6135 results for "canc*"

in 44.90 milliseconds.

• WGS

  Whole-genome sequencing of pancreatic cancer organoids and matched germline controls.

  Dataset EGAD00001010835

• Mult-omics Palbociclib Resistance Study in HR+/HER2– Metastatic Breast Cancer

  This study is multi-omics study of a Asian longitudinal metastatic breast cancer (MBC) cohort treated with palbociclib plus endocrine therapy. It contains NGS of baseline (BL) and progressive disease (PD) from 70 patients, consisting of 79 tumor/normal matched whole exome sequencing (WES) from 62 patients and 90 tumor whole transcriptome sequecing samples (WTS) from 70 patients. There were 56 BL biopsies profiled by WES and 64 by WTS; 23 PD biopsies were profiled by WES and 26 by WTS. Twenty and 23 patients had paired BL and PD biopsies profiled by WES and WTS, respectively.

  Dataset EGAD00001008314

• Transcriptomic analyses of a large cohort of adult B cell acute lyphoblastic leukemia

  Total mononuclear cells (MNC) were isolated from peripheral blood (PB) or bone marrow (BM) samples of 105 Ph-/-/- (B-cell acute lymphoblastic leukemia [B-ALL] triple negative) and 31 Ph+ B-ALL adult patients using Lymphosep (Biowest, Nuaillé, France). A total of 15 samples from healthy subjects were processed including hematopoietic stem-progenitor cells (CD34+) from bone marrow specimens (n = 3), and bone marrow mononuclear cell samples (n = 3) from STEMCELL Technologies (Vancouver, Canada). PB MNC samples (n=5) and cord blood samples (n = 4). CD34+ cells were enriched from cord blood samples by immunomagnetic separation (CD34 MicroBead Kit, Miltenyi Biotec, Bergisch Gladbach, Germany).Libraries were prepared using the TruSight RNA Pan-Cancer Panel Kit (Illumina, San Diego, California, USA), following the manufacturer’s protocol. Sequencing was performed using the Illumina MiSeq instrument.

  Dataset EGAD00001010837

• Serum Proteome Profiling Identifies Early Markers of Therapeutic Response to Neoadjuvant Chemotherapy of Breast Cancer

  We performed a proteomic serum profiling of patients with non-metastasized breast cancer (BC) who received neoadjuvant chemotherapy (NACT). Samples were collected at three timepoints during NACT. Furthermore, we compared serum samples of BC patients pre-NACT to a control group of healthy volunteers.

  Dataset EGAD00010002330

• MethylationEPIC_850K

  This dataset includes IDAT files from 160 samples (57 primary prostate cancers, 95 proste-derived brain metastases, and 7 normal tissues). The samples were profiled using the Illumina Infinium MethylationEPIC BeadChips (850K)

  Dataset EGAD00010002372

• Digital tEchnology For Lung Cancer Treatment

  DEFLeCT metadata: 1) A table containing the mutational profile for all human genes across the cohort of the 81 PROMOLE lung cancer samples. 2) A table containing the expression levels for all human genes across the cohort of the 81 PROMOLE lung cancer samples. 3) A table containing the clinical, histology data and follow-ups of the cohort of 81 PROMOLE lung cancer patients.

  Dataset EGAD00001010838

• Processed bam files for the whole exome sequencing of primary lung adenocarcinoma samples

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes. The extensive single cell profiles depicted a complex cellular atlas and dynamics during lung adenocarcinoma progression which includes cancer, stromal, and immune cells in the surrounding tumor microenvironments.

  Dataset EGAD00001010320

• TRACERx Reduced-representation bisulfite sequencing (RRBS)

  RRBS data from TRACERx non-small cell lung cancer (NSCLC) tumours and matched normal adjacent tissue. TRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC).

