11925 results for "cancer rna-seq"

in 30.48 milliseconds.

• Plasma whole genome sequencing from patients with stage IV colorectal cancer and microsatellite instability

  This dataset contains plasma WGS data from patients with stage IV colorectal cancer (CRC, n = 16) and healthy individuals (n = 21) used in the Pointy manuscript. Patients with CRC provided written consent and samples were collected as performed as described previously (Clinical-Trials.gov number NCT01876511; Georgiadis et al., 2019, Le et al., 2017). Plasma samples from 21 healthy control individuals were procured through BioIVT. Cell-free DNA (cfDNA) was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit. Libraries were prepared with 5 to 250 ng of cfDNA using the NEBNext DNA Library Prep Kit. Libraries were sequenced on HiSeq2000/2500.

  Dataset EGAD00001008999

• RNA-Sequencing of B-Lymphoblastic Leukemia with Glucocorticoids and PI3K Delta Inhibition

  Mononuclear cells were isolated from bone marrow or peripheral blood of 7 pediatric subjects with B-lymphoblastic leukemia at the time of diagnosis. To determine the effect of PI3K delta inhibition on glucocorticoid-induced gene regulation, B-lymphoblastic leukemia cells were treated with glucocorticoids +/- the PI3K delta inhibitor, idelalisib, for 24 hours prior to RNA extraction and sequencing. Biological replicates were performed for 4 of the 7 subjects; 2 of these subjects had specimens demonstrating an additive response to the combination of glucocorticoids and idelalisib in viability testing in vitro, and 2 subjects' specimens demonstrated a synergistic response. All 7 subjects were analyzed together and the 4 specimens with biological replicates were also evaluated in an additional analysis. An average of 40 million reads per specimen and treatment condition were obtained. We compared gene expression in each treatment condition to vehicle-treated cells and expression in glucocorticoid or idelalisib only treated cells to combination treated cells. Using a high dose of dexamethasone (25 or 50 nM) and an equivalent dose of prednisolone, we found that the addition of idelalisib (500 nM) enhanced prednisolone-induced gene regulation but not dexamethasone-induced gene regulation. Surprisingly, the specimens with additive response to combination therapy demonstrated enhancement of prednisolone-induced gene regulation with the addition of idelalisib, while the specimens with synergistic responses did not. Raw sequencing data (FASTQ) and count tables will be available through dbGaP.

  Study phs003085

• 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

• Population Architecture Using Genomics and Epidemiology (PAGE): Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project

  As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of >150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU's overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold: Replicate genome-wide association study (GWAS)- identified variants in European Americans; Identify population-specific and trans-population genotype-phenotype associations; Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt's biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin's lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

  Study phs000559

• EPICC: Evolutionary Predictions in Colorectal Cancer

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

  Dataset EGAD00001008140

• 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

• Improved Detection and Identification of Microsatellite Instability Features in Colorectal Cancer

  In this study, we analyzed a series of colorectal cancers using a new sequencing technology that sequences more than 200 microsatellite regions in parallel. With this technology, we evaluated mono-, di- , tri- and tetranucleotide repeats to provide a broad spectrum of microsatellite instability (MSI) events. This deep sequencing technology provides greater than 2000X coverage and eliminates the need for PCR amplification. Because every read corresponds to a single DNA molecule, we eliminated artifacts introduced by PCR that obscured true MSI events. Also included in our analysis is a new method for detection of somatic copy number changes in 83 cancer genes, with which we can infer chromosomal instability (CIN) status.

  Study phs001914

• 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

• Biomarker Research for Anti EGFR Monoclonal Antibodies by Comprehensive Cancer Genomics

  The purpose of this study is to explore new biomarkers for predicting the effectiveness of an anti-epidermal growth factor receptor (anti-EGFR) antibody in patients with advanced colorectal cancer. The investigation involves analyzing the entire exome, conducting a genome-wide association study (GWAS), and analyzing copy number variations (CNVs) using single nucleotide polymorphism (SNP) arrays. Genetic material from both cancerous and non-cancerous tissues, as well as blood samples from patients who received the antibody treatment, will be used. The goal is to identify genetic mutations and SNPs associated with treatment efficacy and adverse events, and to assess the correlation between their presence and treatment effectiveness using statistical methods.

  Study JGAS000779

• 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

• 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

• PREDICT

  PREDICT/Predicting individual response and resistance to VEGFR/mTOR pathway therapeutic intervention using biomarkers discovered through tumour functional genomics.

