10161 results for "cancer rna-seq"

in 60.22 milliseconds.

• Non-invasive human skin transcriptome analysis using mRNA in skin surface lipids

  Non-invasive acquisition of mRNA data from the skin can be extremely useful for understanding skin physiology and diseases. Inspired by the holocrine process, in which the sebaceous glands secrete cell contents into the sebum, we focused on the possible presence of mRNAs in skin surface lipids (SSLs). We found that measurable levels of human mRNAs exist in SSLs, where the sebum protects them from degradation by RNases. The AmpliSeq transcriptome analysis was modified to measure SSL-RNA levels, and our results revealed that the SSL-RNAs predominantly comprised mRNAs derived from sebaceous glands, the epidermis, and hair follicles. Analysis of SSL-RNAs non-invasively collected from patients with atopic dermatitis revealed significantly increased expression of inflammation-related genes and decreased expression of terminal-differentiation���related genes, consistent with the results of previous reports. Further, we found that lipid-synthesis���related genes were downregulated in the sebaceous glands of patients with atopic dermatitis. These results indicate that the analysis of SSL-RNAs is a promising strategy to understand the pathophysiology of skin diseases.

  Study JGAS000418

• Offspring Sex Impacts DNA Methylation and Gene Expression in Placentae from Women with Diabetes during Pregnancy

  Exposure to diabetes in utero is known to increase the offspring's likelihood of developing metabolic disease in adulthood, but the mechanisms involved are unknown. It has been proposed that early exposure to hyperglycemia and elevated insulin levels may lead to malprogramming of the fetus leading to the subsequent development of diabetes and obesity. Epigenetic modifications of the genome including DNA methylation, provide a plausible mechanism that allows for permanent propagation of gene activity states from one generation of cells to the next. The placenta, a fetal tissue easily accessible for study, is a complex organ that is essential in regulating fetal growth. The changes in placental nutrient transport associated with diabetes during pregnancy (DDP) have significant effects on the developing fetus, indicating that the placenta plays a critical role in fetal programming. The aim of our study was to investigate whether exposure to DDP alters genome-wide DNA methylation in the placenta obtained from term pregnancies resulting in differentially methylated loci of metabolically relevant genes and downstream changes in RNA and protein expression.

  Study phs001535

• Tissue and Fluid Analysis in Ocular Inflammatory Disease

  Granulomas are found in multiple infectious and non-infectious human diseases, yet the pathogenic drivers of granulomatous disease are still poorly understood. To enrich for pathologic inflammatory cells, we leveraged the intrinsic immunologic isolation of the eye by examining granulomatous uveitis, a form of ocular inflammation. We performed single-cell RNA-sequencing to obtain an unbiased gene expression survey of ocular immune cells in the anterior chamber of four live human donors with granulomatous uveitis. We paired this with antigen-receptor sequence analysis to identify clonally expanded lymphocytes. Each individual had robust clonal expansion arising from a single T or B cell lineage, suggesting distinct pathogenic processes in each patient. This variability in clonal expansion was mirrored by individualized combinations of TH1, TH1/TH17, and Tregs in ocular CD4 T cells. In contrast, ocular CD8 T cells were more transcriptionally similar across donors. Finally, clonally expanded, activated B cells displayed evidence of plasmablast differentiation and class-switching within the ocular microenvironment. Collectively, our study identified conserved and individualized features of granulomatous inflammation, illuminating parallel pathophysiologic mechanisms.

  Study phs002449

• The National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis (CLEAR)

  The Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis (CLEAR) Registry and Repository, supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), was established in 2000 to provide a resource for the scientific community to explore genetic and non-genetic factors affecting rheumatoid arthritis (RA) occurrence and outcomes in African Americans. The long term objective is a database and a repository of 1,100 RA and 550 matched healthy African-American subjects. This CLEAR Registry and Repository has two arms: a longitudinal arm for subjects with early RA (enrollment from 2000 to 2005) and a cross-sectional arm for subjects with any disease duration (enrollment from 2006 to 2012). CLEAR has two components: a database and a repository. The database contains extensive demographic, socioeconomic, clinical and radiographic (radiographs of hands and feet) information and bone mineral density data from DEXA scans. The repository contains genomic DNA, plasma and serum on most of the participants. Participants in CLEAR II had RNA isolated from peripheral blood cells.

