Publications

What is a Publication?
25 Publications visible to you, out of a total of 25

Abstract (Expand)

Conventional immune checkpoint inhibitors (ICIs) remain largely ineffective in microsatellite-stable metastatic colorectal cancer (MSS mCRC), where low tumor immunogenicity and molecular heterogeneity across metastatic sites underpin therapeutic resistance. We present a comprehensive transcriptomics analysis of metastatic and primary tumor biopsies from MSS mCRC patients treated with botensilimab (BOT; Fc-enhanced anti-CTLA-4) +/- balstilimab (BAL; anti-PD-1). Self-organizing map (SOM) machine learning stratified tumors into four molecular types, including a liver-like (LIV) subtype characterized by metabolic reprogramming and immunosuppressive signatures, and proliferative (PRO), inflammatory (INF), and mesenchymal (MES) types concordant with pan-cancer classifications. PRO, INF, and MES types were enriched for epithelial tumor cells, immune cells, and fibroblasts, respectively, defining immune-depleted, immune-enriched, and fibrotic states along a plasticity gradient. We observed treatment-related transcriptomic shifts toward immune-enriched states via upregulation of antigen presentation, T cell recruitment, and cytotoxicity pathways. INF and MES tumor types exhibited improved clinical responses and survival vs PRO and LIV types. This study identified distinct tumor microenvironment states that align along an immunophenotype axis marked by CD74, interferon-gamma, and APOBEC3 expression identified previously for primary CRC. Our findings provide novel insights into molecular correlates of immunotherapy response in MSS mCRC, potentially informing future therapeutic strategies to expand ICI efficacy to historically unresponsive tumors.

Authors: T. Konecny, N. Zadirako, A. Grigoryan, M. Tamazyan, S. Mnatsakanyan, L. Stepanyan, H. Loeffler-Wirth, S. Bourdelais, G. Mednick, C. Delepine, D. Chand, H. Binder

Date Published: 16th Jun 2026

Publication Type: Journal

Abstract (Expand)

Lymph node (LN) function requires the organization of cells into higher-order spatial units. However, the principles governing LN architecture in health and disease remain poorly understood. Here, we used single-cell and spatial mapping to investigate the mechanisms directing immune cell organization in human LNs and its disruption in architecturally distinct lymphoma entities: indolent follicular lymphoma (FL) and aggressive diffuse large B cell lymphoma (DLBCL). Our data substantiate the central role of LN-resident stromal cells in chemokine-driven lymphocyte zonation and reveal an inflammatory feedback loop fueled by tumor-reactive T cells that triggers stromal remodeling, progressive loss of homeostatic chemokine gradients, and tissue disorganization from a non-malignant state to FL and DLBCL. Loss of homeostatic chemokines was associated with adverse patient survival, identifying the underlying architectural rearrangement as a key event during lymphomagenesis. Collectively, our results highlight the principles of LN organization and suggest how lymphoma-induced microenvironmental reprogramming drives the loss of tissue organization.

Authors: F. Czernilofsky, A. Mathioudaki, L. Jopp-Saile, R. Lutz, D. Vonficht, X. Wang, C. Schniederjohann, H. Voehringer, T. Roider, M. A. Baertsch, C. Rodemer, H. Loffler-Wirth, M. Grau, D. Fitzgerald, J. Mammen, J. Kosla, N. Liebers, P. M. Bruch, D. Ordonez-Rueda, A. Brobeil, G. Mechtersheimer, C. Pabst, C. Muller-Tidow, A. Trumpp, M. Seifert, F. Neumann, M. Heikenwalder, V. Benes, W. Huber, J. Distler, G. Lenz, H. Binder, R. Siebert, G. P. Nolan, M. Gerstung, J. B. Zaugg, D. Hubschmann, S. Haas, S. Dietrich

Date Published: 31st Mar 2026

Publication Type: Journal

Abstract (Expand)

