7825 results for "canc*"

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• Whole-exome sequences of ovarian clear cell carcinomas and paired normal DNA

  Whole-exome sequencing dataset for the Australian Ovarian Cancer Study (AOCS) and The Jikei University School of Medicine (JIKEI) ovarian clear cell carcinoma (OCCC) clinical outliers project, representing 10 donors. Consists of 20 fastq files: one normal and one tumour from each patient.

  Dataset EGAD00001006004

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Colorectal_LCM (2020-02-20)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2020-02-20.

  Dataset EGAD00001005994

• 108 samples liver cancer and normal controls (54 pairs), whole exome sequencing

  liver cancer paired with normal controls, viral and non-viral origin

  Dataset EGAD00001006005

• WES

  WES was performed on 2 TURP, 6 biopsies, 1 CDX, 1 cell line, 6 CTCs, 1 DNA germline and 1WBC from prostate cancer

  Dataset EGAD00001006009

• Neoantigen study samples for prostate cancer

  Whole exome sequencing and RNA sequencing data from 30 patients with prostate cancer.

  Dataset EGAD00001005713

• UCSF Pediatric Bithalamic Glioma Genome Project

  Targeted next-generation sequencing of 13 pediatric bithalamic diffuse gliomas. BAM files of targeted next-generation DNA sequencing data of 13 pediatric gliomas, with multi-region sequencing data from 2 of these cases (17 total tumor samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from using the QIAamp DNA FFPE Tissue Kit (Qiagen). Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of 480 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001005499

• Sequencing files for "A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma."

  Files from DNA and RNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues.

  Dataset EGAD00001005519

• Bam files for a metastatic bladder cancer patient with BAP1 variants

  This dataset consists of three bam files (two cell-free DNA and one germline DNA) from a metastatic bladder cancer patient with BAP1 variants. Bam files were generated from targeted Illumina sequencing data.

  Dataset EGAD00001005520

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. Finally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Dataset EGAD00001005524

• Ovarian cancer organoid biobank - followup

  WGS of more samples in the ovarian cancer organoid biobank dataset.

  Dataset EGAD00001005707

• MDA whole exome sequencing data for bladder cancer

  46 BAM files from 23 urothelial bladder cancer patients on an immunotherapy clinical trial. PBMC normal samples and solid tumor samples are paired. Alignment was done by BWA with reference genome hg19.

  Dataset EGAD00001005712

• DESIGN-NKI RNA-seq

  RNA-Seq data of 36 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005715

• DESIGN-NKI low coverage WGS

  Low-coverage whole genome sequencing data of 37 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for WGS to a depth of approx. 0.5X.

  Dataset EGAD00001005718

• NKI-MET HNSCC RNA-Seq

  RNA-Seq data of 8 HNSCC specimens from patients diagnosed with metastatic disease at The Netherlands Cancer Institute, Amsterdam. Primary HNSCC biopsy samples obtained prior to treatment were used for polyA mRNA sequencing.

  Dataset EGAD00001005720

• ART-NKI II HNSCC RNA-Seq

  RNA-Seq data of 25 HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam and enrolled in the ARTFORCE trial. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005721

• DESIGN II HNSCC RNA-Seq

  RNA-Seq data of 28 HPV-negative HNSCC specimens from patients treated at the Netherlands Cancer Institute (Amsterdam), VU Medical Centre (Amsterdam) or MAASTRO Clinic (Maastricht) in The Netherlands. HNSCC biopsy samples were obtained prior to treatment (chemo-radiotherapy) for the prospective study within the DESIGN project and used for polyA mRNA sequencing.

  Dataset EGAD00001005722

• Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

  This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

  Dataset EGAD00001005724

• WES of blood and patient-matched cecum tumor tissue samples, Panel NGS of patient-matched cfDNA

  WES using IDT xGen Research Exome on Illumina NovaSeq 2x150bp: Normal sample (buffy coat), cecum tumor biopsy at diagnosis, ileocecal valve region tumor sample at week 19, pericolonic metastasis at week 19, lymph node metastasis at week 19, peritoneal metastasis at week 19. Week 19 samples from hemicolectomy. Deep coverage cfDNA NGS using PanCeq pan-cancer panel on NextSeq 2x150bp: week 2 and week 10.

