mathematical models ; Medicine and Health Sciences ; Metabolism ; Model accuracy ; Multidrug Resistance-Associated Proteins - genetics ; Neurology ; Neurosciences ; NMR ; Nuclear magnetic resonance ; Patients ; Physical Sciences ; Polymorphism, Single Nucleotide - genetics ; Population ; Prediction models ; Proteins ; Research and Analysis Methods ; Sclerosis ; Single-nucleotide polymorphism ; Studies ; Surgery ; Systematic review ; Temporal lobe ; Therapy</subject><ispartof>PloS one, 2017-01, Vol.12 (1), p.e0169214-e0169214</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Silva-Alves et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2017 Silva-Alves et al 2017 Silva-Alves et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c725t-6cfcd4f62f6743bacfc1f1f516ee9897aca5efe3d9acd807e792c74f6348b8c63</citedby><cites>FETCH-LOGICAL-c725t-6cfcd4f62f6743bacfc1f1f516ee9897aca5efe3d9acd807e792c74f6348b8c63</cites><orcidid>0000-0002-9195-1009 ; 0000-0001-9084-7173 ; 0000-0001-9336-9568 ; 0000-0002-2485-9560 ; 0000-0001-7929-2197</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5215688/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5215688/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28052106$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Silva-Alves, Mariana S</creatorcontrib><creatorcontrib>Secolin, Rodrigo</creatorcontrib><creatorcontrib>Carvalho, Benilton S</creatorcontrib><creatorcontrib>Yasuda, Clarissa L</creatorcontrib><creatorcontrib>Bilevicius, Elizabeth</creatorcontrib><creatorcontrib>Alvim, Marina K M</creatorcontrib><creatorcontrib>Santos, Renato O</creatorcontrib><creatorcontrib>Maurer-Morelli, Claudia V</creatorcontrib><creatorcontrib>Cendes, Fernando</creatorcontrib><creatorcontrib>Lopes-Cendes, Iscia</creatorcontrib><title>A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Algorithms</subject><subject>Anticonvulsants - therapeutic use</subject><subject>Antiepileptic agents</subject><subject>ATP Binding Cassette Transporter, Sub-Family B - genetics</subject><subject>Biology and Life Sciences</subject><subject>Breast cancer</subject><subject>Control</subject><subject>CYP1A2 protein</subject><subject>CYP2D6 protein</subject><subject>Cytochrome P-450 CYP1A1 - genetics</subject><subject>Cytochrome P-450 CYP1A2 - genetics</subject><subject>Cytochrome P-450 CYP1B1 - genetics</subject><subject>Cytochrome P-450 CYP2C19 - genetics</subject><subject>Cytochrome P-450 CYP2C9 - genetics</subject><subject>Cytochrome P-450 CYP2D6 - genetics</subject><subject>Cytochrome P-450 CYP2E1 - genetics</subject><subject>Cytochrome P-450 CYP3A - genetics</subject><subject>Cytochrome P450</subject><subject>Drug abuse treatment</subject><subject>Drug metabolism</subject><subject>Drugs</subject><subject>Epilepsy</subject><subject>Epilepsy, Temporal Lobe - drug therapy</subject><subject>Epilepsy, Temporal Lobe - genetics</subject><subject>Evaluation</subject><subject>Gene expression</subject><subject>Genes</subject><subject>Genetics</subject><subject>Genomics</subject><subject>Genotype</subject><subject>Health aspects</subject><subject>Hippocampus</subject><subject>Hippocampus - metabolism</subject><subject>Hippocampus - pathology</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Laws, regulations and rules</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Model accuracy</subject><subject>Multidrug Resistance-Associated Proteins - genetics</subject><subject>Neurology</subject><subject>Neurosciences</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Polymorphism, Single