Cancer Omics

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This Programme offers a comprehensive summary of various projects focused on deciphering the intricate molecular mechanisms of cancer development, progression, and response to treatments, utilizing bioinformatics, computational biology, and machine learning. These are multi-laboratory efforts carried out across several research institutions, supported by various grant programs.

Web page: Not specified

Funding details:

No funding details specified

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Showing 5 out of a possible 10 People Advanced People list for this Programme with search and filtering

Arsen Arakelyan

Projects: Sex-specific differences in long-term gamma and simGCRsim-associated alterations in differential gene expression in the heart tissue, Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Omics-Based Insights into Human Long-Term Exposure to Environmental Metals, Biological pathway activity analysis, ML approaches for omic data analysis, Tools for Telomere Biology, Mental disorders and aging brain, Armenian Wine Genome Program, Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics, Molecular characterization of cancers with long-read RNA sequencing, Low dose radiation risks: present research and future perspectives, Third International Congress "CRISPR-2025", International Summit in Immuno-Oncology, Molecular Diagnostics in Oncology and Related Fields of Medicine, Discovery of new antiviral compounds by combining in silico and in vitro methods, African swine fever virus, Whole genome and whole exome sequencing based analysis of diversity of genomic variants in Armenian population

Institutions: Institute of Molecular Biology NAS RA, Armenian Bioinformatics Institute

https://orcid.org/0000-0002-6851-1056

Meline Hakobyan

Projects: Tools for Telomere Biology, ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing

Institutions: Institute of Molecular Biology NAS RA

https://orcid.org/0000-0002-1089-0655

Siras Hakobyan

Projects: Biological pathway activity analysis, Sex-specific differences in long-term gamma and simGCRsim-associated alterations in differential gene expression in the heart tissue, ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing

Institutions: Institute of Molecular Biology NAS RA

https://orcid.org/0000-0002-6875-2482

Expertise: Bioinformatics

Tools: Cytoscape, Genomics, Microarray analysis, Python, R, Single Cell analysis, Transcriptomics

Siras Hakobyan is currently a Junior Researcher and PhD student in the Research Group of Bioinformatics at the Institute of Molecular Biology NAS RA in Yerevan, Armenia. He received his Master's and Bachelor's degrees in Bioinformatics from Yerevan State University, completing his studies in 2018 and 2016. His PhD project focuses on pathway-centered analysis of high-throughput omics data, particularly examining pathway activity states in cancers based on pathway topology, gene expression, and ...

Lana Karapetyan

Projects: Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics, Development of induced pluripotent stem cell bank of patients with Familial Mediterranean Fever, Molecular Profiling of Cancer Metastases, Sex-specific differences in long-term gamma and simGCRsim-associated alterations in differential gene expression in the heart tissue, Molecular characterization of cancers with long-read RNA sequencing, ML approaches for omic data analysis, Mental disorders and aging brain, Armenian Wine Genome Program, Functional Genomics of Vine, COVID-19, Genetic History of Armenians, Molecular epidemiology of multi-drug resistance, Multi-Omics Molecular Diversity of Cancers, Third International Congress "CRISPR-2025", International Summit in Immuno-Oncology, Low dose radiation risks: present research and future perspectives, Molecular Diagnostics in Oncology and Related Fields of Medicine, Whole genome and whole exome sequencing based analysis of diversity of genomic variants in Armenian population, Discovery of new antiviral compounds by combining in silico and in vitro methods, African swine fever virus

Institutions: Russian-Armenian University, Institute of Molecular Biology NAS RA

https://orcid.org/0009-0009-7992-1366

Expertise: Genetics, Molecular Biology

Tools: PCR, Sequencing, qtPCR, rtPCR

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Advanced Projects list for this Programme with search and filtering

Multi-Omics Molecular Diversity of Cancers

No description specified

Programme: Cancer Omics

Public web page: Not specified

Organisms: Not specified

Molecular Profiling of Cancer Metastases

Together with the scientists at Agenus, a US-based biotech company specializing in innovative forms of cancer treatment, including the discovery and development of immuno-oncology therapies, ABI has formed a team of students and researchers to study cancer metastases and identify potential biomarkers and targets for cancer immunotherapies. This collaboration is also part of our long-term vision of supporting biotech developments in Armenia. The team discovers transcriptomics heterogeneity of Liver ...

Programme: Cancer Omics

Public web page: https://abi.am/research/research-labs/agenus-lab/

Start date: 21st Feb 2024

Organisms: Human

Molecular characterization of cancers with long-read RNA sequencing

The overarching objective of this project is to utilize nanopore sequencing technology and to develop machine learning–based pipelines for the integrative analysis of heterogeneous biological "big data," including multi-omic and phenotypic datasets. The goal is to investigate the molecular mechanisms underlying selected cancers and to identify novel omics-based markers for cancer classification, diagnostics, and prognostics.

