Epigenetic Heterogeneity of B-Cell Lymphoma: Chromatin Modifiers

PSF toolkit: an R package for pathway curation and topology-aware analysis

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

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.

The Transcriptome and Methylome of the Developing and Aging Brain and Their Relations to Gliomas and Psychological Disorders

Mutually linked expression and methylation dynamics in the brain govern genome regulation over the whole lifetime with an impact on cognition, psychological disorders, and cancer. We performed a joint study of gene expression and DNA methylation of brain tissue originating from the human prefrontal cortex of individuals across the lifespan to describe changes in cellular programs and their regulation by epigenetic mechanisms. The analysis considers previous knowledge in terms of functional gene signatures and chromatin states derived from independent studies, aging profiles of a battery of chromatin modifying enzymes, and data of gliomas and neuropsychological disorders for a holistic view on the development and aging of the brain. Expression and methylation changes from babies to elderly adults decompose into different modes associated with the serial activation of (brain) developmental, learning, metabolic and inflammatory functions, where methylation in gene promoters mostly represses transcription. Expression of genes encoding methylome modifying enzymes is very diverse reflecting complex regulations during lifetime which also associates with the marked remodeling of chromatin between permissive and restrictive states. Data of brain cancer and psychotic disorders reveal footprints of pathophysiologies related to brain development and aging. Comparison of aging brains with gliomas supports the view that glioblastoma-like and astrocytoma-like tumors exhibit higher cellular plasticity activated in the developing healthy brain while oligodendrogliomas have a more stable differentiation hierarchy more resembling the aged brain. The balance and specific shifts between volatile and stable and between more irreversible and more plastic epigenomic networks govern the development and aging of healthy and diseased brain.

Deciphering the Transcriptomic Heterogeneity of Duodenal Coeliac Disease Biopsies

Coeliac disease (CD) is a clinically heterogeneous autoimmune disease with variable presentation and progression triggered by gluten intake. Molecular or genetic factors contribute to disease heterogeneity, but the reasons for different outcomes are poorly understood. Transcriptome studies of tissue biopsies from CD patients are scarce. Here, we present a high-resolution analysis of the transcriptomes extracted from duodenal biopsies of 24 children and adolescents with active CD and 21 individuals without CD but with intestinal afflictions as controls. The transcriptomes of CD patients divide into three groups-a mixed group presenting the control cases, and CD-low and CD-high groups referring to lower and higher levels of CD severity. Persistence of symptoms was weakly associated with subgroup, but the highest marsh stages were present in subgroup CD-high, together with the highest cell cycle rates as an indicator of virtually complete villous atrophy. Considerable variation in inflammation-level between subgroups was further deciphered into immune cell types using cell type de-convolution. Self-organizing maps portrayal was applied to provide high-resolution landscapes of the CD-transcriptome. We find asymmetric patterns of miRNA and long non-coding RNA and discuss the effect of epigenetic regulation. Expression of genes involved in interferon gamma signaling represent suitable markers to distinguish CD from non-CD cases. Multiple pathways overlay in CD biopsies in different ways, giving rise to heterogeneous transcriptional patterns, which potentially provide information about etiology and the course of the disease.

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.

SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks

Background: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers. Method: We applied SOM (self-organizing maps) portrayal, a neural network-based machine learning method, to re-analyze the genome-wide Single Nucleotide Polymorphism (SNP) data of nearly eight hundred grapevine cultivars. The method generates genome-specific data landscapes. Their topology reflects the geographical distribution of cultivars, indicates paths of cultivar dissemination in history and genome-phenotype associations about grape utilization. Results: The landscape of vine genomes resembles the geographic map of the Mediterranean world, reflecting two major dissemination paths from South Caucasus along a northern route via Balkan towards Western Europe and along a southern route via Palestine and Maghreb towards Iberian Peninsula. The Mediterranean and Black Sea, as well as the Pyrenees, constitute barriers for genetic exchange. On the coarsest level of stratification, cultivars divide into three major groups: Western Europe and Italian grapes, Iberian grapes and vine cultivars from Near East and Maghreb regions. Genetic landmarks were associated with agronomic traits, referring to their utilization as table and wine grapes. Pseudotime analysis describes the dissemination of grapevines in an East to West direction in different waves of cultivation. Conclusion: In analogy to the tasks of the wine waiter in gastronomy, the sommelier, our ‘SOMmelier’-approach supports understanding the diversity of grapevine genomes in the context of their geographic and historical background, using SOM portrayal. It offers an option to supplement vine cultivar passports by genome fingerprint portraits.

Population Levels Assessment of the Distribution of Disease-Associated Variants With Emphasis on Armenians - A Machine Learning Approach

Background: During the last decades a number of genome-wide association studies (GWASs) has identified numerous single nucleotide polymorphisms (SNPs) associated with different complex diseases. However, associations reported in one population are often conflicting and did not replicate when studied in other populations. One of the reasons could be that most GWAS employ a case-control design in one or a limited number of populations, but little attention was paid to the global distribution of disease-associated alleles across different populations. Moreover, the majority of GWAS have been performed on selected European, African, and Chinese populations and the considerable number of populations remains understudied. Aim: We have investigated the global distribution of so far discovered disease-associated SNPs across worldwide populations of different ancestry and geographical regions with a special focus on the understudied population of Armenians. Data and Methods: We have used genotyping data from the Human Genome Diversity Project and of Armenian population and combined them with disease-associated SNP data taken from public repositories leading to a final dataset of 44,234 markers. Their frequency distribution across 1039 individuals from 53 populations was analyzed using self-organizing maps (SOM) machine learning. Our SOM portrayal approach reduces data dimensionality, clusters SNPs with similar frequency profiles and provides two-dimensional data images which enable visual evaluation of disease-associated SNPs landscapes among human populations. Results: We find that populations from Africa, Oceania, and America show specific patterns of minor allele frequencies of disease-associated SNPs, while populations from Europe, Middle East, Central South Asia, and Armenia mostly share similar patterns. Importantly, different sets of SNPs associated with common polygenic diseases, such as cancer, diabetes, neurodegeneration in populations from different geographic regions. Armenians are characterized by a set of SNPs that are distinct from other populations from the neighboring geographical regions. Conclusion: Genetic associations of diseases considerably vary across populations which necessitates health-related genotyping efforts especially for so far understudied populations. SOM portrayal represents novel promising methods in population genetic research with special strength in visualization-based comparison of SNP data.