  Dataset EGAD00001009707

• RNAseq of GEPARSIXTO

  294 formalin-fixed paraffin-embedded (FFPE) tissue samples were sent to the UNC Lineberger Comprehensive Cancer Center Translational Genomics Lab (TGL) for RNA isolation using the Maxwell 16 MDx Instrument (Promega AS3000) and the Maxwell 16 LEV RNA FFPE Kit (Promega AS1260) following the manufacturer’s protocol (Promega 9FB167). 279 total RNA sequencing libraries were prepared at TGL using a Bravo Automated Liquid-Handling Platform (Agilent G5562A) and the TruSeq Stranded Total RNA Library Prep Gold Kit (Illumina 20020599) following the manufacturer’s protocol (Illumina 1000000040499). RNAseq library quality and quantity were measured using a TapeStation 4200 (Agilent G2991AA) and Qubit 3.0 fluorometer (Life Technologies Q33216), pooled at equal molar ratios, and denatured following the manufacturer’s protocol (Illumina 1000000106351). 271 total RNA sequencing libraries were sequenced at TGL on NovaSeq 6000 (Illumina 20012850) S4 flow cells (Illumina 20028313) following the manufacturer’s protocol (Illumina 1000000019358) using a 2x50 bp paired-end configuration and pool sizes of 91 libraries to target a read depth of 110 million clusters per library on average.

  Dataset EGAD00001010201

• Sequence of breast cancer bone metastases PDX from 2 targeted panels

  Sequence of breast cancer bone metastases PDX obtained by 2 targeted panels

  Dataset EGAD00001010856

• Sequence of breast cancer bone metastases PDX from 2 targeted panels

  Sequence of breast cancer bone metastases PDX obtained by 2 targeted panels

  Dataset EGAD00001010860

• Molecular risk stratification in patients with T1 colorectal cancer_WES

  The introduction of bowel cancer screening has led to a significant increase in the proportion of patients being diagnosed with asymptomatic, early-stage colorectal cancer (CRC). Although the majority of these patients are successfully treated with surgery alone, a small proportion of patients have 'born-to-be-bad' aggressive lesions with early dissemination leading to distant metastases. Current standard of care histological assessment is unable to distinguish between these aggressive versus non-aggressive early lesions which is essential to provide appropriate clinical management decisions. This study aims to carry out molecular and histological profiling of approximately 300 T1 CRCs in order to develop a molecular stratifier based on the risk of relapse in early-invasive CRC. This novel T1 cohort will represent the world's largest molecularly characterised T1 cohort of samples, with digital pathology assessment alongside whole exome sequencing, copy number variation analysis and 3' RNA-seq. This data will be used to generate a robust panel of molecular and/or histological markers applicable to formalin-fixed paraffin embedded (FFPE) archival tissue which discriminates between T1 lesions based on risk of relapse, which will ultimately be used to inform clinical management of CRC at the earliest stages of the disease.

  Dataset EGAD00001010890

• Organoid Derivation Project - GRCh38 - RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011092

• Bone marrow single cell genomics from blood cancer samples

  Four different types of transcriptomic single-cell sequencing of blood from childhood B acute lymphoblastic leukemia.

  Dataset EGAD00001011055

• 47 urothelial cancer patients WES and 38 RNAseq

  We performed a global mutational landscape analysis using tumor samples from the 47 urothelial cancer patients included in MATCH-R or MOSCATO studies, with advanced metastatic disease and WES available (RNAseq is included for 38 patient samples).

  Dataset EGAD00001011063

• Spatially resolved antigen receptor and gene expression data from breast cancer patients

  The dataset includes spatially-resolved and single-cell antigen receptor, as well as gene expression, data from two different HER2+ breast cancer patients. The tumor piece obtained during surgery from each patient was divided into several regions and tissue sections were used for spatial transcriptomics (Visium, 10x genomics). As indicated, some tissue sections were analyzed by a new method (Spatial VDJ) to spatially resolve antigen receptor sequences (target capture), which was developed in our publication. In parallel, tissue pieces from the same tumor were dissociated for single-cell gene expression analysis (10x genomics GEX, VDJ, and feature barcoding/Hash Tag Oligonucleotide). The deposited data is in the form of fastq files. All processed data, metadata, micrographs of the tissue sections (of those used for spatial transcriptomics), and scripts used for the analysis are publicly available at Zenodo (DOI: 10.5281/zenodo.7961605). Final libraries were sequenced on NextSeq2000 (Illumina) or NovaSeq6000 (Illumina) and analyzed with Cell Ranger, Seurat, Space Ranger, and STutility pipelines.

