5731 results for "canc*"

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• ATAC-seq of metastatic prostate tumors

  This dataset includes Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data, in FASTQ format, from 70 metastatic castration-resistant prostate cancer tumor samples from the SU2C/PCF West Coast Dream Team (WCDT) project. The sequencing data is paired-end, 150 bp sequencing data from an Illumina NovaSeq 6000 machine. ATAC-seq libraries were prepared following the protocol described in Buenrostro et al. Nature Methods. 2013 (PMID: 24097267) and Corces et al. Nature Methods. 2017 (PMID: 28846090).

  Dataset EGAD00001009654

• RNA-Seq 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 EGAD00001004280

• The University of Hong Kong Gastric Cancer XClone Study WES Data

  Exome capturing was performed using xGen Exome Research Panel v1.0 based on standard protocols. Paired-end sequencing (2 x 151 bp) was performed using Illumina NovaSeq6000.

  Dataset EGAD00001015382

• The University of Hong Kong Gastric Cancer XClone Study Single Cell CNV Data

  Single cell encapsulation and DNA libraries were prepared by Chromium™ Single Cell DNA Reagent Kits and Chromium™ Single Cell C and D Chip Kits.

  Dataset EGAD00001015383

• Comprehensive cancer predisposition gene testing in an adult multiple primary tumor series shows a broad range of deleterious variants and atypical tumor phenotypes

  Multiple primary tumors (MPT) affect a substantial proportion of cancer survivors and may result from various causes including inherited predisposition. Currently, germline genetic testing of MPT cases for cancer predisposition gene (CPG) variants is mostly targeted by tumor type. We ascertained pre-assessed MPT cases from genetics centers (defined as ≥2 primaries by age 60 years or ≥3 by 70) and performed whole genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment/molecular investigations, pathogenic variants in moderate and high-risk CPGs were detected in 67/440 (15.2%) of probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies including structural variants at low frequency (6/440 (1.4%) of probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant) the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice site CPG variants and at least one discordant tumor type was significantly higher than a control population (χ2=43.642 P=<0.0001). 2/67 (3%) of probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Summing together variant detection rates from a similarly ascertained previous MPT case series, the present results suggest that first-line comprehensive CPG analysis in a clinical genetics referral-based MPT cohort would detect a deleterious variant in about a third of cases.

  Dataset EGAD00001004088

• Development, maturation and maintenance of human prostate inferred from somatic mutations

  Stem cells within prostate epithelium frequently undergo malignant transformation, but there is limited information on their clonal dynamics and mutation burden in healthy human prostates. We sequenced whole genomes from 409 microdissections of prostate epithelium across 8 donors, using phylogenetic reconstruction with spatial mapping in a 59-year-old man’s prostate to provide high-resolution reconstruction of tissue dynamics across the lifespan. Somatic mutation burden increases linearly with age, at ~16 mutations/year/clone, and is higher in peripheral than peri-urethral regions. The 24-30 independent glandular subunits are established as rudimentary ductal structures during fetal development by 5-10 embryonic cells each. Puberty induces formation of further side branches and terminal acini by local stem cells disseminated through the rudimentary ducts during development. During adult tissue maintenance, clonal expansions are small, with limited geographic scope and minimal migration. Driver mutations are rare in normal ageing prostate epithelium, but the one canonical driver we did observe generated a sizable intraepithelial clonal expansion. By resolving unbiased, continuously occurring lineage-marking mutations, we define stem cell dynamics through embryogenesis, puberty and ageing, with relevance for prostate cancer.

  Dataset EGAD00001006591

• 41 CRC Exome bam + 4 CRC paired fastq from EGAS00001002477 study

  This dataset contain 45 pairs of colorectal tumor and adjacent normal tissue. Four of them are from a previous study EGAS00001002477. For each sample a BAM was generated by aligning to GRCh37. Those colorectal cancer all have the MSI phenotype.

  Dataset EGAD00001006666

• Angiopredict Whole genome Shallow Sequencing

  Shallow sequencing of metastatic colorectal cancer samples for the Angiopredict and Nobev cohorts described in: van Dijk et al., JCO, in revision

  Dataset EGAD00001003990

• To profile the landscape of sebaceous tumours_WES

  Sebaceous tumours are a rare cutaneous cancer with potential for aggressive behaviour. However, limited information is available on these cancers with few published cases. Here we wish to exome sequence these cancers to define the first genomic landscape for this malignancy. We will extract DNA from formalin-fixed, paraffin-embedded (FFPE) cores. Cores may be obtained from lesional and non-lesional tissues of primaries as well as matching metastases. The extracted DNA will be used for exome sequencing.

  Dataset EGAD00001015367

• Neoantigen Immunogenicity Landscapes and Evolution of Tumor Ecosystems During Immunotherapy with Nivolumab

  We performed Whole Exome (WXS) and RNASeq sequencing on samples obtained from the same site before and during therapy from our prospective clinical trial (CA209-153, NCT02066636) of nivolumab in advanced Non-small cell lung cancer (NSCLC) patients that progressed on chemotherapy. There are 58 pre and 42 on therapy WXS samples and 24 pre and 12 on therapy RNASeq samples. All WXS tumor samples have matching normal samples.

