14983 results for "cancer studies using rna-seq"

in 31.55 milliseconds.

• Single_cell_ATAC_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008283

• Bulk_RNA_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008278

• Single_cell_ATAC_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008279

• Whole_Genome_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008280

• Whole-genome sequencing of rare disease patients in a national healthcare system

  Most patients with rare diseases do not receive a molecular diagnosis and the aetiologicalvariants and mediating genes for more than half such disorders remain to be discovered. Weimplemented whole-genome sequencing (WGS) in a national healthcare system to streamlinediagnosis and to discover unknown aetiological variants, in the coding and non-coding regionsof the genome. In a pilot study for the 100,000 Genomes Project, we generated WGS data for13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelianassociations between genes and rare diseases, of which 11 have been discovered since 2015and at least 79 are confirmed aetiological. Using WGS of UK Biobank1, we showed that rarealleles can explain the presence of some individuals in the tails of a quantitative red blood cell(RBC) trait. Finally, we reported 4 novel non-coding variants which cause disease through thedisruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates asynergy by using WGS for diagnosis and aetiological discovery in routine healthcare.

  Study EGAS00001004364

• The genetic structure of Norway

  The aim of the present study was to describe the genetic structure of the Norwegian population using genotypes from 6369 unrelated individuals with detailed information about places of residence. Using standard single marker- and haplotype-based approaches, we report evidence of two regions with distinctive patterns of genetic variation, one in the far northeast, and another in the south of Norway, as indicated by fixation indices, haplotype sharing, homozygosity and effective population size. We detect and quantify a component of Uralic Sami ancestry that is enriched in the North. On a finer scale, we find that rates of migration have been affected by topography like mountain ridges. In the broader Scandinavian context, we detect elevated relatedness between the mid- and northern border areas towards Sweden. The main finding of this study is that despite Norway’s long maritime history and as a former Danish territory, the region closest to mainland Europe in the south appears to have been the most isolated region in Norway, highlighting the open sea as a barrier to gene flow into Norway.

  Study EGAS00001004826

• Enrichment of PTPRT and JAK2 mutations in lung cancer from African Americans and evidence for increased GI and HRD in LUSC

  The goal of this study is to compare the somatic mutation landscape between European Americans and African Americans. It includes various histological forms of lung cancer. In LUAD, we find increased prevalence of PTPRT and JAK2 mutations among African Americans. These proteins function in the same pathway and as such, this may be a new actionable target for lung cancer. We have also examined the landscape of copy number profiles in African Americans and European Americans.

  Study phs001895

• Genome-Wide Analysis of Aberrant Position and Sequence of Plasma DNA Fragment Ends in Patients With Cancer

  This study investigated fragmentation characteristics in plasma cell-free DNA and evaluated the relevance of such analysis for cancer diagnostics. Specifically, we performed whole genome sequencing (WGS) of healthy individuals and cancer patients, and evaluated genome-wide coverage and sequences surrounding fragment ends. This was a retrospective study of archived plasma samples. The WGS data are available through this submission. Detailed study design and findings were published in Budhraja et al. Science Translational Medicine 2023.

  Study phs003170

• Human Skin Cancer Atlas, Medical University of Vienna Data Access Policy

  Cancer-associated fibroblasts (CAFs) play a key role in cancer progression and treatment outcome. Here, we elucidate the yet unresolved intra-tumoral CAF variety in three skin cancer types at molecular and spatial single-cell resolution in a large cohort. We show that two out of three CAF subtypes contribute to tumor immune surveillance with distinct mechanisms. Matrix CAFs (mCAFs) ensheath tumor nests and synthesize extracellular-matrix to prevent T cell invasion. Immunomodulatory CAFs (iCAFs), which express proinflammatory and immunomodulatory factors, are only detected in high abundance in aggressive tumors. Strikingly, iCAFs but not tumor cells are (apart from immune cells) the exclusive cell type producing chemokines and, thus, play a key role in immune cell recruitment and activation. Mechanistically, we show that cancer cells transform adjacent healthy fibroblasts into cytokine-expressing iCAFs, which subsequently recruit immune cells and modulate the immune response. In conclusion, targeting CAF variants holds promise for improved efficacy of immunotherapy.

  Dac EGAC50000000154

• WES of breast cancer patients and controls

  This study assessed the variant profiles of 408 samples from 126 breast cancer patients with various survival outcomes, including paired uninvolved mammary gland samples collected at proximal distances from primary lesions. As a reference, control samples from 15 mammoplasty individuals without cancer history were studied. Whole exome sequencing was performed to identify post-zygotic and germline variants present in the studied samples. Variant annotation in known databases, interpretation, and statistical analyses followed to discern the clinical relevance of called variants.

  Study EGAS50000000539

• Uncovering Principles of Adaptive Regulation in Cancer Resistance Through Deep Evolutionary Profiling

  A comprehensive understanding of the emergence of therapy resistance in cancer requires new approaches capable of generating and characterizing drug resistant cell lines at scale. Here, we present the EVolution Of Lineages in Vitro (EVOLV) platform, a high-throughput approach for generating diverse libraries of viable, drug-resistant cell lines from a common progenitor. EVOLV enables deeper insights into the genomics of drug resistance as well as into the complex interplay among genomic, transcriptomic, and functional layers during cancer evolution.

  Study phs003851

• Evaluation of whole genome sequencing utility in identifying driver alterations in cancer genome

  This study aimed to assess the effectiveness of whole genome sequencing (WGS) compared to whole exome sequencing (WES) in identifying driver alterations in cancer genomes. Out of 177 cases with no driver alterations detected by WES, WGS identified driver alterations in 68.4% and likely driver alterations in 22.6% of cases. The most common alterations found were oncogene amplifications, tumor suppressor gene deletions, and small variants outside coding regions. WGS proved valuable in uncovering genomic changes associated with cancer development.

