An Integrated Clinical and Genetic Prediction Model for Tacrolimus Levels in Pediatric Solid Organ Transplant Recipients

Min, Sandar; Papaz, Tanya; Lambert, A Nicole; Allen, Upton; Birk, Patricia; Blydt-Hansen, Tom; Foster, Bethany J; Grasemann, Hartmut; Hamiwka, Lorraine; Litalien, Catherine; Ng, Vicky; Berka, Noureddine; Campbell, Patricia; Daniel, Claude; Saw, Chee Loong; Tinckham, Kathryn; Urschel, Simon; Van Driest, Sara L; Parekh, Rulan et Mital, Seema (2021). An Integrated Clinical and Genetic Prediction Model for Tacrolimus Levels in Pediatric Solid Organ Transplant Recipients Transplantation , vol. 106 , nº 3. pp. 597-606. DOI: 10.1097/tp.0000000000003700.

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Résumé

BACKGROUND: There are challenges in achieving and maintaining therapeutic tacrolimus levels after solid organ transplantation (SOT). The purpose of this genome-wide association study (GWAS) was to generate an integrated clinical and genetic prediction model for tacrolimus levels in pediatric SOT. METHODS: In a multicenter prospective observational cohort study (2015-18), children <18 years old at their first SOT receiving tacrolimus as maintenance immunosuppression were included (455 as discovery cohort; 322 as validation cohort). Genotyping was performed using a genome-wide single nucleotide polymorphism (SNP) array and analyzed for association with tacrolimus trough levels during 1-year follow-up. RESULTS: GWAS adjusted for clinical factors identified 25 SNPs associated with tacrolimus levels; 8 were significant at a genome-wide level (p<1.025x10). Nineteen SNPs were replicated in the validation cohort. After removing SNPs in strong linkage disequilibrium, 14 SNPs remained independently associated with tacrolimus levels. Both traditional and machine learning approaches selected organ type, age at transplant, rs776746, rs12333983 and rs12957142 SNPs as the top predictor variables for dose-adjusted 36-48hr post-tacrolimus initiation (T1) levels. There was a significant interaction between age and organ type with rs776476*1 SNP (p<0.05). The combined clinical and genetic model had lower prediction error and explained 30% of the variation in dose-adjusted T1 levels compared to 18% by the clinical and 12% by the genetic only model. CONCLUSIONS: Our study highlights the importance of incorporating age, organ type and genotype in predicting tacrolimus levels and lays the groundwork for developing an individualized age and organ specific genotype-guided tacrolimus dosing algorithm.

Type de document:	Article
Mots-clés libres:	-
Centre:	Centre INRS-Institut Armand Frappier
Date de dépôt:	30 juin 2022 13:59
Dernière modification:	30 juin 2022 13:59
URI:	https://espace.inrs.ca/id/eprint/12393

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