Search Results - EGA European Genome-Phenome Archive

Alignment BAM files and gene count files of the Illumina Sequencing Data (10 tumor samples)

Reads were processed with the RNA-seq workflow 1.3.0 developed by the DKFZ Omics IT and Data Management Core Facility (https://github.com/DKFZ-ODCF/RNAseqWorkflow). First, FASTQ reads were aligned via two-pass alignment using STAR 2.5.3a. The STAR index was generated from the 1000 Genomes assembly and GENCODE Version 19 gene models with a sjdbOverhang of 200. Duplicate marking of the resultant main alignment file was done with sambamba 0.6.5. Gene-specific read counting was performed using featureCounts (from Subread 1.5.1) over exon features based on GENCODE Version 19 gene models. Both reads of a paired fragment were used for counting, and the quality threshold was set to 255, indicating that STAR found a unique alignment. Strand-specific counting was also used. For RPKM and TPM calculations, all genes on chromosomes X and Y, the mitochondrial genome, as well as rRNA and tRNA genes were omitted as they are likely to introduce library size estimation biases.

Dataset EGAD00001008969

WGBS data (CancerLocator study) of cell-free DNA derived from human blood

The blood samples of eight lung cancer patients and one benign lung tumor patient are collected for this dataset. Blood samples were centrifuged first at 1,600 × g for 10 minutes, and then the plasma was transferred into new micro tubes and centrifuged at 16,000 × g for another 10 minutes. The plasma was collected and stored at -80⁰C. CfDNA was extracted from 5 ml plasma using the Qiagen QIAamp Circulating Nucleic Acids Kit and quantified by Qubit 3.0 Fluoromter (Thermo Fisher Scientific). Bisulfite conversion of cfDNA was performed by using EZ-DNA-Methylation-GOLD kit (Zymo Research). After that, Accel-NGS Methy-Seq DNA library kit (Swift Bioscience) was used to prepare the sequencing libraries. The DNA libraries were then sequenced with 150bp paired-end reads.

Dataset EGAD00001003168

RNA sequencing of 31 pancreatic cancer organiods

Dataset EGAD00001005217

Single nuclei ATAC-Seq data from the human ganglionic eminences

We performed the single nuclei Assay for Transposase-Accessible Chromatin with Sequencing (snATAC-Seq) to map potential regulatory genomic regions operating in individual cell populations of the human GE, using these to test for enrichment of schizophrenia common genetic variant liability and to functionally annotate non-coding variants associated with the disorder.

Study EGAS50000000411

Mutational_burden_in_oesophagus__nanoseq_

We wish to accurately assess mutational burden and signature from patients of different ages using clonal oesophageal epithelium and duplex-seq. We will also attempt to look for differences between smokers and non-smokers and individuals with different levels of alcohol consumption.

Study EGAS00001007695

A Pilot Study of Rapid Autopsy and Procurement of Tissue in Thoracic Malignancy Cancer Patients to Investigate Tumor Heterogeneity

Integrated proteo-genomics studies to characterize tumor heterogeneity of metastatic lung adenocarcinoma and thymic carcinoma are lacking. We carried out whole exome sequencing, RNA sequencing, copy number estimation and mass spectrometry-based proteomics of 40 tumors from five rapid/warm autopsy patients. We found highly variable mutational heterogeneity that was largely driven by APOBEC-mutagenesis with the expression of APOBEC3B (Gene ID: 9582) and APOBEC3A_B (Gene ID: 100913187) due to underlying APOBEC3 region germline variants as the likely mediating mechanism. APOBEC3A_B expression was associated with increased immune tumor microenvironment and CD274 (Gene ID: 29126) expression while smoking was associated with increased APOBEC-mutagenesis in TP53 (Gene ID: 7157) mutant tumors with high APOBEC3B expression. Heterogeneity at the level of the transcriptome and proteome occurred in association with copy number heterogeneity, not APOBEC-induced mutational heterogeneity. Arm and focal copy number alterations (CNAs) occurred as an evolutionarily late event in a subset of patients, with corresponding changes in the transcriptome and proteome, implicating CNAs as a driving force of heterogeneity in the evolution of metastatic disease.