  Dataset EGAD00001011061

• Clonal selection after gene therapy in sickle cell disease

  While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

  Dataset EGAD00001010913

• Novel method for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

  HGSOC patient-derived organoids and their tissue of origin

  Dataset EGAD00001008753

• Organoid Derivation Pilot: RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011091

• Organoid Derivation Project - GRCh38 - WGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011093

• Organoid Derivation Project: WGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011090

• Towards standardized whole exome sequencing (WES) for cancer patients: lessons from a multicentric pilot study

  ZPM WES Pilot consisting of 30 samples paired tumor/normal analyzed with WES at four different laboratories in Germany.

  Dataset EGAD00001011087

• Organoid Derivation Project - GRCh38 - TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011094

• Mechanism of action and resistance to Trastuzumab Deruxtecan in patients with metastatic breast cancer: the DAISY trial

  195 exome sequencing samples

  Dataset EGAD00001011110

• Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Evaluation of somatic mutations in cervicovaginal samples as a non-invasive method for the detection and molecular classification of endometrial cancer

  Dataset EGAD00001011123

• Gene expression profile of mesothelial-derived carcinoma-associated fibroblasts

  RNA-sequencing was carried out on ascetic fluid-isolated mesothelial cells from low-grade serous ovarian cancer patients, high-grade serous ovarian cancer patients, chemotherapy-treated high-grade serous ovarian cancer patients and control mesothelial cells obtained from non-oncologic patients to identify differentially expressed genes associated to mesothelial-to-mesenchymal transition process. The dataset contains 18 samples: - Control mesothelial cells: 4 samples - Group 1, high-grade serous ovarian cancer patients: 3 samples - Group 2, chemotherapy-treated high-grade serous ovarian cancer patients: 5 samples - Group 3, low-grade serous ovarian cancer patients: 6 samples

  Dataset EGAD00001005133

• WGS short read and 10X linked read sequencing of HR Deficient breast cancers

  A set of tumor/normal paired sequencing experiments, performed in short read WGS and 10X linked read whole genomes. BAM files aligned to hg19 are provided. Sample Alias number and Subject ID reflects patient of origin. T/N distinction discriminates between tumor and normal (peripheral blood) tissue.

  Dataset EGAD00001010326

• Total RNA sequencing data from the Triple Negative Trial (TNT)

  BAM files from total RNA sequencing of samples from breast cancer patients in the TNT trial. Data includes 186 primary tumour samples and 13 matched recurrence samples.

  Dataset EGAD00001011141

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_001 for Triple negative breast cancer patient-derived xenograft SA535X9XB03616

  Dataset EGAD00001011233

• Spectrum of Response to Platinum and PARP Inhibitors in Germline BRCA Associated Pancreatic Cancer in the Clinical and Pre-clinical Setting

  Whole genome sequencing data for germline BRCA pancreatic cancer

  Dataset EGAD00001011129

• Cell-free DNA sequencing data of healthy control, atrophic gastritis, and gastric cancer patients’ blood

  Sample cohort (n=48) is consisted of healthy, atrophic gastritis and gastric cancer patients. Some of the gastric cancer patients samples are collected at the separate time points: -1 before the operation; -2 after the operation; -3 during the control visit. For the hybridisation capture of the genes unique 15 gastric cancer-related gene panel was developed and very deep sequencing using TruSight Oncology Unique Molecular Identifier (UMI) Reagents (Illumina) was used.

  Dataset EGAD00001011153

• RNASeq data from one small cell prostate cancer patient (4 samples from 3 time points)

  RNASeq data from one small cell prostate cancer patient - 4 cancer samples (FFPE) from Patient 1 collected at 3 different time points. mRNA was selected using the Magnetic mRNA Isolation Module (NEB) and sequenced on the Illumina NextSeq 500 as 75bp paired end reads (total read length 150bp).

