8253 results for "canc*"

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• Tumour sample for patient SA402

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA402

  Dataset EGAD00001009189

• Tumour sample for patient SA404

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA404

  Dataset EGAD00001009190

• Tumour sample for patient SA530

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA530

  Dataset EGAD00001009191

• Tumour sample for patient SA585

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA585

  Dataset EGAD00001009192

• Tumour sample for patient SA586

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA586

  Dataset EGAD00001009193

• Tumour sample for patient SA588

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA588

  Dataset EGAD00001009194

• Tumour sample for patient SA589

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA589

  Dataset EGAD00001009195

• Tumour sample for patient SA590

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA590

  Dataset EGAD00001009196

• Tumour sample for patient SA591

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA591

  Dataset EGAD00001009197

• Tumour sample for patient SA592

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA592

  Dataset EGAD00001009198

• Tumour sample for patient SA593

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA593

  Dataset EGAD00001009199

• Tumour sample for patient SA595

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA595

  Dataset EGAD00001009200

• Tumour sample for patient SA596

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA596

  Dataset EGAD00001009201

• Tumour sample for patient SA598

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA598

  Dataset EGAD00001009202

• Tumour sample for patient SA599

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA599

  Dataset EGAD00001009203

• Tumour sample for patient SA600

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA600

  Dataset EGAD00001009204

• Tumour sample for patient SA601

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA601

  Dataset EGAD00001009205

• Tumour sample for patient SA654

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA654

  Dataset EGAD00001009206

• Tumour sample for patient SA655

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA655

  Dataset EGAD00001009207

• Tumour sample for patient SA665

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA665

  Dataset EGAD00001009208

• Tumour sample for patient SA666

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA666

  Dataset EGAD00001009209

• Tumour sample for patient SA669

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA669

  Dataset EGAD00001009212

• Bulk RNA-seq of stromal cells from multiple cancer types

  We sorted CD45-CD44+CD90+ stromal cells from multiple tumor types and performed bulk RNA-sequencing.

  Dataset EGAD00001009176

• Tumour sample for patient SA667

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA667

  Dataset EGAD00001009210

• Tumour sample for patient SA668

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA668

  Dataset EGAD00001009211

• Tumour sample for patient SA671

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA671

  Dataset EGAD00001009213

• Tumour sample for patient SA672

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA672

  Dataset EGAD00001009214

• Tumour sample for patient SA535

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA535

  Dataset EGAD00001009215

• Normal sample for patient SA1035

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1035

  Dataset EGAD00001009216

• Normal sample for patient SA604

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA604

  Dataset EGAD00001009217

• Normal sample for patient SA605

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA605

  Dataset EGAD00001009218

• Normal sample for patient SA609

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA609

  Dataset EGAD00001009219

• Normal sample for patient SA218

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA218

  Dataset EGAD00001009220

• Normal sample for patient SA219

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA219

  Dataset EGAD00001009221

• S:CORT Stratification in COloRecTal cancer

  Colorectal cancer samples will be submitted for Illumina sequencing using a custom capture of 116 genes implicated in colorectal tumourigenesis. Driver mutations will be detected and ultimately correlated with phenotypic data.

  Dataset EGAD00001009760

• Single Cell Genome Sequence for DLP+ library A96141A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96141A

  Dataset EGAD00001009777

• Single cell RNA-sequencing of tissues from NSCLC patients treated with chemoimmunotherapy

  The goal of this study is to characterize immune cell populations by single cell RNA-sequencing (scRNA-seq) in tumor and uninvolved normal tissues from non-small cell lung cancer (NSCLC) patients with resectable non-small cell lung cancer and who received neoadjuvant chemoimmunotherapy. scRNA-seq was performed on seven pairs of tumor and normal tissues as well as one lymph node (LN) sample. Data set includes pair-end fastq files for single cell RNA sequencing of 7 neo-immuno patients. (Total 15 samples and 50 runs).

  Dataset EGAD00001009778

• cfDNA from fractionated and paired unfractionated plasma samples of lung cancer patients

  Plasma from lung cancer patients from EDTA tubes was fractionated using size exclusion chromatography. Fractions 1-5, 7-11, 12-15, 16-20 were pooled, cfDNA was extracted from the fractions and paired unfractionated samples and PE150bp sequencing was performed on an Illumina Novaseq S4 flowcell. Samples are provided as raw reads without any prior processing.