Study phs001432

National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

Study phs000634

Developing therapeutics for ovarian cancer using ovarian cancer organoids

To identify effective drugs for clear cell ovarian cancer (CCC) and high-grade serous ovarian cancer (HGSC) through high-throughput drug screening using ovarian cancer organoids and to identify novel therapeutic targets based on the biological characteristics of CCC and HGSC through omics analysis.

Study JGAS000764

Short-read RNA-seq of doxycycline-inducible DUX4 human myoblast cell lines

The dataset includes the short-read RNA sequencing data of DUX4i myoblasts (Clone-5 and Clone-7) with and without 16-hour DOX-treatment (n=1 in each condition).

Dataset EGAD50000000719

RNA-seq in endometrial healthy and tumor tissues

RNA has been extracted by Rneasy mini kit from around 30mg of flash frozen tissue derived from 3 healthy and 3 tumor endometrial tissues of post-menopausal patients. Raw paired-end fastq.gz files are provided.

Dataset EGAD50000000200

Performance assessment of total RNA sequencing of human biofluids and extracellular vesicles

SMARTer Stranded Total RNA-Seq method of human platelet-rich plasma, platelet-free plasma, urine, conditioned medium, and extracellular vesicles (EVs) from these biofluids. Including a titration experiment with spikes.

Dataset EGAD00001006150

Richter Syndrome RNA-seq dataset

Raw FASTQ files for 77 RS + DLBCL + CLL samples. RNA-sequencing with single-end 50 nt reads.

Dataset EGAD00001007922

Bulk RNA data from Wilms Tumors

Bulk RNA-seq data of pediatric Wilms tumors

Dataset EGAD00001009298

RNA-seq samples

Raw FASTQ files obtained by RNA sequencing of tumor samples from patients (age 12-29) with newly diagnosed, recurrent intermediate or high-grade sarcoma.

Dataset EGAD00001008393

Genetic landscape of Extranodal NK/T-cell lymphoma

Whole-exome sequence (WES) data of tumor-normal pairs from 40 ENKTCL patients and RNA sequence (RNA-seq) data of tumors from 20 ENKTCL patients.

Dataset EGAD00001009852

Genome_Diversity_in_Africa_Project___GemCode_libraries

Globally, human populations show structured genetic diversity as a result of geographical dispersion, selection and drift. Understanding this genetic variation can provide insights into the evolutionary processes that shape both human adaptation and variation in disease. Populations from Africa have the highest levels of genetic diversity. This characteristic, in addition to historical genetic admixture, can lead to complexities in the design of studies assessing the genetic determinants of disease and human variation. However, such studies of African populations are also likely to provide new opportunities to discover novel disease susceptibility loci and variants and refine gene–disease association signals. A systematic assessment of genetic diversity within Africa would facilitate genomic epidemiological studies in the region.The Genome Diversity in Africa Project (GDAP) will extend and expand the African Genome Variation (AGV) project, an international collaboration aimed to produce a comprehensive catalogue of human genetic variation in Sub-Saharan Africa (SSA). Using a sequencing-based approach, GDA project aims to capture human genetic variation in Africa, including single nucleotide polymorphisms (SNPs), structural variants, and haplotypes. This resource will make a substantial contribution to understanding patterns of genetic diversity within and among populations in Africa, as well as providing a global resource to help design, implement and interpret genomic studies in African populations and studies comprising globally diverse populations, complementing existing genomic resources. Specifically, we plan to carry out whole genome sequencing at high depth (30x) of up to 1000 individuals across Africa (25 individuals from each ethnolinguistic group). We have already completed sequencing from 5 ethno-linguistic groups and we are now adding others.Our scientific objectives are to: 1) develop a resource that provides a comprehensive catalogue of genetic variation in populations from Africa accessible to the global scientific community; 2) characterise population genetic diversity, structure, gene flow and admixture across Africa; 3) develop a cost-efficient, next-generation genotype array for diverse populations across Africa; and 4) facilitate whole genome-sequencing association studies of complex traits and diseases by developing a reference panel for imputation and resource for enhancing fine-mapping disease susceptibility loci. These scientific objectives will be supported by cross-cutting operational activities, including network and management of the consortium, research ethics, and research capacity building in statistical genetics and bioinformatics.