  Study EGAS00001000094

• 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

• Neoadjuvant Breast Cancer Validations

  This study aims to re-sequence findings from whole genome studies using a bespoke pulldown method to validate mutations in those genomes sequenced.

  Dataset EGAD00001000663

• 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

• Genome and transcriptome sequence data from a wilms tumor tumor patient

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

  Dataset EGAD00001015334

• 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 EGAD00001015335

• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

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

  Dataset EGAD00001015263

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

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

  Dataset EGAD00001015268

• Genome and transcriptome sequence data from a ocular melanoma tumor patient

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

  Dataset EGAD00001015269

• 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 EGAD00001015279

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0163_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0163_001 for Triple negative breast cancer patient-derived xenograft SA609X7XB03505

  Dataset EGAD00001011219

• 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_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

• neoALTTO

  RNAseq fastq files for 254 samples for the neoALTTO study of lapatinib, trastuzumab or combination in HER2+ breast cancer patients. Those are pre-treatment baseline samples.

  Dataset EGAD00001011354

• Bone Cancer - Rare Types Whole Genome

  We propose to definitively characterise the somatic genetics of a selection of rare bone cancers through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Dataset EGAD00001000785

• Single-Cell Multiomics of the Immune Microenvironment in T-Cell Acute Lymphoblastic Leukemia

  We present single-cell transcriptome and surface protein expression (CITE-seq) data from four healthy donors and fifteen pediatric T cell acute lymphoblastic leukemia (T-ALL) patients at diagnosis and at different treatment timepoints (days 1 – 6 and at time of remission). Mononuclear cells from peripheral blood (PB; longitudinal) or bone marrow (BM; diagnosis only) were isolated and CD45high immune cells and CD45dimCD7+ malignant cells were sorted and subsequently pooled to enrich for immune cells. For comparison,CD45high immune cells from four healthy donors were also sorted. Single-cell sequencing of the transcriptome and surface protein expression (204 antibodies) was performed using the 10x Genomics platform.Multiomics analyses resulted in the identification of a T-ALL subgroup characterized by a remodeled immune microenvironment at initial diagnosis. This immune landscape is dominated by the presence of non-malignant CD4-CD8-TCRab T cells that interact with CXCL16 expressing non-classical monocytes. Leukemia cell intrinsic transcriptional rewiring in these patients is associated with activation of Rap1 signaling.

  Study phs004269

• Sequencing_Acute_Myeloid_Leukaemia_

  This project will aim at sequencing and analysing three samples from a patient with acute myeloid leukaemia (AML) with t(15;17) translocation (acute promyelocytic leukaemia). The patient was treated with standard chemotherapy and samples (blood and bone marrow) taken for minimal residual disease measured during and after chemotherapy. 2-3years later the patient presented a second AML containing inv (16).In this project we will sequence both AML and normal genomes to find associated mutations This will allow unprecedented insights into the dynamics of appearance of secondary AML, since we will be able to use the MRD samples to track the emergence of the inv(16) leukaemia. We will also be able to distinguish whether the 2 leukaemias are clonally related or not. This project will entail sequencing and analysis of three samples (whole genome 30x coverage) using Hi-Seq. The funds from this grant will cover the cost of sequencing 9 lanes from the AML t(15;17) sample, 9 lanes from AML inv(16) sample and 8 lanes from the normal genome.

  Study EGAS00001000035

• Genes associated with pancreas development and function maintain open chromatin in iPSCs generated from human pancreatic beta cells

  Current in-vitro islet differentiation protocols suffer from heterogeneity and low efficiency. Induced-pluripotent stem cells (iPSCs) derived from pancreatic beta-cells (BiPSCs) preferentially differentiate towards endocrine pancreas-like cells versus those from fibroblasts (FiPSCs). We interrogated genome-wide open chromatin in BiPSCs and FiPSCs via ATAC-seq, and identified ~8.3k significant, differential open chromatin sites (DOCS) between the two iPSC subtypes (FDR<0.05). DOCS where chromatin was more accessible in BiPSCs (Bi-DOCS) were significantly enriched for known regulators of endodermal development, including bivalent and weak enhancers, and FOXA2 binding sites (FDR<0.05). Bi-DOCS were associated with genes related to pancreas development and beta-cell function, including transcription factors mutated in monogenic diabetes (PDX1, NKX2-2, HNF1A; FDR<0.05). Moreover, Bi-DOCS correlated with enhanced gene expression in BiPSC-derived definitive endoderm and pancreatic progenitor cells. Bi-DOCS therefore highlight genes and pathways governing islet-lineage commitment, which can be exploited for differentiation protocol optimisation, diabetes disease modelling, and therapeutic purposes.

