A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Pan-cancer analysis of telomere maintenance mechanisms.

Telomeres, protective caps at chromosome ends, maintain genomic stability and control cell lifespan. Dysregulated telomere maintenance mechanisms (TMMs) are cancer hallmarks, enabling unchecked cell proliferation. We conducted a pan-cancer evaluation of TMM using RNA sequencing data from The Cancer Genome Atlas for 33 different cancer types and analyzed the activities of telomerase-dependent (TEL) and alternative lengthening of telomeres (ALT) TMM pathways in detail. To further characterize the TMM profiles, we categorized the tumors based on their ALT and TEL TMM pathway activities into five major phenotypes: ALT high TEL low, ALT low TEL low, ALT middle TEL middle, ALT high TEL high, and ALT low TEL high. These phenotypes refer to variations in telomere maintenance strategies, shedding light on the heterogeneous nature of telomere regulation in cancer. Moreover, we investigated the clinical implications of TMM phenotypes by examining their associations with clinical characteristics and patient outcomes. Specific TMM profiles were linked to specific survival patterns, emphasizing the potential of TMM profiling as a prognostic indicator and aiding in personalized cancer treatment strategies. Gene ontology analysis of the TMM phenotypes unveiled enriched biological processes associated with cell cycle regulation (both TEL and ALT), DNA replication (TEL), and chromosome dynamics (ALT) showing that telomere maintenance is tightly intertwined with cellular processes governing proliferation and genomic stability. Overall, our study provides an overview of the complexity of transcriptional regulation of telomere maintenance mechanisms in cancer.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine.

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Transcriptional states of CAR-T infusion relate to neurotoxicity - lessons from high-resolution single-cell SOM expression portraying.

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient's infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

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.

Single-cell trajectories of melanoma cell resistance to targeted treatment.

OBJECTIVE: Cellular heterogeneity is regarded as a major factor affecting treatment response and resistance in malignant melanoma. Recent developments in single-cell sequencing technology have provided deeper insights into these mechanisms. METHODS: Here, we analyzed a BRAFV600E-mutant melanoma cell line by single-cell RNA-seq under various conditions: cells sensitive to BRAF inhibition with BRAF inhibitor vemurafenib and cells resistant to BRAF inhibition with vemurafenib alone or vemurafenib in combination with the MEK1/2 inhibitors cobimetinib or trametinib. Dimensionality reduction by t-distributed stochastic neighbor embedding and self-organizing maps identified distinct trajectories of resistance development clearly separating the 4 treatment conditions in cell and gene state space. RESULTS: Trajectories associated with resistance to single-agent treatment involved cell cycle, extracellular matrix, and de-differentiation programs. In contrast, shifts detected in double-resistant cells primarily affected translation and mitogen-activated protein kinase pathway reactivation, with a small subpopulation showing markers of pluripotency. These findings were validated in pseudotime analyses and RNA velocity measurements. CONCLUSIONS: The single-cell transcriptomic analyses reported here employed a spectrum of bioinformatics methods to identify mechanisms of melanoma resistance to single- and double-agent treatments. This study deepens our understanding of treatment-induced cellular reprogramming and plasticity in melanoma cells and identifies targets of potential relevance to the management of treatment resistance.

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.

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy.

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

Molecular characterization of Burkitt lymphoma in the breast or ovary.

Breast and ovary have been described as rare but typical sites of presentation of Burkitt lymphoma (BL) in females, particularly after puberty. We revised a historic series of 44 lymphomas of the breast or the ovary in women diagnosed between 1973 and 2014 as BL. Fluorescence in situ hybridization (FISH) was applied to all, and array-based copy number analysis as well as expression profiling to a subset of those cases. Of the 42 cases evaluable for FISH, 19 cases showed an IG-MYC translocation but only 9 of those fulfilled the criteria of the current WHO classification for the diagnosis of BL. Those nine cases resembled BL of other sites with regard to molecular features. Our findings along with literature data suggest that breast and ovarian BL (1) seem to be rarer than hitherto assumed, (2) share typical molecular features with other BL, and (3) predominantly affect women in the fertile age.

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas.

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.

Telomere Maintenance Pathway Activity Analysis Enables Tissue- and Gene-Level Inferences.

Telomere maintenance is one of the mechanisms ensuring indefinite divisions of cancer and stem cells. Good understanding of telomere maintenance mechanisms (TMM) is important for studying cancers and designing therapies. However, molecular factors triggering selective activation of either the telomerase dependent (TEL) or the alternative lengthening of telomeres (ALT) pathway are poorly understood. In addition, more accurate and easy-to-use methodologies are required for TMM phenotyping. In this study, we have performed literature based reconstruction of signaling pathways for the ALT and TEL TMMs. Gene expression data were used for computational assessment of TMM pathway activities and compared with experimental assays for TEL and ALT. Explicit consideration of pathway topology makes bioinformatics analysis more informative compared to computational methods based on simple summary measures of gene expression. Application to healthy human tissues showed high ALT and TEL pathway activities in testis, and identified genes and pathways that may trigger TMM activation. Our approach offers a novel option for systematic investigation of TMM activation patterns across cancers and healthy tissues for dissecting pathway-based molecular markers with diagnostic impact.

