Related Experiment Video
Updated: Apr 25, 2026

Author Spotlight: Genetic Profiling for Fluorouracil Response in Gastric Cancer
Published on: May 10, 2024
A novel system for predicting the toxicity of irinotecan based on statistical pattern recognition with UGT1A
Ryouichi Tsunedomi1, Shoichi Hazama1, Yusuke Fujita2
1Department of Digestive Surgery and Surgical Oncology, Yamaguchi University Graduate School of Medicine, Yamaguchi 755-8505, Japan.
Abstract:
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 (TA6>TA7), UGT1A1*60 (-3279T>G), UGT1A7 (387T>G), UGT1A7 (622T>C), and UGT1A9*1b (-118T9>T10, 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, TA6, 211G; UGT1A7: 387T, 622T; UGT1A9: T10) and haplotype-II (UGT1A1: -3279T, TA6, 211A; UGT1A7: 387G, 622C; UGT1A9: T9) 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.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
09:34Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Related Concept Videos
Pharmacogenetics of Phase II Enzymes: N-acetyltransferase, Thiopurine S-methyltransferase, UDP-glucuronosyltransferase
Pharmacogenetics of Drug Targets: β₂-Adrenergic Receptors, Apo E, Thymidylate Synthase
Drug toxicity: Idiosyncratic Reactions
Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu
Pharmacogenetics of Drug Metabolism: Overview