Comparative assessment of differential network analysis methods.

Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.

Commonly altered genomic regions in acute myeloid leukemia are enriched for somatic mutations involved in chromatin remodeling and splicing.

Acute myeloid leukemia (AML) is characterized by molecular heterogeneity. As commonly altered genomic regions point to candidate genes involved in leukemogenesis, we used microarray-based comparative genomic hybridization and single nucleotide polymorphism profiling data of 391 AML cases to further narrow down genomic regions of interest. Targeted resequencing of 1000 genes located in the critical regions was performed in a representative cohort of 50 AML samples comprising all major cytogenetic subgroups. We identified 120 missense/nonsense mutations as well as 60 insertions/deletions affecting 73 different genes (∼ 3.6 tumor-specific aberrations/AML). While most of the newly identified alterations were nonrecurrent, we observed an enrichment of mutations affecting genes involved in epigenetic regulation including known candidates like TET2, TET1, DNMT3A, and DNMT1, as well as mutations in the histone methyltransferases NSD1, EZH2, and MLL3. Furthermore, we found mutations in the splicing factor SFPQ and in the nonclassic regulators of mRNA processing CTCF and RAD21. These splicing-related mutations affected 10% of AML patients in a mutually exclusive manner. In conclusion, we could identify a large number of alterations in genes involved in aberrant splicing and epigenetic regulation in genomic regions commonly altered in AML, highlighting their important role in the molecular pathogenesis of AML.