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 8 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 deferential gene expression in the heart tissue, Functional Genomics of Vine, Molecular Profiling of Cancer Metastases, Study of the molecular mechanisms of familial Mediterranean fever using genetic engineering and functional genomics, Mental disorders and aging brain, 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, Armenian Wine Genome Program, Molecular characterization of cancers with long-read RNA sequencing, Low dose radiation risks: present research and future perspectives

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 deferential 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 ...

Tomas Konecny

Tomas Konecny obtained his bachelor's and master's degrees in Molecular Biology and Genetics at Masaryk University, Czech Republic. He began a PhD in Molecular and Cell Biology at the University of South Bohemia. In 2023, Tomas switched to bioinformatics and started a PhD at Leipzig University, Germany. He is currently a bioinformatician at the Armenian Bioinformatics Institute and the Interdisciplinary Centre for Bioinformatics, Leipzig. His research focuses on grapevine response to climate ...

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Advanced Projects list for this Programme with search and filtering

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/

Showing 5 out of a possible 6 Publications Advanced Publications list for this Programme with search and filtering

Assigning Transcriptomic Subtypes to Chronic Lymphocytic Leukemia Samples Using Nanopore RNA-Sequencing and Self-Organizing Maps.

Molecular characterization of cancers with long-read RNA sequencing

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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

PubMed ID: 40149301

Citation: Cancers (Basel). 2025 Mar 13;17(6):964. doi: 10.3390/cancers17060964.

Created: 9th May 2025 at 06:24, Last updated: 9th May 2025 at 06:27

Pan-cancer analysis of telomere maintenance mechanisms.

Molecular characterization of cancers with long-read RNA sequencing

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Telomeres, protective caps at chromosome ends, maintain genomic stability and control cell lifespan. Dysregulated telomere maintenance mechanisms (TMMs) are cancer hallmarks, enabling unchecked cell …

Authors: M. Hakobyan, H. Binder, A. Arakelyan

Date Published: 2nd Jul 2024

Publication Type: Journal

PubMed ID: 38763334

Citation: J Biol Chem. 2024 Jun;300(6):107392. doi: 10.1016/j.jbc.2024.107392. Epub 2024 May 18.

Created: 11th Feb 2025 at 13:12

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

Biological pathway activity analysis, Molecular characterization of cancers with long-read RNA sequencing

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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.

Authors: S. Hakobyan, A. Stepanyan, L. Nersisyan, H. Binder, A. Arakelyan

Date Published: 8th Sep 2023

Publication Type: Journal

PubMed ID: 37680201

Citation: Front Genet. 2023 Aug 23;14:1264656. doi: 10.3389/fgene.2023.1264656. eCollection 2023.

Created: 6th Mar 2024 at 19:17, Last updated: 3rd Apr 2025 at 08:38

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

Molecular characterization of cancers with long-read RNA sequencing

Abstract (Expand)

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 …

Authors: O. Ashekyan, N. Shahbazyan, Y. Bareghamyan, A. Kudryavzeva, D. Mandel, M. Schmidt, H. Loeffler-Wirth, M. Uduman, D. Chand, D. Underwood, G. Armen, A. Arakelyan, L. Nersisyan, H. Binder

Date Published: 28th Jul 2023

Publication Type: Journal

PubMed ID: 37568651

Citation: Cancers (Basel). 2023 Jul 28;15(15):3835. doi: 10.3390/cancers15153835.

Created: 9th May 2025 at 20:29

Integrated Multi-Omics Maps of Lower-Grade Gliomas.

Molecular characterization of cancers with long-read RNA sequencing

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Abstract (Expand)

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 …

Authors: H. Binder, M. Schmidt, L. Hopp, S. Davitavyan, A. Arakelyan, H. Loeffler-Wirth

Date Published: 4th Jun 2022

Publication Type: Journal

PubMed ID: 35681780

Citation: Cancers (Basel). 2022 Jun 4;14(11):2797. doi: 10.3390/cancers14112797.

Created: 9th May 2025 at 20:32

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