14970 results for "cancer studies using rna-seq"

in 34.64 milliseconds.

• Normal sample for patient SA287

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA287

  Dataset EGAD00001009228

• Tumour sample for patient SA682

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009349

• Tumour sample for patient SA681

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009348

• Tumour sample for patient SA680

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009347

• Tumour sample for patient SA294

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA294

  Dataset EGAD00001009542

• Normal sample for patient SA683

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA683

  Dataset EGAD00001009383

• Normal sample for patient SA289

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009367

• Normal sample for patient SA682

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA682

  Dataset EGAD00001009382

• Tumour sample for patient SA228

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA228

  Dataset EGAD00001009533

• Tumour sample for patient SA224

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA224

  Dataset EGAD00001009530

• Tumour sample for patient SA1058

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009673

• Tumour sample for patient SA409

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA409

  Dataset EGAD00001009518

• Tumour sample for patient SA420

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA420

  Dataset EGAD00001009519

• Tumour sample for patient SA296

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA296

  Dataset EGAD00001009520

• Tumour sample for patient SA597

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA597

  Dataset EGAD00001009521

• Normal sample for patient SA211

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA211

  Dataset EGAD00001009545

• Normal sample for patient SA229

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA229

  Dataset EGAD00001009552

• Normal sample for patient SA1073

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1073

  Dataset EGAD00001009594

• Tumour sample for patient SA289

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA289

  Dataset EGAD00001009359

• Tumour sample for patient SA291

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009360

• Tumour sample for patient SA280

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA280

  Dataset EGAD00001009361

• Tumour sample for patient SA286

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA286

  Dataset EGAD00001009358

• Normal sample for patient SA673

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA673

  Dataset EGAD00001009373

• Normal sample for patient SA678

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA678

  Dataset EGAD00001009378

• Normal sample for patient SA679

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA679

  Dataset EGAD00001009379

• Normal sample for patient SA680

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA680

  Dataset EGAD00001009380

• Normal sample for patient SA681

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA681

  Dataset EGAD00001009381

• Normal sample for patient SA233

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA233

  Dataset EGAD00001009556

• 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_CHIP0142_004

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

  Dataset EGAD00001011243

• Tumour sample for patient SA1026

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA1026

  Dataset EGAD00001009607

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015309

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015316

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015332

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015337

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015338

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015264

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015265

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015267

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015270

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015272

• Normal sample for patient SA533

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA533

  Dataset EGAD00001009571

• Normal sample for patient SA597

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA597

  Dataset EGAD00001009572

• Normal sample for patient SA997

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA997

  Dataset EGAD00001009573

• Normal sample for patient SA1027

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1027

  Dataset EGAD00001009588

• Normal sample for patient SA1028

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1028

  Dataset EGAD00001009589

• Normal sample for patient SA1040

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1040

  Dataset EGAD00001009590

• Normal sample for patient SA1058

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA1058

  Dataset EGAD00001009672

• Genome and transcriptome sequence data from a craniopharyngioma tumor patient

  Genome and transcriptome sequence data from a craniopharyngioma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015273

• Genome and transcriptome sequence data from a neuroblastoma tumor patient

  Genome and transcriptome sequence data from a neuroblastoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015277

• Genome and transcriptome sequence data from a osteosarcoma tumor patient

  Genome and transcriptome sequence data from a osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015278

• Genome and transcriptome sequence data from a angiosarcoma tumor patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015290

• Kidney Cancer Heterogeneity and Evolution Revealed by Multi-Region Exome Sequencing

  Paired end Illumina sequencing of whole exomes of multiple tumour regions.

  Dataset EGAD00001000734

• Solid_WXS_T

  Whole-exome sequencing on AB 5500xl Genetic Analyzer of colorectal cancer primary tumor sample

  Dataset EGAD00001002107

• Lung cancers from admixed Latin American populations (2)

  Raw data from cancer panel sequencing of lung adenocarcinomas from admixed Latin American populations, predominantly samples without known oncogene mutations (n=532).

