Biology subjects

Tercan, B.

Publications and source records attributed to Tercan, B..

6 recordsLinked to original sources

bioRxiv2026

Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution

Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.

systems biology↗

bioRxiv2025

Cellular heterogeneity and therapeutic response profiling of human IDH+ glioma stem cell cultures

Summary/AbstractGlioblastoma stem cell (GSC) cultures are initiated from glioblastoma (GBM) surgical resection tissue. They can capture and propagate key GBM primary tumor molecular and cellular features. We have deeply characterized four isocitrate dehydrogenase (IDH)-expressing (or IDH+) GSC cultures from unrelated adults to serve as cellular models for the majority of adult primary GBM. We demonstrate that GSC cultures can be continuously propagated in defined, serum-free media and 5% oxygen without requiring specialized growth substrates; have well-defined genomic and mtDNA variants and gene/protein expression profiles; and highly reproducible dose-survival curves when treated with the GBM standard-of-care therapies of ionizing radiation (IR) and temozolomide (TMZ). We also illustrate how expressed lentiviral barcodes, mtDNA variants and single cell gene expression profiling can be used to define and track cellular heterogeneity over 40 days after IR treatment. These well-characterized IDH+ GSC cultures can support many high throughput in vitro assay formats, including xenograft, organoid and other GBM disease modeling protocols. They should prove a useful resource to better understand GBM biology, and to identify new and more effective GBM therapies and treatment regimens. HighlightsO_LIGlioblastoma (GBM)-derived IDH-expressing Glioma Stem Cell (GSC) cultures can capture and propagate GBM genomic variants, gene and protein expression programs. C_LIO_LIMitochondrial DNA (mtDNA) variants identified by high accuracy Duplex DNA sequencing are abundant and sub-clonally organized in GSC cultures. C_LIO_LIGSC cultures have highly reproducible dose-survival curves for ionizing radiation and temozolomide, the GBM standard-of-care therapies. C_LIO_LIGSC culture cellular heterogeneity can be captured, characterized and tracked by using expressed lentiviral barcodes, mtDNA variants and scRNA sequencing. C_LI eTOC blurbIDH-expressing glioma stem cell cultures (GSCs) are experimentally tractable and versatile glioblastoma (GBM) cellular disease models. Deeply characterized GSC cultures can enable new work to understand GBM biology, and help identify new and more effective GBM therapies and treatment regimens. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/667532v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@82aceeorg.highwire.dtl.DTLVardef@1277c18org.highwire.dtl.DTLVardef@144188eorg.highwire.dtl.DTLVardef@1a84bb3_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Predicting microsatellite instability from whole slide images using texture features

Identifying MSI in whole slide images (WSIs), one of the most widely used diagnostic imaging formats, is of great importance and in demand. In this study we employed color-based texture features to predict MSI on both a tile and sample based level. We found that within cohorts of hematoxylin and eosin (H&E) stained WSIs, texture morphology is able to predict MSI on a tile level with an AUC of up to 0.95 and on a sample level with an AUC of up to 0.98. This runs in contrast to other methods for predicting MSI in H&E WSIs which either utilized artificial intelligence based models, or achieved lower accuracy scores. Our results demonstrate that texture morphology is a significantly notable factor when it comes to identifying MSI in H&E WSIs, and should be used when constructing future models for MSI identification in a clinical setting.

bioinformatics↗

bioRxiv2023

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗

bioRxiv2022

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

MotivationCancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. ResultsWe show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. Availabilityhttps://bioconductor.org/packages/devel/bioc/html/SPONGE.html Contactmarkus.daniel.hoffmann@tum.de; markus.list@tum.de Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2021

SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, an integrated resource and framework to facilitate the prediction of context-specific SLIs by using cloud-based technologies. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the multiple comparable prediction approaches available. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential SLI partners, using multiple computational strategies. Context-specific synthetic lethality potential can also be compared using the framework. We demonstrate various use cases for our cloud-based computational resource and the utility of this approach for customizable and extensible computational inference of SLIs.

bioinformatics↗