Search Results - EGA European Genome-Phenome Archive

Bladder cancer subtyping study across 4 atezo clinical trials

Checkpoint inhibitors targeting the PD-1/PD-L1 interaction have revolutionized cancer therapy across many indications. In urothelial carcinoma (UC), several PD-1/PD-L1 inhibitors have been approved for treatment in localized and metastatic disease. While these agents provide durable clinical benefit in a number of patients, the majority do not respond. A better understanding of the molecular mechanisms underlying response and resistance is needed to tailor existing treatments and develop new therapeutics in UC. We profiled tumors from 2803 patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab in metastatic UC and muscle-invasive bladder cancer by RNA-seq, a targeted DNA panel, immunohistochemistry, and digital pathology. A machine learning algorithm identified four UC transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care was observed in immune and basal tumors (45% of patients). Digital pathology showed enrichment of human interpretable features in specific molecular subtypes, and a self-supervised approach could classify molecular subtypes from H&E slides with high accuracy, suggesting that machine-based image interpretation could accelerate tumor molecular profiling. Somatic alterations showed limited capacity to identify responders. Finally, mechanisms of benefit with PD-L1 blockade appeared different in immune and basal tumors, with immune tumors benefiting from B/plasma cell enrichment, while basal tumors benefited from granulocyte enrichment. This study represents the largest integration of UC molecular and curated clinical data in randomized clinical trials to date, paving the way for prospective clinical studies tailoring treatment regimens to specific tumor molecular subtypes in UC and other indications.

Study EGAS50000000497

Healthy human B-lymphopoiesis RNA-Seq reference using FACS sorted bone marrow aspirates

To define a transcriptomic reference of human B lymphopoiesis, bone marrow aspirates were obtained from n=4 healthy donors (study registration DRKS00023583). After immunodensity cell separation, samples were FACS-sorted into 7 established lymphopoietic differentiation stages. RNA was extracted from 5,000-320,000 cells per differentiation stage and subjected to ultra-low-input RNA sequencing after generation of stranded sequencing libraries.

Dataset EGAD00001010917

Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

Study phs000206

Whole Transcriptome Sequencing of Human Tumor Cells and Hematopoietic Stem and Progenitor Cells During Aging and Bone Marrow Disorders

Whole transcriptome sequencing analyses were performed on human hematopoietic stem and progenitor cells during aging and bone marrow disorders. For this purpose, normal and diseased bone marrow or peripheral blood-derived mononuclear cells were FACS-purified into hematopoietic stem (CD34+CD38-Lin-) or hematopoietic progenitor (CD34+CD38+Lin-) populations. Primary human samples from healthy individuals were used along with comparative analyses in some pre-malignant and malignant conditions, including myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) and plasma cell dyscrasias (e.g., multiple myeloma). Studies were also performed on total mononuclear cell fractions from patients with newly-diagnosed or relapsed multiple myeloma, or plasma cell leukemia. Briefly, RNA was extracted from all samples and quality was assessed using a Bioanalyzer instrument. Only samples with RNA integrity (RIN) values >7 were subjected to library preparation for RNA-sequencing. The SMARTer v4 cDNA amplification kit was used along with library construction by the NEB Ultra DNA kit for Illumina. Paired-end sequencing was performed on Illumina NextSeq instruments (Scripps Next Generation Sequencing Core), with all samples run over two separate flow-cells (to generate additional sequencing reads); >95M reads were generated in total for each sample. The RNA-sequencing data are available as FASTQ files.

Study phs002291

SAPCS Blood RNA-seq of prostate cancer patients

The study aims to quantify the gene expression in blood samples in 20 prostate cancer patients under the Southern African Prostate Cancer Study (SAPCS). The objective is to identify a list of differentially expressed genes.

Study EGAS50000000702

RNA-seq SE

Transcriptomics of single-end RNA-seq data

Study EGAS00001006521

RNA-seq PE

Transcriptomics of paired-end RNA-seq data

Study EGAS00001006522

iPSC and iPSC derived pericytes

Bulk RNA sequencing of iPSCs and iPSC derived pericytes from three cell lines MNZTASi019-A, MNZTASi021-A and MNZTASi022-A. Data includes one iPSC data set per cell line and iPSC derived pericyte differentiations per cell line (3 differentiations from MNZTASi019-A and MNZTASi021-A, 2 from MNZTASi022-A). RNA-seq data was generated using an Illumina Stranded mRNA 150bp paired-end library preparation.

Dataset EGAD50000000255

RNAseq of paired FL and tFL samples

This dataset contains RNA profiling of paired FL and tFL samples (n=11 pairs) sequenced by Illumina NextSeq500 instrument. Libraries used in this mRNA-seq dataset were prepared with QuantSeq FWD library preparation kit (Lexogen, Catalog Number: 015) with the addition of UMI module. RNAs used for library preparation were isolated from FFPE tissues using ALLPREP RNA/DNA FFPE kit (QIAGEN,Catalog number: 80234). Data are uploaded as FASTQ format.

Dataset EGAD50000001384

Sequencing data for study Dissociation of solid tumour tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses for library TENX038_VOA11267_002

Single-cell RNA-sequencing data generated using the 10X genomics chromium platform, comprising patient derived xenograft, cell line, and primary tumour data. This includes different digestion conditions (37C collagenase vs 6C cold protease) and FACS sorting for live, dying, and dead cells. 3 samples; filetype=fastq

Dataset EGAD00001005165

14977 results for "cancer studies using rna-seq"

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