  Study phs001360

• Proteogenomics reveals two distinct biological pilocytic astrocytoma subgroups

  Pilocytic astrocytoma (PA) is the most common pediatric brain tumor and driven by aberrant MAPK signaling, typically mediated by BRAF alterations. While five-year overall survival rates exceed 95%, tumor recurrence constitutes a major clinical challenge in incompletely resected tumors despite chemotherapeutic or radiation based therapies. Therefore, we used proteogenomics to discern the biological heterogeneity of PA to improve classification of this tumor entity and identify novel therapeutic targets. Our proteogenomics approach integrates RNA sequencing and LC/MS-based proteomic profiling data from a cohort of 58 confirmed, primary PA samples. An integrative genomics approach was conducted to discern the biological heterogeneity of PA and to identify aberrant pathway activation in these biological subgroups. In summary, Pilocytic astrocytomas segregate into two groups where younger patients are significantly associated with Group 1. Importantly, we validate the two distinct biological subgroups in two non-overlapping cohorts. The biological heterogeneity seen here may improve biological classification and reveal novel therapeutic targets specifically useful for non-resectable tumors with high risk of recurrent or progressive disease.

  Study EGAS00001006402

• Transcriptomics sequencing analysis of pre-invasive lung adenocarcinoma in never-smokers

  The prevalence of lung adenocarcinoma (LUAD) has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in non-smokers. We performed whole exome-sequencing and RNA-sequencing for samples from 94 East Asian patients with precancerous lesions (25 with atypical adenomatous hyperplasia [AAH]; 69 with adenocarcinoma in situ [AIS]) and 73 patients with early invasive lesions (minimally invasive adenocarcinoma [MIA]). Cellular analysis revealed that the activities of endothelial and stromal cells could categorize tumors into molecular subtypes within pathology-defined lesion types. These subtypes were linked with the advanced radiology-defined lesion type and coordinated with immune cell infiltration throughout the progression of LUAD. Characterization of these lesion types identified positively selected mutation patterns, and suggested that angiogenesis of the late-stage AIS type potentially contributes to tissue invasion of the MIA type. Our findings offer a novel resource that may help to improve early diagnosis and patient prognosis, and also suggest possible approaches for early disease interception.

  Study EGAS50000000439

• Whole exome sequencing of pre-invasive lung adenocarcinoma in non-smokers

  The prevalence of lung adenocarcinoma (LUAD) has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in non-smokers. We performed whole exome-sequencing and RNA-sequencing for samples from 94 East Asian patients with precancerous lesions (25 with atypical adenomatous hyperplasia [AAH]; 69 with adenocarcinoma in situ [AIS]) and 73 patients with early invasive lesions (minimally invasive adenocarcinoma [MIA]). Cellular analysis revealed that the activities of endothelial and stromal cells could categorize tumors into molecular subtypes within pathology-defined lesion types. These subtypes were linked with the advanced radiology-defined lesion type and coordinated with immune cell infiltration throughout the progression of LUAD. Characterization of these lesion types identified positively selected mutation patterns, and suggested that angiogenesis of the late-stage AIS type potentially contributes to tissue invasion of the MIA type. Our findings offer a novel resource that may help to improve early diagnosis and patient prognosis, and also suggest possible approaches for early disease interception.

  Study EGAS50000000438

• Genomic Analyses Identify Recurrent MEF2D Fusions in Acute Lymphoblastic Leukemia

