Population-scale predictions of DPD and TPMT phenotypes using a quantitative pharmacogene-specific ensemble classifier
Menée à partir de données de séquençage portant sur 138 842 personnes, cette étude évalue la performance d'un algorithme, basé sur des données pharmacogénétiques, pour prédire le phénotype de la dihydropyrimidine déshydrogénase et de la thiopurine S-méthyltransférase et prévoir la toxicité de la fluoropyrimidine et de la thiopurine
Background : Inter-individual differences in dihydropyrimidine dehydrogenase (DPYD encoding DPD) and thiopurine S-methyltransferase (TPMT) activity are important predictors for fluoropyrimidine and thiopurine toxicity. While several variants in these genes are known to decrease enzyme activities, many additional genetic variations with unclear functional consequences have been identified, complicating informed clinical decision-making in the respective carriers.
Methods : We used a novel pharmacogenetically trained ensemble classifier to analyse DPYD and TPMT genetic variability based on sequencing data from 138,842 individuals across eight populations.
Results : The algorithm accurately predicted in vivo consequences of DPYD and TPMT variants (accuracy 91.4% compared to 95.3% in vitro). Further analysis showed high genetic complexity of DPD deficiency, advocating for sequencing-based DPYD profiling, whereas genotyping of four variants in TPMT was sufficient to explain >95% of phenotypic TPMT variability. Lastly, we provided population-scale profiles of ethnogeographic variability in DPD and TPMT phenotypes, and revealed striking interethnic differences in frequency and genetic constitution of DPD and TPMT deficiency.
Conclusion : These results provide the most comprehensive data set of DPYD and TPMT variability published to date with important implications for population-adjusted genetic profiling strategies of fluoropyrimidine and thiopurine risk factors and precision public health.
British Journal of Cancer , résumé, 2020