15167 results for "cancer studies using rna-seq"

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• Whole transcriptome RNA sequencing on bone marrow and peripheral blood samples from patients with acute myeloid leukemia at diagnosis or relapse.

  One hundred cryopreserved bone marrow and peripheral blood samples from patients with acute myeloid leukemia (AML) with 10-90% blasts were selected from the biobank of the Department of Hematology of Leiden University Medical Center (LUMC). The AML cases cover all subtypes, and specifically include known subtype-defining balanced chromosomal translocations according to the WHO classification. The samples were obtained from 96 patients and include three pairs of de novo and relapsed AML and one pair of de novo and presumed therapy-related AML (tAML). Total RNA was isolated from mononuclear cells without prior enrichment for leukemic blasts. The quality and integrity of total RNA was checked and RNA libraries were prepared using the TruSeq RNA library preparation kit v2 (Illumina, San Diego, CA) in an ISO/IEC 17025-accredited protocol. This workflow started with enrichment of messenger RNA by oligo dT magnetic beads. After fragmentation, cDNA synthesis was performed, followed by adaptor ligation and PCR amplification. Paired-end sequencing with a read length of 126 bp was performed on an Illumina HiSeq 2500 v4 sequencer to at least 12.5 Gbp per sample. Image analysis, base calling, and quality check was performed with Illumina data analysis pipeline RTA v1.18.64 and Bcl2fastq v1.8.4. RNAseq reads are provided in compressed Sanger FASTQ format.

  Dataset EGAD00001004187

• ESR1 mutations in tamoxifen-associated endometrial cancer versus spontaneous arisen endometrial tumors

  An increased incidence of endometrial cancer has been described for patients that have received tamoxifen to treat breast cancer. Using samples from endometrial tumors, isolated from surgivcal specimens of patients who previously received tamoxifen treatment for breast cancer, we aimed to identify whether there are specific somatic mutations enriched in this population, relative to endometrial tumors from the general population. For this, WES was performed on matched endometrial tumors and healthy tissue (n=21).

  Dataset EGAD00001009088

• FinHer Breast Cancer Study

  The purpose of this study is to sequence 500 known cancer genes in 960 newly diagnosed high risk breast cancer patients treated with current standard of care therapies and trastuzumab, for somatic alteration and copy number changes. We will be using next gen sequencing technology to determine the prognostic relevance of these somatic genetic alterations and of teh low frequency events to determine if they are associated with trastuzumab benefit or HER2 positive breast cancer, i.e. treatment interaction. The samples will be analysed adn correlated with clinical variables including outcome.

  Dataset EGAD00001000871

• Modeling glioblastoma invasion using human brain organoids and single-cell transcriptomics

  We performed scRNA-seq of patient-derived glioblastoma cells alone or after co-culture with human iPSC-derived cerebral organoid cells. Transcriptional changes implicated in the invasion process that are coherent across patient samples indicate that GBM cells reactively upregulate genes required for their dispersion. Potential interactions between GBM and organoid cells identified by an in silico receptor-ligand pairing screen suggest functional therapeutic targets.

  Study EGAS00001003852

• An integrated single-cell reference atlas of the human endometrium

  This dataset includes both the anchor scRNA-seq and snRNA-seq datasets used to build the Human Endometrial Cell Atlas (HECA). HECA provides a comprehensive definition of endometrial cell types and states throughout the menstrual cycle in donor with and without endometriosis. It identifies consensus cell types across datasets generated by teams worldwide, as well as previously unreported cell types, all of which are validated and mapped in situ using spatial transcriptomics. Processed data is available from ArrayExpress with accession number E-MTAB-14039. See Marečková, M., Garcia-Alonso, L., Moullet, M. et al. An integrated single-cell reference atlas of the human endometrium. Nat Genet 56, 1925–1937 (2024) https://doi.org/10.1038/s41588-024-01873-w for more information.

  Dataset EGAD00001015446

• Single-cell RNA sequencing of pediatric Hodgkin Lymphoma to study the inhibition of T cell subtypes

  Here, we applied single-cell RNA-sequencing (scRNA-seq) on isolated HRS cells and the immune cells from the same pediatric classical Hodgkin Lymphoma (cHL) tumors. Specifically, 13 cHL patients and 3 reactive lymph node control samples were included in this cohort. This allowed us to identify genes of cell surface proteins that are consistently overexpressed in HRS cells and can potentially be used as targets for antibody-drug conjugates or CAR T cells. Finally, we identify potential interactions by which HRS cells inhibit T cells, among which the Galectin-1/CD69 and HLA-DRA/LAG3 interactions. However, high levels of inter-patient heterogeneity of the interaction strength were observed. In conclusion, this study identifies new potential therapeutic targets for cHL and highlights the importance of studying heterogeneity when identifying therapy targets.

  Dataset EGAD50000000626

• METABRIC

  Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

  Study EGAS00000000098

• De novo detection of somatic variants

  The Tumor Profiler Study is an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease. This dataset contains scDNA-seq data for 3 ovarian cancer samples from the Tumor Profiler Study used to validate LongSom. LongSom is a computational workflow leveraging high-quality long-read scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct tumor clonal heterogeneity.

  Dataset EGAD50000001292

• WNT-dependent interaction between inflammatory fibroblasts and FOLR2+ macrophages promotes fibrosis in chronic kidney disease

  Chronic kidney disease (CKD) is a public health problem driven by myofibroblast accumulation, leading to interstitial fibrosis. Heterogeneity is a recently recognised characteristics in kidney fibroblasts in CKD, but the role of different populations is still unclear. Here, we characterize a proinflammatory fibroblast population (named CXCL-iFibro), which corresponds to an early state of myofibroblast differentiation in CKD. By performing immunofluorescence studies and spatial transcriptomics analysis on 2 patients, we demonstrate that CXCL-iFibro co-localize with macrophages in the kidney and participate in their attraction, accumulation, and switch into FOLR2+ macrophages from early CKD stages on. In vitro, we cultured primary inflammatory fibroblasts that we characterized by using bulk RNAseq. We show that macrophages promote the switch of CXCL-iFibro into ECM-secreting myofibroblasts through a WNT/-catenin-dependent pathway, thereby suggesting a reciprocal crosstalk between these populations of fibroblasts and macrophages. Finally, the detection of CXCL-iFibro at early stages of CKD is predictive of poor patient prognosis, which shows that the CXCL-iFibro population is an early player in CKD progression and demonstrates the clinical relevance of our findings.

  Study EGAS50000000101

• Spatial multi-omic map of human myocardial infarction

  Myocardial infarction is a leading cause of mortality worldwide 1 . While advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . Here, we generate an integrative high- resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility, and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from myocardial infarction and control patients. Multimodal data integration allowed us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell-states of major cell-types and analysed them in their spatial context, evaluating their dependency on other cell-types. Our data elucidates molecular principles of human myocardial tissue organisation, recapitulating a gradual cardiomyocyte and myeloid continuum following ischemic injury. In total, our study provides an integrative molecular map of human myocardial infarction and represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease.

  Study EGAS00001006330