  Dataset EGAD00001005737

• Whole exome sequencing of advanced gastric cancer

  To define the cellular characteristics of malignant ascites of advanced gastric cancer patients and search for therapeutic strategies, we obtained 5 malignant ascites and 1 cerebrospinal fluid from five patients with gastric cancer. We analyzed single-cell RNA-seq data of 180 cells from 4 malignant ascites and 1 cerebrospinal fluid metastasis using Fluidigm® C1™ System. Whole exome sequencing data was also generated from blood or tumor tissue.

  Dataset EGAD00001005740

• Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells

  Human Induced Pluripotent Stem Cells (hiPSC) are an established patient-specific model system where opportunities are emerging for cell-based therapies. We compared and contrasted hiPSCs derived from different tissues, skin and blood, in the same individual. We show extensive single-nucleotide mutagenesis in all hiPSC lines, although fibroblast-derived hiPSCs (F-hiPSCs) are particularly heavily mutagenized by ultraviolet (UV)-related damage. We utilized genome sequencing data on 454 F-hiPSCs and 44 blood-derived hiPSCs (B-hiPSCs) to gain further insights. Across 324 whole genome sequenced (WGS) F-hiPSCs derived by the Human Induced Pluripotent Stem Cell Initiative (HipSci), UV-related damage is present in ~72% of cell lines, sometimes causing substantial mutagenesis (range 0.25-15 per Mb). Furthermore, we find remarkable genomic heterogeneity between independent F-hiPSC clones derived from the same reprogramming process in the same donor, due to oligoclonal populations within fibroblasts. Combining WGS and exome-sequencing data of 452 HipSci F-hiPSCs, we identify 272 predicted pathogenic mutations in cancer-related genes, of which 21 genes were hit recurrently three or more times, involving 77 (17%) lines. Notably, 151 of 272 mutations were present in starting fibroblast populations suggesting that more than half of putative driver events in F-hiPSCs were acquired in vivo. In contrast, B-hiPSCs reprogrammed from erythroblasts show lower levels of genome-wide mutations (range 0.28-1.4 per Mb), no UV damage, but a strikingly high prevalence of acquired BCOR mutations in ~57% of lines, indicative of strong selection pressure. All hiPSCs had otherwise stable, diploid genomes on karyotypic pre-screening, highlighting how copy-number-based approaches do not have the required resolution to detect widespread nucleotide mutagenesis. This work strongly suggests that models for cell-based therapies require detailed nucleotide-resolution characterization prior to clinical application.

  Dataset EGAD00001007029

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Single-cell RNA-Sequencing of three primary breast cancers, two primary prostate cancers, and a metastatic melanoma sample from Wu et al. (2021) Genome Medicine study. Each tumour was sequenced across different cryopreservation conditions including Fresh Tissue (FT), cryopreserved single-cell suspensions (CCS), cryopreserved solid tissue fragments (CT) and a cryopreserved after overnight cold storage (CO). Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq platform.

  Dataset EGAD00001007030

• Molecular profiling of tissue autopsies and ctDNA

  The dataset contains high-throughput sequencing data derived from a cancer autopsy series of 10 patients. As part of this study, whole-exome sequencing and RNA-seq were performed for spatially distinct tissue biopsies from the patients. In addition, plasma samples from the patients were sequenced using a custom panelt to profile ctDNA. There are 106 files containing whole-exome sequencing data, 107 files containing RNA-seq data, and 9 files containing plasma sequencing data.

  Dataset EGAD00001007040

• bladder cancer patient data

  This contains H3K27ac ChIP-seq, RNA-seq and HiC fastq files.

