15028 results for "cancer studies using rna-seq"

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• single-cell ATAC-seq

  Single-cell ATAC-seq data for 5 CLL samples (2 controls, 3 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005965

• Primary Neuroblastoma Circle-seq Data

  Circle-seq data for 21 primary neuroblastoma samples supporting Koche et al. Extrachromosomal circular DNA drives oncogenic genome remodeling in neuroblastoma (2020).

  Dataset EGAD00001006580

• release_2: ICGC PedBrain: ChIP-Seq

  release_2: ICGC PedBrain: ChIP-Seq

  Dataset EGAD00001001621

• miRNA seq data of 43 cases out of dataset EGAD00001000650 (+ one read_me file)

  miRNA seq data of 43 cases out of dataset EGAD00001000650 (MMML)

  Dataset EGAD00001001619

• ATAC-seq

  This dataset contains the raw sequencing data (Runs) for all 10x Genomics single-cell ATAC-seq Experiment.

  Dataset EGAD00001009822

• ATAC-seq of cohesin-mutated and -wildtype adult AMLs

  ATAC-seq (Illumina TDE1 Transposase) to profile accessible chromatin regions of cohesin-mutated (STAG2 or RAD21 mutations) and -wildtype adult AMLs.

  Dataset EGAD00001011267

• Single cell RNA sequencing of human umbilical cord blood lymphoid progenitors

  We performed single cell RNA sequencing of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage deeply, our target cells contain CD34+CD38+CD45RA+CD19-CD7-CD10+ cells (CD10SP). All these populations are evaluated by single cell RNA sequencing.

  Study JGAS000551

• Study of tumor RNA expression differences between treated and untreated PitNET patients

  The goal of the study is to evaluate response of PitNET to drug therapy before surgery. Patients with and without drug therapy are compared in RNA level. The RNA is extracted from surgery material, sequenced and deferentially expressed genes (DEG) determined.

  Study EGAS00001004736

• DCIS Whole Exome & Whole Genome Sequencing Data

  Primary DCIS and recurrent DCIS/IBC (total n=82, ipsilateral n=78 and contralateral n=4) as well as non-recurrent DCIS (n=32) cases were identified through hospital databases from Royal Melbourne Hospital (RMH), Nottingham City Hospital (UK), the LifePool cohort and Peter MacCallum Cancer Centre (PMCC). All cases were micro-dissected from haematoxylin stained sections (range 4-20 sections) by manual micro-dissection to achieve >50% tumour tissue purity. DNA was extracted using the FFPE AllPrep kit (Qiagen, MD, USA). Depending on the success and timing of DNA extraction (Figure 1), cases were analysed by a targeted sequencing panel (n=46, samples pre-2020), whole exome sequencing (WES, n=67), or low-coverage whole genome sequencing (LCWGS). WES: DNA was processed by the Australian Genome Research Facility (AGRF) using the Twist Bioscience Human Comprehensive Exome v1 or v2 (Twist Bioscience HQ, South San Francisco, CA, USA) according to the Twist Target Enrichment Standard Hybridisation Protocol. Sequencing was performed on the Illumina NovaSeq 6000 with 150 bp paired-end reads for a median depth of 101.29x, range: 20.39-247x. Whole Genome Sequencing (WGS): Libraries for four paired samples with matched stromal DNA were made, processed and sequenced by AGRF using the IDT xGen Kit (USA) according to the manufacturer’s protocol, aiming to achieve 60x depth for tumour DNA and 30x for matched normal. File type: Paired-end FASTQ.

  Dataset EGAD50000001846

• Diagnosis of pediatric central nervous system tumors using methylation profiling of cfDNA from cerebrospinal fluid

  In this study, we explore the potential of classifying pediatric brain tumors based on methylation profiling of the cell-free DNA in cerebrospinal fluid (CSF). For this proof-of-concept study, we collected 20 cerebrospinal fluid samples of pediatric brain cancer patients via a ventricular drain placed for reasons of increased intracranial pressure. For 11 patients in this study we collected matched tumor DNA. This cohort contains fastQ files of cfRRBS data of these samples.

  Study EGAS50000000377

• Targeted Sequencing of 173 genes

  Targeted sequencing of 173 genes in 2433 primary breast tumours. Data includes 2433 tumour samples, 523 adjacent normal (breast) samples and 127 blood samples. Libraries were prepared with Illumina's Nextera custom enrichment kit targetting all the exons of the most frequently mutated breast cancer genes. Libraries were multiplexed (48 libraries per lane) and sequenced on Illumina HiSeq 2000 (100bp paired-end reads). Somatic mutations were calling with a custom pipeline. We identified 40 mutation-driver (Mut-driver) genes, and determined associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assessed the clonal states of Mut-driver mutations, and estimated levels of intra-tumour heterogeneity using mutant-allele fractions. The results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies. Referece: Pereira et al. (2016) The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nature Communications

  Dataset EGAD00001002115

• Cell-Free, Methylated DNA in Blood Samples Reveals Tissue-Specific, Cellular Damage from Radiation Treatment

  Radiation therapy is an effective cancer treatment, although damage to surrounding healthy tissues can also occur. Cell-type specific DNA methylation patterns can be used to decode the cellular origins of cell-free DNA fragments, allowing for minimally-invasive monitoring of tissue damage. To evaluate whether changes in cell-free DNA methylation can indicate damages to tissues in patients treated with radiation, we collected serum samples from 15 breast cancer patients at three timepoints during their standard-of-care radiation therapy after surgery. A baseline sample was taken for each patient before onset of radiation therapy and a second End-Of-Treatment (EOT) sample was taken 30 minutes after the last treatment fraction. Finally, a recovery sample was taken one month after completion of radiation therapy. From serum samples, cell-free DNA was isolated and bisulfite capture-sequencing DNA methylation data were generated. Bisulfite capture-sequencing DNA methylation data were also generated from buffy coat and tissue-specific endothelial gDNA samples as well as from serum/plasma samples of healthy controls. The analysis of serum samples from breast cancer patients undergoing radiation treatment revealed distinct tissue-specific epithelial and endothelial responses to radiation across multiple organs. In conclusion, as a proof of concept we show that cell-type specific methylation signatures can be applied to detect cellular injury from radiation treatment using minimally invasive cell-free DNA in blood samples.