  Chromosomal rearrangements are initiating events in acute lymphoblastic leukemia (ALL). Here, using RNA-sequencing (RNAseq) of 560 ALL cases, we identify rearrangements between MEF2D (myocyte enhancer factor 2D) and five genes (BCL9, CSF1R, DAZAP1, HNRNPUL1 and SS18) in 22 B progenitor ALL (B-ALL) cases with a distinct gene expression profile, the most common of which is MEF2D-BCL9. Examination of an extended cohort of 1164 B-ALL cases identified 30 cases with MEF2D rearrangements which include an additional fusion partner, FOXJ2, thus MEF2D-rearranged cases comprise 5.3% of cases lacking recurring alterations. MEF2D-rearranged ALL is characterized by a distinct immunophenotype, DNA copy number alterations at the rearrangement sites, older diagnosis age, and poor outcome. The rearrangements result in enhanced MEF2D transcriptional activity, lymphoid transformation, activation of HDAC9 expression and sensitive to histone deacetylase inhibitors treatment. Thus, MEF2D-rearranged ALL represents a distinct form of high-risk leukemia, for which new therapeutic approaches should be considered.

  Study EGAS00001001952

• The mission of the BIOS Consortium is to create a large-scale data infrastructure and to bring together BBMRI researchers focusing on integrative omics studies in Dutch Biobanks.

  The advent of the genome-wide association study (GWAS) led to the successful identification of thousands of variants that are robustly associated with complex disease phenotypes. Dutch biobanks played a substantial role in these discoveries. For most of these variants, however, the mechanisms through which they contribute to these phenotypes remain unknown. The BIOS Consortium applies a functional genomics approach that integrates genome-wide genetic data with data on the epigenome and transcriptome to elucidate these mechanisms. Over 4000 samples from BBMRI-NL biobanks with in-depth information on disease phenotypes and GWAS data are being enriched with RNA-sequencing and genome-wide DNA methylation data. The same is true for samples with whole-genome sequencing data from GoNL. This unique data infrastructure provides a powerful platform to evaluate key questions in integrative omics from establishing comprehensive eQTL and meQTL catalogues to linking molecular pathways across omics levels to phenotypic outcomes.

  Study EGAS00001001077

• TGS___Comprehensive_Molecular_Characterization_of_Colorectal_Cancer_Metastases__MOSAIC_

  Systematic next generation sequencing efforts are beginning to define the genomic landscape across a range of primary tumours, but we know very little of the mutational evolution that contributes to disease progression.We therefore propose to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in a cohort of matched primary and metastatic colorectal cancers, and additionally to explore the extent to which those mutations identified as recurrent in the metastatic setting are able to subvert normal biological processes using both genetically engineered mouse models and established cancer cell lines. This study will enable us to define to what extent primary tumour profiling can capture the biological processes operative in matched metastases as well as the significance of intratumoural heterogeneity.

  Study EGAS00001000958

• Integrative analysis of small cell lung cancer

  Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype. We conducted integrated analysis of genome sequencing, transcriptome, and copy number analysis and found an extremely high mutation rate of 7.4±1 protein-changing mutations per million basepairs. Evidence for inactivation of TP53 and RB1 was found in all sequenced cases. Furthermore, we identified recurrent mutations in CREBBP, EP300, and MLL, observed mutations in PTEN, in SLIT2, and EPHA7, as well as focal amplifications of the FGFR1 locus. As a major feature of SCLC we found mutation in histone modifying genes, observed genome alterations that are therapeutically tractable, and provide a framework for identifying biologically relevant genes in the context of a high mutation rate. Peifer et al., 2012, Nature Genetics.

  Study EGAS00001000299

• Clonal dominance defines metastatic dissemination in pancreatic cancer

  Using patient-derived cells from a pancreatic ductal adenocarcinoma, we created orthotopic clonal replica tumors to trace in an unbiased fashion clonal lineage dynamics of unperturbed tumor expansion and dissemination over time. The model revealed the complex multifaced nature of tumor clonal expansion and uncovered brisk changes in fitness that lead to a continuous reshuffling of tumor clonal architecture and alternating clonal dominance as a distinct feature of cancer growth. Paired analysis of clonal lineages in primary and secondary sites revealed fitness in the primary tumor as a major contributor to dissemination, although other clonal intrinsic and extrinsic factors may contribute to metastatic process. Molecular characterization of lineages with differential metastatic potential identified actionable strategy to suppress dissemination at the subclonal level.