Study EGAS00001001589

Genomic Data of Pediatric MDS

The dataset consists of - 126 whole exome sequencings (SAMD9/9Lmut: 64; GATA2mut 24, MDS wildtype 38/471) performed using SureSelect Human All Exon V6 enrichment (Agilent, cat# 5190-8863). The generated libraries were sequenced on the Illumina Hiseq 2500 with 150bp paired-end reads. FASTQ files were processed using SeqNext platform (JSI medical system, Germany), with gene-based alignment to a virtual panel of 300 genes (including 28 MDS-associated genes, SAMD9, and SAMD9L), consisting of genes relevant to bone marrow failure, MDS predisposition, and hematological cancers as per the Pan-Cancer studies with cohorts of >10,000 cancers. The respective BAM files are provided. - Custom panel targeting SAMD9, SAMD9L, and 22 single nucleotide polymorphisms (SNP) on chromosome 7q (allele frequency >35% in all ethnic sub-populations in gnomAD) (Ampliseq #IAD104171) were performed in 666/669 cases. And Custom panel targeting 28 MDS-associated genes (GATA2, RUNX1, HOXA9, CEBPA, GATA1, KRAS, NRAS, CBL, PTPN11, ASXL1, EZH2, SETBP1, FLT3, KIT, JAK2, JAK3, CSF3R, MPL, SH2, BCOR, BCORL1; RAD21, STAG2, CTCF, TP53, PTEN, CALR, VPS45) was performed in 544 cases (Ampliseq #IAD51150). Both custom panel libraries were prepared using NEBNext Ultra II DNA library prep kit (New England BioLabs, cat#E7645S/L) per manufacturer’s instruction and samples were sequenced on an Illumina Miseq 2000 with 2 x 150 bp reads. The respective BAM files are provided - 4 SAMD9/9L patients were subjected to MissionBio custom single-cell panel (CO-112) targeting 250 heterozygous gnomAD population polymorphisms on 7q arm and 69 amplicons in SAMD9/9L and other cancer genes. All libraries were sequenced on an Illumina NovaSeq6000 with 150 base-paired ending multiplexed runs. Fastq files were processed using the Tapestri Pipeline V2 and python-based Mosaic package (multi-omics analysis, data visualization). The derived BAM and loom files are provided.

Dataset EGAD00001007856

Datasets of RNA-seq of tumor samples from clear cell renal cell carcinoma patients included in the Translational Program of the NIVOREN GETUG-AFU-26 trial

RNA-seq analyses were performed on tumor tissue samples of 115 advanced clear cell renal cell carcinoma patients who participated in the NIVOREN GETUG-AFU-26 trial. The samples were obtained prior to treatment initiation. RNA-seq was performed to evaluate previously published gene expression signatures. These included: the IMmotion T effector (CD8A, EOMES, PRF1, IFNG and CD274) and the IMmotion Myeloid (IL-6, CXCL1, CXCL2, CXCL3, CXCL8, and PTGS2) gene signatures represent gene expression patterns associated with T effector cells and myeloid cells, respectively, while JAVELIN Renal 101 Immuno (T-cell receptor signaling: CD3G, CD3E, CD8B, THEMIS, TRAT1, GRAP2, CD247; T-cell activation, proliferation, and differentiation: CD2, CD96, PRF1, CD6, IL7R, ITK, GPR18, EOMES, SIT1, NLRC3; Natural killer cell mediated cytotoxicity: D2, CD96, PRF1, CD244, KLRD1, SH2D1A; Chemokine: CCL5, XCL2; and Other immune response genes: CST7, GFI1, KCNA3, PSTPIP1) represents gene expression patterns associated with both innate and adaptive immune response.

Dataset EGAD50000001541

ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

Background Liquid biopsies, and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy-number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution. Methods To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity. Results SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour. Conclusions This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts, or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.

Study EGAS00001007077

14982 results for "cancer studies using rna-seq"

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