  Dataset EGAD00001011154

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03304

  Dataset EGAD00001011225

• Colorectal cancer organoid-stroma biobank cohort

  In colorectal cancers (CRC) the tumor microenvironment plays a key role for prognosis and therapy efficacy. Patient-derived tumor organoids (PDTOs) show enormous potential for preclinical testing, however, purely epithelial cultures features including the ‘consensus molecular subtypes’ (CMS) are largely eradicated. To better reflect the cell type heterogeneity, we established the CRC organoid-stroma biobank of matched PDTOs and cancer-associated fibroblasts (CAFs) from 30 patients. Whole exome sequencing and transcriptome analysis in various in vitro and in vivo contexts was performed to study the influence of the TME on the CRC phenotype.

  Dataset EGAD00001011173

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - Ribo-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011193

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226

• Sequencing data for oesophageal and related samples - Mourikis et al (WGS)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. WGS (BAM files) 521 samples

  Dataset EGAD00001004775

• Sequencing data for oesophageal and related samples - Mourikis et al (RNA)

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. RNAseq (BAM files) 137 samples

  Dataset EGAD00001004776

• Sequencing data for ICGC / OCCAMS samples - Perner et al (WGS, sWGS, WES, mutREAD)

  Data supporting: "The mutREAD method detects mutational signatures from low quantities of cancer DNA." Perner et al. WGS, sWGS, WES, and reduced representation sequencing data tumour and normal samples BAM files

  Dataset EGAD00001006170

• Sequencing data for oesophageal and related samples - Katz-Summercorn, Jammula et al (RNA)

  Data supporting: “Multi-omic cross-sectional cohort study of pre-malignant Barrett’s esophagus reveals structural variation and retrotransposon activity occur early in cancer evolution.” Katz-Summercorn, Jammula et al. RNAseq (BAM files)

  Dataset EGAD00001006353

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_001 for Triple negative breast cancer patient-derived xenograft SA535X6XB03101

  Dataset EGAD00001011224

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0195_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0195_001 for Triple negative breast cancer patient-derived xenograft SA535X8XB03664

  Dataset EGAD00001011234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0208_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0208_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03776

  Dataset EGAD00001011237

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_002 for Triple negative breast cancer patient-derived xenograft SA1035X6XB03211

  Dataset EGAD00001011242

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_004 for Triple negative breast cancer sample SA1035X6XB03209

  Dataset EGAD00001011243

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_001 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03338

  Dataset EGAD00001011244

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_002 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03340

  Dataset EGAD00001011245

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_002 for Triple negative breast cancer sample SA1035X7XB03502

  Dataset EGAD00001011246

• Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial)

  Sequencing data from a phase II study of nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary (CheCUP trial). Panel sequencing data from baseline FFPE biopsies were used to perform a comprehensive genomic profiling of CUP metastases. Combined targeted next-generation sequencing of patient-specific hotsport mutations and shallow whole genome sequencing of baseline and follow-up liquid biopsy samples were used to analyze ctDNA and to evaluate response to immunei checkpoint inhibitor treatment. In some cases, whole exome sequencing of peripheral blood mononuclear cells was performed to screen for potential CHIP and germline mutations.

  Dataset EGAD00001011130

• RNAseq of primary mesothelioma cell lines

  Mesothelioma is an aggressive cancer associated with previous exposure to asbestos and dismal prognosis. Since a pemetrexed/cisplatin combination was introduced for treatment of mesothelioma, no new first- or second-line therapies have been discovered. Thus, to better understand what drives mesothelioma carcinogenesis and to identify potential targets for therapy, in this project we aim at performing RNAseq analysis of a panel of mesothelioma cells lines.

