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Showing papers by "Hans-Peter Lenhof published in 2016"


Journal ArticleDOI
TL;DR: The presented use-case demonstrates that GeneTrail2 is well equipped for the integrative analysis of comprehensive -omics data and may help to shed light on complex pathogenic mechanisms in cancer and other diseases.
Abstract: Motivation Gene set analysis has revolutionized the interpretation of high-throughput transcriptomic data. Nowadays, with comprehensive studies that measure multiple -omics from the same sample, powerful tools for the integrative analysis of multi-omics datasets are required. Results Here, we present GeneTrail2, a web service allowing the integrated analysis of transcriptomic, miRNomic, genomic and proteomic datasets. It offers multiple statistical tests, a large number of predefined reference sets, as well as a comprehensive collection of biological categories and enables direct comparisons between the computed results. We used GeneTrail2 to explore pathogenic mechanisms of Wilms tumors. We not only succeeded in revealing signaling cascades that may contribute to the malignancy of blastemal subtype tumors but also identified potential biomarkers for nephroblastoma with adverse prognosis. The presented use-case demonstrates that GeneTrail2 is well equipped for the integrative analysis of comprehensive -omics data and may help to shed light on complex pathogenic mechanisms in cancer and other diseases. Availability and implementation GeneTrail2 can be freely accessed under https://genetrail2.bioinf.uni-sb.de Contact : dstoeckel@bioinf.uni-sb.de Supplementary information Supplementary data are available at Bioinformatics online.

138 citations


Journal ArticleDOI
TL;DR: EDISON-WMW is developed, a new approach to calculate the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties, which can be applied to high-throughput omics datasets, where hundreds or thousands of features are included.

108 citations


Journal ArticleDOI
TL;DR: MiRNA expression profiling of 36 WTs of different subtypes and four normal kidney tissues showed the clear separation of WT from normal kidney samples, but the miRNA pattern yielded better separation ofWT subtypes, and marked deregulation of miRNAs regulating epithelial to mesenchymal transition, especially in the blastemal subtype, and mi RNAs influencing chemosensitivity,especially in regressive subtypes were found.
Abstract: Wilms tumor (WT) is the most common childhood renal cancer. Recent findings of mutations in microRNA (miRNA) processing proteins suggest a pivotal role of miRNAs in WT genesis. We performed miRNA expression profiling of 36 WTs of different subtypes and four normal kidney tissues using microarrays. Additionally, we determined the gene expression profile of 28 of these tumors to identify potentially correlated target genes and affected pathways. We identified 85 miRNAs and 2107 messenger RNAs (mRNA) differentially expressed in blastemal WT, and 266 miRNAs and 1267 mRNAs differentially expressed in regressive subtype. The hierarchical clustering of the samples, using either the miRNA or mRNA profile, showed the clear separation of WT from normal kidney samples, but the miRNA pattern yielded better separation of WT subtypes. A correlation analysis of the deregulated miRNA and mRNAs identified 13,026 miRNA/mRNA pairs with inversely correlated expression, of which 2844 are potential interactions of miRNA and their predicted mRNA targets. We found significant upregulation of miRNAs-183, -301a/b and -335 for the blastemal subtype, and miRNAs-181b, -223 and -630 for the regressive subtype. We found marked deregulation of miRNAs regulating epithelial to mesenchymal transition, especially in the blastemal subtype, and miRNAs influencing chemosensitivity, especially in regressive subtypes. Further research is needed to assess the influence of preoperative chemotherapy and tumor infiltrating lymphocytes on the miRNA and mRNA patterns in WT.

41 citations


Journal ArticleDOI
TL;DR: Principal Component Analysis provided evidence that not only cancer from control tissue but also tissue from adenocarcinoma and SCC can be differentiated.
Abstract: Proteomics analysis of paired cancer and control tissue can be applied to investigate pathological processes in tumors. Advancements in data-independent acquisition mass spectrometry allow for highly reproducible quantitative analysis of complex proteomic patterns. Optimized sample preparation workflows enable integrative multi-omics studies from the same tissue specimens.We performed ion mobility enhanced, data-independent acquisition MS to characterize the proteome of 21 lung tumor tissues including adenocarcinoma and squamous cell carcinoma (SCC) as compared to control lung tissues of the same patient each. Transcriptomic data were generated for the same specimens. The quantitative proteomic patterns and mRNA abundances were subsequently analyzed using systems biology approaches.We report a significantly (p = 0.0001) larger repertoire of proteins in cancer tissues. 12 proteins were higher in all tumor tissues as compared to matching control tissues. Three proteins, CAV1, CAV2, and RAGE, were vice versa higher in all controls. We also identified characteristic SCC and adenocarcinoma protein patterns. Principal Component Analysis provided evidence that not only cancer from control tissue but also tissue from adenocarcinoma and SCC can be differentiated. Transcriptomic levels of key proteins measured from the same matched tissue samples correlated with the observed protein patterns.The applied study set-up with paired lung tissue specimens of which different omics are measured, is generally suited for an integrated multi-omics analysis.

