Biology subjects

Belova, T.

Publications and source records attributed to Belova, T..

8 recordsLinked to original sources

bioRxiv2026

A Pan-Cancer Benchmark of Clustering Strategies for Patient-Specific Gene Regulatory Networks

Cancer patients with clinically similar tumors can exhibit substantial differences in disease progression and treatment response. Patient-specific gene regulatory networks (GRNs) may capture regulatory heterogeneity that is not represented by gene expression alone. However, strategies for clustering patients using these high-dimensional networks have not been systematically evaluated and it remains unclear whether clustering based directly on millions of patient-specific regulatory edge weights is computationally feasible at scale. We benchmarked feature-selection and clustering approaches for patient-specific GRNs inferred with PANDA and LIONESS across 9,778 primary tumor samples from 33 cancer types. We compared three data representations: gene expression, gene indegrees summarizing regulatory input to each gene, and complete networks of transcription factor-gene edge weights. Twenty combinations of four feature-selection methods and five clustering algorithms were evaluated using consensus clustering. Performance was assessed based on mean silhouette score, PAC score, concordance, computational time, and memory usage. Standard deviation-based feature selection combined with k-means clustering achieved the strongest overall performance for both GRN-derived representations, with overall scores of 0.997 for edge weights and 0.985 for indegrees, while remaining computationally efficient. Edge-weight- and indegree-derived clusters showed clinical, biological, and prognostic associations that were complementary to those obtained from gene expression. Together, these findings provide a practical framework for network-based cancer patient stratification and highlight the potential of GRN-derived features to reveal clinically relevant tumor heterogeneity beyond gene expression alone.

bioinformatics↗

bioRxiv2025

SiSaNA: A Python-based command line interface for Single-Sample Network Analysis

MotivationReconstructing and analyzing single-sample regulatory networks has proven useful for understanding mechanisms of regulatory biology and its disruption in disease, but traditionally required a fair amount of programming experience and work from the user. ResultsHere, we present SiSaNA (Single-Sample Network Analysis), a workflow that removes the need of prior programming experience and allows for single-sample regulatory network reconstruction and analysis, all via a command line interface. Using example input files, we walk the user through a tutorial on how to run the SiSaNA pipeline, interpret the results, and obtain high-quality figures summarized in a single HTML file as output. AvailabilitySiSaNA is freely available to install with pip via the Python Package Index. It is additionally available on GitHub at https://github.com/kuijjerlab/sisana. Example input files can be found at https://doi.org/10.5281/zenodo.17190642.

bioinformatics↗

bioRxiv2025

Pan-cancer analysis of patient-specific gene regulatory landscapes identifies recurrent PD-1 pathway dysregulation linked to outcome

Cancer heterogeneity remains a major challenge to the development of broadly effective therapies. Although gene expression-based stratification has advanced our understanding of molecular subtypes, it often overlooks the regulatory mechanisms driving tumor progression and therapeutic resistance. These mechanisms are governed by gene regulatory networks--complex systems of transcription factors and other regulators that control gene expression. To investigate regulatory heterogeneity at the patient level, we inferred individual gene regulatory networks for over 9,000 tumors across 33 cancer types from The Cancer Genome Atlas enabling a comprehensive pancancer analysis of inter-patient regulatory variation. Our analysis uncovered novel regulatory subtypes and revealed recurrent dysregulation of the PD-1 signaling pathway in 23 cancer types. This dysregulation was associated with patient outcomes in 12 cancers and appeared largely independent of established molecular subtypes and immune cell composition, suggesting alternative regulatory mechanisms. We identified both well-characterized (FOS, JUNB, ATF3) and previously unrecognized (RFX7, ELF1, NFYA, NFYC) transcriptional regulators of PD-L1 (CD274), highlighting new candidate targets for modulating immune checkpoint activity across tumor types. Our work demonstrates the power of single-sample gene regulatory networks to uncover hidden layers of tumor biology, improve patient stratification, and inform future research in immunotherapy.

bioinformatics↗

bioRxiv2024

Gene regulatory network integration with multi-omics data enhances survival predictions in cancer

