RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.

Recent studies revealed trajectories of mutational events in early melanomagenesis, but the accompanying changes in gene expression are far less understood. Therefore, we performed a comprehensive RNA-seq analysis of laser-microdissected melanocytic nevi (n = 23) and primary melanoma samples (n = 57) and characterized the molecular mechanisms of early melanoma development. Using self-organizing maps, unsupervised clustering, and analysis of pseudotime (PT) dynamics to identify evolutionary trajectories, we describe here two transcriptomic types of melanocytic nevi (N1 and N2) and primary melanomas (M1 and M2). N1/M1 lesions are characterized by pigmentation-type and MITF gene signatures, and a high prevalence of NRAS mutations in M1 melanomas. N2/M2 lesions are characterized by inflammatory-type and AXL gene signatures with an equal distribution of wild-type and mutated BRAF and low prevalence of NRAS mutations in M2 melanomas. Interestingly, N1 nevi and M1 melanomas and N2 nevi and M2 melanomas, respectively, cluster together, but there is no clustering in a stage-dependent manner. Transcriptional signatures of M1 melanomas harbor signatures of BRAF/MEK inhibitor resistance and M2 melanomas harbor signatures of anti-PD-1 antibody treatment resistance. Pseudotime dynamics of nevus and melanoma samples are suggestive for a switch-like immune-escape mechanism in melanoma development with downregulation of immune genes paralleled by an increasing expression of a cell cycle signature in late-stage melanomas. Taken together, the transcriptome analysis identifies gene signatures and mechanisms underlying development of melanoma in early and late stages with relevance for diagnostics and therapy.

Combined SOM-portrayal of gene expression and DNA methylation landscapes disentangles modes of epigenetic regulation in glioblastoma.

AIM: We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer. MATERIALS & METHODS: The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'. RESULTS: We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states. CONCLUSION: The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.

Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases.

Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent.

Epigenetic Heterogeneity of B-Cell Lymphoma: Chromatin Modifiers.

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.

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

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.

oposSOM: R-package for high-dimensional portraying of genome-wide expression landscapes on bioconductor.

MOTIVATION: Comprehensive analysis of genome-wide molecular data challenges bioinformatics methodology in terms of intuitive visualization with single-sample resolution, biomarker selection, functional information mining and highly granular stratification of sample classes. oposSOM combines those functionalities making use of a comprehensive analysis and visualization strategy based on self-organizing maps (SOM) machine learning which we call 'high-dimensional data portraying'. The method was successfully applied in a series of studies using mostly transcriptome data but also data of other OMICs realms. AVAILABILITY AND IMPLEMENTATION: oposSOM is now publicly available as Bioconductor R package. CONTACT: wirth@izbi.uni-leipzig.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular classification of diffuse cerebral WHO grade II/III gliomas using genome- and transcriptome-wide profiling improves stratification of prognostically distinct patient groups.

Cerebral gliomas of World Health Organization (WHO) grade II and III represent a major challenge in terms of histological classification and clinical management. Here, we asked whether large-scale genomic and transcriptomic profiling improves the definition of prognostically distinct entities. We performed microarray-based genome- and transcriptome-wide analyses of primary tumor samples from a prospective German Glioma Network cohort of 137 patients with cerebral gliomas, including 61 WHO grade II and 76 WHO grade III tumors. Integrative bioinformatic analyses were employed to define molecular subgroups, which were then related to histology, molecular biomarkers, including isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation, 1p/19q co-deletion and telomerase reverse transcriptase (TERT) promoter mutations, and patient outcome. Genomic profiling identified five distinct glioma groups, including three IDH1/2 mutant and two IDH1/2 wild-type groups. Expression profiling revealed evidence for eight transcriptionally different groups (five IDH1/2 mutant, three IDH1/2 wild type), which were only partially linked to the genomic groups. Correlation of DNA-based molecular stratification with clinical outcome allowed to define three major prognostic groups with characteristic genomic aberrations. The best prognosis was found in patients with IDH1/2 mutant and 1p/19q co-deleted tumors. Patients with IDH1/2 wild-type gliomas and glioblastoma-like genomic alterations, including gain on chromosome arm 7q (+7q), loss on chromosome arm 10q (-10q), TERT promoter mutation and oncogene amplification, displayed the worst outcome. Intermediate survival was seen in patients with IDH1/2 mutant, but 1p/19q intact, mostly astrocytic gliomas, and in patients with IDH1/2 wild-type gliomas lacking the +7q/-10q genotype and TERT promoter mutation. This molecular subgrouping stratified patients into prognostically distinct groups better than histological classification. Addition of gene expression data to this genomic classifier did not further improve prognostic stratification. In summary, DNA-based molecular profiling of WHO grade II and III gliomas distinguishes biologically distinct tumor groups and provides prognostically relevant information beyond histological classification as well as IDH1/2 mutation and 1p/19q co-deletion status.