Clinical parameter-based prediction of DNA methylation classification generates a prediction model of prognosis in patients with juvenile myelomonocytic leukemia

Juvenile myelomonocytic leukemia (JMML) is a rare heterogeneous hematological malignancy of early childhood characterized by causative RAS pathway mutations. Classifying patients with JMML using global DNA methylation profiles is useful for risk stratification. We implemented machine learning algori...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2022-08, Vol.12 (1), p.14753-14753, Article 14753
Main Authors: Imaizumi, Takahiro, Meyer, Julia, Wakamatsu, Manabu, Kitazawa, Hironobu, Murakami, Norihiro, Okuno, Yusuke, Yoshida, Taro, Sajiki, Daichi, Hama, Asahito, Kojima, Seiji, Takahashi, Yoshiyuki, Loh, Mignon, Stieglitz, Elliot, Muramatsu, Hideki
Format: Article
Language:English
Subjects:
631/67/1990
692/420/2489
Algorithms
Bayesian analysis
Children
Classification
Decision trees
Deoxyribonucleic acid
DNA
DNA methylation
Humanities and Social Sciences
Juveniles
Learning algorithms
Leukemia
Machine learning
Malignancy
Medical prognosis
multidisciplinary
Myelomonocytic leukemia
Patients
Prediction models
Science
Science (multidisciplinary)
Survival
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Juvenile myelomonocytic leukemia (JMML) is a rare heterogeneous hematological malignancy of early childhood characterized by causative RAS pathway mutations. Classifying patients with JMML using global DNA methylation profiles is useful for risk stratification. We implemented machine learning algorithms (decision tree, support vector machine, and naïve Bayes) to produce a DNA methylation-based classification according to recent international consensus definitions using a well-characterized pooled cohort of patients with JMML (n = 128). DNA methylation was originally categorized into three subgroups: high methylation (HM), intermediate methylation (IM), and low methylation (LM), which is a trichotomized classification. We also dichotomized the subgroups as HM/IM and LM. The decision tree model showed high concordances with 450k-based methylation [82.3% (106/128) for the dichotomized and 83.6% (107/128) for the trichotomized subgroups, respectively]. With an independent cohort (n = 72), we confirmed that these models using both the dichotomized and trichotomized classifications were highly predictive of survival. Our study demonstrates that machine learning algorithms can generate clinical parameter-based models that predict the survival outcomes of patients with JMML and high accuracy. These models enabled us to rapidly and effectively identify candidates for augmented treatment following diagnosis.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-18733-4