6048 results for "canc*"

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• Somatic variants in 344 colorectal cancer samples

  The dataset contains somatic variants in 344 colorectal cancer samples. Variants are called with Mutect2 (GRCh38). Important: VCF-files include also variants, which have been annotated as "str_contraction" and "panel_of_normals". Please, use only "PASS" variants in studies, which are not microsatellite repeat related. Samples are sequenced with Novaseq 6000, HiSeq 2000, and HiSeq X Ten instruments (average coverage depth ~30+). The dataset consists of 257 MSS, 58 MSI, 25 MSS IBD, and 4 POLE mutant CRCs.

  Dataset EGAD00001006572

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Dataset EGAD00001006576

• Prognostic and immunomodulatory consequences of ERV expression in ovarian cancer

  The dataset contains transcriptional data obtained using total RNA sequencing on a Illumina machin. 59 samples are from the Hammersmith Hospital (HH) cohort of human primary ovarian tumours and 20 samples are from ovarian cancer cell lines Kuramochi (3 replicates) and Ovsaho (2 replicates) treated with 1 uM DNMTi guadecitabine or vehicle, at an early (day 5) or late (day 8) timepoint.

  Dataset EGAD00001006589

• EORTC RP1335 SPECTA Lung cancer data - Oncomine

  EORTC RP1335 SPECTA Lung cancer data - Oncomine dataset

  Dataset EGAD00001006594

• Validated Single-cell RNA sequencing in early breast cancer

  Single-cell RNA and VDJ sequencing of early breast cancer

  Dataset EGAD00001006608

• Single cell RNA sequencing of tumor and ascites in high grade ovarian cancer

  Fastq files for single cell RNA sequencing of cells from ovarian cancer tumor and ascites (10X chromium 5' v1.1 libraries). Multiple cases are pooled in each sequencing library.

  Dataset EGAD00001006627

• RNA-seq data from the tumor samples of a head and neck cancer patient

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006654

• Pre and Post BCG treated bulk RNAseq dataset.

  We have in total 16 files, technical duplicates of 8 unique samples from Pre and Post BCG samples collected from four non muscle invasive bladder cancer patients. These are bulk RNAseq samples generated by high-throughput sequencing platform.

  Dataset EGAD00001006634

• WGS files for Zhang GenomePaint paper

  WGS files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006679

• Transcriptomic intra-tumor heterogeneity of colorectal cancer evaluated by RNA sequencing of multi-regional biopsies

  RNA sequencing of a total of 41 tumor biopsies taken from a total of 14 patients with colorectal cancer. Ribosomal RNA was removed using the Ribo-Zero Gold rRNA Removal Kit (Illumina, CA, USA) and Paired-end sequencing were performed using ScriptSeq v2 RNA-seq Library preparation Kit (Illumina). Data processing of the paired raw sequence reads was performed using TopHat2, with mapping to the human reference genome HG19. Forty-one BAM files with reads mapping the the human reference genome (HG19) is enclosed.

  Dataset EGAD00001006636

• High-throughput sequencing data for study of molecular drivers of resistance to castration in localised prostate cancer

  Aligned whole-genome sequencing and RNA-seq of localised prostate cancer for study 'Loss of SNAI2 in prostate cancer correlates with clinical response to androgen deprivation therapy'.

  Dataset EGAD00001006640

• RNASeq files for GenomePaint paper

  RNASeq files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006680

• Cancer-Unknown-Primary-Array-Methylation-H021

  CUP samples using 850k

  Dataset EGAD00010002036

• Combinatorial CRISPR screen identifies fitness effects of paralogues FAM50A and FAM50B

  Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1,191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. This dataset includes CRISPR screening of cancer cell lines, RNA sequencing studies of cancer cell lines and also data from the sequencing of tumour xenografts collected from mice.

  Dataset EGAD00001006648

• Combinatorial CRISPR screen identifies fitness effects of paralogues FAM50A and FAM50B

  Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1,191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. This dataset includes CRISPR screening of cancer cell lines, RNA sequencing studies of cancer cell lines and also data from the sequencing of tumour xenografts collected from mice.

  Dataset EGAD00001006649

• RRBS analysis to characterize the epigenomic conservation between species in the context of Aging and Cancer.

  This dataset contains Raw Reduced Representation DNA bisulfite-sequencing data obtained from human brain samples corresponding to 3 Young and 3 Old individuals (aging context), and 3 normal and 3 Glioblastoma samples (tumor context). RRBS libraries were prepared at Diagenode SA and samples were sequenced using the Illumina Novaseq6000 sequencing platform.The accompanying samples from this study (mouse tissues) are located at the ENA database under the accession number PRJEB41460.

  Dataset EGAD00001006650

• Whole-exome sequencing data from a head and neck cancer patient

  This dataset contains paired-end whole-exome sequencing data (2x50 bp) from the normal sample, three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006653

• UROMOL 2020 - RNA-seq data

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 535 patients. Sequencing of total RNA was performed using ScriptSeq-v2 RNA-Seq Library Preparation Kit (Illumina) and KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 500 ng for both kits. The dataset is composed of 1,596 fastq files.

  Dataset EGAD00001006656

• HiC files for Zhang GenomePaint paper

  HiC files for GenomePaint paper titled "Exploration of coding and non-coding variants in cancer using GenomePaint."

  Dataset EGAD00001006678

• 133 CRC RNA-seq fastq

  This dataset contain 133 pairs of colorectal tumor and adjacent normal tissue. For each sample paired RNA-seq fastq were generated using an Illuma Myseq-2000. Those colorectal cancer comprise 101 MSI and 32 MSS tumors.

  Dataset EGAD00001006667

• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  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 This 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. The 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 EGAD00001006673

• FFPE cohort with RNA-seq data of tumor samples

  This dataset contains RNA-seq raw data in fastq format from 14 tumor samples. The samples are from primary tumors or metastasis and represent various cancer entities. The samples are formalin-fixed paraffin-embedded (FFPE) treated. For target enrichment SureSelect XT Human All Exon V6 was used. The libraries were sequenced in paired-end mode (2 x 50 nt) on a NovaSeq6000 S2 flow cell.

  Dataset EGAD00001006781

• RNAseq of resident memory T cells from human lung tumor

  This dataset includes the RNA sequencing of 14 samples. Samples are FACS sorted CD8+ T cells expressing or not the integrin CD103. The paired samples (TRM and non-TRM) were sorted from the tumor of 7 lung cancer patients.

  Dataset EGAD00001006812

• Colorectal cancer cells possess an equipotent capacity to enter a developmental pausing-like state to survive chemotherapy

  Dataset EGAD00001006847

• Colorectal cancer cells possess an equipotent capacity to enter a developmental pausing-like state to survive chemotherapy

  Cancer cells enter a reversible drug tolerant persister (DTP) state to evade death from both chemotherapies and targeted agents. It is increasingly appreciated that the DTP state is an important driver of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer xenograft models to identify and characterize the cancer cells capable of generating DTPs in response to standard-of-care chemotherapy. Barcode analysis revealed no loss in clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fits a mathematical model in which all cancer cells, and not a small subpopulation, possess an equipotent capacity to enter the DTP state. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides new insights into how cancer cells use a developmentally conserved mechanism to drive the DTP state pointing to novel therapeutic opportunities to target diapause-like DTPs.

  Dataset EGAD00001006849