  Dataset EGAD00001009642

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_001 for Triple negative breast cancer sample SA1035X8XB03420

  Dataset EGAD00001011248

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0019_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0019_001 for Triple negative breast cancer patient-derived xenograft SA604X7XB02089

  Dataset EGAD00001011253

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• Recurrent DNMT3B gene rearrangements are associated with unfavorable outcome in dicentric (9;20)-positive pediatric BCP-ALL

  We provide clinical data sets of Array CGH, targeted RNA-seq, total RNA-seq, whole genome bisulfite (WGBS) and whole genome DNA sequencing (WGS) obtained from bone marrow (BM) or peripheral blood (PB) mononuclear cells of 57 pediatric patients with dicentric chromosome dic(9;20) positive Acute lymphocytic leukemia (ALL), from which in 6 cases DNMT3B gene rearrangement was identified. This data is complemented by total RNA-seq and WGBS of samples from 4 additional ALL patients with a t(12;21) translocation and ETV6-RUNX1 gene fusion. DNA was isolated from BM or PB B-lymphocytes using Qiagen QIAamp DNA Blood Midi Kit to perform i) Array CGH of 58 dic(9;20) positive samples by hybridizing 500ng DNA using a Agilent 400K SurePrint G3 Custom CGH Human Genome Microarray (e-Array design 84704) ii) WGBS of 6 DNMT3B rearrangement positive samples and 4 ETV6-RUNX1 positive samples using Tecan TrueMethyl oxBS-Seq module for library preparation and Illumina NovaSeq 6000 platform to run 2x151 cycles iii) WGS of DNMT3B rearrangement positive samples using Illumina Lotus DNA Library Prep Kit followed by sequencing running 2x160 cycles on an Illumina NovaSeq 6000 platform. RNA was isolated from PB B-lymphocytes using the PerkinElmer Chemagic 360 instrument, followed by i) targeted RNA-seq of 56 dic(9;20) positive samples prepared using Illumina TruSight RNA Pan-Cancer Panel and sequenced on an Illumina MiSeq platform running 2x75 cycles ii) total RNA-seq of 6 DNMT3B rearrangement positive samples and 4 ETV6-RUNX1 positive samples utilizing TruSeq Stranded Total RNA Library Prep Gold kit and running 2x100 cycles on an Illumina NovaSeq 6000 platform.

  Dataset EGAD00001011122

• 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

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• Sequencing data for oesophageal and related samples - OACs 496 release (WGS)

  Data from 496 OCCAMS (Oesophageal Cancer Clinical And Molecular Stratification) cases. WGS BAM files 496x oesophageal adenocarcinoma samples 496x normal samples

  Dataset EGAD00001007785

• WGS of a Li-Fraumeni patient's HSPCs

  WGS data of clonally expanded HSPCs from a Li-Fraumeni patient at the time of second cancer (Burkitt lymphoma and <5% t-MN) after primary osteosarcoma diagnosis and a reference MSC bulk.

  Dataset EGAD00001011257

• 10x Single Cell Gene Expression Library TENX063

  10x Single Cell Gene Expression library TENX063 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011268

• RCRF release 1

  Transcriptome sequencing from the Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendocrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010198

• 10x Single Cell Gene Expression Library TENX068

  10x Single Cell Gene Expression library TENX068 for Triple negative breast cancer patient-derived xenograft SA609X4XB03080

  Dataset EGAD00001011210

• 10x Single Cell Gene Expression Library TENX069

  10x Single Cell Gene Expression library TENX069 for Triple negative breast cancer patient-derived xenograft SA609X4XB03083

  Dataset EGAD00001011211

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0150_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0150_002 for Triple negative breast cancer sample SA609X5XB03231

  Dataset EGAD00001011213

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_002 for Triple negative breast cancer patient-derived xenograft SA609X5XB03223

  Dataset EGAD00001011214

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_001 for Triple negative breast cancer patient-derived xenograft SA609X6XB03401

  Dataset EGAD00001011215

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 9 part 1)

  Whole genome sequencing data of 6 high-grade serous carcinoma (HGSC) patients (11 samples) sequenced with MGISEQ-2000

  Dataset EGAD00001011353

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0152_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0152_002 for Triple negative breast cancer sample SA609X6XB03404

  Dataset EGAD00001011216

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0172_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0172_002 for Triple negative breast cancer sample SA609X7XB03554

  Dataset EGAD00001011220

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• Germline genome sequencing samples from the Hereditary Cancer Syndromes (ICCon) Cancer Flagship

  A prospective study of individuals with suspicion of a hereditary cancer syndrome for whom previous clinical targeted genetic testing was either not informative or was not available. To identify pathogenic disease-causing variants explaining participant presentation, germline whole-genome sequencing and a comprehensive cancer virtual gene panel analysis were undertaken.

