Biology subjects

Demir, E.

Publications and source records attributed to Demir, E..

17 recordsLinked to original sources

bioRxiv2026

scOPE identifies which driver-associated expression programs transfer from bulk tumors to single cells

Single-cell RNA sequencing (scRNA-seq) resolves the phenotypic heterogeneity of tumors but rarely observes the somatic mutations that drive it: a variant is legible only where its gene is expressed, the mutant allele is transcribed, and reads span the variant site, so an absent variant read is fundamentally ambiguous. Bulk tumor cohorts have the opposite profile - matched genotype and expression for hundreds of patients, but no cellular resolution. We present scOPE (single-cell Oncological Prediction Explorer), which learns cancer-specific, driver-associated expression axes from bulk tumors, freezes them, and projects single-cell transcriptomes onto the fixed axes without refitting to the target cohort. Our central finding is that this transfer is selective rather than general: of 158 audited driver - cancer models across seven malignancies, 102 met predefined claim-safety criteria and only 11 reached out-of-fold AUROC [≥] 0.90, led by acute myeloid leukemia (AML) NPM1 (0.971), glioblastoma IDH1 (0.963), and pancreatic adenocarcinoma KRAS (0.944). Determining which programs transfer therefore becomes the central task. We address it with a ground-truth-free confidence score - integrating bulk transferability, spatial coherence, score concentration, and copy number (CNV) agreement - that within AML ranked the three independently supported programs above the remainder (AUROC 0.85 across 12 truth-evaluable drivers, of which three were supported), a triage signal rather than a validated genotype classifier. Against expressed-mutation labels, cell state-residual scores were enriched in mutant-labeled cells for NPM1, TP53, and DNMT3A, and the NPM1 separation survived aggregation to patients. Critically, matched genotype--score maps show that even supported programs occupy restricted transcriptional subspaces rather than uniformly marking mutation-positive tumors, and the NPM1 program contracted during treatment across multiple patients. Transferred scores tracked inferred CNV burden yet also resolved discordant malignant populations invisible to aneuploidy alone. scOPE does not call alleles; it recovers continuous, mutation-associated transcriptional axes from existing scRNA-seq data, together with explicit diagnostics for when that reading should be withheld.

bioinformatics↗

bioRxiv2026

Mesoscale Functional Reorganization of Cortical Networks After Cortical Spreading Depression

BackgroundResting-state functional imaging is increasingly used to understand how cortical networks modulate and respond to pain. Awake imaging with a minimally invasive approach is key to observe the natural state of the brain. As migraine with aura, a common headache disorder, can be experimentally modeled by cortical spreading depressions (CSD) in rodents, it is essential to understand the impact of CSD on functional connectivity and network topology to find imaging cues of trigeminovascular activation and headache. MethodsWe used awake widefield intrinsic optical-signal imaging (IOSI) on optically cleared windows to non-invasively characterize the impact of CSDs on bihemispheric resting-state static and dynamic functional connectivity patterns and network topology. A subset of mice was chronically treated with amitriptyline to examine the effect of susceptibility to CSD on connectivity. After baseline imaging, CSD was triggered optogenetically and confirmed by laser speckle contrast imaging. A group of mice received intraperitoneal naproxen after CSD to suppress headache. IOSI was repeated at 30 minutes, 60 minutes, 4 hours, and 24 hours after CSD. The mouse grimace scale was scored at each time point for behavioral headache documentation. ResultsWe observed time-dependent changes in resting-state functional connectivity that were reversed by naproxen. Amitriptyline, a prophylactic migraine medication, decreased susceptibility to CSD and modified resting-state functional connectivity differently than controls. Network analysis with graph-theoretical methods revealed barrel and retrosplenial cortices as potential key players in trigeminal pain processing after CSD. Dynamic functional connectivity analysis demonstrated functional connectivity states, with fractional occupancy and mean dwell time of these states showing distinct CSD and pain-modulated states. A support vector machine was utilized to predict CSD-mediated dynamic connectivity changes in controls. ConclusionOur results bring insight into potentially headache-associated changes in resting-state cortical functional connectivity after CSD and how this functional reorganization is influenced by acute and chronic medications for migraine.