  Dataset EGAD00001006571

• Acute Respiratory Distress Network (ARDSNet) Studies 01 and 03 Lower Versus Higher Tidal Volume, Ketoconazole Treatment and Lisofylline Treatment (ARMA/KARMA/LARMA) (ARDSNet-ARMA/KARMA/LARMA-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Biospecimens Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-ARMA/KARMA/LARMA include plasma, serum and urine. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives The ARDS Network is a consortium of clinical centers and a coordinating center to design and test novel therapies for the treatment of Acute Lung Injury (ALI) and Acute Respiratory Distress Syndrome (ARDS). The primary objective of the KARMA trial was to investigate the efficacy and safety of Ketoconazole and Respiratory Management in the treatment of ALI and ARDS. The Ketoconazole arm of the study was later stopped due to an inability to show efficacy. Participants continued to be randomized to the respiratory management arms of the study (ARMA), which compared two ventilator strategies: a tidal volume of 6 mL/kg versus 12 mL/kg. The LARMA phase of the study investigated the efficacy of Lisofylline and Respiratory Management. Background Participants suffering from ARDS are extremely ill, require mechanical ventilation and, despite improvements in medical care and technology, had a mortality rate as high as 50 percent. An excessive inflammatory response is characteristic of ALI of which ARDS represents the most severe end of the pathophysiologic spectrum. The inflammatory response includes increased numbers of neutrophils activated to produce cytokines, proteases, and reactive oxygen intermediates. Pulmonary injury may also be enhanced by alveolar and tissue macrophages as a producer of vasoactive substances, neutrophil chemoattractants, and procoagulant substances. Ketoconazole, a synthetic antifungal imidazole, also has anti-inflammatory activities and may inhibit neutrophil recruitment via several different pathways known to be involved in the development of ALI and ARDS. Lisofylline causes a marked decrease in the circulating levels of the major oxidizable species of free fatty acids and also inhibits proinflammatory intracellular signaling. Mechanical ventilation in participants with ALI and ARDS have traditionally used tidal volumes of 10 to 15 ml per kilogram of body weight. These large tidal volumes are often necessary to achieve normal partial pressure of arterial carbon dioxide and pH, but may induce inflammatory responses through disruption of pulmonary epithelium and endothelium. Mechanical ventilation at lower tidal volumes may reduce injurious lung stretch and decrease the inflammatory response. Participants The Ketoconazole and Lisofylline trials were designed as 2 x 2 factorials and included 220 participants in each trial. A total of 860 participants were randomized into the ventilator management trial. Participants enrolled in the Lisofylline or Ketoconozole studies had to be concurrently enrolled in the ventilator management study and were first randomized into a ventilator strategy and then to drug or placebo. Conclusions Ketoconazole was found to be safe but did not reduce mortality, duration of mechanical ventilation, or improve lung function. Lisofylline was also found to be safe and to have no beneficial effect for participants with ALI or ARDS. Ventilation at lower tidal volumes resulted in reduced mortality and an increase in the number of days without ventilator support. (PMIDs: 10789668, 11902249, 10793162).

  Study phs003734

• Targeted DNA sequencing and mRNA sequencing data from patients with peritoneal metastasis from colorectal cancer

  Patients with peritoneal metastasis from colorectal cancer (PM-CRC) have inferior prognosis and respond particularly poorly to chemotherapy. This study aims to identify the molecular explanation for the observed clinical behavior and suggest novel treatment strategies in PM-CRC. Tumor samples (230) from a Norwegian national cohort undergoing surgery and hyperthermic intraperitoneal chemotherapy (HIPEC) with mitomycin C (MMC) for PM-CRC were subjected to targeted DNA sequencing, and associations with clinical data were analyzed. mRNA sequencing was conducted on a subset of 30 samples to compare gene expression in tumors harboring BRAF or KRAS mutations and wild-type tumors.

  Dataset EGAD50000000593

• GeoMx DSP of NGS mRNA expression in pre-treatment biopsies from patients with metastatic triple-negative breast cancer treated with PARP inhibitors.

  This dataset contains GeoMx digital spatial profiling (DSP) data from 12 FFPE pre-treatment biopsies of metastatic HR-altered breast cancer patients subsequently treated with PARP inhibitors (Olaparib or Talazoparib). Samples were collected from primary breast tumors (n=7) and metastatic sites including lymph nodes (n=2), bone (n=2), and brain (n=1). Tissue sections were stained for pan-cytokeratin (PANCK) to selectively profile tumor cells, with NGS-based transcriptome data generated for both PANCK+ and PANCK- regions. The dataset includes raw data files (176 fastq files) along with clinical annotations.