  Study phs003290

• University of Texas PDX Development and Trial Center Grant

  The goal for the University Texas PDX Development and Trial Center (UTPDTC) is to optimize personalized biomarker-based cancer therapy and identify effective targeted drugs based on the molecular characteristics of each tumor. Our short-term goals are to establish a biobank of clinically, and molecularly-annotated Patient-Derived Xenografts (PDXs) and to use PDXs as a platform for preclinical drug development and biomarker discovery. The primary goal for UTPDTC investigators will be to develop PDX trial strategies for preclinical testing of single agents and drug combinations. These models will allow the determination of the optimal treatments (single drugs or combinations) that should be tested in clinical trials in increasingly individualized, molecularly defined subsets of tumors. The goal of the Patient-Derived Xenograft Core is to provide high-quality clinically relevant and molecularly annotated PDX models for the research projects proposed in the University of Texas PDX Development and Trial Center (UTPDTC) grant application and to the research activity of the NCI PDX Development and Trial Centers Research Network (PDXNet) by leveraging PDX resources at our institutions and developing new PDX models from human cancer specimens using rigorous quality standards so that the models can be used to guide clinical trial development. The PDX models developed and/or characterized by the PDX Core will be available to other cancer researchers through the PDXNet and NCI Patient-Derived Models Repository (PDMR).

  Study phs001980

• National Sleep Research Resource (NSRR): Cleveland Family Study (CFS)

  The Cleveland Family Study (CFS) is a family-based study of sleep apnea, consisting of 2,284 individuals (46% African American) from 361 families studied on up to 4 occasions over a period of 16 years. The study began in 1990 with the initial aims of quantifying the familial aggregation of sleep apnea. National Institutes of Health (NIH) renewals provided expansion of the original cohort, including increased minority recruitment, and longitudinal follow-up, with the last exam occurring in February 2006. The CFS was designed to provide fundamental epidemiological data on risk factors for sleep disordered breathing (SDB). The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first-degree relatives, spouses and available second-degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first-degree relatives. Each exam, occurring at approximately 4-year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. Currently, data of 710 individuals are available for use through BioData Catalyst (with genotype data available through dbGaP).The National Sleep Research Resource (NSRR) is a NIH-supported sleep data repository that offers free access to large collections of de-identified physiological signals and related clinical data from a large range of cohort studies, clinical trials and other data sources from children and adults, including healthy individuals from the community and individuals with known sleep or other health disorders. The goals of NSRR are to facilitate rigorous research that requires access to large or more diverse data sets, including raw physiological signals, to promote a better understanding of risk factors for sleep and circadian disorders and/or the impact of sleep disturbances on health-related outcomes. Data from over 15 data sources and more than 40,000 individual sleep studies, many linked to dozens if not hundreds of clinical data elements, are available (as of Feb. 2022). Query tools are available to identify variables of interest, and their meta-data and provenance.

  Study phs002715

• Cystic Fibrosis Nasal Epithelium Gene Expression by RNAseq

  Cystic fibrosis (CF) is a monogenic disorder leading to multi-organ system dysfunction, with the primary cause of morbidity and mortality being lung disease. Mutations in CFTR result in defective ion channel transport leading to hyperviscous mucus, airway infection, inflammation and lung function decline. While the Phe508del mutation is the most common CFTR mutation in North American people with CF, it is well known that there is a broad range of lung disease severity even amongst Phe508del homozygotes, including twins/siblings. Heritability analyses showing the relative contribution of non-CFTR modifier genes in such differences in phenotype, have led to our genomic and transcriptomic studies of gene modifiers in CF. Complementary to two international CF GWAS studies, this study was performed to a) determine differential expression of genes relative to a quantitative lung disease severity phenotype score (KNoRMA; as used in CF GWAS studies) and b) to assess expression quantitative trait loci. To better understand genetic modification of CF lung disease severity, we analyzed gene expression (transcriptomic) profiles by mRNA sequencing from nasal mucosal biopsy specimens of 134 people with CF. Each study participants was characterized for CF lung disease severity phenotype by averaging three years of lung function data (forced expiratory volume in 1 second; FEV1), normalized to a CF-specific reference population matching for age, sex, and height. Participants completed nasal lavage and nasal curettage mucosal sampling, with directed sampling to the inferior nasal turbinate, a well validated surrogate for cellular characteristics and CFTR function of the lower CF airways. Participants were further invited to complete a second set of these sampling procedures for validation analysis. Specimen and concurrent clinical data collection occurred across four North American study sites and nasal mucosal specimens were subjected to RNASeq analysis. These data were to be made available in the dbGaP. Our association analyses of gene expression relative to quantitative CF lung disease phenotype, taken together with previously identified GWAS signals, identified biological pathways correlated with CF lung disease pathogenesis. Our further association analyses of gene expression relative to single nucleotide polymorphism variation at significant CF GWAS loci further identified potential mechanisms of genetic variants associated with CF lung disease. This resource is intended to aid researchers in characterizing airway mucosal gene expression from the CF airways for the purpose of gene expression profiling in CF and in muco-obstructive airways disease.

  Study phs002254