title: A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia.
abstract: A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, 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 5 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 by 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 11 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.
authors.affiliation: 1 Salgomed Inc. , Del Mar, California.
authors.forename: Edison
authors.initials: E
authors.lastname: Ong
authors.forename: Anthony
authors.initials: A
authors.lastname: Szedlak
authors.forename: Yunyi
authors.initials: Y
authors.lastname: Kang
authors.forename: Peyton
authors.initials: P
authors.lastname: Smith
authors.forename: Nicholas
authors.initials: N
authors.lastname: Smith
authors.forename: Madison
authors.initials: M
authors.lastname: McBride
authors.forename: Darren
authors.initials: D
authors.lastname: Finlay
authors.forename: Kristiina
authors.initials: K
authors.lastname: Vuori
authors.forename: James
authors.initials: J
authors.lastname: Mason
authors.forename: Edward D
authors.initials: ED
authors.lastname: Ball
authors.forename: Carlo
authors.initials: C
authors.lastname: Piermarocchi
authors.forename: Giovanni
authors.initials: G
authors.lastname: Paternostro
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D015973:Gene Expression Regulation, Leukemic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000188:drug therapy / Q000235:genetics* / Q000378:metabolism; D058990:Molecular Targeted Therapy; D009154:Mutation; D060066:Protein Interaction Maps; D047428:Protein Kinase Inhibitors / Q000494:pharmacology; D015203:Reproducibility of Results; D059467:Transcriptome
chemical_list: D000970:Antineoplastic Agents; D047428:Protein Kinase Inhibitors
keywords: acute myeloid leukemia; gene networks
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