14982 results for "cancer studies using rna-seq"

in 21.35 milliseconds.

• SF6996

  RNA-exome

  Dataset EGAD00001006760

• SF9640

  RNA-exome

  Dataset EGAD00001006771

• SF6587

  RNA-exome

  Dataset EGAD00001006772

• SF10750

  RNA-exome

  Dataset EGAD00001006748

• SF9135

  RNA-exome

  Dataset EGAD00001006747

• SF12165

  RNA-exome

  Dataset EGAD00001006745

• SF4324

  RNA-exome

  Dataset EGAD00001006766

• SF8963

  RNA-exome

  Dataset EGAD00001006764

• SF10931

  RNA-exome

  Dataset EGAD00001006765

• SE10497

  RNA-exome

  Dataset EGAD00001006834

• SF6136

  RNA-exome

  Dataset EGAD00001006754

• SE6136

  RNA-exome

  Dataset EGAD00001006833

• SE4987

  RNA-exome

  Dataset EGAD00001006835

• SF11094

  RNA-exome

  Dataset EGAD00001006755

• SF11780

  RNA-exome

  Dataset EGAD00001006757

• SF7196

  RNA-exome

  Dataset EGAD00001006762

• SF10770

  RNA-exome

  Dataset EGAD00001006761

• SF11248

  RNA-exome

  Dataset EGAD00001006752

• SF11587

  RNA-exome

  Dataset EGAD00001006770

• SF8642

  RNA-exome

  Dataset EGAD00001006749

• SF9490

  RNA-exome

  Dataset EGAD00001006753

• SF9301

  RNA-exome

  Dataset EGAD00001006759

• SF11215

  RNA-exome

  Dataset EGAD00001006763

• SF6839

  RNA-exome

  Dataset EGAD00001006769

• SF10228

  RNA-exome

  Dataset EGAD00001006756

• Gene expression matrix

  Log2 gene expression count data from RNA sequencing.

  Dataset EGAD00001008793

• SE4831

  RNA-exome

  Dataset EGAD00001006830

• SE9037

  RNA-exome

  Dataset EGAD00001006832

• SF10127

  RNA-exome

  Dataset EGAD00001006767

• SF10064

  RNA exome

  Dataset EGAD00001006744

• SF4209

  RNA-exome

  Dataset EGAD00001006768

• SE8570

  RNA-exome

  Dataset EGAD00001006831

• SF11082

  RNA-exome

  Dataset EGAD00001006773

• SE4567

  RNA-exome

  Dataset EGAD00001006836

• SE9581

  RNA-exome

  Dataset EGAD00001006837

• SF6953

  RNA-exome

  Dataset EGAD00001006750

• SF9259

  RNA-exome

  Dataset EGAD00001006751

• Accurate mapping of mitochondrial DNA deletions and duplications using deep sequencing

  Deletions and duplications in mitochondrial DNA (mtDNA) cause mitochondrial disease and accumulate in conditions such as cancer and age-related disorders, but validated high-throughput methodology that can readily detect and discriminate between these two types of events is lacking. Here we present MitoSAlt, a computational method for accurate identification, quantification and visualization of mtDNA deletions and duplications from whole genome, whole exome or transcriptome sequencing data. MitoSAlt was tested on simulated sequencing reads and human patient samples with single deletions and duplications to verify its accuracy. Application to mouse models of mtDNA maintenance disease further demonstrated the ability to detect deletions and duplications even at low levels of heteroplasmy. MitoSAlt paves the way for simple and reliable determination of mtDNA deletions and duplications across a wide range of relevant conditions and available sequencing datasets.  

  Study EGAS00001004380

• Exceptional Responders Initiative

  The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is < 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient. This pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights. Clinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.

