Genome-wide DNA methylation is predictive of outcome in juvenile myelomonocytic leukemia

Elliot Stieglitz (), Tali Mazor, Adam B. Olshen, Huimin Geng, Laura C. Gelston, Jon Akutagawa, Daniel B. Lipka, Christoph Plass, Christian Flotho, Farid F. Chehab, Benjamin S. Braun, Joseph F. Costello and Mignon L. Loh ()
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Elliot Stieglitz: University of California, San Francisco
Tali Mazor: University of California, San Francisco
Adam B. Olshen: University of California, San Francisco
Huimin Geng: University of California
Laura C. Gelston: University of California, San Francisco
Jon Akutagawa: University of California, San Francisco
Daniel B. Lipka: German Cancer Research Center (DKFZ)
Christoph Plass: German Cancer Research Center (DKFZ)
Christian Flotho: Im Neuenheimer Feld 280
Farid F. Chehab: University of California, San Francisco
Benjamin S. Braun: University of California, San Francisco
Joseph F. Costello: University of California, San Francisco
Mignon L. Loh: University of California, San Francisco

Nature Communications, 2017, vol. 8, issue 1, 1-8

Abstract: Abstract Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder of childhood caused by mutations in the Ras pathway. Outcomes in JMML vary markedly from spontaneous resolution to rapid relapse after hematopoietic stem cell transplantation. Here, we hypothesized that DNA methylation patterns would help predict disease outcome and therefore performed genome-wide DNA methylation profiling in a cohort of 39 patients. Unsupervised hierarchical clustering identifies three clusters of patients. Importantly, these clusters differ significantly in terms of 4-year event-free survival, with the lowest methylation cluster having the highest rates of survival. These findings were validated in an independent cohort of 40 patients. Notably, all but one of 14 patients experiencing spontaneous resolution cluster together and closer to 22 healthy controls than to other JMML cases. Thus, we show that DNA methylation patterns in JMML are predictive of outcome and can identify the patients most likely to experience spontaneous resolution.

Date: 2017
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DOI: 10.1038/s41467-017-02178-9

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