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55 Publications visible to you, out of a total of 55
Analysis of Large-Scale OMIC Data Using Self Organizing Maps
LHA - Leipzig Health Atlas

Abstract (Expand)

The data produced by high-throughput bioanalytics is usually given as a feature matrix of dimension N x M (see Figure 1) where N is the number of features measured per sample and M is the number of … samples referring, e.g., to different treatments, time points or individuals. As a convention, each row of the matrix will be termed profile of the respective feature. The columns on the other hand will be termed states referring to each of the conditions studied. In general, the number of features can range from several thousands to millions, depending on the experimental screening technique used. Typically, this number largely exceeds the number of states studied, i.e. N>>M. SOM machine learning aims at reducing the number of relevant features by grouping the input data into clusters of appropriate size, and thus to transform the matrix of input data into a matrix of so-called meta-data with a reduced number of meta-features, K<<N (Figure 1a and b). In other words, SOM aims at mapping the space of the high-dimensional input data onto meta-data space of reduced dimensionality.

Authors: Hans Binder, Henry Löffler-Wirth

Date Published: 2015

Publication Type: Not specified

Molecular characterization of long-term survivors of glioblastoma using genome- and transcriptome-wide profiling.
GGN - German Glioma Network

Abstract (Expand)

The prognosis of glioblastoma, the most malignant type of glioma, is still poor, with only a minority of patients showing long-term survival of more than three years after diagnosis. To elucidate the … molecular aberrations in glioblastomas of long-term survivors, we performed genome- and/or transcriptome-wide molecular profiling of glioblastoma samples from 94 patients, including 28 long-term survivors with >36 months overall survival (OS), 20 short-term survivors with <12 months OS and 46 patients with intermediate OS. Integrative bioinformatic analyses were used to characterize molecular aberrations in the distinct survival groups considering established molecular markers such as isocitrate dehydrogenase 1 or 2 (IDH1/2) mutations, and O(6) -methylguanine DNA methyltransferase (MGMT) promoter methylation. Patients with long-term survival were younger and more often had IDH1/2-mutant and MGMT-methylated tumors. Gene expression profiling revealed over-representation of a distinct (proneural-like) expression signature in long-term survivors that was linked to IDH1/2 mutation. However, IDH1/2-wildtype glioblastomas from long-term survivors did not show distinct gene expression profiles and included proneural, classical and mesenchymal glioblastoma subtypes. Genomic imbalances also differed between IDH1/2-mutant and IDH1/2-wildtype tumors, but not between survival groups of IDH1/2-wildtype patients. Thus, our data support an important role for MGMT promoter methylation and IDH1/2 mutation in glioblastoma long-term survival and corroborate the association of IDH1/2 mutation with distinct genomic and transcriptional profiles. Importantly, however, IDH1/2-wildtype glioblastomas in our cohort of long-term survivors lacked distinctive DNA copy number changes and gene expression signatures, indicating that other factors might have been responsible for long survival in this particular subgroup of patients.

Authors: G. Reifenberger, R. G. Weber, V. Riehmer, K. Kaulich, E. Willscher, H. Wirth, J. Gietzelt, B. Hentschel, M. Westphal, M. Simon, G. Schackert, J. Schramm, J. Matschke, M. C. Sabel, D. Gramatzki, J. Felsberg, C. Hartmann, J. P. Steinbach, U. Schlegel, W. Wick, B. Radlwimmer, T. Pietsch, J. C. Tonn, A. von Deimling, H. Binder, M. Weller, M. Loeffler

Date Published: 15th Oct 2014

Publication Type: Not specified

Human Diseases: brain glioma

Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures.
LHA - Leipzig Health Atlas

Abstract (Expand)

There is a critical need for standard approaches to assess, report and compare the technical performance of genome-scale differential gene expression experiments. Here we assess technical performance … with a proposed standard 'dashboard' of metrics derived from analysis of external spike-in RNA control ratio mixtures. These control ratio mixtures with defined abundance ratios enable assessment of diagnostic performance of differentially expressed transcript lists, limit of detection of ratio (LODR) estimates and expression ratio variability and measurement bias. The performance metrics suite is applicable to analysis of a typical experiment, and here we also apply these metrics to evaluate technical performance among laboratories. An interlaboratory study using identical samples shared among 12 laboratories with three different measurement processes demonstrates generally consistent diagnostic power across 11 laboratories. Ratio measurement variability and bias are also comparable among laboratories for the same measurement process. We observe different biases for measurement processes using different mRNA-enrichment protocols.

Authors: S. A. Munro, S. P. Lund, P. S. Pine, H. Binder, D. A. Clevert, A. Conesa, J. Dopazo, M. Fasold, S. Hochreiter, H. Hong, N. Jafari, D. P. Kreil, P. P. Labaj, S. Li, Y. Liao, S. M. Lin, J. Meehan, C. E. Mason, J. Santoyo-Lopez, R. A. Setterquist, L. Shi, W. Shi, G. K. Smyth, N. Stralis-Pavese, Z. Su, W. Tong, C. Wang, J. Wang, J. Xu, Z. Ye, Y. Yang, Y. Yu, M. Salit

Date Published: 25th Sep 2014

Publication Type: Not specified

A systems biology approach for defining the molecular framework of the hematopoietic stem cell niche.
LHA - Leipzig Health Atlas

Abstract (Expand)

Despite progress in identifying the cellular composition of hematopoietic stem/progenitor cell (HSPC) niches, little is known about the molecular requirements of HSPC support. To address this issue, … we used a panel of six recognized HSPC-supportive stromal lines and less-supportive counterparts originating from embryonic and adult hematopoietic sites. Through comprehensive transcriptomic meta-analyses, we identified 481 mRNAs and 17 microRNAs organized in a modular network implicated in paracrine signaling. Further inclusion of 18 additional cell strains demonstrated that this mRNA subset was predictive of HSPC support. Our gene set contains most known HSPC regulators as well as a number of unexpected ones, such as Pax9 and Ccdc80, as validated by functional studies in zebrafish embryos. In sum, our approach has identified the core molecular network required for HSPC support. These cues, along with a searchable web resource, will inform ongoing efforts to instruct HSPC ex vivo amplification and formation from pluripotent precursors.

