Biology subjects

Izumchenko, E.

Publications and source records attributed to Izumchenko, E..

9 recordsLinked to original sources

bioRxiv2026

scRNA-seq and genomics analyses reveal key mechanisms of inverted papilloma-associated sinonasal squamous cell carcinoma malignant transformation

Sinonasal inverted papilloma (IP) carries a 10% risk of malignant transformation to IP-associated sinonasal squamous cell carcinoma (IP-SNSCC), yet the molecular and immune drivers of this progression remain poorly defined. This study integrates single-cell RNA sequencing, multiplexed spatial proteomics, whole-exome sequencing, and functional assays across IP and IP-SNSCC cohorts to define mechanisms of malignant transformation. Respiratory epithelial basal cells are identified as the putative cell of origin, with progression marked by recurrent CDKN2A loss and TP53 mutations. Spatial profiling reveals immune reorganization at the lesional interface in IP-SNSCC, characterized by enrichment of alternatively activated M2 macrophages. CXCL14 is shown to directly induce an immunosuppressive myeloid phenotype that suppresses T-cell IFN{gamma} production through an IDO-pathway-dependent mechanism. Integration of these multimodal datasets defines a previously unrecognized CXCL14-IDO mechanism that constrains anti-tumor immunity at the tumor-stroma interface. These findings establish IDO-targeted immunomodulation as a rational adjuvant strategy and provide a comprehensive molecular framework for understanding IP-SNSCC pathogenesis.

genomics↗

bioRxiv2026

Integrated analysis of stemness-associated immune modulatory circuits in squamous cell carcinomas

Emerging evidence indicates that a subset of cancer cells enriched for stemness-related gene signatures possess distinct immunomodulatory capacities, enabling these tumor-initiating stem cells (tSCs) to more effectively evade or resist anti-tumor immunity. Despite these advances, the tSC-specific molecular circuits orchestrating their specialized immune privilege program are not well defined. Here, in squamous cell carcinomas of the skin and oral cavity, we comprehensively delineate the unique immune-evasive properties of tSCs and dissect the transcriptional regulation shaping their immunomodulatory programs. By integrating transcriptome profiling, chromatin landscape mapping, genetic perturbation, and single-cell RNA sequencing, we found that the tSC-specific immune program is broadly governed by SOX2, a stemness-associated transcription factor. We demonstrate that SOX2 enables tSCs to sustain immature tumor-associated neutrophils (TANs) and subsequently trigger these myeloid cells to foster the development of tumor-associated macrophages (TAMs). This SOX2-directed tSC-TAN-TAM axis establishes a localized immunosuppressive niche for protecting tSC. SIGNIFICANCEHere, we uncover SOX2 as a master regulator that orchestrates conserved immune modulatory circuits in tSCs to sustain pro-tumor myeloid cell states. These findings place tSCs at the apex of immune landscape remodeling, asserting a central role of stemness-associated program in organizing the immunosuppressive tumor microenvironment.

cancer biology↗

bioRxiv2026

Integrating AI and causal genetics to prioritize therapeutic targets for aging and age-related diseases

Aging is increasingly viewed as a pathologic process and a principal driver of diverse age-related diseases (ARDs). Framing aging as a disease offers an opportunity to identify therapeutic targets capable of modifying multiple chronic disorders simultaneously. Here, we developed an AI-driven target discovery framework that integrates large-scale multi-omic datasets to prioritise therapeutic targets shared between aging and 12 ARDs across four major disease areas: neurological, inflammatory, metabolic, and fibrotic disorders. We identified 29 high-confidence and 16 previously unrecognized aging-associated targets implicated across selected disease areas, together with convergent pathway perturbations characterized by robust upregulation of interferon and inflammatory signaling, alongside coordinated downregulation of MYC-driven proliferative programs, consistent with heightened inflammatory activation and reduced anabolic activity during aging. Hallmarks of aging assessment revealed chronic inflammation as the most enriched hallmark across aging and ARDs. Mendelian randomization provided genetic causal support for IL6, IL6R, NLRP3, NOS2, TLR4, and GLP1R in aging-related traits and multiple ARDs, highlighting potential opportunities for drug repurposing. Co-localization analysis further demonstrated a shared genetic signal at the IL6R locus between gene expression levels and parental survival. Together, our findings outline a scalable AI-guided multi-omic framework for identifying causal and repurposable therapeutic targets for aging and ARDs. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/705676v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@b94af7org.highwire.dtl.DTLVardef@e89e80org.highwire.dtl.DTLVardef@1fde804org.highwire.dtl.DTLVardef@8b817b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2025