  Dataset EGAD00001009780

• BCSA_Exome+SmartSeq3

  Multiple regions were cut out of each tumor extracted form breast cancer patients and two experiments were run: - Whole exome sequencing on each of the regions plus an adjacent normal tissue sample - Smart-Seq3 Single cell RNA sequencing on EPCAM+ CD45- sorted cells from different tumor regions

  Dataset EGAD00001009781

• Tumor Educated Platelets (TEPs) for breast cancer detection

  The TEP dataset consists of 549 Fastq samples which are divided into two experiments: a training data cohort, used to train the classifier, and a validation data cohort, used to assess classifier performance

  Dataset EGAD00001009790

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 2)

  Whole genome sequencing data of 8 High-grade serous carcinoma (HGSC) patients (20 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001009791

• HGSOC-celllines-SmartSeq3

  Smart-seq3 scRNA-seq of cells from primary (OV2295) and metastatic (OV2295R2) high-grade serous ovarian cancer cell-line

  Dataset EGAD00001009798

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - DNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009812

• Distinct embryonic phylogenies and driver events of infant Wilms tumor - RNA

  Cancers of adults typically arise through progressive rounds of clonal diversification and intratumoral selective sweeps which generate a long mutational trunk with shorter subclonal branches. Here, we investigated whether tumors of young children exhibit the same phylogenetic configuration. We studied three infants, including two newborns, with the childhood kidney cancer, Wilms tumour, through whole genome sequencing of bulk tissues, of single cell derived organoids, and of microdissections. All three cancers exhibited unusual driver events, with tumours of newborns harbouring FOXR2 rearrangements, delineating a distinct variant of Wilms tumour. Phylogenetic analyses suggest that tumors were seeded in an early, possibly confined window of development. Unusually, following seeding there was extensive polyclonal diversification with little evidence of clonal sweeps, leading to a distinct phylogenetic configuration more reminiscent of normal tissues rather than of adult cancers. These findings indicate that some childhood cancers may diversify via unorthodox phylogenetic pathways.

  Dataset EGAD00001009813

• Concatenated long-read single-cell RNA sequencing of momentum biopsies of ovarian cancer patients

  Concatenated long-read single-cell RNA sequencing samples prepared using 10X and the HIT-scIsoSeq protocol. The sequencing was performed on Sequel II Pacbio machine. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 bam files per metastasis sample, 2 bam files per healthy sample.

  Dataset EGAD00001009814

• Illumina single-cell RNA sequencing of omentum biopsies of ovarian cancer patients

  Illumina Novaseq paired-end single-cell RNA sequencing samples prepared using 10X Genomics platform. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 HGSOC metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 paired fastq files per sample.

  Dataset EGAD00001009815

• Sequencing data for the manuscript "Multi-focal sampling of de novo metastatic prostate cancer reveals complex polyclonality and enables accurate clinical genotyping"

  This submission includes targeted and whole exome paired-end fastq files.

  Dataset EGAD00001009651

• 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

• Nanopore low-pass WGS of human brain tumors

  Nanopore low-pass WGS of human brain tumors for evaluation of DNA methylation-based classification of cancer

  Dataset EGAD00001009663

• Somatic mutations in facial skin from countries of contrasting skin cancer risk

  The incidence of non-melanoma skin cancer is 17-fold lower in Singapore compared to the UK1, despite Singapore receiving 2-3 times more year-round ultraviolet radiation (UV)2,3. The ageing epidermis of the skin comprises competing somatic mutant clones4,5, from which such cancers develop. We question if differences in keratinocyte skin cancer incidence are reflected in the mutational landscape by comparing ageing facial epidermis from donors of Singapore and the UK. We find UK skin to be a highly competitive, densely mutated landscape with 4-fold greater mutation burden compared to Singaporean skin and differences in clonal selection by country. We disproportionately observe multiple features common to keratinocyte skin cancers6,7,8 in UK skin, such as UV mutagenesis, copy number aberration and hotspot mutations (in particular TP53 R248W). We conclude that keratinocyte skin cancer incidence is reflected in the somatic clones of non-cancerous epidermis. Finally, we re-analyse squamous cell carcinoma exomes from Korea9 to show, even in low incidence populations, carcinogenesis is driven by UV damage.

  Dataset EGAD00001009666