  Dataset EGAD50000001932

• Identification of New Therapeutic Targets for Renal Medullary Carcinoma via Integrated Genomic and Transcriptomic Profiling

  Renal medullary carcinoma (RMC) is a rare, aggressive kidney cancer associated with sickle cell trait, characterized by SMARCB1 loss and resistance to conventional therapies used for other renal cell carcinomas (RCC). We conducted whole exome sequencing (WES) on RMC samples. This study provides a large molecular characterization of RMC, highlighting TROP2 and other cell surface markers as promising therapeutic targets.

  Study EGAS50000001280

• Myelodysplastic_Syndrome_Exome_Sequnecing

  Cancer is driven by mutation. Illumina GA massively parallel sequencing technology will be applied to Agilent exome hybridisation capture libraries and total genomic paired-end libraries. Bespoke algorithms are being developed to identify the somatically acquired structural variants, point mutations, insertions and deletions in these samples. This project will give unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with Myelodysplastic Syndrome development.

  Study EGAS00001000089

• Diverse modes of genomic alterations in hepatocellular carcinoma

  Hepatocellular carcinoma is the most prevalent type of liver cancer. We intended to study the genomic landscape of this disease. We conducted whole-genome, exome and transcriptome sequencing for 42 patients. We show diverse modes in which the HCC genome is affected in these patients, including well-known and novel mutations, structural variations, and Hepatitis B-virus integration into the genome.

  Study EGAS00001000824

• Reconstruction of complex rearrangement patterns causing the initiation of clear cell renal cell carcinoma.

  The genomic hallmark of clear cell renal cell carcinoma is the loss of the short arm of chromosome three. This appears to be the earliest genomic event in the formation of these cancers. Often chromosome 3 is lost at the same time as part of chromosome 5 is duplicated via an unbalanced translocation, often with features consistent with focal chromothripsis. In this study, we sought to reconstruct the chromothriptic event that underlies the initiation of kidney cancer. We used long read sequencing (promethION, Oxford Nanopore Technologies) of patient tumour-derived DNA to elucidate how a single cell division error can generate cancer genome complexity.

  Dataset EGAD00001005495

• sWGS on cfDNA and matching tumor DNA in pediatric cancer

  This dataset contains shallow whole genome sequencing data from paired cfDNA - tumor DNA samples of various pediatric cancer entities (total 215 samples). Files are provided in fastq format. Samples were sequenced on an Ion Proton sequencer (Thermo Fisher Scientific) or a Hiseq3000 (Illumina). Data analysis is available at https://github.com/rmvpaeme/sWGS_pediatric_cancer.

  Dataset EGAD00001007508

• Normal prostatectomy project analysis and leftovers

  Data collected as part of the Normal prostatectomy project analysis. Whole genome sequencing (WGS, targeted at 30X for normal tissue and 50X for tumour tissue) was performed on morphologically normal tissue samples from 30 patients with prostate cancer. In addition, seven prostate tissue samples were sequenced from 7 non-cancer patients: two collected after a cystoprostatectomy and five from samples collected at autopsy. Matched blood controls were included for all patients. An extra five samples were sequenced from the stroma of cell cultured fibroblasts. In addition a few tumour samples obtained at prostatectomy and their blood matched controls are included in this dataset from the main study that are not included elsewhere.

  Dataset EGAD00001004125

• ICR96 exon CNV validation series

  A resource for assessment of exon CNV calling methods in targeted NGS data, we here present the ICR96 exon CNV validation series. The dataset includes high-quality sequencing data from a targeted NGS assay (the TruSight Cancer Panel) together with Multiplex Ligation-dependent Probe Amplification (MLPA) results for 96 independent samples. 66 samples contain at least one validated exon CNV and 30 samples have validated negative results for exon CNVs in 26 genes. The dataset includes 46 exon CNVs in BRCA1, BRCA2, TP53, MLH1, MSH2, MSH6, PMS2, EPCAM and PTEN, giving excellent representation of the cancer predisposition genes most frequently tested in clinical practice. Moreover, the validated exon CNVs include 25 single exon CNVs the most difficult exon CNV to detect.