The emergence of high-throughput omics technologies has resulted in their wide application to cancer studies, greatly increasing our understanding of the disruptions occurring at different molecular levels. The role of gene regulation as a core driver of biological processes and its role in the development and progression of cancer has been well established. Transcriptional regulation, a crucial aspect of gene regulation, can be represented as a network of interactions between regulators (such as transcription factors) and their target genes. These networks are known as gene regulatory networks (GRNs). Several joint dimensionality reduction (JDR) methods and tools for integrating multi-modal data have been developed in recent years. This study presents a new approach to integrate multi-modal data of different dimensions to consider sample-specific GRNs with multi-omics data in ten cancer datasets from The Cancer Genome Atlas (TCGA). We compare JDR models that include GRNs with those that do not, assessing their association with patient survival. We find that including GRNs can improve associations with patient survival across several cancer types. Focusing on liver cancer, our methodology identifies potential mechanisms of gene regulatory dysregulation associated with cancer progression.

systems biology↗

bioRxiv2023

Temporal transcriptome analysis of the endothelial response to tumour necrosis factor

The vascular endothelium acts as a dynamic interface between blood and tissue and provides an anti-inflammatory and anti-thrombotic surface under normal conditions. Tumour necrosis factor- (TNF), a cytokine that drives acute and chronic inflammation, induces numerous transcriptional changes in endothelial cells (EC). However, the overall temporal dynamics of this response have not been fully elucidated. Here, we conducted an extended time-course analysis of the EC response to TNF, from 30 minutes to 72 hours. We identified TNF-regulated genes and used weighted gene correlation network analysis (WGCNA) to decipher co-expression profiles, uncovering two distinct temporal phases - an acute response initiated between 1-4 hours, followed by a later phase initiated between 12-24 hours. Several previously uncharacterised genes were strongly regulated during the acute phase, while the majority in the later phase were interferon-stimulated genes (ISGs). A lack of interferon transcription indicated that this TNF-induced ISG expression was independent of de novo interferon production and autocrine signalling. Furthermore, we observed two different groups of genes whose transcription was inhibited by TNF, those that resolved towards baseline levels over time, and those that did not. Our study provides insights into the global temporal dynamics of the EC transcriptional response to TNF, highlighting distinct gene expression patterns during the acute and later phases. These findings may be useful in understanding the role of EC in inflammation and developing TNF signalling-targeting therapies.

cell biology↗

bioRxiv2022

Heterogeneity in the gene regulatory landscape of leiomyosarcoma.

Soft-tissue sarcomas are group of rare, tremendously heterogeneous, and highly aggressive malignancies. Characterizing inter-tumor heterogeneity is crucial for selecting suitable sarcoma therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms driving sarcoma heterogeneity. We subtyped soft-tissue sarcomas based on patient-specific, genome-wide gene regulatory networks and found pronounced regulatory heterogeneity in leiomyosarcoma--one of the most common soft-tissue sarcomas subtypes that arises in smooth muscle tissue. To characterize this regulatory heterogeneity, we developed a new computational framework. This method, PORCUPINE, combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways that represent potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. In addition, we showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

bioinformatics↗

bioRxiv2021

Calpain DEK1 acts as a developmental switch gatekeeping cell fate transitions

Calpains are cysteine proteases that control cell fate transitions. Although calpains are viewed as modulatory proteases displaying severe, pleiotropic phenotypes in eukaryotes, human calpain targets are also directed to the N-end rule degradatory pathway. Several of these destabilized targets are transcription factors, hinting at a gene regulatory role. Here, we analyze the gene regulatory networks of Physcomitrium patens and characterize the regulons that are deregulated in DEK1 calpain mutants. Predicted cleavage patterns of regulatory hierarchies in the five DEK1-controlled subnetworks are consistent with the genes pleiotropy and the regulatory role in cell fate transitions targeting a broad spectrum of functions. Network structure suggests DEK1-gated sequential transition between cell fates in 2D to 3D development. We anticipate that both our method combining phenotyping, transcriptomics and data science to dissect phenotypic traits and our model explaining the calpains role as a switch gatekeeping cell fate transitions will inform biology beyond plant development.

developmental biology↗

bioRxiv2021

Regulation of PD1 signaling is associated with prognosis in glioblastoma multiforme

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in expression of particular genes, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms that associate with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas (n=522 and 431). We performed a comparative network analysis between patients with long- and short-term survival, correcting for patient age, sex, and neoadjuvant treatment status. We identified seven pathways associated with survival, all of which were involved in immune system signaling. Differential regulation of PD1 signaling was validated in an independent dataset from the German Glioma Network (n=70). We found that transcriptional repression of genes in this pathway--for which treatment options are available--was lost in short-term survivors and that this was independent of mutation burden and only weakly associated with T-cell infiltrate. These results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival, and identify new potential therapeutic interventions, thus underscoring the value of analyzing gene regulatory networks in individual cancer patients.

systems biology↗