Programme: Cancer Omics

Public web page: Not specified

Start date: 1st Sep 2021

End date: 30th Sep 2026

Organisms: Human

Advanced Institutions list for this Programme with search and filtering

Armenian Bioinformatics Institute

Country: Armenia

City: Yerevan

Web page: https://www.abi.am

Institute of Molecular Biology NAS RA

Country: Armenia

City: Yerevan

Web page: https://molbiol.sci.am

Interdisciplinary Center for Bioinformatics (IZBI)

Country: Germany

City: Leipzig

Web page: https://www.izbi.uni-leipzig.de/

Russian-Armenian University

Country: Armenia

City: Yerevan

Web page: https://www.rau.am

Showing 5 out of a possible 9 Data files Advanced Data files list for this Programme with search and filtering

oposSOM-Browser view of 65 recurrent/resistant MSS CRC samples

Molecular Profiling of Cancer Metastases

No description specified

Creator: RNAseq data by AGENUS

Submitter: Nate Zadirako

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

Created: 24th Jun 2026 at 08:20, Last updated: 25th Jun 2026 at 06:29

Raw BRCA1/2 variants in breast cancer patients and healthy relatives produced with GATK.

Multi-Omics Molecular Diversity of Cancers

Aligned sequencing data is available in the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) under accession SRP095082. Variants were called using GATK HaplotypeCaller (version 3.6). After joint performing joint genotyping multi-sample vcf file was generated. Next, SNPs and indels were extracted into two different vcf files and specific set of filters were applied for each case.

Creator: Arsen Arakelyan

Submitter: Lana Karapetyan

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 26th May 2026 at 16:52

BRCA1- and BRCA2- mutation associated transcriptome landscapes in breast and ovarian cancers: ml-SOM results

Multi-Omics Molecular Diversity of Cancers

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This is the submission accompanying raw result files for multiple-layer SOM (ml-SOM) analysis for the paper "Transcriptome patterns of BRCA1- and BRCA2- mutated breast and ovarian cancers".

The dataset contains the results of the ml-SOM analysis of RNA-sequencing data from TCGA-OV (ovarian cancer) and TCGA-BRCA (breast cancer) projects.

The dataset is organized as follows:

Folder "12.BC.40 - Results" - ml-SOM analysis of TCGA-BRCA (breast cancer) dataset Folder "12.OV.40 - Results" - ml-SOM ...

Creators: Arsen Arakelyan, Maria Nikoghosyan, Siras Hakobyan, Arman Simonyan, Lilit Nersisyan, Hans Binder

Submitter: Lana Karapetyan

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 26th May 2026 at 16:30, Last updated: 26th May 2026 at 16:32

oposSOM-BROWSER – ANALYTICS TOOL

Multi-Omics Molecular Diversity of Cancers

oposSOM is a comprehensive data analysis software combining diversity analyses, biomarker selection, functional information mining, and visualization to the machine learning based ‘high-dimensional data portraying’.

Creator: Henry Loeffler-Wirth

Submitter: Lana Karapetyan

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 26th May 2026 at 16:13

Single-cell transcriptomics of chimeric antigen receptor T-cell infusion products

Multi-Omics Molecular Diversity of Cancers

Single-cell RNA sequencing of residual cells from 24 CAR T-cell infusion products.

Creators: None

Submitter: Lana Karapetyan

Investigations: No Investigations

Studies: No Studies

Assays: No Assays

External Link

Created: 26th May 2026 at 15:59

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Showing 5 out of a possible 25 Publications Advanced Publications list for this Programme with search and filtering

Transcriptomic profiling identifies immunotherapy-responsive phenotypes in microsatellite-stable metastatic colorectal cancer.

Molecular Profiling of Cancer Metastases

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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 …

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

PubMed ID: 42298180

Citation: Oncogene. 2026 Jun 16. doi: 10.1038/s41388-026-03861-2.

Created: 24th Jun 2026 at 07:29

Reprogramming of stroma-derived chemokine networks drives the loss of tissue organization in nodal B cell lymphoma.

Molecular Profiling of Cancer Metastases

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 …

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

PubMed ID: 41882179

Citation: Nat Cancer. 2026 Mar;7(3):538-552. doi: 10.1038/s43018-026-01136-z. Epub 2026 Mar 25.

Created: 24th Jun 2026 at 14:03

Integrated analysis of the blood metallome and DNA methylation, gene expression, and protein levels of immune-associated genes in residents from mining region

Molecular Profiling of Cancer Metastases, Multi-Omics Molecular Diversity of Cancers

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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 …

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

PubMed ID: 41485189

DOI: 10.1007/s10653-025-02958-z

Citation: Schizophrenia research,62(1-2):51-8

Created: 9th Jan 2026 at 10:30

Topology-aware pathway analysis of spatial transcriptomics

ML approaches for omic data analysis, Molecular characterization of cancers with long-read RNA sequencing

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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

DOI: 10.7717/peerj.19729

Citation:

Created: 18th Aug 2025 at 10:05

Telomere Maintenance Pathways in Lower-Grade Gliomas: Insights from Genetic Subtypes and Telomere Length Dynamics

Molecular characterization of cancers with long-read RNA sequencing

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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 …

Authors: Meline Hakobyan, Hans Binder, Arsen Arakelyan

Date Published: 28th Apr 2025

Publication Type: Journal

DOI: 10.3390/ijms26094175

Citation: International Journal of Molecular Sciences,26(9):4175

Created: 5th Jul 2025 at 10:47

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