  Study EGAS00001006358

• Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

  Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitors. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Here we evaluate bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples. In CheckMate 026, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne CDx™ assay. TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. Parameter variation can therefore impact TMB calculations, highlighting the need for standardization. Encouragingly, WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90) and differences could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms and centers is achievable.

  Dataset EGAD00001005035

• Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

  Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009398

• Randomized Phase II 2 x 2 Factorial Trial of the Addition of Carboplatin +/- Bevacizumab to Neoadjuvant Weekly Paclitaxel Followed by Dose-Dense AC in Hormone Receptor-Poor/HER2-Negative Resectable Breast Cancer

  This randomized phase II trial studies how well paclitaxel with or without carboplatin and/or bevacizumab followed by doxorubicin and cyclophosphamide works in treating patients with breast cancer that can be removed by surgery. Drugs used in chemotherapy, such as paclitaxel, carboplatin, doxorubicin, and cyclophosphamide, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Bevacizumab may stop the growth of tumor cells by blocking blood flow to the tumor. Giving chemotherapy together with bevacizumab before surgery may make the tumor smaller and reduce the amount of normal tissue that needs to be removed. All participants in this study were female.

  Study phs001863

• CIDR Estrogen Receptor Negative Breast Cancer in African American Women: DNA Methylation, Reproductive Events, and Mammary Epithelial Cell Populations

  We conducted methylation profiling of breast tumor DNA from Formalin-Fixed Paraffin-Embedded (FFPE) samples for 1,550 cases from a cohort of Black women from the Black Women’s Health Study (BWHS), the Women’s Circle of Health Study (WCHS), and from the Pathways Study using the Illumina MethylationEPIC array. We will examine differentially methylated loci (DML) between ER+ and ER- tumors, and by age at onset of breast cancer. Combining these data with previously analyzed samples (n=385), we will consider associations between reproductive risk factors and top DML by age at diagnosis and ER status, examining the complex relationships between methylation, tumor and patient characteristics, and reproductive risk factors using structural equation modeling.

  Study phs002688

• Translating Gene-Calcium Interactions to Precision Medicine for Colorectal Cancer

  Identify functional and rare SNPs and their interactions with Ca intake levels in relation to risk of incident adenoma using whole-exome resequencing of 2468 colorectal adenoma cases and controls from PLCO. We will genotype promising rare and functional SNPs associated with incident adenoma in 570 incident cancer cases, 1,710 healthy controls, 710 adenoma recurrence cases and 966 recurrent adenoma controls from the intervention arm of PLCO to both confirm the associations with adenoma, and to identify when, in the natural history of CRC, chemoprevention by Ca-gene interactions occurs. We hypothesize that Ca-gene interactions prevent early-stage (incident adenoma) and mid-stage (adenoma recurrence) carcinogenesis as well as span the entire CRC carcinogenesis spectrum (i.e. incident CRC).

  Study phs002164

• DMET Genes, Nicotine Metabolism and Prospective Abstinence

  This study (DA033813; PI: Andrew W Bergen; PMID:26132489) includes samples from two laboratory studies of nicotine metabolism. The Pharmacokinetics of Nicotine Metabolism in Twins study (PKTWIN; PI: Gary E Swan; PMID: 15527659) was based on recruitment from a twin registry (PMID: 23084148). The Integrated Research Project on Tobacco Use and Dependence (IRP; PI: Gary E Swan; PMID: 14578134) was based on recruitment from a pedigree-based longitudinal study of risk factors for substance use, the Smoking in Families study (SMOFAM; DA03706; PI: Hy Hops). These two laboratory studies (PKTWIN and IRP/SMOFAM) served as the Stage I dataset to interrogate Drug Metabolizing Enzyme and Transporter genes with a targeted SNP array for association with the Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), an established biomarker of nicotine metabolism. In addition to the laboratory studies, samples from eight RCTs (PMID: 23249876) with the NMR and smoking-related measures used to test SNPs identified in Stage I (PMID: 26132489). In a third stage, a lung cancer meta-analysis database (PMID: 24880342) was used to assess association of SNPs identified in Stage II with lung cancer. The objectives of the study were to identify novel genes and SNPs contributing to nicotine metabolism (Stage I), and to validate PK SNPs associated with the NMR from individuals participating in a clinical laboratory protocol with the NMR obtained from treatment-seeking smokers, and then to investigate association with prospective smoking cessation (Stage II). This study built upon existing studies of nicotine metabolism and randomized trials of smoking cessation therapies. Enhanced knowledge of the genes influencing nicotine metabolism and prospective abstinence may help personalize smoking cessation treatment and risk assessment for smoking-related diseases. For Stage I, both subject [fixed-dose NMR, covariates (age, BMI, ethnicity, sex, smoking status, and hormone use), and pedigree relationships] and sample (common DMET SNP genotype, genotyping quality control) data are available in this accession. The analysis protocol, quality control summaries, summary genotype, summary phenotype, and analysis results are available for Stage I, II and III samples (PMID: 26132489). Extensive discussion of the prior CYP2A6 association literature with the NMR, abstinence, smoking heaviness and lung cancer risk is available (PMID: 26132489). The NMR has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.