  Dataset EGAD00001011260

• Comparison of single/pooled CTCs and bulk exome sequencing

  Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

  Dataset EGAD00001009052

• Exome sequencing data from two small cell prostate cancer patients

  Exome sequencing data from two small cell prostate cancer patients - 4 cancer samples (FFPE) from Patient 1 collected at 3 different time points and 2 cancer samples (FFPE) from Patient 2 collected at 1 time point. Exonic DNA was enriched using the TruSeq Exome Kit (Illumina) and sequenced on the Illumina NextSeq 500 as 75bp paired end reads (total read length 150bp).

  Dataset EGAD00001011149

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8a)

  Whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients (87 samples) sequenced with MGISEQ-2000 and HiSeq X Ten.

  Dataset EGAD00001011304

• ScRNA-seq of 6 human FL

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Dataset EGAD00001007691

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 7a)

  Whole genome sequencing data of 36 high-grade serous carcinoma (HGSC) patients (89 samples) sequenced with HiSeq X Ten.

  Dataset EGAD00001010845

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 7b)

  Whole genome sequencing data of 21 high-grade serous carcinoma (HGSC) patients (59 samples) sequenced with MGISEQ-2000.

  Dataset EGAD00001011086

• Translation of non-canonical open reading frames as a cancer cell survival mechanism in childhood medulloblastoma - RNA-Seq

  This study aims to investigate the dysregulation of RNA translation and identify functional non-canonical open reading frames (ORFs) as potential targets for medulloblastoma treatment. The study involves ribosome profiling and RNAseq of medulloblastoma tissues and cell lines to observe the translation of non-canonical ORFs. Multiple CRISPR-Cas9 screens will be used to identify functional non-canonical ORFs implicated in medulloblastoma cell survival.

  Dataset EGAD00001011192

• Genotype data for African Esophageal squamous cell cancer cases and controls from South Africa

  This dataset contains the cleaned genotype data from 2173 African eosphageal squamous cell cancer cases and population controls. The genotype data was generated using the H3Africa Illumina Custom microarray.

  Dataset EGAD00010002575

• November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001007529

• 252 human breast cancer samples in WGS and WES

  We analyzed chromothripsis in 252 human breast cancers from two patient cohorts (149 metastatic breast cancers, 63 untreated primary tumors, 29 local relapses, 11 longitudinal pairs) using whole-genome and whole-exome (paired) sequencing. A lot of the WGS samples were sequenced on Illumina HiSeq X-Ten using Illumina TruSeq Nano DNA. For exome sequencing Agilent_SureSelect_V5+UTRs has been used (sequencing on Hiseq2000, Hiseq2500 and Hiseq4000). For exome sequencing Agilent_SureSelect_V5+UTRs has been used (sequencing on Hiseq2000, Hiseq2500 and Hiseq4000).

  Dataset EGAD00001007563

• Evolutionary trajectories and clonal migration underlying tumor progression and lymph node metastasis in resectable lung cancer

  In this prospective study, targeted deep sequencing was performed on a total of 160 primary tumors (474 regions) and 112 lymph nodes from 125 patients with stage I-III lung cancer (LuCaTH). Progressive evolution at clonal divergence scale was observed while specific driver events were positively selected for clonal sweeps during tumor development. Between-region genetic divergence (BRGD) of tumors were assessed and positively correlated with tumor differentiation. A machine learning algorithm was employed to evaluate clinicopathological and molecular parameters of primary tumors underlying lymph node metastasis. By analyzing clonal lineages and metastatic trajectories across multiple nodal stations, we unraveled a common sequential LNM seeding pattern but with divergent modes of clonal spread.

  Dataset EGAD00001007587

• RBWES

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Whole-Exome-Sequencing.

  Dataset EGAD00001007591