A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia

TL;DR

This paper introduces a scalable network reconstruction method for acute myeloid leukemia that integrates diverse datasets efficiently, identifies key molecular interactions, and suggests potential therapeutic targets, validated through experimental kinase inhibitor responses.

Contribution

The authors developed a scalable, efficient network reconstruction approach for AML that combines multiple datasets and validates findings with experimental data, advancing systems biology methods.

Findings

Reduced computational time compared to existing methods

Identified key kinase targets such as CDK1, CDK2, CDK4, and CDK6

Validated network predictions with patient cell responses

Abstract

A key aim of systems biology is the reconstruction of molecular networks, however we do not yet have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1) we have used gene expression data (both microarray and RNA-seq) from five different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed optimizing the coefficient of variation of gene expression, using a validated gold standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from eleven AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4 and CDK6 and other kinases as potential therapeutic targets in AML.