Advancing Target Discovery Through Disease-Specific Integration of Multi-Modal Target Identification Models and Comprehensive Target Benchmarking System

Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting translational potential remains challenging. Moreover, the absence of a systematic evaluation framework for model performance limits confidence in target reliability. We present a unified platform combining machine learning-based target identification with comprehensive benchmarking. As a testbed, we developed Target Identification Pro (TargetPro), a disease-specific model spanning 38 diseases across oncology, metabolic, immune, fibrotic, and neurological categories. TargetPro shows strong predictive performance for clinical-stage targets and reveals disease-specific patterns, underscoring the need for tailored target detection models. We next created Target Identification Benchmark (TargetBench 1.0) to rigorously assess target identification systems, including large language models, based on their ability to recover established targets and find high-quality novel candidates. This integrated approach offers a streamlined strategy to evaluate target discovery models, ultimately improving drug development efficiency.

bioinformatics↗

bioRxiv2025

Tumor-Initiating Cells Fine-tune the Plasticity of Neutrophils to Sculpt a Protective Niche

The abundant accumulation of neutrophils in various solid cancers has been well recognized, but the functions of tumor-associated neutrophils (TANs) remain controversial. TANs have long been believed to be immune suppressive and have thus been referred to as "myeloid-derived suppressor cells". However, effective tumor control induced by immunotherapy was recently found to be associated with strong neutrophil signatures. These seemingly contradictory findings highlight the unexpected degree of plasticity and heterogeneity unique to TANs. How the cellular plasticity and functional heterogeneity of TANs are regulated remains unknown. Here, we show that, while anti-PDL1/CD40 agonist immunotherapy can induce interferon responses to reprogram many TANs, allowing them to become plastic and regain anti-tumor activities in squamous cell carcinomas, a subset of TANs residing at the tumor-stroma interface can preserve their immune suppressive state. Importantly, by designing a reverse genetic screening, we identified a group of Sox2Hi tumor-initiating cells (TICs) at the tumor-stroma interface that could upregulate Fatty Acid Desaturase 1 (Fads1) to produce arachidonic acid. This TIC-specific pathway can disrupt the interferon responsive potentials of TANs, preventing the interferon-mediated reprogramming. Thus, by fine-tuning the plasticity of neutrophils, TICs shape neutrophil heterogeneity and sculpt a protective micro-niche to survive from immunotherapy and drive cancer relapse.

cancer biology↗

bioRxiv2025

DORA AI Scientist: Multi-agent Virtual Research Team for Scientific Exploration Discovery and Automated Report Generation

Modern goal-oriented scientific research process involves hierarchical teams of researchers of diverse backgrounds performing generalist and domain-specific tasks. Many of these tasks include hypothesis generation, literature review, data collection, cleanup, processing and analysis, experimental design, virtual and physical experiments, research report and academic paper writing, reference management, bibliography and quality control. Most of these tasks can be performed automatically or in a co-pilot mode by the generative reinforcement learning systems. In this paper, we introduce a versatile multi-agent scientific exploration and draft outline research assistant (DORA), which provides multiple templates and workflows for automated or semi-automated research studies and report generation. Under user guidance, it employs hierarchical teams of AI agents based on the plug-and-play generalist and domain-specific large language models (LLMs) exploiting a variety of specialized research tools and open data repositories and generates high-quality research outputs publication drafts with maximally-accurate references. DORA is designed to minimize the time and effort required for manuscript preparation, thereby enabling researchers to devote more attention to high-value discovery tasks. The system is constantly evolving with user feedback with regular feature and resource updates. The platform is available at https://dora.insilico.com.

bioinformatics↗

bioRxiv2023

Latent transcriptional programs reveal histology-encoded tumor features spanning tissue origins

Precision medicine in cancer treatment depends on deciphering tumor phenotypes to reveal the underlying biological processes. Molecular profiles, including transcriptomics, provide an information-rich tumor view, but their high-dimensional features and assay costs can be prohibitive for clinical translation at scale. Recent studies have suggested jointly leveraging histology and genomics as a strategy for developing practical clinical biomarkers. Here, we use machine learning techniques to identify de novo latent transcriptional processes in squamous cell carcinomas (SCCs) and to accurately predict their activity levels directly from tumor histology images. In contrast to analyses focusing on pre-specified, individual genes or sample groups, our latent space analysis reveals sets of genes associated with both histologically detectable features and clinically relevant processes, including immune response, collagen remodeling, and fibrosis. The results demonstrate an approach for discovering clinically interpretable histological features that indicate complex, potentially treatment-informing biological processes.

cancer biology↗

bioRxiv2020

TMPRSS2, a SARS-CoV-2 internalization protease is downregulated in head and neck cancer patients.

