Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia

Integration of gene expression and DNA-methylation profiles improves molecular subtype classification in acute myeloid leukemia

Taskesen, E.
Babaei, S.
Reinders, M.J.M.
De Ridder, J.

Electrical Engineering, Mathematics and Computer Science

Intelligent Systems

2015-02-23

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.

Acute Myeloid Leukemia
gene expression profiles
DNA-methylation profiles
AML subtypes classification
OA-Fund TU Delft

http://resolver.tudelft.nl/uuid:ccf969f3-b2c8-4219-8bd9-89f207ee8b60

https://doi.org/10.1186/1471-2105-16-S4-S5

BioMed Central

1471-2105

http://www.biomedcentral.com/1471-2105/16/S4/S5

BMC Bioinformatics, 16 (suppl 4), 2015

Institutional Repository

journal article

© 2015 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.