11762 results for "cancer rna-seq"

in 17.59 milliseconds.

• Data Use Ontology (DUO) at EGA

  For those of you that follow the updates of GA4GH you will have seen the unanimous approval of the steering committee for DUO to be included in its suite of technical standards for the sharing of genomic and health related data. DUO has three main features: Each term has been generated by the community and includes a human readable definition that can be expanded where necessary. It is a machine-readable file that encodes how that data can be used and how a researcher intends to use the data. Can be implemented alongside an advanced search algorithm which would allow authenticated users to query and gain access to datasets pertaining to their research. e.g., an industry researcher working on cancer could potentially be matched to any dataset that is allowed for commercial use and for cancer research and offered the opportunity to fetch them automatically. So, what does this mean for EGA? EGA as a driver project has adopted this standard and will be utilizing it for two main purposes in the first instance. i) It will allow EGA users to instantly identify what terms they will need to agree to as part of any Data Access Agreements. This will save valuable time for both applicants and Data Access Committees alike - as you will be able to see if you are likely to be able to access the data based on your research intentions and working background. ii) In the future it will allow users to be able to search for data based on Data Use Ontology terms e.g., you could search for all data that can be used for General Research Use (GRU). As EGA moves forwards submitters will be asked to align their data access policies with DUO so that their dataset(s) can be tagged appropriately on our web pages. If you would like to add DUO to any of your existing datasets, or to any future datasets, do please just let us know.

  Blog data-use-ontology

• Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

  Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We therefore investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population, with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of the cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

  Study EGAS00001004407

• Childhood Cancer Data Initiative (CCDI): Clonal Evolution During Metastatic Spread in High-Risk Neuroblastoma

  The goal of this study is to deliver a detailed characterization of the patterns of disease dissemination at diagnosis, during progression and in response to therapy in high-risk neuroblastoma. Longitudinal and spatially distinct tumors were collected from patients. Clinical data includes diagnosis and treatment information. Biospecimen data includes whole genome sequencing (WGS) and whole transcriptome sequencing (WTS).

  Study phs003111

• National Cancer Institute (NCI) Waldenstrom Macroglobulinemia Genome-wide Association Study

  A discovery GWAS is performed, consisting of genotyping 221 Waldenstrom macroglobulinemia (WM) cases on the Illumina Omni Express 12v1.1 platform and analyzing the genotypes together with those of 3798 previously genotyped controls in a model taking into account gender, age, and principal components of ancestry (i.e., significant eigenvectors). Directly genotyped data are supplemented by imputation using the Haplotype Reference Consortium.

  Study phs001284

• Methylation sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA"

  Methylation sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA." This dataset was created by running samples through Northstar Response, where bisulfite converted samples are amplified in a multiplex PCR targeting more than 500 genomic regions. Samples in this dataset include contrived samples made from 84 different tumors DNA, each mixed with their matched buffy coat DNA at varying ratios, as well as cfDNA and buffy coat DNA samples collected from 67 cancer patients undergoing treatment. All samples were sequenced on Illumina NextSeq 2000 with single end 100 cycle sequencing; files are in fastq.gz format.

  Dataset EGAD50000001015

• Targeted panel somatic variant sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA"

  Targeted panel somatic variant sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA." This dataset was created by running 60 cfDNA samples from patients with metastatic cancer to the lung through a research-use, tumor-naive, targeted-panel assay for measuring somatic mutations across a panel of 80 genes. All samples were sequenced on Illumina NextSeq 2000 with 150x2 paired end sequencing; files are in fastq.gz format. For the original study, the variant allele fractions of these somatic mutations were compared with the Tumor Methylation Scores calculated from Northstar Response in the same samples.

  Dataset EGAD50000001016

• Subclonal variation in patients with pediatric T-lymphoblastic leukemia (T-ALL)

  Variations in TP53 and KRAS genes indicate a particularly poor prognosis in relapsed pediatric T-ALL. We hypothesized that these variations might be subclonally present at disease onset and contribute to relapse risk. To test this, we examined 386 children diagnosed with T-ALL, including more than 100 patients who relapsed as well as matched non-relapsing controls.

  Study EGAS50000000794

• Genotyping microarray data of molecular tumorboard patients in the context of the HRD-manuscript published in IJC

  Intensity files (.idat) derived from the Infinium CytoSNP-850K BeadChip microarray (Illumina) were used to analzye a possible Homologous-Recombination-Repair Deficiency (Molecular Rationale for a PARP-Inhibitor therapy) of n= 39 tumor patients (each n= 39 .idat files of the green and red chanel respectively) presented in the Molecular Tumorboard at the University Hospital Erlangen. Using these files a Genomic Instability Score was bioinformatically deducted (R-based scripts, e.g. ASCAT, scarHRD) based upon the presence of three parameteres "Loss of Heterozygosity, LOH", "Large Scale Transitions, LST" and "Telomeric Allelic Imbalances, TAIs". The associated manuscript is published in International Journal of Cancer, IJC.

  Dataset EGAD00010002736

• Clonal hematopoiesis in rheumatoid arthritis

  To evaluate the clinical impact of CHIP on RA phenotypes and outcomes, we collected DNA samples from four distinct RA patient cohorts of newly diagnosed, previously untreated, RA patients without hematologic malignancies. A total of 573 RA patients and 163 healthy controls underwent targeted NGS sequencing using a sequencing panel consisting of 65 genes recurrently mutated in myeloid malignancies.

  Study EGAS50000000890

• Labcorp® Plasma Detect™ assay: whole genome sequencing analyses of plasma cfDNA, white blood cells and FFPE tumor tissue

  The dataset "Labcorp® Plasma Detect™ assay: whole genome sequencing analyses of plasma cfDNA, white blood cells and FFPE tumor tissue" includes CRAM and CRAI files derived from WGS of 555x plasma cfDNA, 209x white blood cells (normal DNA) and 209x FFPE tumor tissue (tumor DNA) samples for 209 stage III colorectal cancer patients. WGS (150bp PE) was performed on a NovaSeq 6000 with a target depth of coverage of 80x for tumor DNA, 40x for normal DNA and 30x for cfDNA. Reads were aligned to GRCh37 (plasma cfDNA) or GRCh38 (tumor/normal DNA).

  Dataset EGAD50000001180