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Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic/myeloproliferative neoplasm that develops during infancy and early childhood. The array-based international consensus definition of DNA methylation has recently classified patients with JMML into the following 3 groups: high (HM), intermediate (IM), and low methylation (LM). To develop a simple and robust methylation clinical test, 137 patients with JMML were analyzed using the Digital Restriction Enzyme Analysis of Methylation (DREAM), which is a next-generation sequencing–based methylation analysis. Unsupervised consensus clustering of the discovery cohort (n = 99) using DREAM data identified HM (HM_DREAM; n = 35) and LM subgroups (LM_DREAM; n = 64). Of the 98 cases that could be compared with the international consensus classification, 90 HM (n = 30) and LM (n = 60) cases had 100% concordance with DREAM clustering results. Of the remaining 8 cases comprising the IM group, 4 were classified as belonging to the HM_DREAM group and 4 to the LM_DREAM group. A machine-learning classifier was successfully constructed using a support vector machine (SVM), which divided the validation cohort (n = 38) into HM (HM_SVM, n = 18) and LM (LM_SVM; n = 20) groups. Patients with the HM_SVM profile had a significantly poorer 5-year overall survival rate than those with the LM_SVM profile. In conclusion, we developed a robust methylation test using DREAM for patients with JMML. This simple and straightforward test can be easily incorporated into diagnosis to generate a methylation classification for patients so they can receive risk-adapted treatment in the context of future clinical trials.

Juvenile myelomonocytic leukemia (JMML) is a rare myelodysplastic/myeloproliferative neoplasm that develops during infancy and early childhood. The array-based international consensus definition of DNA methylation has recently classified patients with JMML into the following 3 groups: high (HM), intermediate (IM), and low methylation (LM). To develop a simple and robust methylation clinical test, 137 patients with JMML were analyzed using the Digital Restriction Enzyme Analysis of Methylation (DREAM), which is a next-generation sequencing–based methylation analysis. Unsupervised consensus clustering of the discovery cohort (n = 99) using DREAM data identified HM (HM_DREAM; n = 35) and LM subgroups (LM_DREAM; n = 64). Of the 98 cases that could be compared with the international consensus classification, 90 HM (n = 30) and LM (n = 60) cases had 100% concordance with DREAM clustering results. Of the remaining 8 cases comprising the IM group, 4 were classified as belonging to the HM_DREAM group and 4 to the LM_DREAM group. A machine-learning classifier was successfully constructed using a support vector machine (SVM), which divided the validation cohort (n = 38) into HM (HM_SVM, n = 18) and LM (LM_SVM; n = 20) groups. Patients with the HM_SVM profile had a significantly poorer 5-year overall survival rate than those with the LM_SVM profile. In conclusion, we developed a robust methylation test using DREAM for patients with JMML. This simple and straightforward test can be easily incorporated into diagnosis to generate a methylation classification for patients so they can receive risk-adapted treatment in the context of future clinical trials.

Melphalan is widely used for hematopoietic stem cell transplantation (HSCT) conditioning. However, the relationship between its pharmacokinetic (PK) and transplantation outcomes in children has not been thoroughly investigated. We prospectively analyzed the relationship between melphalan area under the curve (AUC) and transplantation outcome and examined the development of a predictive model for melphalan clearance in children. This study included 43 children aged 0 to 19 years who underwent HSCT following a melphalan-based conditioning regimen from 2017 to 2021. In univariable analysis, high-melphalan AUC resulted in a significantly lower cumulative incidence of acute graft-versus-host disease and a higher cumulative incidence of thrombotic microangiopathy, although no significant difference was observed in survival. Regression analysis of a randomly selected derivation cohort (n= 21) revealed the following covariate PK model: predicted melphalan clearance (mL/min) = 6.47 × 24-h urinary creatinine excretion rate (CER, g/day) × 24-h creatinine clearance rate (CCR, mL/min) + 92.8. In the validation cohort (n= 22), the measured melphalan clearance values were significantly correlated with those calculated based on the prediction equation (R2= 0.663). These results indicate that melphalan exposure may be optimized by adjusting the melphalan dose according to CER and CCR.

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Graft failure is a majorcause of severe aplasia after bone marrow transplant (BMT) in patients with acquired bone marrow failure (aBMF). However, we have encountered several patients with aBMF who showed persistent aplasia with full donor chimerism after BMT (donor-type aplasia). The mechanism and risk factors of donor-type aplasia are not clarified.