  Study EGAS00001002591

• TGF-β signaling mediates microglial resilience to spatiotemporally restricted myelin degeneration

  Microglia survey and regulate central nervous system myelination during embryonic development and adult homeostasis. However, whether microglia-myelin interactions are spatiotemporally regulated remains unexplored. By examining spinal cord white matter tracts, we found that myelin degeneration was particularly prominent in the dorsal column (DC) during normal aging. This was accompanied by molecular and functional changes in DC microglia as well as an upregulation of TGF-β signaling. Disrupting TGF-β signaling in microglia led to unchecked microglial responses and myelin loss in the DC, accompanied by neurological deficits exacerbated with aging. snRNA-seq revealed the emergence of a TGF-β signaling-sensitive microglial subset and a disease-associated oligodendrocyte subset, both spatially restricted to the DC. We further discovered that microglia rely on a TGFβ autocrine mechanism to prevent damage of myelin in the DC. These findings demonstrate that TGF-β signaling is indispensable for maintaining microglial resilience to myelin degeneration in the DC during the aging process. This highlights a previously unresolved checkpoint mechanism of TGF-β signaling with regional specificity and spatially restricted microglia-oligodendrocyte interactions.

  Study EGAS50000001413

• Multifocal_Breast_Project

  Comparison of genomic rearrangements and DNA methylationpatterns between different foci of multiple synchronous(multifocal and multicentric) invasive breast cancers

  Study EGAS00001000004

• COLO-829/COLO-829BL

  COLO-829 is a publicly available immortal cancer cell line and COLO-829BL is a lymphoblastoid cell line derived from the same patient

  Dataset EGAD00000000055

• scRNAseq data from metastatic breast cancer samples

  Paired-end SMARTseq2 data of EpCAM+ sorted cells from liquid biopsies from 3 patients. Sequencing was performed on Illumina HiSeq 2500.

  Dataset EGAD50000000816

• MD Anderson Cancer Center HGSOC LRS Data Access Comittee

  Oxford Nanopore long-read sequencing was performed on six paired tumor-normal samples from patients with high-grade serous ovarian carcinoma. Reads were mapped to the GRCh38 reference genome, incorporating base modification information.

  Dac EGAC50000000597

• DAC for lymphoma IFZ Essen

  This DAC is set in place to handle acess to data on lymphomas and immune cells submitted by scientists from the Institute of Cell Biology (Cancer Research) at the University of Duisburg-Essen in Essen, Germany.

  Dac EGAC50000000521

• Myeloproliferative_Neoplasms__MPN__Exome_Validation_Study

  Experiments using targeted pulldown methods will be sequenced to validate findings in the exomes of patients with Myeloproliferative Neoplasms (MPN).

  Study EGAS00001000404

• Gastric Cancer Organoid Cultures and Tumors RNASeq Data

  RNASeq data generated from organoid cultures established from gastric cancers and normal mucosae, paired tumor frozen tissues, and cultured fibroblast.

  Dataset EGAD00001004302

• Illumina_WXS_MET-CELL-XEN

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

  Dataset EGAD00001002097

• GEL WGS Comparison

  These samples are to be analysed with the CGP Developed cancer panel and the results will be compared with WGS data from 4 different comercial providers.

  Dataset EGAD00001000872

• Sequencing data for Murtaza et al. Nature Communications 2015 (doi:10.1038/ncomms9760)

  Exome and targeted amplicon sequencing data for tumor, germline and plasma samples from a patient with metastatic breast cancer.

  Dataset EGAD00001002108

• Illumina_WGS_T-XEN-CELL

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

  Dataset EGAD00001002092

• Illumina_RNA_T-CELL-XEN

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

  Dataset EGAD00001002079

• Illumina_WGS_MET-CELL-XEN

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

  Dataset EGAD00001002086

• Illumina_RNA_MET-CELL_Passages

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample at early/late passages

  Dataset EGAD00001002884

• Gastric Cancer Organoid Cultures and Tumors Whole Exome Sequencing Data

  Whole exome sequencing data generated from organoid cultures established from gastric cancers, paired gastric tumor frozen tissues and blood leukocyte DNA.

  Dataset EGAD00001004301

• Illumina_WGS_T-CELL-XEN

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

  Dataset EGAD00001002090