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.

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.

Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq

Recent technological advances in single-cell genomics make it possible to analyze cellular heterogeneity of tumor samples. Here, we applied single-cell RNA-seq to measure the transcriptomes of 307 single cells cultured from three biopsies of three different patients with a BRAF/NRAS wild type, BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant melanoma metastasis, respectively. Analysis based on self-organizing maps identified sub-populations defined by multiple gene expression modules involved in proliferation, oxidative phosphorylation, pigmentation and cellular stroma. Gene expression modules had prognostic relevance when compared with gene expression data from published melanoma samples and patient survival data. We surveyed kinome expression patterns across sub-populations of the BRAF/NRAS wild type sample and found that CDK4 and CDK2 were consistently highly expressed in the majority of cells, suggesting that these kinases might be involved in melanoma progression. Treatment of cells with the CDK4 inhibitor palbociclib restricted cell proliferation to a similar, and in some cases greater, extent than MAPK inhibitors. Finally, we identified a low abundant sub-population in this sample that highly expressed a module containing ABC transporter ABCB5, surface markers CD271 and CD133, and multiple aldehyde dehydrogenases (ALDHs). Patient-derived cultures of the BRAF mutant/NRAS wild type and BRAF wild type/NRAS mutant metastases showed more homogeneous single-cell gene expression patterns with gene expression modules for proliferation and ABC transporters. Taken together, our results describe an intertumor and intratumor heterogeneity in melanoma short-term cultures which might be relevant for patient survival, and suggest promising targets for new treatment approaches in melanoma therapy.

Gene mutations and actionable genetic lesions in mantle cell lymphoma

Mutations and epigenetic alterations are key events in transforming normal cells to cancer cells. Mantle cell lymphoma (MCL), a non-Hodgkin's lymphoma of the B-cell, is an aggressive malignancy with poor prognosis especially for those patients who are resistant to the frontline drugs. There is a great need to describe the molecular basis and mechanism of drug resistance in MCL to develop new strategies for treatment. We reviewed frequent somatic mutations and mutations involving the B-cell pathways in MCL and discussed clinical trials that attempted to disrupt these gene pathways and/or epigenetic events. Recurrent gene mutations were discussed in the light of prognostic and therapeutic opportunity and also the challenges of targeting these lesions. Mutations in the ATM, CCND1, TP53, MLL2, TRAF2 and NOTCH1 were most frequently encountered in mantle cell lymphoma. Translational models should be built that would assess mutations longitudinally to identify important compensatory, pro-survival and anti-apoptic pathways and actionable genetic targets.

Integrated Cellular and Plasma Proteomics of Contrasting B-cell Cancers Reveals Common, Unique and Systemic Signatures

Approximately 800,000 leukemia and lymphoma cases are diagnosed worldwide each year. Burkitt's lymphoma (BL) and chronic lymphocytic leukemia (CLL) are examples of contrasting B-cell cancers; BL is a highly aggressive lymphoid tumor, frequently affecting children, whereas CLL typically presents as an indolent, slow-progressing leukemia affecting the elderly. The B-cell-specific overexpression of the myc and TCL1 oncogenes in mice induce spontaneous malignancies modeling BL and CLL, respectively. Quantitative mass spectrometry proteomics and isobaric labeling were employed to examine the biology underpinning contrasting Eμ-myc and Eμ-TCL1 B-cell tumors. Additionally, the plasma proteome was evaluated using subproteome enrichment to interrogate biomarker emergence and the systemic effects of tumor burden. Over 10,000 proteins were identified (q<0.01) of which 8270 cellular and 2095 plasma proteins were quantitatively profiled. A common B-cell tumor signature of 695 overexpressed proteins highlighted ribosome biogenesis, cell-cycle promotion and chromosome segregation. Eμ-myc tumors overexpressed several methylating enzymes and underexpressed many cytoskeletal components. Eμ-TCL1 tumors specifically overexpressed ER stress response proteins and signaling components in addition to both subunits of the interleukin-5 (IL5) receptor. IL5 treatment promoted Eμ-TCL1 tumor proliferation, suggesting an amplification of IL5-induced AKT signaling by TCL1. Tumor plasma contained a substantial tumor lysis signature, most prominent in Eμ-myc plasma, whereas Eμ-TCL1 plasma contained signatures of immune-response, inflammation and microenvironment interactions, with putative biomarkers in early-stage cancer. These findings provide a detailed characterization of contrasting B-cell tumor models, identifying common and specific tumor mechanisms. Integrated plasma proteomics allowed the dissection of a systemic response and a tumor lysis signature present in early- and late-stage cancers, respectively. Overall, this study suggests common B-cell cancer signatures exist and illustrates the potential of the further evaluation of B-cell cancer subtypes by integrative proteomics.