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. 2020 Jan 10;5(1):3.
doi: 10.1038/s41392-019-0090-5.

DNA methylation markers in the diagnosis and prognosis of common leukemias

Hua Jiang  1 , Zhiying Ou  2 , Yingyi He  2 , Meixing Yu  2 , Shaoqing Wu  2 , Gen Li  2 , Jie Zhu  2 , Ru Zhang  2 , Jiayi Wang  2 , Lianghong Zheng  3 , Xiaohong Zhang  2 , Wenge Hao  2 , Liya He  2 , Xiaoqiong Gu  2 , Qingli Quan  2 , Edward Zhang  2 , Huiyan Luo  4 , Wei Wei  4 , Zhihuan Li  3 , Guangxi Zang  3 , Charlotte Zhang  2 , Tina Poon  2 , Daniel Zhang  2 , Ian Ziyar  3 , Run-Ze Zhang  3 , Oulan Li  3 , Linhai Cheng  3 , Taylor Shimizu  3 , Xinping Cui  5 , Jian-Kang Zhu  6 , Xin Sun  7 , Kang Zhang  8   9   10
Affiliations

DNA methylation markers in the diagnosis and prognosis of common leukemias

Hua Jiang et al. Signal Transduct Target Ther. .

Abstract

The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis, treatment selection, and prognosis prediction of patients. Using genome-wide methylation profiling and machine learning methods, we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia (ALL) or acute myelogenous leukemia (AML) from normal blood. We established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and specificity. We then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups, with significant differences in clinical outcome in each leukemia type. Together, these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML, with implications for the prediction of prognosis and treatment selection.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Methylation profile can differentiate AML blood and normal blood using 4 markers.
a Unsupervised hierarchical clustering and the heat map associated with the methylation profile (according to the color scale shown) in AML blood vs normal blood. b The accuracy of predicting AML as assessed by the ROC curve.
Fig. 2
Fig. 2. Methylation profile can differentiate ALL blood and normal blood using 7 markers.
a Unsupervised hierarchical clustering and the heat maps associated with the methylation profile (according to the color scale shown) in ALL blood versus normal blood samples. b The accuracy of predicting ALL as assessed by the ROC curve.
Fig. 3
Fig. 3. Methylation profile can differentiate subtypes of leukemia using 5 markers.
a Unsupervised hierarchical clustering and the heatmap with the methylation profile (according to the color scale shown) in ALL versus AML samples. b The accuracy of predicting AML and ALL as assessed by the ROC curve.
Fig. 4
Fig. 4. Using 11 markers, the methylation profile can differentiate the leukemia subtype and normal blood.
Unsupervised hierarchical clustering and the heatmap associated with ALL, AML, and normal blood.
Fig. 5
Fig. 5. Methylation markers can predict the five-year overall survival of patients.
a AML training set (n = 125); b AML validation set (n = 55); c ALL training set (n = 55); and d ALL validation set (n = 34).
See this image and copyright information in PMC

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