neuroscience↗

bioRxiv2025

MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units

Cancer tissue analysis in digital pathology is typically conducted across different spatial scales, ranging from high-resolution cell-level modeling to lower-resolution tile-based assessments. However, these perspectives often overlook the structural organization of functional tissue units (FTUs), the small, repeating structures which are crucial to tissue function and key factors during pathological assessment. The incorporation of FTU information is hindered by the need for detailed manual annotations, which are costly and time-consuming to obtain. While artificial intelligence (AI)-based solutions hold great promise to accelerate this process, there is currently no comprehensive workflow for building the large, annotated cohorts required. To remove these roadblocks and advance the development of more interpretable approaches, we developed MiroSCOPE, an end-to-end AI-assisted platform for annotating FTUs at scale, built on QuPath. MiroSCOPE integrates a fine-tunable multiclass segmentation model and curation-specific usability features to enable a human-in-the-loop system that accelerates AI annotation by a pathologist. The system is used to efficiently annotate over 71,900 FTUs on 184 prostate cancer hematoxylin and eosin (H&E)-stained tissue samples and demonstrates ready translation to breast cancer. Furthermore, we publicly release a dataset named Miro-120, consisting of 120 prostate cancer H&E with 30,568 annotations, which can be used by the community as a high-quality resource for FTU-level machine learning aims. In summary, MiroSCOPE provides an adaptable AI-driven platform for annotating functional tissue units, facilitating the use of structural information in digital pathology analyses.

bioinformatics↗

bioRxiv2025

Unifying Multimodal Single-Cell Data Using a Mixture of Experts β-Variational Autoencoder-Based Framework

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. We present UniVI (Unified Variational Inference), a scalable mixture-of-experts {beta}-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/de-coders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or pre-annotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin--a non-hematopoietic tissue with continuous differentiation hierarchies--UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to tri-modal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell-type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, tri-modal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

bioinformatics↗

bioRxiv2023

Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers

CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To improve understanding of how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets suggests a diverse set of tumor adaptations to CDK4/6i therapy. Malignant cells may adapt to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression during therapy. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to better understand HR+ breast cancer tumor therapeutic adaptations to CDK4/6i therapy.

cancer biology↗

bioRxiv2023

Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment

Mapping spatial interactions of cancer, immune and stromal cells present novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate tumors, there is currently no understanding of how immune cell heterogeneity impacts spatial coordination between tumor and stromal cells in localized tumors. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of 699,461 single-cells that show epigenetic and molecular differences in distinct clinical grades. We report unique populations of mast cells that differentially express CD44, CD90 and Granzyme B (GZMB) and demonstrate GZMB+ mast cells are spatially associated with M2 macrophages in prostate tumors. Finally, we uncover recurrent neighborhoods that are primarily driven by androgen receptor positive (AR+) stromal cells and identify transcriptional networks active in AR+ prostate stroma.

cancer biology↗

bioRxiv2023

Computational modeling of methylation impact of AML drivers reveals new pathways and refines AML risk-stratification