Melanoma Single-Cell Biology in Experimental and Clinical Settings.

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.

Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers.

Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype "portrayal" with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.

Coxsackievirus B3 Infection of Human iPSC Lines and Derived Primary Germ-Layer Cells Regarding Receptor Expression.

The association of members of the enterovirus family with pregnancy complications up to miscarriages is under discussion. Here, infection of two different human induced pluripotent stem cell (iPSC) lines and iPSC-derived primary germ-layer cells with coxsackievirus B3 (CVB3) was characterized as an in vitro cell culture model for very early human development. Transcriptomic analysis of iPSC lines infected with recombinant CVB3 expressing enhanced green fluorescent protein (EGFP) revealed a reduction in the expression of pluripotency genes besides an enhancement of genes involved in RNA metabolism. The initial distribution of CVB3-EGFP-positive cells within iPSC colonies correlated with the distribution of its receptor coxsackie- and adenovirus receptor (CAR). Application of anti-CAR blocking antibodies supported the requirement of CAR, but not of the co-receptor decay-accelerating factor (DAF) for infection of iPSC lines. Among iPSC-derived germ-layer cells, mesodermal cells were especially vulnerable to CVB3-EGFP infection. Our data implicate further consideration of members of the enterovirus family in the screening program of human pregnancies. Furthermore, iPSCs with their differentiation capacity into cell populations of relevant viral target organs could offer a reliable screening approach for therapeutic intervention and for assessment of organ-specific enterovirus virulence.

Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example.

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental "vector fields" using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

oposSOM-Browser: an interactive tool to explore omics data landscapes in health science.

BACKGROUND: oposSOM is a comprehensive, machine learning based open-source data analysis software combining functionalities such as diversity analyses, biomarker selection, function mining, and visualization. RESULTS: These functionalities are now available as interactive web-browser application for a broader user audience interested in extracting detailed information from high-throughput omics data sets pre-processed by oposSOM. It enables interactive browsing of single-gene and gene set profiles, of molecular 'portrait landscapes', of associated phenotype diversity, and signalling pathway activation patterns. CONCLUSION: The oposSOM-Browser makes available interactive data browsing for five transcriptome data sets of cancer (melanomas, B-cell lymphomas, gliomas) and of peripheral blood (sepsis and healthy individuals) at www.izbi.uni-leipzig.de/opossom-browser .

Telomere Length Maintenance and Its Transcriptional Regulation in Lynch Syndrome and Sporadic Colorectal Carcinoma.

Background: Activation of telomere maintenance mechanisms (TMMs) is a hallmark of most cancers, and is required to prevent genome instability and to establish cellular immortality through reconstitution of capping of chromosome ends. TMM depends on the cancer type. Comparative studies linking tumor biology and TMM have potential impact for evaluating cancer onset and development. Methods: We have studied alterations of telomere length, their sequence composition and transcriptional regulation in mismatch repair deficient colorectal cancers arising in Lynch syndrome (LS-CRC) and microsatellite instable (MSI) sporadic CRC (MSI s-CRC), and for comparison, in microsatellite stable (MSS) s-CRC and in benign colon mucosa. Our study applied bioinformatics analysis of whole genome DNA and RNA sequencing data and a pathway model to study telomere length alterations and the potential effect of the "classical" telomerase (TEL-) and alternative (ALT-) TMM using transcriptomic signatures. Results: We have found progressive decrease of mean telomere length in all cancer subtypes compared with reference systems. Our results support the view that telomere attrition is an early event in tumorigenesis. TMM gets activated in all tumors studied due to concerted overexpression of a large fraction of genes with direct relation to telomere function, where only a very small fraction of them showed recurrent mutations. TEL-related transcriptional state was dominating in all CRC subtypes, showing, however, subtype-specific activation patterns; while contribution of the ALT-TMM was slightly more prominent in the hypermutated MSI s-CRC and LS-CRC. TEL-TMM is mainly activated by over-expression of DKC1 and/or TERT genes and their interaction partners, where DKC1 is more prominent in MSS than in MSI s-CRC and can serve as a transcriptomic marker of TMM activity. Conclusions: Our results suggest that transcriptional patterns are indicative for TMM pathway activation with subtle differences between TEL and ALT mechanisms in a CRC subtype-specific fashion. Sequencing data potentially provide a suited measure to study alterations of telomere length and of underlying transcriptional regulation. Further studies are needed to improve this method.