15037 results for "cancer studies using rna-seq"

in 14.84 milliseconds.

• Single_Cell_RNAseq_at_various_stages_of_HiPSCs_differentiating_toward_definitive_endoderm_and_endoderm_derived_lineages

  When comparing the differentiation capacities of pluripotent stem cell lines that have different genetic backgrounds, batch to batch experimental variablility poses a significant challenge, especially when trying to identify smaller effects. One way to address this issue is to differentiate several different lines in the same culture dish, thereby elimating experimental variation. In addition, it allows researchers to analyze many more lines with less experiments. Parallel single cell RNA-Seq exploits that individual cells are tagged and hence each cell can be reliably assigned to the donor of origin based on the genetic variants it contains. In addition, analyzing the genetic signature of single cells within a differentiating population can reveal differentation stages that are not easily detected in bulk RNAseq data. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002278

• Myoepithelial progenitors as founder cells of hyperplastic human breast lesions upon PIK3CA transformation

  The myoepithelial (MEP) lineage of human breast comprises bipotent and multipotent progenitors in ducts and terminal duct lobular units (TDLUs). We here assess whether this heterogeneity impacts on oncogenic PIK3CA transformation. Single cell RNA sequencing (scRNA-seq) and multicolor imaging reveal that terminal ducts represent the most enriched source of cells with ductal MEP markers including a-smooth muscle actin (a-SMA), keratin K14, K17 and CD200. Furthermore, we find neighboring CD200high and CD200low progenitors within terminal ducts. When sorted and kept in ground state conditions, their CD200low and CD200high phenotypes are preserved. Upon differentiation, progenitors remain multipotent and bipotent, respectively. Immortalized progenitors are transduced with shp53 and mutant PIK3CA. Upon transplantation, CD200low MEP progenitors distinguish from CD200high by the formation of multilayered structures with a hyperplastic inner layer of luminal epithelial cells. We suggest a model with spatially distributed MEP progenitors as founder cells of biphasic breast lesions with implications for early detection and prevention strategies.

  Study EGAS00001005933

• Oncogenic cooperation in a human de novo T-ALL model

  We previously modelled T-cell acute lymphoblastic leukemia (T-ALL) by over-expressing four oncogenes (activated NOTCH1, LMO2, TAL1, and BMI1) in healthy human umbilical cord blood cells through lentiviral transduction (Kusakabe et al, Nat Commun 10, 2913 (2019). https://doi.org/10.1038/s41467-019-10510-8). We have now tested individual effects of the four oncogenes by transducing human cells with all possible combinations of one, two, three, and four oncogenes. We also tested the effects of additional known T-ALL oncogenes including HOXA9, LYL1, MEF2C, NXK2.1, TLX1, and TLX3. In addition to culture in vitro on OP9-DL1 feeders, we also injected transduced cells into immunodeficient NSG mice. We observed differing abilities of the various oncogene combinations to support growth in vitro and frank leukemia in vivo. This study includes RNA-seq data from samples of transduced cells cultured in vitro as well as cells harvested from clinically moribund mice after development of leukemia in vivo.

  Study EGAS00001006055

• Collagen XVII Promotes Pancreatic Cancer Through Regulation of PIK3R5

  Pancreatic ductal adenocarcinoma (PDAC) contains a desmoplastic stroma that promotes the aggressive biology of this disease. While the varied mechanisms by which fibrillar collagens contribute to PDAC have been defined, little is known about the contribution of transmembrane collagens. Among this class of collagens, Collagen XVII uniquely contains an extensive intracellular domain that connects intracellular cytoplasmic components with the extracellular matrix. Collagen XVII is best characterized as the defining component of the type I hemidesmosome, an anchoring complex that attaches epithelial cells to the underlying basement membrane. In this study, we characterize the expression of collagen XVII in PDAC and the mechanisms by which it contributes to PDAC biology. We demonstrate that crosstalk between PDAC cells and cancer associated fibroblasts drive collagen XVII expression in vitro and xenograft tumor models in vivo. Genetic depletion of collagen XVII in low-passage patient derived cell lines diminishes their metastatic potential and ability to form tumors in mice. Whole transcriptome sequencing of tumors formed by cells with diminished collagen XVII levels revealed dramatically lower levels of PIK3R5, a regulatory subunit of class IB phosphatidylinositol-3-kinase (PI3K) that interacts with G protein-coupled receptors. Consistent with this, collagen XVII-deficient tumors exhibited lower levels of phosphorylated AKT, a downstream effector of PI3K. The studies highlight the importance of collagen XVII signaling in PDAC and define a novel mechanism by which collagen XVII mediates activation of PI3K.