  Study phs000931

• Novel regional age-associated DNA methylation changes within human common disease-associated loci

  Background: Advancing age progressively impacts on risk and severity of chronic disease. It also modifies the epigenome, with changes in DNA methylation, due to both random drift and variation within specific functional loci. Results: In a discovery set of 2,238 peripheral-blood genome-wide DNA methylomes (MeDIP-seq) aged 19-82 years, we identified 71 age-associated Differentially Methylated Regions (a-DMRs, p < 1.85 x 10-8) within the Linkage Disequilibrium (LD) blocks of the NIH Catalogue of published GWAS SNPs. This included 52 novel regions, 29 within loci not covered by 450k or 27k Illumina array, and with marked enrichment for Poised Promoters and Enhancers across multiple cell types. In a replication set of 2,084 DNA methylomes, 95.7% of the a-DMRs showed the same direction of ageing effect, with 80.3% and 53.3% replicated to p < 0.05 and p < 1.85 x 10-8, respectively. Conclusion: By analysing the functionally enriched disease and trait-associated regions of the human genome, we identified novel epigenetic ageing changes, which could be useful biomarkers or provide mechanistic insights into age-related common diseases.

  Study EGAS00001001910

• Mutational bias in spermatogonia impacts the anatomy of regulatory sites in the human genome

  Mutation in the germline is the ultimate source of genetic variation, but little is known about the influence of germline chromatin structure on mutational processes. Using ATAC-seq, we profile the open chromatin landscape of human spermatogonia, the most proliferative cell-type of the germline, identifying transcription factor binding sites (TFBSs) and PRDM9-binding sites, a subset of which will initiate meiotic recombination. We observe an increase in rare structural variant (SV) breakpoints at PRDM9-bound sites, implicating meiotic recombination in the generation of structural variation. Many germline TFBSs, such as NRF, are also associated with increased rates of SV breakpoints, apparently independent of recombination. Singleton short insertions (>=5 bp) are highly enriched at TFBSs, particularly at sites bound by testis active TFs, and their rates correlate with those of structural variant breakpoints. Short insertions often duplicate the TFBS motif, leading to clustering of motif sites near regulatory regions in this male-driven evolutionary process. Increased mutation loads at germline TFBSs disproportionately affect neural enhancers with activity in spermatogonia, potentially altering neurodevelopmental regulatory architecture. Local chromatin structure in spermatogonia is thus pervasive in shaping both evolution and disease.

  Study EGAS00001005366

• NIH Roadmap Epigenomics Program - Broad Institute

  For the NIH Roadmap Epigenomics project, we applied ChlP-Seq, HTBS and WGBS pipelines to generate comprehensive high-resolution maps of chromatin state and DNA methylation for 100 diverse cell types. Cell types were selected for their biological and medical importance, and for their potential to maximize the comprehensiveness of acquired epigenomic data. They include human ES cells, ES-derived cells, mesenchymal stem cells, reprogrammed stem cells and primary tissues. Comprehensive characterization of epigenetic marks ("the epigenome") is a critical step towards a global understanding of the human genome in health and disease. In this study we provide unprecedented views of the human epigenetic landscape and its variation across cell states, which offer fundamental insight into the functions and interrelationships of epigenetic marks, and provide a framework for future studies of normal and diseased epigenomes. The Roadmap Epigenomics Broad cohort is utilized in the following dbGaP sub-study. To view molecular data and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000700 the Roadmap Epigenomics Broad cohort. phs000610 RM_Epigenomics_Broad_Alz