  Dataset EGAD00001003335

• Whole-exome sequencing performed on a patient with chronic myelomonocytic leukemia and B cell acute lymphoblastic leukemia

  We studied the rare case of a patient who underwent transformation of myelodysplastic syndrome to chronic myelomonocytic leukemia and B cell acute lymphoblastic leukemia. We used fluorescence-activated cell sorting and whole-exome sequencing to identify driver mutations in the various clones. This allowed us to propose a model of branching clonal evolution with a possible germline predisposition to cancer.

  Study EGAS00001005117

• Whole-exome sequencing of Fanconi anemia-like inherited bone marrow failure syndrome

  We identified three fibroblast cultures derived from aplastic anemia patients in the Japanese Cancer Research Resources Bank (JCRB) Cell Bank that showed high levels of sister chromatid exchanges (SCEs) in PHA-stimulated lymphoblasts. To elucidate the genetic basis of the high SCE, we carried out whole exome sequencing of these samples. Subsequently, germline variants were analysed for these patients.

  Study EGAS00001003809

• Clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint.

  Study EGAS00001004445

• Developing somatic copy number and mutation calling tools for a bespoke sequencing platform

  We previously developed a linked-read sequencing platform (DigiPico) for analysing whole cancer genomes from picogram quantities of tumour DNA. The aim of this study was to develop and evaluate the specialised computational approaches required to obtain clinically useful results from the raw data. To this end, we performed matched DigiPico, bulk tumour and germline WGS on samples from four patients.

  Study EGAS00001007195

• 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

• WES and RNAseq of Simultaneous Bilateral Breast Cancer

  This dataset includes NGS profiling of 13 women with simultaneous bilateral breast cancer. Seven women have WES of untreated surgical resections and matched healthy tissue. The six other women have WES of healthy tissue, WES+RNAseq of pre-neoadjuvant tumor biopsies, and when residual disease was present (6 tumors in 4 patients), WES+RNAseq of residual disease from post-neoadjuvant therapy surgery. One patient from the WES+RNAseq cohort had multifocal bilateral disease at diagnosis so there are 2 pre-neoadjuvant biopsy samples from each breast.

  Dataset EGAD00001009987

• EPICC: Evolutionary Predictions in Colorectal Cancer

  661 bam files generated from high-throughput RNAseq of tumour biopsies from colorectal cancer patients

  Dataset EGAD00001007828

• Transcriptome and epigenome characterization of BMP signaling effects on H3.3K27M DIPG

  We profiled transcriptome and epigenome of BMP signaling effects on H3.3K27M DIPG.

  Dataset EGAD00001007768

• RNA Transcriptomic and DNA Methylation Landscape in FTLD-TDP and Controls

  Frontotemporal lobar degeneration (FTLD) is one of the leading causes of dementia in individuals younger than 65 years, with the aggregation of TDP-43 as one of the most common forms of FTLD. FTLD-TDP is clinically, genetically and pathologically heterogeneous, with GRN and C9orf72 as the most common genetic forms (although more than 50% of the cases are genetically unexplained), and A, B and C as the most common pathological subtypes. To investigate the molecular differences between the different forms of FTLD-TDP, we performed bulk RNA sequencing (RNAseq) and reduced representation bisulfite sequencing (RRBS) from different brain regions of FTLD-TDP patients and controls. The RNAseq dataset includes samples from the frontal cortex of 149 individuals, comprising 127 FTLD-TDP cases (genetically unexplained types A (27), B (20) and C (22), 24 GRN mutation carriers, and 34 C9orf72 expansion carriers) and 22 controls. The RRBS dataset includes samples from the frontal cortex of 158 individuals —119 FTLD-TDP cases (genetically unexplained types A (25), B (24), and C (25), 20 GRN mutation carriers, and 25 C9orf72 expansion carriers) and 39 controls; and from the cerebellum of 151 individuals —113 FTLD-TDP cases (genetically unexplained types A (24), B (21), and C (23), 20 GRN mutation carriers, and 25 C9orf72 expansion carriers) and 38 controls. In total, the study consists of data from 183 unique individuals, with some overlap across the different datasets.

