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A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes
To predict precisely severe toxicity of irinotecan, we evaluated the association of UGT1A variants, haplotypes and the combination of UGT1A genotypes to severe toxicity of irinotecan. UGT1A1*6 (211G>A), UGT1A1*28 (TA(6)>TA(7)), UGT1A1*60 (−3279T>G), UGT1A7 (387T>G), UGT1A7 (622T>C), a...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151810/ https://www.ncbi.nlm.nih.gov/pubmed/25175642 http://dx.doi.org/10.3892/ijo.2014.2556 |
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author | TSUNEDOMI, RYOUICHI HAZAMA, SHOICHI FUJITA, YUSUKE OKAYAMA, NAOKO KANEKIYO, SHINSUKE INOUE, YUKA YOSHINO, SHIGEFUMI YAMASAKI, TAKAHIRO SUEHIRO, YUTA KA OBA, KOJI MISHIMA, HIDEYUKI SAKAMOTO, JUNICHI HAMAMOTO, YOSHIHIKO OKA, MASAKI |
author_facet | TSUNEDOMI, RYOUICHI HAZAMA, SHOICHI FUJITA, YUSUKE OKAYAMA, NAOKO KANEKIYO, SHINSUKE INOUE, YUKA YOSHINO, SHIGEFUMI YAMASAKI, TAKAHIRO SUEHIRO, YUTA KA OBA, KOJI MISHIMA, HIDEYUKI SAKAMOTO, JUNICHI HAMAMOTO, YOSHIHIKO OKA, MASAKI |
author_sort | TSUNEDOMI, RYOUICHI |
collection | PubMed |
description | To predict precisely severe toxicity of irinotecan, we evaluated the association of UGT1A variants, haplotypes and the combination of UGT1A genotypes to severe toxicity of irinotecan. UGT1A1*6 (211G>A), UGT1A1*28 (TA(6)>TA(7)), UGT1A1*60 (−3279T>G), UGT1A7 (387T>G), UGT1A7 (622T>C), and UGT1A9*1b (−118T(9)>T(10)(,) also named *22) were genotyped in 123 patients with metastatic colorectal cancer who had received irinotecan-based chemotherapy. Among the 123 patients, 73 were enrolled in either of two phase II studies of the FOLFIRI (leucovorin, 5-fluorouracil and irinotecan) regimen; these patients constituted the training population, which was used to construct the predicting system. The other 50 patients constituted the validation population; these 50 patients either had participated in a phase II study of irinotecan/5′-deoxy-5-fluorouridine or were among consecutive patients who received FOLFIRI therapy. This prediction system used sequential forward floating selection based on statistical pattern recognition using UGT1A genotypes, gender and age. Several UGT1A genotypes [UGT1A1*6, UGT1A7 (387T>G), UGT1A7 (622T>C) and UGT1A9*1b] were associated with the irinotecan toxicity. Among the haplotypes, haplotype-I (UGT1A1: −3279T, TA(6), 211G; UGT1A7: 387T, 622T; UGT1A9: T(10)) and haplotype-II (UGT1A1: −3279T, TA(6), 211A; UGT1A7: 387G, 622C; UGT1A9: T(9)) were also associated with irinotecan toxicity. Furthermore, our new system for predicting the risk of irinotecan toxicity was 83.9% accurate with the training population and 72.1% accurate with the validation population. Our novel prediction system using statistical pattern recognition depend on genotypes in UGT1A, age and gender; moreover, it showed high predictive performance even though the treatment regimens differed among the training and validation patients. |
format | Online Article Text |
id | pubmed-4151810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-41518102014-09-03 A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes TSUNEDOMI, RYOUICHI HAZAMA, SHOICHI FUJITA, YUSUKE OKAYAMA, NAOKO KANEKIYO, SHINSUKE INOUE, YUKA YOSHINO, SHIGEFUMI YAMASAKI, TAKAHIRO SUEHIRO, YUTA KA OBA, KOJI MISHIMA, HIDEYUKI SAKAMOTO, JUNICHI HAMAMOTO, YOSHIHIKO OKA, MASAKI Int J Oncol Articles To predict precisely severe toxicity of irinotecan, we evaluated the association of UGT1A variants, haplotypes and the combination of UGT1A genotypes to severe toxicity of irinotecan. UGT1A1*6 (211G>A), UGT1A1*28 (TA(6)>TA(7)), UGT1A1*60 (−3279T>G), UGT1A7 (387T>G), UGT1A7 (622T>C), and UGT1A9*1b (−118T(9)>T(10)(,) also named *22) were genotyped in 123 patients with metastatic colorectal cancer who had received irinotecan-based chemotherapy. Among the 123 patients, 73 were enrolled in either of two phase II studies of the FOLFIRI (leucovorin, 5-fluorouracil and irinotecan) regimen; these patients constituted the training population, which was used to construct the predicting system. The other 50 patients constituted the validation population; these 50 patients either had participated in a phase II study of irinotecan/5′-deoxy-5-fluorouridine or were among consecutive patients who received FOLFIRI therapy. This prediction system used sequential forward floating selection based on statistical pattern recognition using UGT1A genotypes, gender and age. Several UGT1A genotypes [UGT1A1*6, UGT1A7 (387T>G), UGT1A7 (622T>C) and UGT1A9*1b] were associated with the irinotecan toxicity. Among the haplotypes, haplotype-I (UGT1A1: −3279T, TA(6), 211G; UGT1A7: 387T, 622T; UGT1A9: T(10)) and haplotype-II (UGT1A1: −3279T, TA(6), 211A; UGT1A7: 387G, 622C; UGT1A9: T(9)) were also associated with irinotecan toxicity. Furthermore, our new system for predicting the risk of irinotecan toxicity was 83.9% accurate with the training population and 72.1% accurate with the validation population. Our novel prediction system using statistical pattern recognition depend on genotypes in UGT1A, age and gender; moreover, it showed high predictive performance even though the treatment regimens differed among the training and validation patients. D.A. Spandidos 2014-07-22 /pmc/articles/PMC4151810/ /pubmed/25175642 http://dx.doi.org/10.3892/ijo.2014.2556 Text en Copyright © 2014, Spandidos Publications http://creativecommons.org/licenses/by/3.0 This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported License. The article may be redistributed, reproduced, and reused for non-commercial purposes, provided the original source is properly cited. |
spellingShingle | Articles TSUNEDOMI, RYOUICHI HAZAMA, SHOICHI FUJITA, YUSUKE OKAYAMA, NAOKO KANEKIYO, SHINSUKE INOUE, YUKA YOSHINO, SHIGEFUMI YAMASAKI, TAKAHIRO SUEHIRO, YUTA KA OBA, KOJI MISHIMA, HIDEYUKI SAKAMOTO, JUNICHI HAMAMOTO, YOSHIHIKO OKA, MASAKI A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title | A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title_full | A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title_fullStr | A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title_full_unstemmed | A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title_short | A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A genotypes |
title_sort | novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with ugt1a genotypes |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151810/ https://www.ncbi.nlm.nih.gov/pubmed/25175642 http://dx.doi.org/10.3892/ijo.2014.2556 |
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