14 citations


Journal ArticleDOI
TL;DR: DrugTargetInspector (DTI), an interactive assistance tool for treatment stratification that analyzes genomic, transcriptomic, and proteomic datasets and provides information on deregulated drug targets, enriched biological pathways, and deregulated subnetworks, as well as mutations and their potential effects on putative drug targets and genes of interest is presented.
Abstract: Cancer is a large class of diseases that are characterized by a common set of features, known as the Hallmarks of cancer. One of these hallmarks is the acquisition of genome instability and mutations. This, combined with high proliferation rates and failure of repair mechanisms, leads to clonal evolution as well as a high genotypic and phenotypic diversity within the tumor. As a consequence, treatment and therapy of malignant tumors is still a grand challenge. Moreover, under selective pressure, e.g., caused by chemotherapy, resistant subpopulations can emerge that then may lead to relapse. In order to minimize the risk of developing multidrug-resistant tumor cell populations, optimal (combination) therapies have to be determined on the basis of an in-depth characterization of the tumor's genetic and phenotypic makeup, a process that is an important aspect of stratified medicine and precision medicine. We present DrugTargetInspector (DTI), an interactive assistance tool for treatment stratification. DTI analyzes genomic, transcriptomic, and proteomic datasets and provides information on deregulated drug targets, enriched biological pathways, and deregulated subnetworks, as well as mutations and their potential effects on putative drug targets and genes of interest. To demonstrate DTI's broad scope of applicability, we present case studies on several cancer types and different types of input -omics data. DTI's integrative approach allows users to characterize the tumor under investigation based on various -omics datasets and to elucidate putative treatment options based on clinical decision guidelines, but also proposing additional points of intervention that might be neglected otherwise. DTI can be freely accessed at http://dti.bioinf.uni-sb.de.

12 citations


Journal ArticleDOI
TL;DR: The resulting interaction network, which is based on quantitative analysis of the abundance of miRNAs, mRNAs and proteins each taken from the same lung cancer tissue and from theSame autologous normal lung tissue confirms molecular pathological changes and further contributes to the discovery of altered signaling cascades in lung cancer.
Abstract: // Christina Backes 1, * , Nicole Ludwig 2, * , Petra Leidinger 2 , Hanno Huwer 3 , Stefan Tenzer 4 , Tobias Fehlmann 1 , Andre Franke 5 , Eckart Meese 2 , Hans-Peter Lenhof 6 , Andreas Keller 1 1 Chair for Clinical Bioinformatics, Saarland University, Germany 2 Department of Human Genetics, Saarland University, Germany 3 SHG Clinics, Volklingen, Germany 4 Institute for Immunology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany 5 IKMB, Kiel, Germany 6 Chair for Bioinformatics, Saarland University, Germany * These authors have contributed equally to this work Correspondence to: Andreas Keller, email: andreas.keller@ccb.uni-saarland.de Keywords: systems biology, transcriptomics, miRNomics, proteomics, lung cancer Received: March 15, 2016 Accepted: August 01, 2016 Published: August 31, 2016 ABSTRACT High-throughput omics analyses are applied to elucidate molecular pathogenic mechanisms in cancer. Given restricted cohort sizes and contrasting large feature sets paired multi-omics analysis supports discovery of true positive deregulated signaling cascades. For lung cancer patients we measured from the same tissue biopsies proteomic- (6,183 proteins), transcriptomic- (34,687 genes) and miRNomic data (2,549 miRNAs). To minimize inter-individual variations case and control lung biopsies have been gathered from the same individuals. Considering single omics entities, 15 of 2,549 miRNAs (0.6%), 752 of 34,687 genes (2.2%) and 141 of 6,183 proteins (2.3%) were significantly deregulated. Multivariate analysis also revealed that effects in miRNA were smaller compared to genes and proteins indicating that expression changes of miRNAs might also have limited impact of pathogenicity. However, a new algorithm for modeling the complex mutual interactions of miRNAs and their target genes facilitated precise prediction of deregulation in cancer genes (92.3% accuracy, p=0.007). Lastly, deregulation of genes in cancer matched deregulation of proteins coded by the genes in 80% of cases. The resulting interaction network, which is based on quantitative analysis of the abundance of miRNAs, mRNAs and proteins each taken from the same lung cancer tissue and from the same autologous normal lung tissue confirms molecular pathological changes and further contributes to the discovery of altered signaling cascades in lung cancer.

7 citations