Abstract
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.