Decades before its clinical onset, epigenetic changes start to accumulate in the progenitor cells of Acute Myelogenous Leukemia (AML). Delineating these changes can improve risk-stratification for patients and shed insights into AML etiology, dynamics and mechanisms. Towards this goal, we extracted "epigenetic signatures" through two parallel machine learning approaches: a supervised regression model using frequently mutated genes as labels and an unsupervised topic modeling approach to factorize covarying epigenetic changes into a small number of "topics". First, we created regression models for DNMT3A and TET2, the two most frequently mutated epigenetic drivers in AML. Our model differentiated wild-type vs. mutant genotypes based on their downstream epigenetic impacts with very high accuracy: AUROC 0.9 and 0.8, respectively. Methylation loci frequently selected by the models recapitulated known downstream pathways and identified several novel recurrent targets. Second, we used topic modeling to systematically factorize the high dimensional methylation profiles to a latent space of 15 topics. We annotated identified topics with biological and clinical features such as mutation status, prior malignancy and ELN criteria. Topic modeling successfully deconvoluted the combined effects of multiple upstream epigenetic drivers into individual topics including relatively infrequent cytogenetic events, improving the methylation-based subtyping of AML. Furthermore, they revealed complimentary and synergistic interactions between drivers, grouped them based on the similarity of their downstream methylation impact and linked them to prognostic criteria. Our models identify new signatures and methylation pathways, refine risk-stratification and inform detection and drug response studies for AML patients. KEY POINTSO_LISupervised and unsupervised models reveal new methylation pathways of AML driver events and validate previously known associations. C_LIO_LIIndividual DNMT3A and TET2 signatures are accurate and robust, despite the complex genetic and epigenetic make-up of samples at diagnosis. C_LIO_LIUnsupervised topic modeling factorizes covarying methylation changes and isolates methylation signatures caused by rare mutations. C_LIO_LITopic modeling reveals a group of mutations with similar downstream methylation impacts and mapped to adverse-risk class by ELN. C_LIO_LITopic modeling uncovers methylation signatures of infrequent cytogenetic events, significantly improving methylation-based subtyping. C_LIO_LIOur models can be leveraged to build predictive models for AML-risk. C_LIO_LIOur models show that cytogenetic events, such as t(15;17) have widespread trans downstream methylation impacts. C_LI

cancer biology↗

bioRxiv2022

Predicting transcription factor activity using prior biological information

Transcription factors are critical regulators of cellular gene expression programs. Disruption of normal transcription factor regulation is associated with a broad range of diseases. In order to understand the mechanisms that underly disease pathogenesis, it is critical to detect aberrant transcription factor activity. We have developed Priori, a computational method to predict transcription factor activity from RNA sequencing data. Priori has several key advantages over existing methods. Priori utilizes literature-supported regulatory relationship information to identify known transcription factor target genes. Using these transcriptional relationships, Priori uses linear models to determine the impact and direction of transcription factor regulation on the expression of its target genes. In our work, we evaluated the ability of Priori and 16 other methods to detect aberrant activity from 124 single-gene perturbation experiments. We show that Priori identifies perturbed transcription factors with greater sensitivity and specificity than other methods. Furthermore, our work demonstrates that Priori can be used to discover significant determinants of survival in breast cancer as well as identify mediators of drug response in leukemia from primary patient samples.

bioinformatics↗

bioRxiv2022

Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles

Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluid and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of medium and small EVs and non-vesicular carriers. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from vesicular and non-vesicular subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in the amount of cf-mRNA carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each vesicular and non-vesicular subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EVs subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.

genomics↗

bioRxiv2022

Ongoing Replication Stress Response and New Clonal T Cell Development Discriminate Between Liver and Lung Recurrence Sites and Patient Outcomes in Pancreatic Ductal Adenocarcinoma

Background and AimsMetastatic pancreatic adenocarcinoma (mPDAC) is lethal, yet a subset of patients who have metastatic disease that spreads only to the lung have better outcomes. We identified unique transcriptomic and immune features that distinguish patients who develop metastases in the liver (liver cohort) versus those with lung-avid but liver-averse mPDAC (lung cohort). MethodsWe used clinical data from the Oregon Pancreas Tissue Registry to identify PDAC patients with liver and/or lung metastases. Gene expression and genomic alteration data from 290 PDAC tumors were used to identify features unique to patients from the liver and lung cohorts. In parallel, T cell receptor sequencing data from 289 patients were used to identify immune features unique to patients in the lung cohort. ResultsLung cohort patients had better survival outcomes than liver cohort patients. Primary tumors from patients in the liver cohort expressed a novel gene signature associated with ongoing replication stress (RS) response predictive of poor patient outcome independent from known subtypes. In contrast, patients with tumors lacking the RS response signature survived longer, especially if their tumors had alterations in DNA damage repair genes. A subset of patients in the lung cohort demonstrated new T cell clonal development in their primary and metastatic tumors leading to diverse peripheral blood TCR repertoires. ConclusionLiver-avid metastatic PDAC is associated with an ongoing RS response, whereas tumors lacking the RS response with ongoing T cell clonal responses may have unique vulnerabilities allowing long-term survival in patients with lung-avid, liver-averse metastatic PDAC.