Authors: P. Charbord, C. Pouget, H. Binder, F. Dumont, G. Stik, P. Levy, F. Allain, C. Marchal, J. Richter, B. Uzan, F. Pflumio, F. Letourneur, H. Wirth, E. Dzierzak, D. Traver, T. Jaffredo, C. Durand

Date Published: 4th Sep 2014

Publication Type: Not specified

Gene Set- and Pathway- Centered Knowledge Discovery Assigns Transcriptional Activation Patterns in Brain, Blood, and Colon Cancer: A Bioinformatics Perspective
LHA - Leipzig Health Atlas

Abstract (Expand)

Genome-wide ‘omics'-assays provide a comprehensive view on the molecular landscapes of healthy and diseased cells. Bioinformatics traditionally pursues a ‘gene-centered' view by extracting lists … of genes differentially expressed or methylated between healthy and diseased states. Biological knowledge mining is then performed by applying gene set techniques using libraries of functional gene sets obtained from independent studies. This analysis strategy neglects two facts: (i) that different disease states can be characterized by a series of functional modules of co-regulated genes and (ii) that the topology of the underlying regulatory networks can induce complex expression patterns that require analysis methods beyond traditional genes set techniques. The authors here provide a knowledge discovery method that overcomes these shortcomings. It combines machine learning using self-organizing maps with pathway flow analysis. It extracts and visualizes regulatory modes from molecular omics data, maps them onto selected pathways and estimates the impact of pathway-activity changes. The authors illustrate the performance of the gene set and pathway signal flow methods using expression data of oncogenic pathway activation experiments and of patient data on glioma, B-cell lymphoma and colorectal cancer.

Authors: L. Nersisyan, Henry Löffler-Wirth, A. Arakelyan, Hans Binder

Date Published: 1st Jun 2014

Publication Type: Not specified

Portraying the expression landscapes of cancer subtypes. A case study of glioblastoma multiforme and prostate cancer
HNPCC-Sys - Genomic and transcriptomic heterogeneity of colorectal tumours arising in Lynch syndrome, GGN - German Glioma Network

Abstract (Expand)

Self-organizing maps (SOM) portray molecular phenotypes with individual resolution. We present an analysis pipeline based on SOM machine learning which allows the comprehensive study of large scale … clinical data. The potency of the method is demonstrated in selected applications studying the diversity of gene expression in Glioblastoma Multiforme (GBM) and prostate cancer progression. Our method characterizes relationships between the samples, disentangles the expression patterns into well separated groups of co-regulated genes, extracts their functional contexts using enrichment techniques, and enables the detection of contaminations and outliers in the samples. We found that the four GBM subtypes can be divided into two “localized” and two “intermediate” ones. The localized subtypes are characterized by the antagonistic activation of processes related to immune response and cell division, commonly observed also in other cancers. In contrast, each of the “intermediate” subtypes forms a heterogeneous continuum of expression states linking the “localized” subtypes. Both “intermediate” subtypes are characterized by distinct expression patterns related to translational activity and innate immunity as well as nervous tissue and cell function. We show that SOM portraits provide a comprehensive framework for the description of the diversity of expression landscapes using concepts of molecular function.

Authors: Lydia Hopp, Henry Löffler-Wirth, M. Fasold, Hans Binder

Date Published: 27th Jan 2014

Publication Type: Not specified

Human Diseases: brain glioma, prostate cancer

Methylation associated pathway activity deregulation in lung adenocarcinoma
CapSys - Systems Medicine of Community Acquired Pneumonia

Abstract (Expand)

Methylation impairments are tightly associated with gene expression and molecular pathway deregulation in cancer development. However, other regulatory mechanisms exist, making it important to distinguish … those from methylation driven changes. Here we specifically assessed molecular pathway states associated with gene methylation in lung adenocarcinoma. Paired gene expression and methylation data (id:GSE32867) were obtained from Gene Expression Omnibus. Self organizing maps (Wirth, H. et al.BMC Bioinformatics 2011;12:306 ) and in-house pathway signal flow algorithms were applied to describe expression (PSF) and methylation (mPSF) states in KEGG pathways. 35 and 24 KEGG pathways had at least one branch deregulated at significance levels p<0.05 and 0.05<p < 0.1, respectively. Because many pathways are multibranch, our analysis totalled in 54 up- (PSF>1) and 73 down-regulated (PSF<1) branches. From these branches, 19 were positively (mPSF<0) or negatively (mPSF>0) correlated with methylation states (see table).

Authors: L. Nersisyan, Henry Löffler-Wirth, A. Gevorgyan, Hans Binder, A. Arakelyan

Date Published: 2014

Publication Type: Not specified

Human Diseases: lung disease

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