Unraveling expression and DNA methylation landscapes in cancer

Cancer is a complex, heterogeneous disease and associated with a pluralism of distinct molecular events occurring on multiple layers of cell activity. It is a disease of genomic regulation driven by genetic and epigenetic mechanisms. Consideration of these regulatory levels is inevitable for understanding cancer genesis and progression. Improved high-throughput techniques developed in the last decades enable a highly resolved view on these mechanisms but at the same time the technologies produce an incredible amount of molecular data. Hence it needs advances in computational methods to master the data. In this thesis we demonstrate how to cope with high-dimensional data to characterize molecular aspects of cancer. The main aim of this thesis is to develop and to apply bioinformatics methods to unravel molecular mechanisms, with special focus on gene expression and epigenetics, underlying cancer. Therefore, we selected two cancer entities, B-cell lymphoma and glioblastoma, for a more detailed, exemplary study. Bioinformatics methods dealing with molecular cancer data have to tackle tasks like data integration, dimension reduction, data compression and proper visualization. One effective method that fulfills the mentioned tasks is self organizing map (SOM) machine learning, a technique to ‘organize’ complex, multivariate data. We present an analytic framework based on SOMs that aims at characterizing single-omics landscapes, here either regarding genome wide expression or methylation, to describe the heterogeneity of cancer on the molecular level. Molecular data of each sample is presented in terms of ‘individual’ maps, which enable their evaluation by visual inspection. The portrayal method also realizes comprehensive downstream analysis tasks such as marker selection and clustering of co-regulated features into modules, stratification of cases into subtypes, knowledge discovery, function mining and pathway analysis. Further, we describe how to detect and to correct outlier samples. In a novel combining approach all these analytic tasks of the single-omics SOM are embedded in a workflow to integratively analyze gene expression and DNA methylation data of unmatched patient cohorts. We showed that this approach provides detailed insights into the transcriptome and methylome landscapes of cancer. Furthermore, we developed a new inter-omics method based on SOM machine learning for the combined analysis of gene expression and DNA methylation data obtained from the same patient cohort. The method allows the visual inspection of the data landscapes of each sample on a personalized and class-related level, where the relative contribution of each of both data entities can be tuned either to focus on expression or methylation landscapes or on a combination of both. Using the single-omics SOM approach, we studied molecular subtypes of B-cell lymphoma based on gene expression data. The method disentangles tumor heterogeneity and provides suited markers for the cancer subtypes. We proposed a refined subtyping of B-cell lymphoma into four subtypes, rather than a previously assumed three-group classification. In a second application of the single-omics SOM we studied a gene expression data set concerning glioblastoma for which we confirmed an established four-subtype classification. Our results suggested a similar gene activation pattern as observed in the lymphoma study characterized by an antagonistic switching between transcriptional modes related to immune response and cell division. Our integrative study on a larger lymphoma cohort comprising additional subtypes confirmed previous results about the role of stemness genes during development and maturation of B-cells. Their dysfunctions in lymphoma 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 subtype-specific signatures that associate 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. While studying the transcription of epigenetic modifiers in lymphoma and healthy controls, we found that the expression levels of nearly all modifiers are strongly disturbed in lymphoma and concluded that the epigenetic machinery is highly deregulated. Our results suggested that Burkitt’s lymphoma and diffuse large B-cell lymphoma differ by an imbal-ance of repressive and poised promoters, which is associated with an imbalance of the activity of histone- and DNA-modifying enzymes. Our inter-omics method was applied to a high-grade glioblastomas. Their expression and methylation landscapes were segmented into modes of co-expressed and co-methylated genes, which reflect underlying regulatory modes of cell activity. We found antagonistic methylation and gene expression changes between the IDH1 mutated and IDH1 wild type subtypes, which affect predominantly poised and repressed chromatin states. Therefore we assume that these effects deregulate developmental processes either by their blockage or by aberrant activation. Our methods presented in this thesis enable a holistic view on high-dimensional molecular data collected in large-scale cancer studies. The examples chosen illustrate the mutual dependence of regulatory effects on genetic, epigenetic and transcriptomic levels. Our finding revealed that epigenetic deregulation in cancer must go beyond simple schemes using only a few modes of regulation. By applying the tools and methods described above to large-scale cancer cohorts we could confirm and supplement previous findings about underlying cancer biology

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It “portrays” the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers

Transcriptome-Guided Drug Repositioning

Drug repositioning can save considerable time and resources and significantly speed up the drug development process. The increasing availability of drug action and disease-associated transcriptome data makes it an attractive source for repositioning studies. Here, we have developed a transcriptome-guided approach for drug/biologics repositioning based on multi-layer self-organizing maps (ml-SOM). It allows for analyzing multiple transcriptome datasets by segmenting them into layers of drug action- and disease-associated transcriptome data. A comparison of expression changes in clusters of functionally related genes across the layers identifies “drug target” spots in disease layers and evaluates the repositioning possibility of a drug. The repositioning potential for two approved biologics drugs (infliximab and brodalumab) confirmed the drugs’ action for approved diseases (ulcerative colitis and Crohn’s disease for infliximab and psoriasis for brodalumab). We showed the potential efficacy of infliximab for the treatment of sarcoidosis, but not chronic obstructive pulmonary disease (COPD). Brodalumab failed to affect dysregulated functional gene clusters in Crohn’s disease (CD) and systemic juvenile idiopathic arthritis (SJIA), clearly indicating that it may not be effective in the treatment of these diseases. In conclusion, ml-SOM offers a novel approach for transcriptome-guided drug repositioning that could be particularly useful for biologics drugs

Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome

We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host’s response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

Background: The blood transcriptome is expected to provide a detailed picture of an organism’s physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research.We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration. Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features. Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes. Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II–III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context

Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma

Burkitt lymphoma (BL) is the most common B-cell lymphoma in children. Within the International Cancer Genome Consortium (ICGC), we performed whole genome and transcriptome sequencing of 39 sporadic BL. Here, we unravel interaction of structural, mutational, and transcriptional changes, which contribute to MYC oncogene dysregulation together with the pathognomonic IG-MYC translocation. Moreover, by mapping IGH translocation breakpoints, we provide evidence that the precursor of at least a subset of BL is a B-cell poised to express IGHA. We describe the landscape of mutations, structural variants, and mutational processes, and identified a series of driver genes in the pathogenesis of BL, which can be targeted by various mechanisms, including IG-non MYC translocations, germline and somatic mutations, fusion transcripts, and alternative splicing

The genomic and transcriptional landscape of primary central nervous system lymphoma

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations

Unraveling expression and DNA methylation landscapes in cancer

Cancer is a complex, heterogeneous disease and associated with a pluralism of distinct molecular events occurring on multiple layers of cell activity. It is a disease of genomic regulation driven by genetic and epigenetic mechanisms. Consideration of these regulatory levels is inevitable for understanding cancer genesis and progression. Improved high-throughput techniques developed in the last decades enable a highly resolved view on these mechanisms but at the same time the technologies produce an incredible amount of molecular data. Hence it needs advances in computational methods to master the data. In this thesis we demonstrate how to cope with high-dimensional data to characterize molecular aspects of cancer. The main aim of this thesis is to develop and to apply bioinformatics methods to unravel molecular mechanisms, with special focus on gene expression and epigenetics, underlying cancer. Therefore, we selected two cancer entities, B-cell lymphoma and glioblastoma, for a more detailed, exemplary study. Bioinformatics methods dealing with molecular cancer data have to tackle tasks like data integration, dimension reduction, data compression and proper visualization. One effective method that fulfills the mentioned tasks is self organizing map (SOM) machine learning, a technique to ‘organize’ complex, multivariate data. We present an analytic framework based on SOMs that aims at characterizing single-omics landscapes, here either regarding genome wide expression or methylation, to describe the heterogeneity of cancer on the molecular level. Molecular data of each sample is presented in terms of ‘individual’ maps, which enable their evaluation by visual inspection. The portrayal method also realizes comprehensive downstream analysis tasks such as marker selection and clustering of co-regulated features into modules, stratification of cases into subtypes, knowledge discovery, function mining and pathway analysis. Further, we describe how to detect and to correct outlier samples. In a novel combining approach all these analytic tasks of the single-omics SOM are embedded in a workflow to integratively analyze gene expression and DNA methylation data of unmatched patient cohorts. We showed that this approach provides detailed insights into the transcriptome and methylome landscapes of cancer. Furthermore, we developed a new inter-omics method based on SOM machine learning for the combined analysis of gene expression and DNA methylation data obtained from the same patient cohort. The method allows the visual inspection of the data landscapes of each sample on a personalized and class-related level, where the relative contribution of each of both data entities can be tuned either to focus on expression or methylation landscapes or on a combination of both. Using the single-omics SOM approach, we studied molecular subtypes of B-cell lymphoma based on gene expression data. The method disentangles tumor heterogeneity and provides suited markers for the cancer subtypes. We proposed a refined subtyping of B-cell lymphoma into four subtypes, rather than a previously assumed three-group classification. In a second application of the single-omics SOM we studied a gene expression data set concerning glioblastoma for which we confirmed an established four-subtype classification. Our results suggested a similar gene activation pattern as observed in the lymphoma study characterized by an antagonistic switching between transcriptional modes related to immune response and cell division. Our integrative study on a larger lymphoma cohort comprising additional subtypes confirmed previous results about the role of stemness genes during development and maturation of B-cells. Their dysfunctions in lymphoma 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 subtype-specific signatures that associate 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. While studying the transcription of epigenetic modifiers in lymphoma and healthy controls, we found that the expression levels of nearly all modifiers are strongly disturbed in lymphoma and concluded that the epigenetic machinery is highly deregulated. Our results suggested that Burkitt’s lymphoma and diffuse large B-cell lymphoma differ by an imbal-ance of repressive and poised promoters, which is associated with an imbalance of the activity of histone- and DNA-modifying enzymes. Our inter-omics method was applied to a high-grade glioblastomas. Their expression and methylation landscapes were segmented into modes of co-expressed and co-methylated genes, which reflect underlying regulatory modes of cell activity. We found antagonistic methylation and gene expression changes between the IDH1 mutated and IDH1 wild type subtypes, which affect predominantly poised and repressed chromatin states. Therefore we assume that these effects deregulate developmental processes either by their blockage or by aberrant activation. Our methods presented in this thesis enable a holistic view on high-dimensional molecular data collected in large-scale cancer studies. The examples chosen illustrate the mutual dependence of regulatory effects on genetic, epigenetic and transcriptomic levels. Our finding revealed that epigenetic deregulation in cancer must go beyond simple schemes using only a few modes of regulation. By applying the tools and methods described above to large-scale cancer cohorts we could confirm and supplement previous findings about underlying cancer biology