  Study phs000700

• Single-Cell Transcriptomic Analysis of Kaposi Sarcoma

  Summary: Single-cell transcriptome analysis of skin and blood samples from individuals with Kaposi's sarcoma Aims: Elucidate the cellular composition and gene expression profiles of Kaposi sarcoma herpesvirus (KSHV) infected cells and non-infected tumor infiltrating cells, and peripheral blood samples from the same individuals, with or without specific therapies, such as antiretroviral therapy for HIV, immunotherapies (such as immune checkpoint inhibitors and immune modulator drugs like pomalidomide), or angiogenesis inhibitory agents. Population: Age 18 and older, both genders, including individuals from North America and other areas of the world (including Africa). Individuals with or without HIV are included. Molecular Technologies: Single-cell RNAseq using 10x Genomics Principal Findings: Two distinct populations of endothelial cells were found infected with KSHV, one CD34+ and one CD34-Both clusters include lytic and latent KSHV gene expressionThe KSHV infected cell clusters differ in expression of housekeeping genes, proliferative markers, ribosomal and secretory vesicle genesBiomarkers of KSHV infected cells were identified, including sodium channel SCN9A Changes in cell composition with therapy are noted Clonal amplification of TCR+ T-cells were noted in the skin and blood Data Available through dbGaP: scRNA-seq data

  Study phs003800

• Distinct immune cell infiltration patterns in PDAC exhibit divergent immune cell selection and immunosuppressive mechanisms

  Pancreatic ductal adenocarcinoma has a dismal prognosis. A comprehensive analysis of single-cell multi-omic data from matched tumour-infiltrated CD45+ cells and peripheral blood in 12 patients, and two published datasets, reveals a complex immune infiltrate. Patients have either a myeloid-enriched or adaptive-enriched tumour microenvironment. Lymphocyte enrichment is intrinsically linked with highly distinct B and T cell clonal selection, diversification, and differentiation. Using TCR data, we see the largest clonal expansions in CD8 effector memory, senescent cells, and highly activated regulatory T cells which are induced within the tumour from naïve cells. We identify pathways that potentially lead to a suppressive microenvironement, including targets of immunotherapy and checkpoints including TIGIT/PVR and SIRPA/CD47. PDAC patient tumors from the APACT clinical trial revealed that patients with myeloid enrichment had the shorter overall survival compared to those adaptive cell enrichment. We identify multiple therapeutic targets that should form the basis for rational design including boosting of B cell responses, targeting immunosuppressive macrophages, and specific Treg depletion approaches. The dataset is scRNA-seq data from PDAC patients.

  Study EGAS50000000726

• 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

• TSG_knock_out_in_hiPSCs

  We will perform RNAseq to evaluate the effects of the loss of a list of TSGs on the transcriptome.

  Study EGAS00001002262

• 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

• 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

• 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_RNA_T-XEN-CELL

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

  Dataset EGAD00001002081

• 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_WGS_MET-XEN

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

  Dataset EGAD00001002087

• 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

• 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_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

• 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

• Spatial Maps of Prostate Cancer Transcriptomes

  A total of 14 samples that has been analyzed with the Spatial Transcriptomics method. H&E stain can be sent if requested.

  Dataset EGAD00001004121

• 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

• 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

• Single cell multiomics sequencing and analyses of human colorectal cancer

  Whole genome sequencing data of tumor tissues, adjacent normal tissues, and peripheral blood from CRC patients.