Environmental exposure to toxic and essential metals can disrupt host immune function through mechanisms involving epigenetic, transcriptional, and post-transcriptional regulation. Although numerous studies have investigated these regulatory layers separately, integrative analyses across molecular levels in relation to metallome is missing. In this study, we performed a targeted multi-omics analysis of six immune-associated genes (NFKB1, CDKN2A, IGF2, H19, ESR1, and APOA5) and corresponding proteins in healthy residents from a long-term mining region (MRR, n = 46) and a non-mining region (NMR, n = 48). Transcriptome data were generated by mRNA sequencing, while DNA methylation data were obtained using targeted bisulfite sequencing by analyzing previously identified differentially methylated positions. Plasma protein levels were measured by enzyme-linked immunosorbent assay, and plasma metal concentrations were quantified using inductively coupled plasma mass spectrometry. We observed significantly higher plasma levels of NFKB1 and CDKN2A proteins, along with lower ESR1 transcript levels, in residents of the mining region compared to the non-mining region. NFKB1 protein levels were associated with both promoter methylation and residence in mining region, suggesting a regulatory cascade from DNA methylation to protein expression. IGF2 protein levels were higher in males and showed positive associations with age and the cumulative Z-score of essential metal mixture burden. Our results show that long-term residence in mining regions is associated with changes in NFKB1 at both the DNA methylation and protein levels, which may serve as a sensitive biomarker of metal exposure.

Authors: Yeva Bareghamyan, Arpine Minasyan, Suren Davitavyan, Anna Petrackova, Jakub Savara, Romana Nesnadna, Eva Kriegova, Jonathan Schug, Arsen Arakelyan, Ani Stepanyan

Date Published: 4th Jan 2026

Publication Type: Journal

Abstract (Expand)

Spatial transcriptomics (ST) has transformed genomics by mapping gene expression onto intact tissue architecture, uncovering intricate cellular interactions that bulk and single-cell RNA sequencing often overlook. Traditional ST workflows typically involve clustering spots, performing differential expression analyses, and annotating results via gene-set methods such as overrepresentation analysis (ORA) or gene set enrichment analysis (GSEA). More recent spatially-aware techniques extend these approaches by incorporating tissue organization into gene-set scoring. However, because they operate primarily at the level of individual genes, they may overlook the connectivity and topology of biological pathways, limiting their capacity to trace the propagation of signaling events within tissue regions. In this study, we address that gap by translating gene expression into pathway-level activity using the Pathway Signal Flow (PSF) algorithm. PSF integrates expression data with curated interaction networks to compute numeric activity scores for each branch of a biological pathway, producing a functionally annotated feature space that captures downstream signaling effects as branch-specific activity values. We applied PSF to two public 10x Genomics Visium datasets (human melanoma and mouse brain) and compared clustering based on PSF-derived pathway activities from 40 curated Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene expression with standard Seurat Louvain clustering and spatially aware methods (Vesalius, spatialGE). We observed good correspondence between PSF-based and expression-based clustering when spatially aware clustering methods were used. This suggests that branch-level pathway activities can themselves drive clustering and pinpoint spatially deregulated processes. To assess cluster-specific functional annotation, we compared PSF results to conventional ORA (based on marker genes) and GSDensity (based on cluster-specific gene sets). PSF identified a broader set of significant pathways with substantial overlap with both ORA and GSDensity, providing increased sensitivity due to its branch-level resolution. We further demonstrated that PSF-derived activity values can be used to detect spatially deregulated pathway branches, yielding results comparable to those obtained with spatially aware gene set analysis approaches such as GSDensity and spatialGE. The availability of pathway topology and branch-specific information also enabled the identification of potential intercellular communication via ligand-receptor interactions between deregulated pathways in adjacent tumor regions. To support interactive exploration of results, we developed the PSF Spatial Browser, an R Shiny application for visualizing pathway activities, gene expression patterns, and deregulated pathway networks.

Authors: Siras Hakobyan, Maria Schmidt, H. Binder, A. Arakelyan

Date Published: 14th Aug 2025

Publication Type: Journal

Abstract (Expand)

