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

Autor: Imaizumi T; Department of Advanced Medicine, Nagoya University Hospital, Nagoya, Japan., Meyer J; Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, USA., Wakamatsu M; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Kitazawa H; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Murakami N; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Okuno Y; Department of Virology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan., Yoshida T; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Sajiki D; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Hama A; Department of Hematology and Oncology, Children's Medical Center, Japanese Red Cross Nagoya First Hospital, Nagoya, Japan., Kojima S; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Takahashi Y; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan., Loh M; Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, USA., Stieglitz E; Department of Pediatrics, Benioff Children's Hospital, University of California, San Francisco, San Francisco, USA., Muramatsu H; Department of Pediatrics, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8560, Japan. hideki-muramatsu@med.nagoya-u.ac.jp.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Aug 30; Vol. 12 (1), pp. 14753. Date of Electronic Publication: 2022 Aug 30.
DOI: 10.1038/s41598-022-18733-4
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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