Nucleotide - genetics</subject><subject>Population</subject><subject>Prediction models</subject><subject>Proteins</subject><subject>Research and Analysis Methods</subject><subject>Sclerosis</subject><subject>Single-nucleotide polymorphism</subject><subject>Studies</subject><subject>Surgery</subject><subject>Systematic review</subject><subject>Temporal lobe</subject><subject>Therapy</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1Fv0zAQxyMEYmPwDRBYQkLw0BLbie28TCpljEpFm8bg1XKcc-spiYudAHvkm-Os2dSiPUx5iB3_7n-X__mS5CVOp5hy_OHK9b5V9XTjWpimmBUEZ4-SQ1xQMmEkpY931gfJsxCu0jSngrGnyQERaU5wyg6TvzN07qGyurOuRbN65bzt1g0yzqNPvl-hCwgxQwBkW3SuOgttF9DvyKCvEKyq0SU0G-fjYulKQCcbW8MmXKOPKkCFoua8tq3V8Vy1FTqFFjqr0aKNCRo1JH2ePDGqDvBifB8l3z-fXM6_TJZnp4v5bDnRnOTdhGmjq8wwYhjPaKniFhtscswAClFwpVUOBmhVKF2JlAMviOYxgGaiFJrRo-T1VndTuyBH94LEIs8pIZQWkVhsicqpK7nxtlH-Wjpl5c0H51dS-Vh9DdJUChvCjNY5yUCByEqesVIQgLzkRkSt4zFbXzZQ6Whb9GhPdP-ktWu5cr9k7EvOxCDwbhTw7mcPoZONDRrqWrXg-pu6i0xQRvOHoDkXnPEBffMfer8RI7VS8V9t7FUsUQ-icpZxltEMk8HQ6T1UfCporI630sSrsB_wfi8gMh386VaqD0Euvl08nD37sc--3WHXoOpuHVzdD7cr7IPZFtTeheDB3PUDp3IYqls35DBUchyqGPZqt5d3QbdTRP8BG6gd8A</recordid><startdate>20170104</startdate><enddate>20170104</enddate><creator>Silva-Alves, Mariana S</creator><creator>Secolin, Rodrigo</creator><creator>Carvalho, Benilton S</creator><creator>Yasuda, Clarissa L</creator><creator>Bilevicius, Elizabeth</creator><creator>Alvim, Marina K M</creator><creator>Santos, Renato O</creator><creator>Maurer-Morelli, Claudia V</creator><creator>Cendes, Fernando</creator><creator>Lopes-Cendes, Iscia</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9195-1009</orcidid><orcidid>https://orcid.org/0000-0001-9084-7173</orcidid><orcidid>https://orcid.org/0000-0001-9336-9568</orcidid><orcidid>https://orcid.org/0000-0002-2485-9560</orcidid><orcidid>https://orcid.org/0000-0001-7929-2197</orcidid></search><sort><creationdate>20170104</creationdate><title>A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c725t-6cfcd4f62f6743bacfc1f1f516ee9897aca5efe3d9acd807e792c74f6348b8c63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Anticonvulsants - therapeutic use</topic><topic>Antiepileptic agents</topic><topic>ATP Binding Cassette Transporter, Sub-Family B - genetics</topic><topic>Biology and Life Sciences</topic><topic>Breast cancer</topic><topic>Control</topic><topic>CYP1A2 protein</topic><topic>CYP2D6 protein</topic><topic>Cytochrome P-450 CYP1A1 - genetics</topic><topic>Cytochrome P-450 CYP1A2 - genetics</topic><topic>Cytochrome P-450 CYP1B1 - genetics</topic><topic>Cytochrome P-450 CYP2C19 - genetics</topic><topic>Cytochrome P-450 CYP2C9 - genetics</topic><topic>Cytochrome P-450 CYP2D6 - genetics</topic><topic>Cytochrome P-450 CYP2E1 - genetics</topic><topic>Cytochrome P-450 CYP3A - genetics</topic><topic>Cytochrome P450</topic><topic>Drug abuse treatment</topic><topic>Drug metabolism</topic><topic>Drugs</topic><topic>Epilepsy</topic><topic>Epilepsy, Temporal Lobe - drug therapy</topic><topic>Epilepsy, Temporal Lobe - genetics</topic><topic>Evaluation</topic><topic>Gene expression</topic><topic>Genes</topic><topic>Genetics</topic><topic>Genomics</topic><topic>Genotype</topic><topic>Health aspects</topic><topic>Hippocampus</topic><topic>Hippocampus - metabolism</topic><topic>Hippocampus - pathology</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Laws, regulations and rules</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Metabolism</topic><topic>Model accuracy</topic><topic>Multidrug Resistance-Associated Proteins - genetics</topic><topic>Neurology</topic><topic>Neurosciences</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Patients</topic><topic>Physical Sciences</topic><topic>Polymorphism, Single Nucleotide - genetics</topic><topic>Population</topic><topic>Prediction models</topic><topic>Proteins</topic><topic>Research and Analysis Methods</topic><topic>Sclerosis</topic><topic>Single-nucleotide polymorphism</topic><topic>Studies</topic><topic>Surgery</topic><topic>Systematic review</topic><topic>Temporal lobe</topic><topic>Therapy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Silva-Alves, Mariana S</creatorcontrib><creatorcontrib>Secolin, Rodrigo</creatorcontrib><creatorcontrib>Carvalho, Benilton S</creatorcontrib><creatorcontrib>Yasuda, Clarissa L</creatorcontrib><creatorcontrib>Bilevicius, Elizabeth</creatorcontrib><creatorcontrib>Alvim, Marina K M</creatorcontrib><creatorcontrib>Santos, Renato O</creatorcontrib><creatorcontrib>Maurer-Morelli, Claudia V</creatorcontrib><creatorcontrib>Cendes, Fernando</creatorcontrib><creatorcontrib>Lopes-Cendes, Iscia</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Silva-Alves, Mariana S</au><au>Secolin, Rodrigo</au><au>Carvalho, Benilton S</au><au>Yasuda, Clarissa L</au><au>Bilevicius, Elizabeth</au><au>Alvim, Marina K M</au><au>Santos, Renato O</au><au>Maurer-Morelli, Claudia V</au><au>Cendes, Fernando</au><au>Lopes-Cendes, Iscia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2017-01-04</date><risdate>2017</risdate><volume>12</volume><issue>1</issue><spage>e0169214</spage><epage>e0169214</epage><pages>e0169214-e0169214</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28052106</pmid><doi>10.1371/journal.pone.0169214</doi><tpages>e0169214</tpages><orcidid>https://orcid.org/0000-0002-9195-1009</orcidid><orcidid>https://orcid.org/0000-0001-9084-7173</orcidid><orcidid>https://orcid.org/0000-0001-9336-9568</orcidid><orcidid>https://orcid.org/0000-0002-2485-9560</orcidid><orcidid>https://orcid.org/0000-0001-7929-2197</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2017-01, Vol.12 (1), p.e0169214-e0169214 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1855322339 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Anticonvulsants - therapeutic use Antiepileptic agents ATP Binding Cassette Transporter, Sub-Family B - genetics Biology and Life Sciences Breast cancer Control CYP1A2 protein CYP2D6 protein Cytochrome P-450 CYP1A1 - genetics Cytochrome P-450 CYP1A2 - genetics Cytochrome P-450 CYP1B1 - genetics Cytochrome P-450 CYP2C19 - genetics Cytochrome P-450 CYP2C9 - genetics Cytochrome P-450 CYP2D6 - genetics Cytochrome P-450 CYP2E1 - genetics Cytochrome P-450 CYP3A - genetics Cytochrome P450 Drug abuse treatment Drug metabolism Drugs Epilepsy Epilepsy, Temporal Lobe - drug therapy Epilepsy, Temporal Lobe - genetics Evaluation Gene expression Genes Genetics Genomics Genotype Health aspects Hippocampus Hippocampus - metabolism Hippocampus - pathology Humans Laboratories Laws, regulations and rules Mathematical models Medicine and Health Sciences Metabolism Model accuracy Multidrug Resistance-Associated Proteins - genetics Neurology Neurosciences NMR Nuclear magnetic resonance Patients Physical Sciences Polymorphism, Single Nucleotide - genetics Population Prediction models Proteins Research and Analysis Methods Sclerosis Single-nucleotide polymorphism Studies Surgery Systematic review Temporal lobe Therapy |
| title | A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information |
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