Biology subjects

Dincer, C.

Publications and source records attributed to Dincer, C..

9 recordsLinked to original sources

bioRxiv2026

Structure-function analysis of PI3K signalling cascade base editing screens in cancer cells

Knowledge of protein structure and function underpins rational drug discovery, yet many targets lack known selectively druggable sites. Furthermore, the identification of secondary druggable sites offers a strategy to overcome drug resistance. Fragment-based drug discovery (FBDD) can identify new ligandable binding pockets, though how to triage those with the ability to exert biologically relevant effects can be unclear. Systematic approaches to identify novel functionally-important protein sites for therapeutic intervention, such as allosteric pockets or protein-protein interaction (PPI) interfaces, has the potential to accelerate drug discovery, particularly when combined with structure-based hit-finding modalities. The phosphoinositide-3 kinase (PI3K) signalling pathway is frequently altered in human cancer and resistance to approved inhibitors is an ongoing challenge. Here, we performed large-scale CRISPR base editing mutagenesis screens across 30 PI3K pathway proteins in three disease-relevant cancer cell models to systematically map functional residues. Integration of base editing data with structural information identified residues corresponding to known catalytic sites, fragment-binding pockets and PPI interfaces, providing validation for the approach. Additionally, we identified putative allosteric pockets near regions of undefined function. Together, these findings establish high-throughput base editing mutagenesis combined with structural analysis as a scalable strategy to delineate structure-function relationships and inform drug development.

genomics↗

bioRxiv2025

On-target mutations confer resistance to WRN helicase inhibitors in Microsatellite Unstable Cancer Cells.

Werner helicase inhibitors (WRNi) are in clinical development for microsatellite-unstable (MSI) tumors with defective DNA mismatch repair. Here, we investigate how cancer cell evolution shapes response to WRN inhibition and informs potential resistance mechanisms. Genome-wide CRISPR screens combined with WRN knockout did not identify bypass mechanisms, underscoring WRNs essential, non-redundant function in MSI cells. Pharmacogenomic screens identified modulators of WRNi sensitivity, including SMARCAL1, which links it to WRN-MSI synthetic lethality. Semi-saturation mutagenesis of WRN and prolonged drug treatment identified on-target WRN mutations driving acquired resistance to multiple WRNi in vitro and in vivo, which was mitigated by combination with standard chemotherapies. Some resistance mutations conferred broad cross-resistance, whereas others preserved sensitivity to alternative clinical-grade WRNi with distinct mechanism of action. Our findings could inform clinical trial design by suggesting the feasibility of real-time tracking of emerging resistance and enabling early therapeutic adaptations. SignificanceWe present the first exploration of how MSI cancer cells evolve under the selective pressure of WRN helicase inhibition, providing a framework for understanding adaptive responses to this newly identified synthetic-lethal dependency. This study identifies on-target WRN mutations as key drivers of resistance in MSI cancers, supporting the use of combination strategies with other standard-of-care treatments to prevent resistance. It highlights how mutation tracking can guide therapeutic switching to clinically available WRN inhibitors with distinct mechanisms of action, thereby refining clinical development and potentially improving biomarker-informed patient outcomes.

cancer biology↗

bioRxiv2025

BEstimate: a computational tool for the design and interpretation of CRISPR base editing experiments

CRISPR base editors enable scalable targeted DNA mutagenesis and are a powerful tool for the interpretation of gene function, the study of variants of unknown significance, and disease modelling. Existing guide RNA (gRNA) design tools focus on engineering a small number of targeted variants in a given gene or lack comprehensive functional annotation of target sequences. Here we developed BEstimate, a flexible computational pipeline that systematically specifies base editor gRNA target sites, generates gRNA off-target predictions, and rich functional, structural and clinical annotations of targeted edits. BEstimate accommodates custom gRNA design against variant alleles, improving editing accuracy and facilitating the programmed reversion of disease variants. BEstimate is a freely available, versatile tool for designing gRNA libraries and analysing base editor screens.

bioinformatics↗

bioRxiv2022

Optimised whole-genome CRISPR interference screens identify ARID1A-dependent growth regulators in human induced pluripotent stem cells

Perturbation of gene function is a powerful way to understand the role of individual genes in cellular systems. Whole-genome CRISPR/Cas-based screens have parallelized this approach and identified genes that modulate growth in many contexts. However, the DNA break-induced stress upon Cas9 action limits the efficacy of these screens in important models, such as human induced pluripotent stem cells (iPSCs). Silencing with a catalytically inactive Cas9 is a less stressful alternative, but has been considered less effective so far. Here, we first tested the efficiency of several dCas9 fusion proteins for target repression in human iPSCs, and identified dCas9-KRAB-MeCP2 as the most potent. We then produced monoclonal and polyclonal cell lines carrying this construct from multiple iPSC donors, and optimized genome-wide screens with them. We found silencing in a 200bp window around the transcription start site to be as effective as using wild-type Cas9 for identifying essential genes in iPSCs, but with a reduced cost due to better cell survival. Monoclonal lines performed better, but data from polyclonal lines were of sufficient quality for screening for larger effects. Finally, we performed whole-genome screens to identify dosage sensitivities that depend on the functionality of ARID1A, a commonly mutated cancer and developmental disorder gene. We observed differential growth upon depletion of NF2, TAF6L, EZH2, and PSMB2 genes in ARID1A+/-lines compared to wild type, and an enrichment of proteasome genes. Further, we confirmed that the context-specific growth decrease was phenocopied by treating the cells with a proteasome inhibitor, suggesting a pharmacologically targetable synthetic lethal interaction between the proteasome and ARID1A. We propose that many more plausible targets in challenging cell models can be efficiently identified with our approach.

