Single Cell RNAseq of blood and tumor from renal cancer patients
Ontology highlight
ABSTRACT:
PROVIDER: EGAC00001001494 | EGA |
REPOSITORIES: EGA
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Project description:Data Access Committee EGAC00001001605
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Project description:Single-cell RNASeq (scRNAseq) showed that IL8 is primarily expressed in circulating and intratumoral myeloid cells and high IL8 expression was associated with the downregulation of the antigen presentation machinery in myeloid cells.
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Project description:Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.
| S-EPMC10837414 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.
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Project description:Patients with colorectal cancer (CRC) following renal transplantation require long-term immunosuppressants to prevent graft rejection. However, the impact of these immunosuppressants on the tumor immune microenvironment and the roles of immune cells within it remain poorly understood. We conducted comprehensive single-cell RNA sequencing on tumor and normal tissues from four CRC patients post renal transplantation and compared these with published data from 23 non-transplant CRC patients. We set four groups for detailed comparative analysis based on the renal transplantation status and tissue origin: non-renal transplantation normal (nRT_Normal), non-renal transplantation tumor (nRT_Tumor), renal transplantation normal (RT_Normal), renal transplantation tumor (RT_Tumor). Our analysis revealed significant tumor immune microenvironment landscape alterations in the transplantation group. CD8+effector T cells of RT_Tumor showed significantly diminished cytotoxicity and tumor neoantigen recognition (p < 0.0001), while CD4+FOXP3 regulatory T cells of RT_Tumor displayed a higher inhibitory score (p < 0.05), indicating preserved immunomodulatory potential compared with non-transplant CRC. Notably, significantly increased CTLA4 expression in T cells of RT_Tumor was found and testified (p < 0.05). Our findings provide novel mechanistic insights for understanding the immune landscape in renal transplant recipients with CRC and pave the way for potential immunotherapeutic strategies that may improve survival and quality of life for this patient population.
| S-EPMC11786312 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.
| S-EPMC6456841 | biostudies-literature
Project description:Bladder cancer, one of the most prevalent malignancies worldwide, remains hard to classify due to a staggering molecular complexity. Despite a plethora of diagnostic tools and therapies, it is hard to outline the key steps leading up to the transition from high-risk non-muscle-invasive bladder cancer (NMIBC) to muscle-invasive bladder cancer (MIBC). Carcinogen-induced murine models can recapitulate urothelial carcinogenesis and natural anti-tumor immunity. Herein, we have developed and profiled a novel model of progressive NMIBC based on 10 weeks of OH-BBN exposure in hepatocyte growth factor/cyclin dependent kinase 4 (R24C) (Hgf-Cdk4R24C) mice. The profiling of the model was performed by histology grading, single cell transcriptomic and proteomic analysis, while the derivation of a tumorigenic cell line was validated and used to assess in vivo anti-tumor effects in response to immunotherapy. Established NMIBC was present in females at 10 weeks post OH-BBN exposure while neoplasia was not as advanced in male mice, however all mice progressed to MIBC. Single cell RNA sequencing analysis revealed an intratumoral heterogeneity also described in the human disease trajectory. Moreover, although immune activation biomarkers were elevated in urine during carcinogen exposure, anti-programmed cell death protein 1 (anti-PD1) monotherapy did not prevent tumor progression. Furthermore, anti-PD1 immunotherapy did not control the growth of subcutaneous tumors formed by the newly derived urothelial cancer cell line. However, treatment with CpG-oligodeoxynucleotides (ODN) significantly decreased tumor volume, but only in females. In conclusion, the molecular map of this novel preclinical model of bladder cancer provides an opportunity to further investigate pharmacological therapies ahead with regards to both targeted drugs and immunotherapies to improve the strategies of how we should tackle the heterogeneous tumor microenvironment in urothelial bladder cancer to improve responses rates in the clinic.
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Project description:Glioblastoma (GBM) is the most common malignant primary brain tumor and remains incurable. Previous work has shown that systemic administration of Decitabine (DAC) induces sufficient expression of NY-ESO-1 in GBM for targeting by adoptive T-cell therapy in vivo. However, the mechanisms by which DAC enhances immunogenicity in GBM remain to be elucidated. Using patient tissue, immortalized glioma cells, and primary patient-derived gliomaspheres, we demonstrate in vitro that basal NY-ESO-1 expression is restricted by promoter hypermethylation in gliomas. DAC treatment of glioma cells specifically inhibits DNA methylation silencing and renders NY-ESO-1 an inducible tumor antigen. Targeting of DAC-induced NY-ESO-1 in primary GBM cells promotes specific and polyfunctional NY-ESO-1 TCR-T cell responses. DAC further upregulates other tumor-associated cancer testis antigens concomitantly with tumor-intrinsic reactivation of human endogenous retroviruses (hERV) and type I interferon. Overall, we demonstrate that DAC promotes an inducible tumor antigen and enhances T cell functionality against GBM.
2024-06-04 | GSE261190 | GEO
Project description:Glioblastoma (GBM) is the most common malignant primary brain tumor and remains incurable. Previous work has shown that systemic administration of Decitabine (DAC) induces sufficient expression of NY-ESO-1 in GBM for targeting by adoptive T-cell therapy in vivo. However, the mechanisms by which DAC enhances immunogenicity in GBM remain to be elucidated. Using patient tissue, immortalized glioma cells, and primary patient-derived gliomaspheres, we demonstrate in vitro that basal NY-ESO-1 expression is restricted by promoter hypermethylation in gliomas. DAC treatment of glioma cells specifically inhibits DNA methylation silencing and renders NY-ESO-1 an inducible tumor antigen. Targeting of DAC-induced NY-ESO-1 in primary GBM cells promotes specific and polyfunctional NY-ESO-1 TCR-T cell responses. DAC further upregulates other tumor-associated cancer testis antigens concomitantly with tumor-intrinsic reactivation of human endogenous retroviruses (hERV) and type I interferon. Overall, we demonstrate that DAC promotes an inducible tumor antigen and enhances T cell functionality against GBM.
2024-06-03 | GSE261188 | GEO
Project description:Glioblastoma (GBM) is the most common malignant primary brain tumor and remains incurable. Previous work has shown that systemic administration of Decitabine (DAC) induces sufficient expression of NY-ESO-1 in GBM for targeting by adoptive T-cell therapy in vivo. However, the mechanisms by which DAC enhances immunogenicity in GBM remain to be elucidated. Using patient tissue, immortalized glioma cells, and primary patient-derived gliomaspheres, we demonstrate in vitro that basal NY-ESO-1 expression is restricted by promoter hypermethylation in gliomas. DAC treatment of glioma cells specifically inhibits DNA methylation silencing and renders NY-ESO-1 an inducible tumor antigen. Targeting of DAC-induced NY-ESO-1 in primary GBM cells promotes specific and polyfunctional NY-ESO-1 TCR-T cell responses. DAC further upregulates other tumor-associated cancer testis antigens concomitantly with tumor-intrinsic reactivation of human endogenous retroviruses (hERV) and type I interferon. Overall, we demonstrate that DAC promotes an inducible tumor antigen and enhances T cell functionality against GBM.
2024-06-03 | GSE261187 | GEO