  Dataset EGAD00001011302

• Low-coverage Whole Genome Sequencing, Colorectal adenomas, NKI-AvL TGO series Stool-Proteomics

  For this tissue dataset, we applied low-pass whole genome sequencing to 98 non-advanced and advanced adenomas. As small number of lesions was sequenced multiple times, this dataset consists of 103 fastq files. These adenomas were classified as lesions with low-risk or high-risk of progression, according to the presence of specific DNA copy number changes (Carvalho et al, CancerPrevRes, 2018).

  Dataset EGAD00001004075

• PGDx elio™ plasma resolve assay: targeted sequencing analyses of plasma cfDNA

  The dataset "PGDx elio™ plasma resolve assay: targeted sequencing analyses of plasma cfDNA" includes paired end FASTQ reads of 183 cfDNA samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 33 genes, covering over 237,000 bp, targeting 25,000x depth across the targeted regions.

  Dataset EGAD00001009719

• PGDx elio™ plasma resolve assay: targeted sequencing analyses of WBC DNA

  The dataset "PGDx elio™ plasma resolve assay: targeted sequencing analyses of WBC DNA" includes paired end FASTQ reads of 49 white blood cell (WBC) genomic DNA samples from metastatic colorectal cancer patients. Sequencing was performed using a panel consisting of 33 genes, covering over 237,000 bp, targeting 25,000x depth across the targeted regions.

  Dataset EGAD00001009721

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - patient metadata files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - patient metadata files (Mutographs)

  Dataset EGAD00001015387

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - filtered vcf files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - filtered vcf files (Mutographs)

  Dataset EGAD00001015388

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - structural variation vcf files (Mutographs)

  Dataset EGAD00001015389

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - copy number variants (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - copy number variants (Mutographs)

  Dataset EGAD00001015390

• The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  The complexity of tobacco smoke induced mutagenesis in head and neck cancer - sequence data (Mutographs)

  Dataset EGAD00001015386

• FIT interval CRCs versus Screen-detected CRCs

  Formalin-fixed, paraffin-embedded samples from 27 FIT interval CRC and 54 screen-detected CRCs collected in a pilot-program of FIT-based CRC screening in the southwest and northwest regions in the Netherlands, were used in this study. DNA was extracted for 1) Shallow Sequencing (copy number analysis) and 2) TSACP Amplicon Cancer Gene Panel (mutations) of 22 FIT Interval CRCs and 45 screen-detected CRCs.

  Dataset EGAD00001006409

• EuCanImage_GUMED_UC7

  This dataset contains the standardized clinical information related to the Use case 7 - Breast cancer from EuCanImage

  Dataset EGAD50000000190

• CZ_TEST3_END-2-END

  TEST3 END-2-END demo dataset with one sample of a synthetic variants from fake brest cancer patient.

  Dataset EGAD50000000473

• CZ_END-2-END_final

  CZ FINAL END-2-END demo dataset with one sample of a synthetic variants from fake brest cancer patient.

  Dataset EGAD50000000475

• CINECA_synthetic_cohort_ita_ingestion_test

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk\n\nThis dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. \nThe genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD50000000369

• SYNTHETIC DATA: EOSC4Cancer Colorectal Cancer (CRC) synthetic Whole Genome Sequencing dataset

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) is utilized to synthetize these 10 pairs tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) are learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aims to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient is down-sampled randomly, which includes 50% germline variants of such a patient. It is then mixed together with an in silico germline variant profile that is modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learn from a real CRC patient is added to the germline variant profile used for creating normal synthetic WGS data of the same patient, which is utilized to produce simulated sequences. Neither copy number profile nor structural variation profile is introduced into the tumor synthetic WGS data. Tumor content and ploidy are assumed to be 100% and 2.

  Dataset EGAD50000000384

• Whole-Genome and Transcriptomic Data from Colorectal Cancer Patients and Matched Healthy Controls in Sweden

  This dataset contains whole-genome sequencing (WGS) and RNA-seq data generated from tumor samples, matched blood samples, and healthy control samples collected from 350 Swedish individuals between 2019 and 2023. Tumor biopsies were obtained from colorectal cancer (CRC) patients, while control samples were collected from age- and sex-matched healthy volunteers with no history of CRC. For tumor samples, paired-end RNA-seq was performed to capture gene expression profiles, and WGS was carried out on DNA extracted from both tumor tissue and matched blood samples to identify somatic and germline variants. For controls, WGS was performed on peripheral blood DNA to provide baseline genomic profiles for comparison. The resulting dataset includes: RNA-seq files in FASTQ format (~2.8 TB total) WGS files in CRAM format (~4.2 TB total) Variant calls in VCF format (~150 GB) Clinical and sample metadata in CSV format Data are stored in FEGA Sweden under controlled access. Access requests will be reviewed by the Lund University RDO as the data controller. Ethical approval was obtained from the Swedish Ethical Review Authority (Dnr: 2023-01234).

  Dataset EGAD50000000155