cell biology↗

bioRxiv2022

Base editing screens map mutations affecting IFNγ signalling in cancer

IFN{gamma}signalling underpins host responses to infection, inflammation and anti-tumour immunity. Mutations in the IFN{gamma}signalling pathway cause immunological disorders, haematological malignancies, and resistance to immune checkpoint blockade (ICB) in cancer, however the function of most clinically observed variants remain unknown. Here, we systematically investigate the genetic determinants of IFN{gamma}response in colorectal cancer cells using CRISPR-Cas9 screens and base editing mutagenesis. Deep mutagenesis of JAK1 with cytidine and adenine base editors, combined with pathway-wide screens, reveal loss-of-function and gain-of-function mutations with clinical precedence, including causal variants in haematological malignancies and mutations detected in patients refractory to ICB. We functionally validate variants of uncertain significance in primary tumour organoids, where engineering missense mutations in JAK1 enhanced or reduced sensitivity to autologous tumour-reactive T cells. By classifying > 300 missense variants altering IFN{gamma} pathway activity, we demonstrate the utility of base editing for mutagenesis at scale, and generate a resource to inform genetic diagnosis.

cancer biology↗

bioRxiv2021

OptoAssay - Light-controlled Dynamic Bioassay Using Optogenetic Switches

Circumventing the limitations of current bioassays, we introduce the first light-controlled assay, the OptoAssay, towards wash- and pump-free point-of-care diagnostics. Extending the capabilities of standard bioassays with light-dependent and reversible interaction of optogenetic switches, OptoAssays enable a bi-directional movement of assay components, only by changing the wavelength of light. Combined with smartphones, OptoAssays obviate the need for external flow control systems like pumps or valves and signal readout devices.

bioengineering↗

bioRxiv2020

CEN-tools: An integrative platform to identify the contexts of essential genes

I.An emerging theme from large-scale genetic screens that identify genes essential for fitness of a cell, is that essentiality of a given gene is highly context-specific and depends on a number of genetic and environmental factors. Identification of such contexts could be the key to defining the function of the gene and also to develop novel therapeutic interventions. Here we present CEN-tools (Context-specific Essentiality Network-tools), a website and an accompanying python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels, and drug response levels. We show that CEN-tools is suitable for both the systematic identification of genetic dependencies as well as for targeted queries into the dependencies of specific user-selected genes. The associations between genes and a given context within CEN-tools are represented as dependency networks (CENs) and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

systems biology↗

bioRxiv2019

CRISPR/Cas13a powered electrochemical microfluidic biosensor for nucleic acid amplification-free miRNA diagnostics

Non-coding small RNAs, such as microRNAs, are becoming the biomarkers of choice for multiple diseases in clinical diagnostics. A dysregulation of these microRNAs can be associated to many different diseases, such as cancer, dementia or cardiovascular conditions. The key for an effective treatment is an accurate initial diagnosis at an early stage, improving the patients survival chances. Here, we introduce a CRISPR/Cas13a powered microfluidic, integrated electrochemical biosensor for the on-site detection of microRNAs. Through this unique combination, the quantification of the potential tumor markers microRNA miR-19b and miR-20a has been realized without any nucleic acid amplification. With a readout time of 9 minutes and an overall process time of less than 4 hours, a limit of detection of 10 pM was achieved, using a measuring volume of less than 0.6 {micro}l. Furthermore, we demonstrate the feasibility of our versatile sensor platform to detect miR-19b in serum samples of children, suffering from brain cancer. The validation of our results with a standard qRT-PCR method shows the ability of our system to be a low-cost and target amplification-free tool for nucleic acid based diagnostics.

molecular biology↗

bioRxiv2019

3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients

Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.\n\nAuthor SummaryGlioblastoma (GBM) is the most aggressive brain tumor type with a 15 months of survival on average. The mutation distribution of the GBM patients is very heterogeneous and standard treatments fail to consider the inter-tumor heterogeneity. In our study, we follow a systems level approach that integrates mutation profiles with protein-protein interaction networks. We hypothesized that although the mutations are heterogeneous, the mutations that are close in 3D of the same protein may affect the protein function similarly and this information can be used to get meaningful relation between disease state and mutations. Therefore, we spatially grouped these mutations as \"patches\" and reconstructed patient specific protein interaction networks. When we cluster these networks based on their pathway similarities, we found four patient groups in GBM. Then, the comparison of groups revealed overrepresentation of 3D patches. This finding can be used for patient specific therapy.

systems biology↗