  Dataset EGAD00001007052

• Early breast cancer (WGS)

  Low-coverage whole genome sequencing of 29 early breast cancer samples.

  Dataset EGAD00001007056

• Early breast cancer (WES)

  Whole-exome sequencing of 30 early breast cancer samples.

  Dataset EGAD00001007057

• CITE-seq of early breast cancer

  CITE-seq of early breast cancer samples.

  Dataset EGAD00001007058

• Circulating cell-free DNA analyses for Patient Monitoring in Small Cell Lung Cancer

  This study consists of over 200 data files from cfDNA and germline DNA from 69 patients and 32 healthy normal volunteers discussed in this publication.

  Dataset EGAD00001007070

• Whole Exome Sequencing of Mixed Histology Lung Cancer

  We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histologic patterns including 6 LUAD, 6 LCNEC, 3 SCLC, 3 LUSC, and one poorly differentiated NSCLC-NOS.

  Dataset EGAD00001007076

• WGS of HSPC clones of healthy individual sequenced with Illumina NovaSeq 6000

  Dataset contains WGS sequecing data from clonally expanded hematopoietic stem cells from 7 individual pediatric cancer patients. Samples were taken before (DX - diagnosis) or Follow-up (DX2/REM/FU - Diagnosis 2, remission or follow-up, respectively). In addtion, cord blood clones (Designated CB) treated with X-ray radiation, Cisplatin, Maphosphamide, Vincristine and Doxorubicin and untreated cord blood hematopoietic stem/progenitor cells were have been whole-genome sequenced. (Abbreviations RAD, CISPL, MAPH, VINC, DOX and CTRL, respectively)

  Dataset EGAD00001007078

• Exome sequencing of replication repair deficient brain cancers

  This dataset contains exome sequencing from replication repair deficient brain tumor samples.

  Dataset EGAD00001006920

• Transcriptome sequencing of replication repair deficient brain cancers

  Hypermutant tumors which harbor many somatic mutations may obscure the interpretation of targetable genomic events. This dataset contains transcriptome sequencing from 21 replication repair deficient brain tumor samples as well as healthy controls.

  Dataset EGAD00001006921

• Longitudinal single-cell RNA-seq data from metastatic ovarian cancer

  Longitudinal single-cell RNA-seq data of prospectively collected tumor tissue samples before and after chemotherapy from 11 HGSOC patients.

  Dataset EGAD00001006922

• Assessment of cannabidiol and Δ9-tetrahydrocannabiol in mouse models of medulloblastoma

  These data were used in the following publication: Andradas, C.; Byrne, J.; Kuchibhotla, M.; Ancliffe, M.; Jones, A.C.; Carline, B.; Hii, H.; Truong, A.; Storer, L.C.D.; Ritzmann, T.A.; et al. Assessment of Cannabidiol and delta9-Tetrahydrocannabiol in Mouse Models of Medulloblastoma and Ependymoma. Cancers 2021, 13, 330. https://doi.org/10.3390/cancers13020330 There are 4 paired-end RNA-seq samples from paediatric brain cancer cell lines.

  Dataset EGAD00001006923

• MutWP5: CRUK Mutographs of Cancer: Lung: PD37920 (Targeted) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006929

• MutWP5: CRUK Mutographs of Cancer: Lung: PD38233 (WG) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006930

• MutWP5: CRUK Mutographs of Cancer: Lung: PD43291_Novaseq (Exome) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006932

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006933

• Exome Sequencing of Gastric Cancer Samples

  We perform whole exome sequencing on 50 pairs of gastric cancer and matched normal samples.

  Dataset EGAD00001006957

• Bulk tumor RNAseq of bladder cancer patients

  RNAseq fastq files from 611 bulk pre-treatment tumors from two indications: metastatic urothelial bladder cancer patients (IMvigor210) and metastatic renal cell carcinoma (IMmotion150)

  Dataset EGAD00001006960

• Whole-genome sequencing of gastric cancer with peritoneal metastasis

  Whole-genome sequencing of 135 tumor samples and 98 normal samples of gastric cancer with peritoneal metastasis

  Dataset EGAD00001006963

• MicroRNA profiling by next-generation sequencing for colorectal cancer screening

  The dataset contains raw miRNA sequencing data of plasma samples from 20 newly diagnosed colorectal cancer cases and 20 controls free of colorectal neoplasms matched by age and sex. It includes files in the FASTQ compressed (.gz) format.