  Dataset EGAD00001004495

• 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

• Normal sample for patient SA609

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

  Dataset EGAD00001009219

• Normal sample for patient SA287

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

  Dataset EGAD00001009228

• Normal sample for patient SA275

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA275

  Dataset EGAD00001009224

• Tumour sample for patient SA667

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

  Dataset EGAD00001009210

• Tumour sample for patient SA668

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

  Dataset EGAD00001009211

• Tumour sample for patient SA669

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

  Dataset EGAD00001009212

• Tumour sample for patient SA671

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

  Dataset EGAD00001009213

• Tumour sample for patient SA672

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

  Dataset EGAD00001009214

• Tumour sample for patient SA535

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

  Dataset EGAD00001009215

• Normal sample for patient SA1035

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

  Dataset EGAD00001009216

• Normal sample for patient SA604

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

  Dataset EGAD00001009217

• Normal sample for patient SA605

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

  Dataset EGAD00001009218

• Normal sample for patient SA279

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA279

  Dataset EGAD00001009226

• Normal sample for patient SA218

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

  Dataset EGAD00001009220

• Normal sample for patient SA219

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

  Dataset EGAD00001009221

• Normal sample for patient SA272

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA272

  Dataset EGAD00001009222

• Normal sample for patient SA274

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA274

  Dataset EGAD00001009223

• Normal sample for patient SA276

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA276

  Dataset EGAD00001009225

• Normal sample for patient SA283

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA283

  Dataset EGAD00001009227

• Normal sample for patient SA394

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA394

  Dataset EGAD00001009229

• Normal sample for patient SA395

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA395

  Dataset EGAD00001009230

• Normal sample for patient SA398

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA398

  Dataset EGAD00001009231

• Normal sample for patient SA402

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

  Dataset EGAD00001009232

• Normal sample for patient SA404

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

  Dataset EGAD00001009233

• Normal sample for patient SA530

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

  Dataset EGAD00001009234

• Normal sample for patient SA535

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

  Dataset EGAD00001009235

• Normal sample for patient SA585

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

  Dataset EGAD00001009236

• Normal sample for patient SA586

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

  Dataset EGAD00001009237

• Normal sample for patient SA588

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

  Dataset EGAD00001009238

• Normal sample for patient SA589

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

  Dataset EGAD00001009239

• Normal sample for patient SA590

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

  Dataset EGAD00001009240

• Normal sample for patient SA591

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

  Dataset EGAD00001009241

• Normal sample for patient SA592

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

  Dataset EGAD00001009242

• Normal sample for patient SA593

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

  Dataset EGAD00001009243

• Normal sample for patient SA595

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

  Dataset EGAD00001009244

• Tumour sample for patient SA599

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

  Dataset EGAD00001009203

• Normal sample for patient SA598

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

  Dataset EGAD00001009246

• Normal sample for patient SA599

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

  Dataset EGAD00001009247

• Normal sample for patient SA600

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

  Dataset EGAD00001009248

• Normal sample for patient SA601

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

  Dataset EGAD00001009249

• Normal sample for patient SA654

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

  Dataset EGAD00001009250

• Normal sample for patient SA655

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

  Dataset EGAD00001009251

• Normal sample for patient SA665

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

  Dataset EGAD00001009252

• Normal sample for patient SA667

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

  Dataset EGAD00001009253

• Normal sample for patient SA668

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

  Dataset EGAD00001009254

• Normal sample for patient SA669

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

  Dataset EGAD00001009255

• Normal sample for patient SA671

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

  Dataset EGAD00001009256

• Normal sample for patient SA672

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

  Dataset EGAD00001009257

• Tumour sample for patient SA1035

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

  Dataset EGAD00001009258

• Tumour sample for patient SA994

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA994

  Dataset EGAD00001009259

• Tumour sample for patient SA604

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

  Dataset EGAD00001009260

• Tumour sample for patient SA605

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

  Dataset EGAD00001009261

• Tumour sample for patient SA609

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

  Dataset EGAD00001009262

• Normal sample for patient SA596

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

  Dataset EGAD00001009245

• Tumour sample for patient SA600

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

  Dataset EGAD00001009204

• Tumour sample for patient SA601

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

  Dataset EGAD00001009205

• Tumour sample for patient SA654

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

  Dataset EGAD00001009206

• Tumour sample for patient SA655

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

  Dataset EGAD00001009207

• Tumour sample for patient SA665

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

  Dataset EGAD00001009208