  Study phs004075

• Single Cell Genome Variation Induced by Mutational Processes in Cancer - HGSOC Trios Study

  Mutational signatures derived from whole genome sequencing have potential prognostic significance. Identification of high grade serous ovarian cancer patients with deficiency in homologous recombination repair pathways could identify a subset of patients that would benefit from homologous recombination deficiency-specific treatments independent of identification of a pathological BRCA1/2 gene mutation. Clinical protocols rely on a pathologist's assessment of formalin-fixed paraffin-embedded (FFPE) tissues, and thus FFPE tissue samples are readily available. However, the bulk of mutational signature analyses have been performed on fresh frozen tissues. In this study, we aimed to assess the reliability of whole genome sequencing, variant calling, and mutational signature analysis from FFPE tissue samples.

  Study phs003036

• Sequence Analysis of Mutations and Translocations Across Breast Cancer Subtypes

  This study combined whole exome (103 samples) and whole genome (22 samples) sequencing over a total of 108 breast tumors and matched normal DNA to identify novel mutations and translocations. Samples were subjected to paired-end Illumina sequencing with goal of 30x coverage of tumor/normal for whole genomes and 100x tumor/normal coverage for whole exomes. From these sequences, we used various computational techniques to identify somatic point mutations, insertion/deletions and structural rearrangements in these tumors. From these data, we identified new insights into the rates of background mutations in these cancers, novel recurrent mutated genes, and multiple gene rearrangements. One of these rearrangements appears to be a recurrent event in breast cancer.

  Study phs000369

• A Combined Omics and Tissue Biobank for Paediatric Cancers

  The Swedish Childhood Tumour Biobank (BTB) is a nonprofit national infrastructure for samples and genomic data collection from pediatric patients diagnosed with central nervous system and other solid tumours in Sweden. Fresh frozen tumour tissue and patient’s matched blood are collected from affected patients and genomic data is continuously generated and made available for approved medical research projects. We expect that the accessibility of biological samples and/or data to the research community will contribute to further advances in the understanding of tumour biology, which in turn will impact many aspects of clinical care of children with cancer, by refining diagnosis and/or identifying oncogenic drivers and therapeutic targets.

  Study EGAS50000000209

• Insights into BRCA1 and TP53 associated breast cancer development from integrated whole genome analysis of mouse model mammary tumors

  Germline mutations in BRCA1 are a leading cause of hereditary breast cancer. We generated Brca1;Trp53 double conditional knockout (CKO) and Trp53 single CKO mouse models, both with either one or two copies of Trp53 deleted by Cre. We used whole-genome sequencing and optical genome mapping to interrogate the genomes of the resulting mammary tumors. Our comprehensive genomic analyses show that the Brca1;Trp53-CKO tumors recapitulate major genomic features of the human BRCA1 breast samples indicative of conserved fundamental mutational processes. This study also provides new insights into the genetic mechanisms of tumor development with potential implications for the treatment of BRCA1 cancers.

  Study EGAS50000001402

• Elevated somatic mutation burdens in normal human cells due to defective DNA polymerases

  Mutation accumulation over time in normal somatic cells contributes to cancer development and is proposed as a cause of ageing. DNA polymerases POLE and POLD1 replicate DNA with high fidelity during normal cell divisions. However, in some cancers defective proofreading due to acquired mutations in the exonuclease domains of POLE or POLD1 causes markedly elevated somatic mutation burdens with distinctive mutational signatures. POLE and POLD1 exonuclease domain mutations also cause familial cancer predisposition when inherited through the germline. Here, we sequenced normal tissue DNA from individuals with germline POLE or POLD1 exonuclease domain mutations. Increased mutation burdens with characteristic mutational signatures were found to varying extents in all normal adult somatic cell types examined, during early embryogenesis and in sperm. Mutation burdens were further markedly elevated in neoplasms from these individuals. Thus human physiology is able to tolerate ubiquitously elevated mutation burdens. Indeed, with the exception of early onset cancer, individuals with germline POLE and POLD1 exonuclease domain mutations are not reported to show abnormal phenotypic features, including those of premature ageing. The results, therefore, do not support a simple model in which all features of ageing are attributable to widespread cell malfunction directly resulting from somatic mutation burdens accrued during life.