ObjectivesTwo of the main target tissues of SARS-coronavirus 2 are the oral cavity pharynx-larynx epithelium, the main virus entry site, and the lung epithelium. The virus enters host cells through binding of the Spike protein to ACE2 receptor and subsequent S priming by the TMPRSS2 protease. Herein we aim to assess differences in both ACE2 and TMPRSS2 expression in normal tissues from oral cavity-pharynx-larynx and lung tissues as well as neoplastic tissues from the same histological areas. The information provided in this study may contribute to better understanding of SARS-coronavirus 2 ability to interact with different biological systems and contributes to cumulative knowledge on potential mechanisms to inhibit its diffusion. Materials and MethodsThe study has been conducted using The Cancer Genome Atlas (TCGA) and the Regina Elena Institute (IRE) databases and validated by experimental model in HNSCC and Lung cancer cells. Data from one COVID19 positive patient who was operated on for HNSCC was also included. We have analyzed 478 tumor samples and 44 normal samples from TCGA HNSCC cohort for whom both miRNA and mRNA sequencing was available. The dataset included 391 HPV- and 85 HPV+ cases, with 331 P53 mutated and 147 P53 wild type cases respectively. 352 out of 478 samples were male and 126 female. In IRE cohort we analyzed 66 tumor samples with matched normal sample for miRNA profiling and 23 tumor\normal matched samples for mRNA profiling. 45 out of 66 tumors from IRE cohort were male and 21 female, 38 were P53 mutated and 27 wild type. Most patients (63 of 66) in IRE cohort were HPV negative. Normalized TCGA HNSCC gene expression and miRNA expression data were obtained from Broad Institute TCGA Genome Data Analysis Center (http://gdac.broadinstitute.org/). mRNA expression data from IRE cohort used in this study has been deposited to NCBIs Gene Expression Omnibus and is accessible through GEO series accession number GSE107591. In order to inference about potential molecular modulation of TMPRSS2, we also included miRNAs expression for the 66 IRE cohort matched tumor and normal samples from Agilent platform. DNA methylation data for TCGA tumors were obtained from Wanderer (http://maplab.imppc.org/wanderer/). We used miRWalk and miRNet web tools for miRNA-target interaction prediction and pathway enrichment analysis. The correlation and regression analyses as well as the miRNA and gene modulation and the survival analysis were conducted using Matlab R2019. ResultsTMPRSS2 expression in HNSCC was significantly reduced compared to the normal tissues and had a prognostic value in HNSCC patients. Reduction of TMPRSS2 expression was more evident in women than in men, in TP53 mutated versus wild TP53 tumors as well as in HPV negative patients compared to HPV positive counterparts. Functionally, we assessed the multivariate effect on TMPRSS2 in a single regression model. We observed that all variables had an independent effect on TMPRSS2 in HNSCC patients with HPV negative, TP53 mutated status and with elevated TP53-dependent Myc-target genes associated with low TMPRSS2 expression. Investigation of the molecular modulation of TMPRSS2 in both HNSCC and lung cancers revealed that expression of microRNAs targeting TMPRSS2 anti-correlated in both TCGA and IRE HNSCC datasets, while there was not evidence of TMPRSS2 promoter methylation in both tumor cohorts. Interestingly, the anti-correlation between microRNAs and TMPRSS2 expression was corroborated by testing this association in a SARS-CoV-2 positive HNSCC patient. ConclusionsCollectively, these findings suggest that tumoral tissues, herein exemplified by HNSCC and lung cancers might be more resistant to SARS-CoV-2 infection due to reduced expression of TMPRSS2. The protective mechanism might occur, at least partially, through the aberrant activation of TMPRSS2 targeting microRNAs; thereby providing strong evidence on the role of non-coding RNA molecule in host viral infection. These observations may help to better assess the frailty of SARS-CoV-2 positive cancer patients.

cancer biology↗

bioRxiv2017

Untangling The Gene-Epigenome Networks: Timing Of Epigenetic Regulation Of Gene Expression In Acquired Cetuximab Resistance

BACKGROUNDTargeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model.\n\nRESULTSAn unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically.\n\nCONCLUSIONSGenes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

cancer biology↗