BackgroundCoordinated efforts to collect large-scale data sets provide a basis for systems level understanding of complex diseases. In order to translate these fragmented and heterogeneous data sets into knowledge and medical benefits, advanced computational methods for data analysis, integration and visualization are needed.MethodsWe introduce a novel data integration framework, Anduril, for translating fragmented large-scale data into testable predictions. The Anduril framework allows rapid integration of heterogeneous data with state-of-the-art computational methods and existing knowledge in bio-databases. Anduril automatically generates thorough summary reports and a website that shows the most relevant features of each gene at a glance, allows sorting of data based on different parameters, and provides direct links to more detailed data on genes, transcripts or genomic regions. Anduril is open-source; all methods and documentation are freely available.ResultsWe have integrated multidimensional molecular and clinical data from 338 subjects having glioblastoma multiforme, one of the deadliest and most poorly understood cancers, using Anduril. The central objective of our approach is to identify genetic loci and genes that have significant survival effect. Our results suggest several novel genetic alterations linked to glioblastoma multiforme progression and, more specifically, reveal Moesin as a novel glioblastoma multiforme-associated gene that has a strong survival effect and whose depletion in vitro significantly inhibited cell proliferation. All analysis results are available as a comprehensive website.ConclusionsOur results demonstrate that integrated analysis and visualization of multidimensional and heterogeneous data by Anduril enables drawing conclusions on functional consequences of large-scale molecular data. Many of the identified genetic loci and genes having significant survival effect have not been reported earlier in the context of glioblastoma multiforme. Thus, in addition to generally applicable novel methodology, our results provide several glioblastoma multiforme candidate genes for further studies.Anduril is available at http://csbi.ltdk.helsinki.fi/anduril/The glioblastoma multiforme analysis results are available at http://csbi.ltdk.helsinki.fi/anduril/tcga-gbm/

BackgroundThe global effect of copy number and epigenetic alterations on miRNA expression in cancer is poorly understood. In the present study, we integrate genome-wide DNA methylation, copy number and miRNA expression and identify genetic mechanisms underlying miRNA dysregulation in breast cancer.ResultsWe identify 70 miRNAs whose expression was associated with alterations in copy number or methylation, or both. Among these, five miRNA families are represented. Interestingly, the members of these families are encoded on different chromosomes and are complementarily altered by gain or hypomethylation across the patients. In an independent breast cancer cohort of 123 patients, 41 of the 70 miRNAs were confirmed with respect to aberration pattern and association to expression. In vitro functional experiments were performed in breast cancer cell lines with miRNA mimics to evaluate the phenotype of the replicated miRNAs. let-7e-3p, which in tumors is found associated with hypermethylation, is shown to induce apoptosis and reduce cell viability, and low let-7e-3p expression is associated with poorer prognosis. The overexpression of three other miRNAs associated with copy number gain, miR-21-3p, miR-148b-3p and miR-151a-5p, increases proliferation of breast cancer cell lines. In addition, miR-151a-5p enhances the levels of phosphorylated AKT protein.ConclusionsOur data provide novel evidence of the mechanisms behind miRNA dysregulation in breast cancer. The study contributes to the understanding of how methylation and copy number alterations influence miRNA expression, emphasizing miRNA functionality through redundant encoding, and suggests novel miRNAs important in breast cancer.

Lymphatic invasion and accumulation of continuous collagen bundles around tumor cells are associated with poor melanoma prognosis, but the underlying mechanisms and molecular determinants have remained unclear. We show here that a copy-number gain or overexpression of the membranetype matrix metalloproteinase MMP16 (MT3-MMP) is associated with poor clinical outcome, collagen bundle assembly around tumor cell nests, and lymphatic invasion. In cultured WM852 melanoma cells derived from human melanoma metastasis, silencing of MMP16 resulted in cell-surface accumulation of the MMP16 substrate MMP14 (MT1-MMP) as well as L1CAM cell adhesion molecule, identified here as a novel MMP16 substrate. When limiting the activities of these trans-membrane protein substrates toward pericellular collagen degradation, cell junction disassembly, and blood endothelial transmigration, MMP16 supported nodular-type growth of adhesive collagen-surrounded melanoma cell nests, coincidentally steering cell collectives into lymphatic vessels. These results uncover a novel mechanism in melanoma pathogenesis, whereby restricted collagen infiltration and limited mesenchymal invasion are unexpectedly associated with the properties of the most aggressive tumors, revealing MMP16 as a putative indicator of adverse melanoma prognosis. Cancer Res; 75(10); 2083-94. Ó2015 AACR.

Somatic copy-number alterations (SCNAs) are an important type of structural variation affecting tumor pathogenesis. Accurate detection of genomic regions with SCNAs is crucial for cancer genomics as these regions contain likely drivers of cancer development. Deep sequencing technology provides single-nucleotide resolution genomic data and is considered one of the best measurement technologies to detect SCNAs. Although several algorithms have been developed to detect SCNAs from whole-genome and whole-exome sequencing data, their relative performance has not been studied. Here, we have compared ten SCNA detection algorithms in both simulated and primary tumor deep sequencing data. In addition, we have evaluated the applicability of exome sequencing data for SCNA detection. Our results show that (i) clear differences exist in sensitivity and specificity between the algorithms, (ii) SCNA detection algorithms are able to identify most of the complex chromosomal alterations and (iii) exome sequencing data are suitable for SCNA detection.

CNAmet R-package and user guide are freely available under GNU General Public License at http://csbi.ltdk.helsinki.fi/CNAmet.