Biology subjects

Arakelyan, A.

Publications and source records attributed to Arakelyan, A..

6 recordsLinked to original sources

bioRxiv2026

2,234 attempts to harmonize a multi-platform B-cell lymphoma bulk transcriptomic dataset: ComBat is superior by cross-batch prediction quality

Background. Cross-platform transcriptomic harmonization benchmarks rely on individual success metric each: correlation with a reference profile, classifier accuracy, or a composite imported from single-cell integration. We found no published study that computes multiple orthogonal metric classes over one set of harmonization approaches and measures overlapping between the best approaches by these metrics. Methods. We used the 2,234 approaches of the recent real-world benchmark on 7,174 B-cell lymphoma transcriptomes. They combine 31 harmonization methods, 14 batch-removal strategies, three imputations and two post-removal settings. We assigned its 87 polarity-adjusted scoring metrics, out of 344 registered, to five classes (global, local, distributional, structural and composite) and added three biology-facing classes: within-cohort marker correlation (L), cross-batch rank agreement of a marker panel (M), and leave-one-batch-out (LOBO) prediction of lymphoma subtypes and normal B cells (N). Each class resulted in top 112 approaches (5%), and we measured overlap by the Jaccard index. Results. Harmonization methods of high harshness such as quantile normalization matched different batches to the same shape of distribution but were inferior by local and global metrics. Local and composite metrics resulted in the largest overlap of best approaches (71 shared). Classes L and M, M and N shared no approach, whereas L and N shared one approach without harmonization under the rare-batches-removed strategy, and all three overlaps were significantly below the random expectation. Class N resulted in the ComBat family in 84 of its 112 places. Class M elected only 10_mnn and 12_scanorama, methods that completely corrupted biology by correlation. Conclusions. Best transcriptomic harmonization approach is highly contingent on the quality metrics used, with metric classes trade-offing between each other. We provide a six-step recipe for metric classes, their thresholds and the cross-batch tests for selecting the best approaches.

bioinformatics↗

bioRxiv2026

Benchmarking of bulk transcriptomic harmonization tools in a multi-platform B-cell lymphoma cohort identifies feature-specific quantile normalization and surrogate variable analysis as top-performing methods

Cross-platform harmonization of bulk transcriptomic datasets remains a fundamental challenge for developing cancer biomarkers because of persistent unresolved batch effects. Most harmonization tools are benchmarked on datasets with large inter-group biological differences (for example TCGA tumor types), whereas actionable biomarker mining requires preserving subtle transcriptional distinctions between closely related diagnoses. Here we present ComboBatch, a benchmarking pipeline that evaluates the full cross-product of 14 batch-removal strategies, 3 imputation methods, 33 harmonization algorithms and 2 post-removal conditions across 7,174 samples from 88 germinal-center B-cell lymphoma cohorts spanning four transcriptomic platforms. Scoring 87 quality metrics across 2,234 harmonization approaches, we show that method choice (R2 0.36) and batch-removal strategy (0.26) are the principal determinants of harmonization quality, whereas imputation (0.016) and post-removal (<0.01) are secondary. Feature Specific Quantile Normalization and Surrogate Variable Analysis were the top methods, jointly resolving follicular lymphoma, diffuse large B-cell lymphoma and normal germinal-center B-cell differences in multi-platform and RNA-seq-only compositions, respectively. We provide a data-driven five-scenario decision tree for harmonization method selection, applicable to any retrospective multi-platform transcriptomic study. The ComboBatch pipeline is available on GitHub and can be used for harmonization, allowing bioinformaticians to utilize 33 harmonization and 3 imputation methods according to their needs.

cancer biology↗

bioRxiv2023

Development of an optimized, non-stem cell line for intranasal delivery of therapeutic cargo to the central nervous system