  Study phs001145

• Identification of Somatic Changes in Tumors from Fanconi Anemia Patients

  This study focused on the identification of genomic signatures present in tumors from Fanconi anemia patients. Fanconi anemia is a rare hereditary disease caused by defects in DNA interstrand crosslink repair. The primary patient phenotypes are bone marrow failure and cancer predisposition. We concentrated on squamous cell carcinomas and used whole genome Illumina sequencing (22 tumor samples and 12 normal samples) , whole exome sequencing (37 tumor samples and 33 normal samples), PacBio long-read whole-genome (nine tumor and four normal samples),10X linked-read whole-genome sequencing, 10x single-cell/nuclei 3’ RNA sequencing (three tumor samples), 10x Visium spatial transcriptomics (one tumor sample), EPIC 850K methylation array (six samples), and bulk RNAseq (six tumor samples) to identify somatic changes present in tumors. The study aimed to identify changes that drive tumorigenesis in Fanconi anemia, give insight into the early development and aggressive nature of these tumors, and suggest therapeutic avenues for these patients. Another goal was to compare the genomic changes identified in tumors from Fanconi anemia patients with sporadic head and neck cancers. This study demonstrated that the tumors from Fanconi anemia individuals are generally not driven by HPV. Instead, they acquire mutations in the p53 tumor suppressor. The main characteristic of the tumors is the presence of a high number of structural variants in the form of small deletions, unbalanced translocations, and fold-back inversions, which often form complex rearrangements. These rearrangements result in amplifications and deletions of a multitude of oncogenes and tumor suppressors, respectively. The study also shows that lack of the Fanconi anemia DNA repair pathway leads to an epithelial-to-mesenchymal transition in a mouse model and in human samples that may influence the behavior of tumors in Fanconi anemia. Finally, comparing genomic changes in Fanconi anemia and sporadic tumors of the head and neck, we propose that the structural variants present in sporadic cancer result in a functional deficiency of the Fanconi anemia DNA repair pathway.

  Study phs002652

• Integrated Genomic Analyses of Cutaneous T Cell Lymphomas Reveal the Molecular Bases for Disease Heterogeneity

  Cutaneous T cell lymphomas (CTCLs) are a clinically diverse collection of lymphomas of the skin-homing T cell. In the present study, we performed DNA and RNA sequencing on a cohort of CTCLs with representative samples from varying disease subtypes and stages. We characterized the landscape of putative driver genes, identified genetic relationships between CTCLs across disease stages, and inferred molecular subtypes in patients with stage-matched leukemic disease. Collectively, our findings clarify CTCL genetics and provide novel insights into pathways that drive diverse disease phenotypes. DNA and RNA sequencing was performed on 114 and 96 samples respectively. Tumor samples were obtained from University of Chicago (Chicago, IL), Northwestern University (Chicago, IL) and Yale University (New Haven, CT). 

  Study phs002456

• Primary human hepatocyte ATAC-seq

  Profiling the chromatin accessibility landscape of cultured primary human hepatocytes (PHH) from three female and three male adult, non-diabetic donors.

  Dataset EGAD50000001751

• β-catenin ChIP sequencing in HCC models.

  This dataset contains ChIP sequencing data for β-catenin, H3K27ac and H3K27me3 from three HCC cell lines and five tumour organoids under normal conditions and oleic acid treatment.

  Dataset EGAD50000001816

• Bulk exome sequencing of primary GBM - SF 10282

  We performed bulk exome-seq on a primary GBM and a blood sample from SF10282

  Dataset EGAD00001002275

• Bulk exome sequencing of primary GBM - SF 10345

  We performed bulk exome-seq on a primary GBM and a blood sample from SF10345

  Dataset EGAD00001002273

• ChIP-Seq of human stimulated and cultured CD4+ Treg cells

  This dataset includes whole genome sequence data for ChIPmentation assays (18 H3K4me3, 20 H3K27ac and 3 input samples) of human stimulated and cultured CD4+ Treg cells.

  Dataset EGAD00001005816

• scRNA-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015545

• scATAC-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015546

• Single-cell RNA sequencing of a PBMC collected from a male with 45,X/48,XYYY karyotype

  Dac EGAC00001002373

• Strand-specific RNA Sequencing of paired initial and recurrent gliomas

  Dataset EGAD00001001613