Telomere maintenance mechanisms (TMMs) play a critical role in cancer biology, particularly in lower-grade gliomas (LGGs), where telomere dynamics and pathway activity remain poorly understood. In this study, we analyzed TCGA-LGG and CGGA datasets, focusing on telomere length variations, pathway activity, and survival data across IDH subtypes. Additional validation was performed using the GEO COPD and GBM datasets, ensuring consistency in data processing and batch effect correction. Our analysis revealed significant differences in TEL pathway activation between Short- and Long-TL groups, emphasizing the central role of TERT in telomere maintenance. In contrast, ALT pathway activation displayed subtype-specific patterns, with IDH-wt tumors exhibiting the highest ALT activity, primarily driven by the RAD51 branch. Validation using CGGA data confirmed these findings, demonstrating consistent TEL and ALT pathway behaviors across datasets. Additionally, genetic subtype analysis revealed substantial telomere length variability associated with ATRX and IDH mutation status. Notably, IDHwt-ATRX WT tumors exhibited the shortest telomere length and the highest ALT pathway activity. These findings highlight distinct telomere regulatory dynamics across genetic subtypes of LGG and provide new insights into potential therapeutic strategies targeting telomere maintenance pathways.

Authors: Meline Hakobyan, Hans Binder, Arsen Arakelyan

Date Published: 28th Apr 2025

Publication Type: Journal

Abstract (Expand)

Background/Objectives: Massively parallel sequencing technologies have advanced chronic lymphocytic leukemia (CLL) diagnostics and precision oncology. Illumina platforms, while offering robust performance, require substantial infrastructure investment and a large number of samples for cost-efficiency. Conversely, third-generation long-read nanopore sequencing from Oxford Nanopore Technologies (ONT) can significantly reduce sequencing costs, making it a valuable tool in resource-limited settings. However, nanopore sequencing faces challenges with lower accuracy and throughput than Illumina platforms, necessitating additional computational strategies. In this paper, we demonstrate that integrating publicly available short-read data with in-house generated ONT data, along with the application of machine learning approaches, enables the characterization of the CLL transcriptome landscape, the identification of clinically relevant molecular subtypes, and the assignment of these subtypes to nanopore-sequenced samples. Methods: Public Illumina RNA sequencing data for 608 CLL samples were obtained from the CLL-Map Portal. CLL transcriptome analysis, gene module identification, and transcriptomic subtype classification were performed using the oposSOM R package for high-dimensional data visualization with self-organizing maps. Eight CLL patients were recruited from the Hematology Center After Prof. R. Yeolyan (Yerevan, Armenia). Sequencing libraries were prepared from blood total RNA using the PCR-cDNA sequencing-barcoding kit (SQK-PCB109) following the manufacturer's protocol and sequenced on an R9.4.1 flow cell for 24-48 h. Raw reads were converted to TPM values. These data were projected into the SOMs space using the supervised SOMs portrayal (supSOM) approach to predict the SOMs portrait of new samples using support vector machine regression. Results: The CLL transcriptomic landscape reveals disruptions in gene modules (spots) associated with T cell cytotoxicity, B and T cell activation, inflammation, cell cycle, DNA repair, proliferation, and splicing. A specific gene module contained genes associated with poor prognosis in CLL. Accordingly, CLL samples were classified into T-cell cytotoxic, immune, proliferative, splicing, and three mixed types: proliferative-immune, proliferative-splicing, and proliferative-immune-splicing. These transcriptomic subtypes were associated with survival orthogonal to gender and mutation status. Using supervised machine learning approaches, transcriptomic subtypes were assigned to patient samples sequenced with nanopore sequencing. Conclusions: This study demonstrates that the CLL transcriptome landscape can be parsed into functional modules, revealing distinct molecular subtypes based on proliferative and immune activity, with important implications for prognosis and treatment that are orthogonal to other molecular classifications. Additionally, the integration of nanopore sequencing with public datasets and machine learning offers a cost-effective approach to molecular subtyping and prognostic prediction, facilitating more accessible and personalized CLL care.

Authors: A. Arakelyan, T. Sirunyan, G. Khachatryan, S. Hakobyan, A. Minasyan, M. Nikoghosyan, M. Hakobyan, A. Chavushyan, G. Martirosyan, Y. Hakobyan, H. Binder

Date Published: 13th Mar 2025

Publication Type: Journal

Abstract

Not specified

Authors: A. Stubenvoll, M. Schmidt, J. Moeller, M. A. L. Chango, C. Schultz, O. Antoniadou, H. Loeffler-Wirth, S. Bernhart, F. Grosse, B. Thier, A. Paschen, U. Anderegg, J. C. Simon, M. Ziemer, C. T. Schoeder, H. Binder, M. Kunz

Date Published: 15th Jan 2025

Publication Type: Journal

Powered by
(v.1.15.0-main)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH