15037 results for "cancer studies using rna-seq"

in 50.78 milliseconds.

• Characterization of the oral and gut microbiome of melanoma patients before initiation of ant-PD-1 therapy, and confirmation of the phenotype in germ-free mice

  The oral and gut microbiomes of melanoma patients were characterized before the initiation of ant-PD1 immunotherapy, and compared to treatment response. Validation studies were performed in germ-free mice using stool from patients who responded/did not respond to ant-PD1 immunotherapy. All baseline oral(n=86) and gut (n=43) microbiome samples were subject to 16S sequencing - V4 region ( merged fastq files have been made available through this portal). Whole genome shotgun sequencing (WGS) was performed on a subset of fecal samples (n=25)- these files are also available( paired end reads). Also available are 16S sequencing results of stool samples from donors (n=2) used in fecal microbiota transplant and murine samples (n=12) from germ-free mice transplanted with stool from responder/non-responder patients. The fastq files associated with this dataset are stored at ENA under the following links: Fecal 16S – PRJEB22894 https://www.ebi.ac.uk/ena/browser/view/PRJEB22894 Oral 16S – PRJEB22874 https://www.ebi.ac.uk/ena/browser/view/PRJEB22874 Murine 16S – PRJEB22895 https://www.ebi.ac.uk/ena/browser/view/PRJEB22895 Fecal WGS – PRJEB22893 https://www.ebi.ac.uk/ena/browser/view/PRJEB22893

  Dataset EGAD00001003943

• Recurrent epimutations activate gene body promoters in primary glioblastoma

  Aberrant DNA hypomethylation may play an important role in the growth rate of glioblastoma (GBM), but the functional impact on transcription remains poorly understood. We assayed the GBM methylome with MeDIP-seq and MRE-seq, adjusting for copy number differences, in a small set of non-glioma CpG island methylator phenotype (non-G-CIMP) primary tumors. Recurrent hypomethylated loci were enriched within a region of chromosome 5p15 that is specified as a cancer amplicon and also encompasses TERT, encoding telomerase reverse transcriptase, which plays a critical role in tumorigenesis. Overall, 76 gene body promoters were recurrently hypomethylated, including TERT and the oncogenes GLI3 and TP73. Recurring hypomethylation also affected previously unannotated alternative promoters, and luciferase reporter assays for three of four of these promoters confirmed strong promoter activity in GBM cells. Histone H3 lysine 4 trimethylation (H3K4me3) ChIP-seq on tissue from the GBMs uncovered peaks that coincide precisely with tumor-specific decrease of DNA methylation at 200 loci, 133 of which are in gene bodies. Detailed investigation of TP73 and TERT gene body hypomethylation demonstrated increased expression of corresponding alternate transcripts, which in TP73 encodes a truncated p73 protein with oncogenic function and in TERT encodes a putative reverse transcriptase-null protein. Our findings suggest that recurring gene body promoter hypomethylation events, along with histone H3K4 trimethylation, alter the transcriptional landscape of GBM through the activation of a limited number of normally silenced promoters within gene bodies, in at least one case leading to expression of an oncogenic protein.

  Study EGAS00001000685

• NHGRI GREGoR Consortium: Genomics Research to Elucidate the Genetics of Rare Disease