  Study phs003641

• Deep single-cell RNA sequencing data for 12346 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive NSCLC patients

  Cancer immunotherapies have shown sustained clinical responses in treating non-small cell lung cancer (NSCLC), but efficacy varies between patients and is believed to depend in part on the amount and properties of tumor infiltrating lymphocytes (TILs). To comprehensively depict and dissect the baseline landscape of the composition, lineage and functional states of TILs in lung cancer, here we generated deep single-cell RNA sequencing data for 12,346 T cells from the primary tumour, adjacent normal tissues and peripheral blood of 14 treatment-naive NSCLC patients. Combined expression and TCR-based lineage tracking revealed a significant proportion of effector T cells with common origins and similar functional states across peripheral blood and tumours pointing towards a highly migratory nature of these T cells. We also observed tumour-infiltrating CD8+ T cells undergoing extensive clonal expansion and exhaustion, with two clusters of cells exhibiting states preceding exhaustion. Survival analysis on independent datasets suggested that high ratio of pre-exhausted to exhausted T cells is associated with better prognosis of lung adenocarcinoma (LUAD). In addition, we observed further heterogeneity within the tumour regulatory T cells (Tregs), characterized by the bimodal distribution of TNFRSF9, an activation marker for antigen-specific Tregs. The gene signature of this group of activated tumour Tregs, which included IL1R2, correlated with poor prognosis in LUAD. The T cell clusters revealed by our single cell analyses provide a new approach for patient stratification, and the accompanying compendium of data will help the research community to gain further insight into the functional states and dynamics of T cell responses in lung cancer.

  Study EGAS00001002430

• arcOGEN_HumanCoreExome-24v1-0_subset_2

  Subset 2 of osteoarthritis cases from the arcOGEN Consortium (http://www.arcogen.org.uk/) genotyped on HumanCoreExome-24v1-0 with consent for osteoarthritis studies only.

  Dataset EGAD00010000927

• arcOGEN_HumanCoreExome-12v1-1_subset_2

  Subset 2 of osteoarthritis cases from the arcOGEN Consortium (http://www.arcogen.org.uk/) genotyped on HumanCoreExome-12v1-1 with consent for osteoarthritis studies only.

  Dataset EGAD00010000924

• arcOGEN_HumanCoreExome-12v1-0_subset_2

  Subset 2 of osteoarthritis cases from the arcOGEN Consortium (http://www.arcogen.org.uk/) genotyped on HumanCoreExome-12v1-0 with consent for osteoarthritis studies only.

  Dataset EGAD00010000923

• Dataset for 9 samples from the study EGAS00001007891

  This dataset links 9 samples from the study "Phosphoproteomics adds value to treatment recommendations in molecular tumor boards" (EGAS00001007891) to the study EGAS00001007934. Further information about the samples can be found in the respective studies.

  Dataset EGAD50000002316

• University of Miami Study on Genetics of Autism and Related Disorders (AutismDisorders)

  Autism spectrum disorders (ASD) are neurodevelopmental in origin and characterized by impairments in reciprocal social interaction and communication accompanied by restricted and repetitive patterns of interest or behavior. With improved surveillance and a broadening of the diagnostic criteria, the most recent prevalence studies suggest that ASD may affect as many as 1/166 children in the US. Treatments are limited resulting in little impact on the profound morbidity. Little is known about the etiology of ASD, but there is a strong genetic component. Genetic studies over the past decade have failed to identify definitive ASD genes, but have clearly shown that the underlying genetics are more complex than anticipated with the likelihood that multiple genes are acting independently and/or interactively. With this realization the field of ASD genetics is at a critical juncture. To move forward we must embrace new and creative paradigms to successfully dissect the genetic etiology of this disease. Specifically the current study will: Extend our ascertainment to the full range of ASD. We will increase our ASD dataset by collecting 600 families who meet clinical diagnostic criteria for the three most common ASDs: autism (AUT), Asperger disorder (ASP) and Pervasive Developmental Disorder-NOS (PDD-NOS). Phenotype assessment will be expanded to include the Social Responsiveness Scale (SRS) and detailed seizure data. Ascertainment will focus on parent-child trios. Further characterize the ASD-associated variations in the Gamma-Aminobutyric Acid (GABA) receptor (GABR) subunits. We will complete our GABR subunit variant analysis, including coding, UTR, regulatory, and conserved intronic sequence, concentrating on GABRA4 and GABRB1. We will also investigate the role of variation in other GABR subunits. All AUT specific findings will be examined in the entire ASD dataset. Identify the chromosome 19p ASD gene. Multiple genome-wide screens have provided significant evidence for linkage to Chromosome 19p and we have found evidence for significant association in the linked families within an ~3 Mb region. We will genotype 768 SNPs spaced at ~4 kb to refine the candidate region and delineate specific candidate genes for further testing. We will screen the strongest associated candidate genes for additional variants and evaluate evidence for association in the entire ASD dataset (SA5). Develop and apply the Phenotypic Homogeneity Distinction (PHD) algorithm for identification of the phenotypic signature of linkage and/or association signals. The PHD algorithm identifies phenotypic covariates that discriminate a target sample subset (e.g. the positively linked families or families carrying the associated risk allele) from its complement. The resulting PHD discriminators will define a homogeneous subset from our newly-ascertained ASD dataset to use, as a confirmatory dataset and for localization within genes under study in our other aims. Test for gene-gene interactions. To fully explain the spectrum of autism risk, we will test for interactions between genetic variations associated with ASD (such as those in GABR subunit genes). We will build upon our experience using the MDR method and extend our analyses across biological pathways using the entire ASD data set.

  Study phs000436