cancer biology↗

bioRxiv2022

Dual Targeting of FLT3 and LSD1 Disrupts the MYC Super-Enhancer Complex in Acute Myeloid Leukemia

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood super-enhancer, suppressing super-enhancer activation as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies provide preclinical rationale for the investigation of dual FLT3/LSD1 inhibition in a clinical trial.

cancer biology↗

bioRxiv2021

Capturing scientific knowledge in computable form

Technological advances in computing provide major opportunities to accelerate scientific discovery. The wide availability of structured knowledge would allow us to take full advantage of these by enabling efficient human-computer interaction. Traditionally, biological knowledge is captured in publications and knowledge bases, however, the information in articles is not directly accessible to computers, and knowledge bases are constrained by finite resources available for manual curation. To accelerate knowledge capture and communication and to keep pace with the rapid growth of scientific reports, we developed the Biofactoid (biofactoid.org) software suite, which crowdsources structured knowledge in articles from authors. Biofactoid is a web-based system that lets scientists draw a network of interactions between genes, their products, and chemical compounds and employs smart-automation to translate user input into a structured language using the expressive power of a formal ontology. The resulting data is shared via public information resources, enabling author-curated knowledge to be appreciated in the context of all existing computable knowledge. Authors of recently published papers across a range of journals have already contributed their pathway information, much of which is novel and extends existing pathway databases into new biological areas. We envision the adoption of Biofactoid for crowdsourced curation by scientists and publishers as part of an ecosystem of tools that accelerate scientific communication and discovery. AvailabilityBiofactoid server at https://biofactoid.org

bioinformatics↗

bioRxiv2020

Ribavirin shows antiviral activity against SARS-CoV-2 and downregulates the activity of TMPRSS2 and the expression of ACE2 in vitro

Ribavirin is a guanosine analog and has a broad-spectrum antiviral activity against RNA viruses. Based on this, we aimed to show the anti-SARS-CoV-2 activity of this drug molecule via in vitro, in silico and molecular techniques. Ribavirin showed antiviral activity in Vero E6 cells following SARS-CoV-2 infection. In silico analysis suggested that Ribarivin has a broad-spectrum impact on Vero E6 cells. According to the detailed molecular techniques, Ribavirin was shown to decrease TMPRSS2 expression both at mRNA and protein level 48 hours after treatment. The suppressive effect of Ribavirin in ACE2 protein expression was shown to be dependent on cell types. Finally, proteolytic activity assays showed that Ribavirin also showed an inhibitory effect on TMPRSS2 enzyme. As a conclusion, Ribavirin is a potential antiviral drug for the treatment against SARS-CoV-2, and it interferes with the effect of TMPRSS2 and ACE2 expression.

pharmacology and toxicology↗

bioRxiv2020

An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer

Mechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancers genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Genes implicated in tumorigenesis often exhibit diverse sets of genomic variants in the tumor cohorts within which they are frequently mutated. We sought to identify the downstream expression effects of these perturbations and to find whether or not this heterogeneity at the genomic level is reflected in a corresponding heterogeneity at the transcriptomic level. Applying a novel hierarchical framework for organizing the mutations present in a cohort along with machine learning pipelines trained on sample expression profiles we systematically interrogated the signatures associated with combinations of perturbations recurrent in cancer. This allowed us to catalogue the mutations with discernible downstream expression effects across a number of tumor cohorts as well as to uncover and characterize a multitude of cases where subsets of a genes mutations are clearly divergent in their function from the remaining mutations of the gene.

bioinformatics↗

bioRxiv2019

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

bioRxiv2018

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.

systems biology↗