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21 Publications visible to you, out of a total of 21
Personalized disease phenotypes from massive omics data.
LHA - Leipzig Health Atlas

Abstract (Expand)

Application of new high-throughput technologies in molecular medicine collects massive data for hundreds to thousands of persons in large cohort studies by characterizing the phenotype of each individual … on a personalized basis. The chapter aims at increasing our understanding of disease genesis and progression and to improve diagnosis and treatment. New methods are needed to handle such "big data." Machine learning enables one to recognize and to visualize complex data patterns and to make decisions potentially relevant for diagnosis and treatment. The authors address these tasks by applying the method of self-organizing maps and present worked examples from different disease entities of the colon ranging from inflammation to cancer.

Authors: Hans Binder, Lydia Hopp, K. Lembcke, Henry Löffler-Wirth

Date Published: 2017

Publication Type: Not specified

Epigenetic Heterogeneity of B-Cell Lymphoma: Chromatin Modifiers.
MMML-MYC-SYS

Abstract (Expand)

We systematically studied the expression of more than fifty histone and DNA (de)methylating enzymes in lymphoma and healthy controls. As a main result, we found that the expression levels of nearly all … enzymes become markedly disturbed in lymphoma, suggesting deregulation of large parts of the epigenetic machinery. We discuss the effect of DNA promoter methylation and of transcriptional activity in the context of mutated epigenetic modifiers such as EZH2 and MLL2. As another mechanism, we studied the coupling between the energy metabolism and epigenetics via metabolites that act as cofactors of JmjC-type demethylases. Our study results suggest that Burkitt's lymphoma and diffuse large B-cell Lymphoma differ by an imbalance of repressive and poised promoters, which is governed predominantly by the activity of methyltransferases and the underrepresentation of demethylases in this regulation. The data further suggest that coupling of epigenetics with the energy metabolism can also be an important factor in lymphomagenesis in the absence of direct mutations of genes in metabolic pathways. Understanding of epigenetic deregulation in lymphoma and possibly in cancers in general must go beyond simple schemes using only a few modes of regulation.

Authors: L. Hopp, L. Nersisyan, H. Loffler-Wirth, A. Arakelyan, H. Binder

Date Published: 21st Oct 2015

Publication Type: Not specified

Human Diseases: non-Hodgkin lymphoma

Epigenetic Heterogeneity of B-Cell Lymphoma: DNA Methylation, Gene Expression and Chromatin States.
MMML-MYC-SYS

Abstract (Expand)

Mature B-cell lymphoma is a clinically and biologically highly diverse disease. Its diagnosis and prognosis is a challenge due to its molecular heterogeneity and diverse regimes of biological dysfunctions, … which are partly driven by epigenetic mechanisms. We here present an integrative analysis of DNA methylation and gene expression data of several lymphoma subtypes. Our study confirms previous results about the role of stemness genes during development and maturation of B-cells and their dysfunction in lymphoma locking in more proliferative or immune-reactive states referring to B-cell functionalities in the dark and light zone of the germinal center and also in plasma cells. These dysfunctions are governed by widespread epigenetic effects altering the promoter methylation of the involved genes, their activity status as moderated by histone modifications and also by chromatin remodeling. We identified four groups of genes showing characteristic expression and methylation signatures among Burkitt's lymphoma, diffuse large B cell lymphoma, follicular lymphoma and multiple myeloma. These signatures are associated with epigenetic effects such as remodeling from transcriptionally inactive into active chromatin states, differential promoter methylation and the enrichment of targets of transcription factors such as EZH2 and SUZ12.

Authors: L. Hopp, H. Loffler-Wirth, H. Binder

Date Published: 7th Sep 2015

Publication Type: Not specified

Human Diseases: non-Hodgkin lymphoma, B-cell lymphoma

Function Shapes Content: DNA-Methylation Marker Genes and their Impact for Molecular Mechanisms of Glioma
GGN - German Glioma Network

Abstract (Expand)

Glioma is a clinically and biologically diverse disease. It challenges diagnosis and prognosis due to its molecular heterogeneity and diverse regimes of biological dysfunctions which are driven by … genetic and epigenetic mechanisms. We discover the functional impact of sets of DNA methylation marker genes in the context of brain cancer subtypes as an exemplary approach how bioinformatics and particularly machine learning using self organizing maps (SOM) complements modern high-throughput genomic technologies. DNA methylation changes in gliomas comprise both, hyper- and hypomethylation in a subtype specific fashion. We compared pediatric (2 subtypes) and adult (4) glioblastoma and non-neoplastic brain. The functional impact of differential methylation marker sets is discovered in terms of gene set analysis which comprises a large collection of markers related to biological processes, literature data on gliomas and also chromatin states of the healthy brain. DNA methylation signature genes from alternative studies well agree with our signatures. SOM mapping of gene sets robustly identifies similarities between different marker sets even under conditions of noisy compositions. Mapping of previous sets of glioma markers reveals high redundancy and mixtures of subtypes in the reference cohorts. Consideration of the regulatory level of DNA methylation is inevitable for understanding cancer genesis and progression. It provides suited markers for diagnosis of glioma subtypes and disentangles tumor heterogeneity.

Authors: E. Willscher, H. Loffler-Wirth, H. Binder, Lydia Hopp

Date Published: 2015

Publication Type: Not specified

Human Diseases: brain glioma

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

Portraying the Expression Landscapes of B-CellLymphoma-Intuitive Detection of Outlier Samples and of Molecular Subtypes.
MMML-MYC-SYS

Abstract (Expand)

We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene … expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics.

Authors: L. Hopp, K. Lembcke, H. Binder, H. Wirth

Date Published: 2nd Dec 2013

Publication Type: Not specified

Human Diseases: non-Hodgkin lymphoma, B-cell lymphoma

Analysis of microRNA expression using machine learning.
LHA - Leipzig Health Atlas

Abstract (Expand)

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter … we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

Authors: H. Wirth, M. V. Cakir, L. Hopp, H. Binder

Date Published: 26th Nov 2013

Publication Type: Not specified

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