  The NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare Disease) Consortium was established in June 2021 with the goal of developing novel tools and approaches to advance the discovery of the genetic basis of rare conditions. Numerous types of molecular data are generated in GREGoR and available on the AnVIL cloud platform via dbGaP application, including short- and long-read genome and exome sequencing, transcriptomics, metabolomics, methylomics, and proteomics. De-identified clinical and demographic data is obtained, with a focus on standardized ontologies.Visit the GREGoR Consortium data webpage for summary information about the GREGoR Dataset, including numbers of participants and data types, methods documentation, and Release Notes. The Consortium comprises five Research Centers (RCs - see below), a Data Coordinating Center (DCC), and various partner members and external collaborators.Baylor College of Medicine Research Center (BCM-GREGoR) The Baylor College of Medicine GREGoR program, which is part of the GREGoR consortium, enrolls individuals, families, and cohorts with suspected rare disease across a range of syndromic and non-syndromic phenotypes. Subjects are enrolled from national and international collaborating physician referrals. Subjects provide written informed consent for future re-contact. Data generated and shared include family structure, detailed phenotypes, exome or short-read genome data, and in some cases long-read genome or RNA-sequencing, and these are shared upon completion of standard quality control checks and annotation. Broad Institute (Broad) The Broad Center for Mendelian Genomics, part of the GREGoR consortium uses next-generation sequencing (exome, genome, transcriptome, and long read sequencing), computational approaches, and functional studies to discover the variants and genes that underlie Mendelian conditions with a particularly focus on neuromuscular, neurodevelopmental, and syndromic phenotypes. Samples come from collaborators and direct enrollment through the Rare Genomes Project and we are committed to rapid data sharing without an embargo period. University of California, Irvine (UCI-GREGoR) To accelerate the pace of Mendelian disease gene discovery and clinical implementation, we propose a Mendelian Genomics Research Center, part of the GREGoR Consortium, leveraging the broad pediatric and adult clinical and research expertise at Children's National Hospital and University of California, Irvine. Our goal is to develop best practices to increase the diagnostic yield of rare diseases, engage the community to reduce health disparities for complex diagnoses, while creating a dataset accessible to all. Our Center will unite world class experts combining basic and translational research with innovative approaches to phenotyping, variant identification and functional investigation of both coding and non-coding sequence changes with the goals of discovering novel Mendelian gene variations and identifying variants not detected on current sequencing pipelines, disambiguating uncertain variants into disease-causing versus benign categorizations, and sharing information by working collaboratively with the GREGoR community.GREGoR Stanford Site (GSS) The goal of the GREGoR Stanford Site (GSS) is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease. Participants included individuals with undiagnosed suspected Mendelian disease who had non-diagnostic exome sequencing and their immediate family members. Participants and their family members provided written, informed consent and biological samples from which DNA, RNA, plasma, fibroblasts, PBMCs and other cell types were generated and stored. Samples from research participants and their immediate family members may have undergone short and long-read genome sequencing, transcriptome sequencing, metabolomics and/or lipidomics profiling, methyl-capture-sequencing and ATAC-sequencing. De-identified clinical data extracted from participant medical records are linked to the samples. University of Washington Center for Rare Disease Research (UW-CRDR) The goals of the University of Washington Center for Rare Disease Research are to: (1) maximize novel gene discovery for Mendelian conditions by recruitment, short- and long-read whole genome sequencing, transcriptome sequencing and analysis of families with rare conditions for which the gene is either unknown or the gene is known but no pathogenic variant can be identified via clinical testing; (2) develop new strategies for gene discovery for Mendelian conditions caused by variants that are difficult to detect using conventional testing strategies, variants of unknown function effect (e.g., regulatory, structural variants) or have unusual modes of inheritance; and (3) implement high-throughput screening and targeted follow-up functional studies to prioritize and validate candidate non-coding variants. De-identified data and phenotypic information are shared via MyGene2, ClinVar, and AnVIL.

  Study phs003047

• Myeloid cancer sequencing data

  SmMIP libraries using DNA from patients diagnosed with myeloid malignancies were generated in replicates and were sequenced on the NovaSeq SP platform (Illumina)

  Dataset EGAD00001007791

• Whole genome sequencing files for St. Jude Clinical Pilot

  This dataset contains whole genome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004290

• Whole exome sequencing files for St. Jude Clinical Pilot

  This dataset contains whole exome sequencing BAM files for 78 tumor-normal pairs (a total of 156 samples) used in the St. Jude Clinical Pilot. Mapping was performed using BWA. This dataset accompanies the paper "Clinical Cancer Genomic Profiling by Three-Platform Sequencing of Whole Genome, Whole Exome and Transcriptome"

  Dataset EGAD00001004287

• PGDx elio™ tissue complete assay: targeted sequencing analyses of tissue DNA

  The dataset "PGDx elio™ tissue complete assay: targeted sequencing analyses of tissue DNA" includes paired end FASTQ reads of 28 tissue samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 505 genes, targeting 2,500x depth across the targeted regions.

  Dataset EGAD00001009720

• GELATO clinical trial whole exome sequencing data (metastatic lesions)

  Paired-end whole exome sequencing of 19 lobular breast cancer metastatic tumors and matched normal samples obtained from 19 unique patients from the GELATO clinical trial. The included raw sequencing data in fastq format was generated using Illumina NovaSeq 6000 from fresh frozen material.

  Dataset EGAD00001009837

• Targeted Illumina sequencing of 3399 plasma cfDNA samples and 1988 leukocyte DNA samples from 1995 metastatic prostate cancer patients

  Blood samples were collected from eight clinical trials and a British Columbia-based biobank. All plasma cfDNA and leukocyte DNA samples were processed with uniform methodology, and underwent targeted sequencing using a custom hybridization capture panel and Illumina instruments. Four different generations of custom hybridization capture panel were used, all providing coverage of the complete coding regions of 72 prostate cancer genes, and later panel generations also providing exhaustive coverage of the AR locus (including introns and flanking regulatory regions), as well as a genome-wide SNP grid for copy number analysis.

  Dataset EGAD50000001594

• Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

  The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1,070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8,887 TCGA, and 1,261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n=1,261) including our lung cancer cohort (n=497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

  Study EGAS50000000324