Neural stem cells (NSC) are considered to be valuable candidates for delivering a variety of anti-cancer agents to brain tumors, including oncolytic viruses. However, owing to the previously reported tumorigenic potential of NSC cell line after intranasal administration (INA), here we identified the human hepatic stellate cell line (LX-2) as a cell type capable of longer resistance to replication of oncolytic adenoviruses (OAV) as therapeutic cargo, and being non-tumorigenic after INA. Our data show that LX-2 cells can longer withstand the OAV XVir-N-31 replication and oncolysis than NSCs. By selecting the highly migratory cell population out of LX-2, an offspring cell line with a higher and more stable capability to migrate was generated. Additionally, as a safety backup, we applied genomic HSV-TK integration into LX-2 leading to the high vulnerability to Ganciclovir. Histopathological analyses confirmed the absence of neoplasia in the respiratory tracts and brains of immuno-compromised mice 3 months after INA of LX-2 cells. Our data suggest that LX-2 is a novel robust and safe cell line for delivering anti-cancer and other therapeutic agents to the brain.

molecular biology↗

bioRxiv2022

Retrotransposons facilitates tissue specific horizontal Transfer of circulating tumor DNA

A variety of organisms have been shown to have altered physiology or developed pathology due to gene transfer, but mammals have never been shown to do so. Here, we show that circulating tumor DNA (ct) can promote cell-specific horizontal gene transfer (HGT) between human cancer cells and explain the mechanisms behind this phenomenon. Once ctDNA enters the host cell, it migrates to the nucleus and integrates into the cells genome, thereby transferring its genetic information. We determine that retrotransposons of the ERVL, SINE, and LINE families are necessary for cell targeting and the integration of ctDNA into host DNA. Using chemically synthesized retrotransposons, we found that AluSp and MER11C reproduced multiple myelomas (MM) ctDNAs cell targeting and integration into MM cells. We also discovered that ctDNA might, as a result of HGT, influence the treatment response of multiple myeloma and pancreatic cancer models. Overall, this is the first study to show that retrotransposon-directed HGT can promote genetic material transfer in cancer. There is, however, a broader impact of our findings than just cancer since cell-free DNA has also been found in physiological and other pathological conditions as well. Furthermore, with the discovery of transposons-mediated tissue-specific targeting, a new avenue for the delivery of genes and therapies will emerge.

cancer biology↗

bioRxiv2021

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.

bioinformatics↗

bioRxiv2017

A transcriptome and literature guided algorithm for reconstruction of pathways to assess activity of telomere maintenance mechanisms

Activation of telomere maintenance mechanisms (TMMs) is a crucial factor for indefinite proliferation of cancer cells. The most common TMM is based on the action of telomerase, but in some cancers telomeres are elongated via homologous recombination based alternative mechanism (ALT). Despite their importance, little is known about TMM regulation and factors responsible for TMM phenotype choice in different cells. Currently, many studies address the involvement of few genes in TMMs, but a consensus unified picture of the full process is missing.\n\nWe have developed a computational biology framework combining knowledge- and data-driven approaches to aid in understanding of TMMs. It is based on a greedy algorithm with three core modules: (1) knowledge-based construction/modification of molecular pathways for telomerase-dependent and alternative TMMs, (2) coupled with gene expression data-based validation with an in-house pathway signal flow (PSF) algorithm, and (3) iteration of these two coupled steps until converging at pathway topologies that best reflect state of the art knowledge and are in maximum accordance with the data. We have used gene expression data derived from cell lines and tumor tissues and have performed extensive literature search and multiple cycles of greedy iterations until reaching TMM assessment accuracy of 100% and 77%, respectively.\n\nAvailability of TMM pathways that best reflect recent knowledge and data will facilitate better understanding of TMM processes. As novel experimental findings in TMM biology emerge, and new datasets are generated, our approach may be used to further expand/improve the pathways, possibly allowing for making distinctions not only between telomerase-dependent and ALT TMMs, but also among their different subtypes. Moreover, this method may be used for assessment of TMM phenotypes from gene expression data, which is crucial for studies where experimental detection of TMM states is missing. Furthermore, it can also be used to assess TMM activities in proliferating healthy cells.

bioinformatics↗