Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia. Issue 4 (December 2015)

• Record Type: Journal Article
• Authors: Taskesen, Erdogan; Babaei, Sepideh; Reinders, Marcel; de Ridder, Jeroen
• Is Part Of: BMC bioinformatics. Volume 16:Issue 4(2015)
• Page Start: 1
• Page End: 8
• Publication Date: 2015-12
• Subjects: Acute Myeloid Leukemia -- gene expression profiles -- DNA-methylation profiles -- AML subtypes classification; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S4-S5
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Acute Myeloid Leukemia (AML) is characterized by various cytogenetic and molecular abnormalities. Detection of these abnormalities is important in the risk-classification of patients but requires laborious experimentation. Various studies showed that gene expression profiles (GEP), and the gene signatures derived from GEP, can be used for the prediction of subtypes in AML. Similarly, successful prediction was also achieved by exploiting DNA-methylation profiles (DMP). There are, however, no studies that compared classification accuracy and performance between GEP and DMP, neither are there studies that integrated both types of data to determine whether predictive power can be improved. Approach Here, we used 344 well-characterized AML samples for which both gene expression and DNA-methylation profiles are available. We created three different classification strategies including early, late and no integration of these datasets and used them to predict AML subtypes using a logistic regression model with Lasso regularization. Results We illustrate that both gene expression and DNA-methylation profiles contain distinct patterns that contribute to discriminating AML subtypes and that an integration strategy can exploit these patterns to achieve synergy between both data types. We show that concatenation of features from both data sets, i.e. early integration, improves the predictive power compared to classifiers trained on GEP or DMP alone. A more sophisticated strategy, i.e. the late integration strategy, employs a two-layer classifier which outperforms the early integration strategy. Conclusion We demonstrate that prediction of known cytogenetic and molecular abnormalities in AML can be further improved by integrating GEP and DMP profiles.