  Dataset EGAD00001006212

• The 5-hydroxymethylcytosine Landscape of Prostate Cancer

  This dataset consists of genome-wide 5hmC methylomes at various stages of prostate cancer, including not only 93 metastases from castration-resistant prostate cancer (mCRPC) patients, but also 5hmC patterns in cell-free DNA (cfDNA). There are 2000 runs in total as fastq files.

  Dataset EGAD00001008462

• EPICC: Evolutionary Predictions in Colorectal Cancer

  ATAC-seq profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008140

• Methylated DNA immunoprecipitation sequencing of untreated and castration resistant prostate cancer

  Methylated DNA immunoprecipitation sequencing of 28 untreated prostate cancers, 11 castration resistant prostate cancers, and 12 benign prostatic hyperplasias.

  Dataset EGAD00001000610

• Colorectal cancer methylation profiling

  4 AC samples, each with adenoma, carcinoma and normal colon tissue (12 samples in total) were analysed on the Infinium MethylationEPIC BeadChip for copy number alteration analyses.

  Dataset EGAD00010001691

• WES bam files associated with HNSCC patients in the LIONESS study

  This dataset contains 85 WES bam/bai files of HNSCC cancer patients as part of with the LIONESS study

  Dataset EGAD50000001487

• Myeloma_Follow_up_Pilot

  Targeted capture of exonic and intronic regions of interest for the study of genomic alterations in multiple myeloma.

  Study EGAS00001000743

• Illumina_WXS_T-CELL-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002101

• Illumina_RNA_T-XEN_Validation_Cohort

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer sample of a validation cohort of 60 PDX

  Dataset EGAD00001002883

• Illumina_WXS_T-XEN-CELL

  Whole-exome sequencing on Illumina HiSeq2000/2500 of PDO culture derived from Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002103

• Illumina_RNA_MET-CELL-GFP

  RNAseq on Illumina HiSeq2000/2500 of WNT reporter of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002074

• OT2_Illumina_RNA_MET_XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003366

• Illumina_RNA_T-XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer primary tumor sample

  Dataset EGAD00001002080

• Whole transcriptome sequencing of untreated and castration resistant prostate cancer

  Whole transcriptome sequencing of 28 untreated prostate cancers, 13 castration resistant prostate cancers, and 12 benign prostatic hyperplasias.

  Dataset EGAD00001000609

• Illumina_WXS_MET-XEN

  Whole-exome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002098

• OT2_Illumina_WGS_MET_XEN

  Whole-genome sequencing on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from colorectal cancer metastasis sample (OT2_cohort)

  Dataset EGAD00001003374

• Angiopredict Whole genome Shallow Sequencing

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

  Dataset EGAD00001003990

• Res1_PC9_exp2_MC_01_04_22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Study EGAS00001006170

• Res1_PC9_exp1_MC_29_03_22

  8 cell pellet samples for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kosuke primers.

  Study EGAS00001006169

• 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_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 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

• 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_CHIP0173_001

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

  Dataset EGAD00001011233

• 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_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_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_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 TENX063

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

  Dataset EGAD00001011268

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015299

• 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

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015298

• 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 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

• 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

• 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_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_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_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_CHIP0020_002

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

  Dataset EGAD00001011254

• Whole exome sequencing of serially sampled TNBC patients on pre/on PARP inhibitor

  Novaseq whole exome raw sequence files (FastQ) for breast cancer tumor core biopsies and blood normal control.

  Dataset EGAD00001007916

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0063_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0063_000 for Triple negative breast cancer patient-derived xenograft SA609X3XB01584

  Dataset EGAD00001011209

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015305

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a metastatic osteosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015306

• Genome and transcriptome sequence data from a relapsed osteosarcoma tumor patient

  Genome and transcriptome sequence data from a relapsed osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015311

• Genome and transcriptome sequence data from a synovial sarcoma tumor patient

  Genome and transcriptome sequence data from a synovial sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015313

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015315

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015318

• Genome and transcriptome sequence data from a anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015319

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015324

• Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015330

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

  Genome and transcriptome sequence data from a pIlomyxoid astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015333