  Dataset EGAD00001006966

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• Targeted sequencing of Human esophageal epithelium microbiopsies

  NOTCH1 mutant clones occupy the majority of normal human esophagus by middle age, but are comparatively rare in esophageal cancers, suggesting NOTCH1 mutations may promote clonal expansion but impede carcinogenesis. Here we test this hypothesis. Visualizing and sequencing NOTCH1 mutant clones in aging normal human esophagus, reveals frequent biallelic mutations that block NOTCH1 signaling.  In mouse esophagus, heterozygous Notch1 mutation confers a competitive advantage over wild type cells, an effect enhanced by loss of the second allele.  Notch1 loss alters transcription but has minimal effects on epithelial structure and cell dynamics. In a carcinogenesis model, Notch1 mutations were less prevalent in tumors than normal epithelium. Deletion of Notch1 reduced tumor growth, an effect recapitulated by anti-NOTCH1 antibody treatment.  We conclude that Notch1 mutations in normal epithelium are beneficial as wild type Notch1 promotes tumor expansion. NOTCH1 blockade has therapeutic potential in esophageal squamous tumors.

  Dataset EGAD00001006969

• Clinical phenotype data

  Clinical data corresponding to the patients with ovarian cancer studied using scRNA-seq and bulk RNA-seq. Variables include molecular subtype, predicted immune phenotype, reviewing pathologist comment, final immune phenotype, histology characterization, and tumor stage.

  Dataset EGAD00001006973

• scRNA-seq using 10X Genomics platform

  Matrices of counts from single-cell RNA-seq data for 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert). Dissociated cells from each tumor sample have been sorted to isolate live cells from the 3 compartments: tumor, immune and stromal. Each of the compartments has been analyzed separately by scRNAseq, excluding some desert tumors for which cells from the stromal and immune compartments have been pooled. Sequencing was performed using 10X Genomics Chromium Single Cell platform (v2 Chemistry).

  Dataset EGAD00001006974

• Bulk RNAseq fastq files

  RNAseq FASTq files from 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert).

  Dataset EGAD00001006975

• HER2-negative primary breast cancers WES

  Temporal HER2-negative breast cancers WES

  Dataset EGAD00001006980

• Breast Cancer TNBC Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of five TNBC primary breast cancers from Wu et al. (2020) EMBO J study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001006981

• Single-cell RNA-seq data from metastatic ovarian cancer for quality control study

  This study includes treatment-naïve fresh tissue sample from 4 HGSOC patients.

  Dataset EGAD00001006984

• Single-cell gene expression data from CD8+ T cells from two Austrian COVID19 patients stimulated with wildtype and mutant SARS-Cov-2 peptides

  The data contains single-cell gene sequencing data (10x Genomics) from FACS-purified CD8 T lymphocytes from two Austrian patients. The cells were stimulated with one MHC class I peptides obtained from a common (wild type) variant and an emerging mutant variant of the SARS-Cov-2 virus. Then the samples were multiplexed using hashtag oligos. We provide the raw and aligned sequence data for: i. The single-cell experiments ii. The PCR-amplified samples for enrichment of the hashtag oligo multiplexing barcodes iii. The PCR-amplified samples for enrichment of the T Cell Receptor (TCR) VDJ region for immuno-profiling. The samples and libraries were processed and obtained in collaboration between St. Anna Children's Cancer Research Institute (CCRI), CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, and the Medical University of Vienna. The cell barcodes and processed data has been submitted to the GEO database with GEO accession GSE166651.

  Dataset EGAD00001006995