  Study JGAS000715

• Metabolomic and transcriptomic analyses identify metabolic alterations and immune suppression in ovarian cancer

  The objective of this study is to identify metabolic alterations and related phenotypes specific to ovarian cancer by performing metabolomic analysis of plasma and tissue samples. In addition, transcriptomic analysis is conducted to investigate the relationship between metabolic status and immune-related gene expression. By integrating metabolomic and transcriptomic data, this study aims to clarify how metabolic reprogramming contributes to tumor progression and immune suppression, particularly the reduction of natural killer (NK) cell activity, in ovarian cancer.

  Study JGAS000831

• A Comparative Analysis of Algorithms for Somatic SNV Detection in Cancer

  Four recently published algorithms for the detection of somatic SNV sites in matched cancer-normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer- normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterised and compared to investigate the strengths and weaknesses of each SNV calling algorithm.

  Study EGAS00001000927

• The T cell receptor repertoire of tumor infiltrating T cells is predictive and prognostic for cancer survival

  Tumor infiltration by T cells is paramount for effective anti-cancer immune responses. We hypothesized that the T cell receptor (TCR) repertoire of tumor infiltrating T lymphocytes (TIL/Tc) could determine disease course at different stages in cancer progression. We show that the diversity of the TIL/Tc TCR at baseline is prognostic in various cancers, whereas in metastatic melanoma TIL/Tc TCR clonality at baseline is predictive for activity and efficacy of PD1 blockade immunotherapy.

  Study EGAS00001005201

• Defining structural variation associated with breast cancer susceptibility by long-read genome sequencing

  Germline structural variants (SVs) are challenging to identify by conventional genetic testing assays. Long-read sequencing has improved the global characterization of SVs, but its sensitivity at genetic loci associated with high- and moderate-penetrance cancer susceptibility has not been reported. This study used long-read genome sequencing performed on the Oxford Nanopore Technologies' PromethION to resolve variants underlying breast cancer susceptibility in sixteen individuals with pathogenic germline SVs in BRCA1, BRCA2, CHEK2 or PALB2.

  Study EGAS00001005872

• Whole Genome Sequencing Data of High Grade Serous Ovarian Cancer

  Longitudinal whole genome sequencing data to study chemoresponse in ovarian cancer. These samples belong to an ongoing sample collection and the data already includes hundreds of samples from over 200 patients. Samples are from primary and metastatic tissues and ascites. All patients have been treated with platinum-taxane based chemotherapy and majority of them are diagnosed with high-grade serous ovarian cancer.

  Study EGAS00001006775

• Dr. Fatima Mechta-Grigoriou, PhD, DR1 Head of Stress and Cancer Laboratory Deputy Director Genetic and Biology of Cancer Dpt Institut Curie - Inserm U830

  Dac EGAC00001000581

• ICGC prostate cancer whole genome sequencing

  ICGC prostate cancer whole genome sequencing

  Dataset EGAD00001000306

• Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Data access committe for the project: Converging and evolving immuno-genomic routes towards immune escape in breast cancer

  Dac EGAC50000000074

• Thoracic Patient-Derived Xenografts

  Targeted capture sequencing of 341 cancer associated genes in thoracic patient-derived xenografts.

  Study phs001192

• scRNAseq of ovarian cancer patient derived organoids

  scRNAseq of ovarian cancer patient derived organoids with or without estradiol

  Dataset EGAD50000002058

• Genentech gallbladder cancer study - whole genome sequencing

  Genentech gallbladder cancer study - whole genome sequencing

  Dataset EGAD00001004855

• Plasma methylome of metastatic prostate cancer

  The dataset contains plasma DNA methylation data derived from metastatic prostate cancer patients.

  Dataset EGAD00001005433

• 65 prostate cancer cases wgs sequencing

  65 prostate cancer cases wgs sequencing

  Dataset EGAD00001000983

• 65 prostate cancer cases wgs sequencing

  65 prostate cancer cases wgs sequencing

  Dataset EGAD00001001004

• Premalignant SOX2 in ovarian cancer patients

  Whole Genome Sequencing data set for the study "Premalignant SOX2 in ovarian cancer patients"

  Dataset EGAD00001002734

• Whole exome sequencing of 103 pairs BLCA-CN

  We sequenced 50M exomes of 103 pairs bladder cancer of chinese

  Study EGAS00001000677

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumouroids

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008273

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008275

• Single_cell_ATAC_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008276

• Application of organoid culture system of human gastrointestinal and thoracic cancer in genome medicine

  In order to establish preclinical models, we have established three kinds of esophageal tumoroids derived from esophageal cancer patient. We performed whole exome sequencing to analyze genetic background of the tumoroids.

  Study JGAS000749

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004915

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004910

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004900

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004927

• Genome and transcriptome sequence data from a metastatic rectal cancer patient

  Genome and transcriptome sequence data from a metastatic rectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004918

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004926

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004913

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003613

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003619

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004912

• Genome and transcriptome sequence data from a metastatic cervical cancer patient

  Genome and transcriptome sequence data from a metastatic cervical cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002561

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

  Genome and transcriptome sequence data from a metastatic pancreatic cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003740

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003741

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004687

• Genome and transcriptome sequence data from a lung cancer patient

  Genome and transcriptome sequence data from a lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001961

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002540

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002532

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003058