AIMS: Acute lymphoblastic leukemia (ALL) is the most common of all paediatric cancers. Aside from predisposing to ALL, polymorphisms could also be associated with poor outcome. Indeed, genetic variations involved in drug metabolism could, at least partially, be responsible for heterogeneous responses to standardized leukemia treatments, hence requiring more personalized therapy. The aims of this study were to (a) to determine the prevalence of seven common genetic polymorphisms including those that affect the folate and/or thiopurine metabolic pathways, i.e. cyclin D1 (CCND1-G870A), γ-glutamyl hydrolase (GGH-C452T), methylenetetrahydrofolate reductase (MTHFR-C677T and MTHFR-A1298C), thymidylate synthase promoter (TYMS-TSER), thiopurine methyltransferase (TPMT*3A and TPMT*3C) and inosine triphosphate pyrophosphatase (ITPA-C94A), in Caucasian (n = 94, age < 20) and Vietnamese (n = 141, age < 16 years) childhood ALL and (b) to assess the impact of a multilocus genetic risk score (MGRS) on relapse-free survival (RFS) using a Cox proportional-hazards regression model. RESULTS: The prevalence of MTHFR-677TT genotype was significantly higher in Caucasians (P = 0.008), in contrast to the prevalence of TYMS-TSER*3R/3R and ITPA-94AA/AC genotypes which were significantly higher in Vietnamese (P < 0.001 and P = 0.02, respectively). Compared with children with a low MGRS (≤ 3), those with a high MGRS (≥ 4) were 2.06 (95% CI = 1.01, 4.22; P = 0.04) times more likely to relapse. Adding MGRS into a multivariate Cox regression model with race/ethnicity and four clinical variables improved the predictive accuracy of the model (AUC from 0.682 to 0.709 at 24 months). CONCLUSION: Including MGRS into a clinical model improved the predictive accuracy of short and medium term prognosis, hence confirming the association between well determined pharmacogenotypes and outcome of paediatric ALL. Whether variants on other genes associated with folate metabolism can substantially improve the predictive value of current MGRS is not known but deserves further evaluation.
AIMS: Acute lymphoblastic leukemia (ALL) is the most common of all paediatric cancers. Aside from predisposing to ALL, polymorphisms could also be associated with poor outcome. Indeed, genetic variations involved in drug metabolism could, at least partially, be responsible for heterogeneous responses to standardized leukemia treatments, hence requiring more personalized therapy. The aims of this study were to (a) to determine the prevalence of seven common genetic polymorphisms including those that affect the folate and/or thiopurine metabolic pathways, i.e. cyclin D1 (CCND1-G870A), γ-glutamyl hydrolase (GGH-C452T), methylenetetrahydrofolate reductase (MTHFR-C677T and MTHFR-A1298C), thymidylate synthase promoter (TYMS-TSER), thiopurine methyltransferase (TPMT*3A and TPMT*3C) and inosine triphosphate pyrophosphatase (ITPA-C94A), in Caucasian (n = 94, age < 20) and Vietnamese (n = 141, age < 16 years) childhood ALL and (b) to assess the impact of a multilocus genetic risk score (MGRS) on relapse-free survival (RFS) using a Cox proportional-hazards regression model. RESULTS: The prevalence of MTHFR-677TT genotype was significantly higher in Caucasians (P = 0.008), in contrast to the prevalence of TYMS-TSER*3R/3R and ITPA-94AA/AC genotypes which were significantly higher in Vietnamese (P < 0.001 and P = 0.02, respectively). Compared with children with a low MGRS (≤ 3), those with a high MGRS (≥ 4) were 2.06 (95% CI = 1.01, 4.22; P = 0.04) times more likely to relapse. Adding MGRS into a multivariate Cox regression model with race/ethnicity and four clinical variables improved the predictive accuracy of the model (AUC from 0.682 to 0.709 at 24 months). CONCLUSION: Including MGRS into a clinical model improved the predictive accuracy of short and medium term prognosis, hence confirming the association between well determined pharmacogenotypes and outcome of paediatric ALL. Whether variants on other genes associated with folate metabolism can substantially improve the predictive value of current MGRS is not known but deserves further evaluation.
Authors: O Dewit; T Moreels; F Baert; H Peeters; C Reenaers; M de Vos; Ph Van Hootegem; V Muls; G Veereman; F Mana; M Van Outryve; J Holvoet; S Naegels; H Piessevaux; Y Horsmans; J L Gala Journal: Clin Biochem Date: 2011-06-24 Impact factor: 3.281
Authors: Marcello Tiseo; Elisa Giovannetti; Carmelo Tibaldi; Andrea Camerini; Francesco Di Costanzo; Fausto Barbieri; Jacobus A Burgers; Andrew Vincent; Godefridus J Peters; Egbert F Smit; Andrea Ardizzoni Journal: Lung Cancer Date: 2012-08-11 Impact factor: 5.705
Authors: Julie R Gilbert; Himalee S Sabnis; Roman Radzievski; Deon B Doxie; Deborah DeRyckere; Sharon M Castellino; Kavita Dhodapkar Journal: J Immunother Cancer Date: 2022-06 Impact factor: 12.469