BACKGROUND: Interindividual differences in therapeutic efficacy in patients treated with thiopurines might be explained by the presence of thiopurine S-methyltransferase (TPMT) alleles that encode for reduced TPMT enzymatic activity. It is therefore of value to know an individual's inherent capacity to express TPMT. METHOD: We developed a pyrosequencing method to detect 10 single-nucleotide polymorphisms (SNPs) in TPMT. A Swedish population (n = 800) was examined for TPMT*3A, TPMT*3B, TPMT*3C, and TPMT*2. Patients with inflammatory bowel disease (n = 24) and healthy volunteers (n = 6), selected on the basis of TPMT enzymatic activity, were investigated for all 10 SNPs to determine the relationship between TPMT genotype and phenotype. RESULTS: In the general population we identified the following genotypes with nonfunctional alleles: TPMT*1/*3A (*3A allelic frequency, 3.75%), TPMT*1/*3C (*3C allelic frequency, 0.44%), TPMT*1/*3B (*3B allelic frequency, 0.13%), and TPMT*1/*2 (*2 allelic frequency, 0.06%). All nine individuals with normal enzymatic activity were wild-type TPMT*1/*1. Thirteen individuals with intermediate activity were either TPMT*1/*3A (n = 12) or TPMT*1/*2 (n = 1). Eight individuals with low enzymatic activity were TPMT*3A/*3A (n = 4), TPMT*3A/*3C (n = 2), or TPMT*1/*3A (n = 2). CONCLUSION: Next to wild type, the most frequent alleles in Sweden are TPMT*3A and TPMT*3C. A previously established phenotypic cutoff for distinguishing normal from intermediate metabolizers was confirmed. To identify the majority of cases (90%) with low or intermediate TPMT activity, it was sufficient to analyze individuals for only 3 of the 10 SNPs investigated. Nevertheless, this investigation indicates that other mutations might be of relevance for decreased enzymatic activity.
BACKGROUND: Interindividual differences in therapeutic efficacy in patients treated with thiopurines might be explained by the presence of thiopurine S-methyltransferase (TPMT) alleles that encode for reduced TPMT enzymatic activity. It is therefore of value to know an individual's inherent capacity to express TPMT. METHOD: We developed a pyrosequencing method to detect 10 single-nucleotide polymorphisms (SNPs) in TPMT. A Swedish population (n = 800) was examined for TPMT*3A, TPMT*3B, TPMT*3C, and TPMT*2. Patients with inflammatory bowel disease (n = 24) and healthy volunteers (n = 6), selected on the basis of TPMT enzymatic activity, were investigated for all 10 SNPs to determine the relationship between TPMT genotype and phenotype. RESULTS: In the general population we identified the following genotypes with nonfunctional alleles: TPMT*1/*3A (*3A allelic frequency, 3.75%), TPMT*1/*3C (*3C allelic frequency, 0.44%), TPMT*1/*3B (*3B allelic frequency, 0.13%), and TPMT*1/*2 (*2 allelic frequency, 0.06%). All nine individuals with normal enzymatic activity were wild-type TPMT*1/*1. Thirteen individuals with intermediate activity were either TPMT*1/*3A (n = 12) or TPMT*1/*2 (n = 1). Eight individuals with low enzymatic activity were TPMT*3A/*3A (n = 4), TPMT*3A/*3C (n = 2), or TPMT*1/*3A (n = 2). CONCLUSION: Next to wild type, the most frequent alleles in Sweden are TPMT*3A and TPMT*3C. A previously established phenotypic cutoff for distinguishing normal from intermediate metabolizers was confirmed. To identify the majority of cases (90%) with low or intermediate TPMT activity, it was sufficient to analyze individuals for only 3 of the 10 SNPs investigated. Nevertheless, this investigation indicates that other mutations might be of relevance for decreased enzymatic activity.
Authors: Marzena Skrzypczak-Zielinska; Pawel Borun; Katarzyna Milanowska; Ludwika Jakubowska-Burek; Oliwia Zakerska; Agnieszka Dobrowolska-Zachwieja; Andrzej Plawski; Ursula G Froster; Marlena Szalata; Ryszard Slomski Journal: Genet Test Mol Biomarkers Date: 2012-12-19
Authors: U Hindorf; M Lindqvist; C Peterson; P Söderkvist; M Ström; H Hjortswang; A Pousette; S Almer Journal: Gut Date: 2006-03-16 Impact factor: 23.059
Authors: Lilla M Roy; Richard M Zur; Elizabeth Uleryk; Chris Carew; Shinya Ito; Wendy J Ungar Journal: Pharmacogenomics Date: 2016-03-29 Impact factor: 2.533
Authors: Jeeshan Chowdhury; Govind V Kaigala; Sudeep Pushpakom; Jana Lauzon; Alistair Makin; Alexey Atrazhev; Alex Stickel; William G Newman; Christopher J Backhouse; Linda M Pilarski Journal: J Mol Diagn Date: 2007-08-09 Impact factor: 5.568