Biology subjects

Aybey, B.

Publications and source records attributed to Aybey, B..

3 recordsLinked to original sources

bioRxiv2026

Multi-omics, organoid-based modeling reveals an SRC/mTOR-dependent fetal-like stem cell trajectory in colorectal cancer

BackgroundSingle-cell atlases have described diverse stem cell states in colorectal cancer (CRC), however, the overarching trajectories of those states and the underlying functional mechanisms, including their relevance for drug sensitivity, need better understanding. MethodsWe established 64 patient-derived organoids from microsatellite-stable colorectal cancers, characterized their transcriptomes and genomes, and performed drug screening with 62-140 clinically approved substances. We analyzed additional published transcriptome data from patient-derived organoids (72 patients from three independent datasets), TCGA-CRC data (466 patients), and single-cell transcriptomes of tumor biopsies (123,000 cells from six independent cohorts) to establish a functional and molecular landscape of CRC stem cells. We performed mechanistic follow-up analyses by mass-spectrometry-based proteomics, large-scale kinase inhibition assays and immunofluorescence analyses. ResultsWe find a continuous landscape of CRC stem cells that is characterized by distinct developmental programs: adult stem cell-to fetal-like regenerative states and transition between differentiation programs. By large-scale drug perturbations and multi-omics modeling, we identify a regenerative/fetal-like stem cell trajectory characterized by PI3K/mTOR dependency. We find the identified developmental axes conserved in organoid, clinical, as well as single-cell data, and the fetal-like PI3K/mTOR-dependent state to be associated with poor clinical prognosis. Mechanistically, PI3K/mTOR vulnerability is linked to a lack of adaptive capability due to suppressed mRNA translation and associated with an upregulated SRC signaling network. ConclusionsOur work moves beyond a molecular CRC landscape by combined functional perturbation analyses in organoids. This enables mechanistic modeling of stem cell state regulation and identifies an SRC/mTOR-dependent regenerative state in CRC, which might allow improved therapeutic targeting in the future.

cancer biology↗

bioRxiv2023

Stromal signals dominate gene expression signature scores that aim to describe cancer-intrinsic stemness or mesenchymality characteristics

PurposeEpithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory ability, a crucial aspect of tumor metastasis that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, and mesenchymality (EMT-related) characteristics of tumors based on bulk tumor expression profiling. However, recent studies have suggested that non-cancerous cells in the tumor micro- or macroenvironment heavily influence individual signature profiles. Experimental DesignWe analyzed scores of 11 published and frequently referenced gene expression signatures in bulk, single cell, and pseudo bulk expression data across multiple cancer types. ResultsOur study strengthens and extends the influence of non-cancerous cells on signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. The cell type composition, especially the amount of tumor cells, of a tumor sample frequently dominates EMT-related signature scores. Additionally, our analyses revealed that stromal cells, most often fibroblasts, are the main drivers of the EMT-related signature scores. ConclusionsWe call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low tumor cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymality/EMT characteristics.

bioinformatics↗

bioRxiv2023

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets

BackgroundRobust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. ResultsWe established a novel gene expression similarity-based workflow for the discovery of immune cell type signatures that leverages multiple datasets, here four single cell expression datasets from three different cancer types. We used our immune cell signatures to train random forest classifiers for immune cell type assignment of single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in two independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in our random forest approach. Discussion and conclusionWe demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for 90% of all genes whose expression profiles have not been used for cell type classification.

genomics↗