15075 results for "cancer studies using rna-seq"

in 30.40 milliseconds.

• Clonal expansion of mutated cell population in bladder urothelium

  1cm biospies of from patients undergoing bladder cystectomy will be collected. The underlying muscle and stroma will be removed and the remaining epithelia dissected into small sequential areas which will be sent for ultra-deep exome sequencing using a panel of known cancer and viral genes. Sequence analysis using similar methods to Martincorena I et al (Science 2015, 348:880) will provide an idea of the somatic mutational landscape in these patient samples. Individual patient muscle samples will also be sequenced as a reference.

  Dataset EGAD00001003250

• Genomic landscape of poorly differentiated thyroid carcinoma

  Poorly differentiated thyroid carcinoma (PDTC) is a rare and aggressive subtype of thyroid cancer with limited molecular characterization. This study aims to define the somatic mutational landscape of PDTC using whole-genome sequencing. Paired tumour and matched normal blood samples were collected from six patients undergoing surgery. The overarching goal is to identify recurrent mutations, structural variants, and other genomic alterations associated with tumour aggressiveness and poor clinical outcomes.

  Study EGAS50000001134

• Bulk RNASeq of metastatic colorectal cancer organoids with ATOH1 knockout

  Bulk RNA sequencing was performed on mCRC organoids subjected to cetuximab treatment, comparing ATOH1 knockout and control conditions. The dataset is intended to elucidate the contribution of ATOH1-regulated secretory cell population to cetuximab persistence mechanisms.

  Dataset EGAD50000002060

• Accurate detection of tumor-specific fusion genes reveals strongly immunogenic personal neo-antigens

  Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.

  Study EGAS00001004877

• Pan Cancer Investigation of Human Leukocyte Antigen Loss of Heterozygosity

  Cancer cells can escape immune recognition through human leukocyte antigen (HLA) loss of heterozygosity (LOH) which results in deletion of HLA alleles, causing a reduction in presentation of tumor neoantigens. Despite its influence on immunotherapy response, few methods exist to detect HLA LOH, and accuracy of the approaches is poorly defined. Here, we present DASH (Deletion of Allele-Specific HLAs), a machine learning-based algorithm developed to detect HLA LOH from paired tumor-normal sequencing data. Using DASH to characterize HLA LOH in 610 patients across 15 cancer types, we found that 18% of patients had loss of heterozygosity in at least one HLA allele, suggesting the HLA LOH is a widespread, and potentially key immune resistance strategy in multiple cancers.

  Study phs002783

• Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

  Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

  Dataset EGAD00001003883

• Development of New Diagnostics, Therapeutics, and Prevention Methods for Personalized Medicine Based on Comprehensive Cancer-Related Gene Exome Analysis and Information Analysis Using Cancer Specimens Stored in TMDU Biobank.

  Biobanked tumor samples are a valuable resource in real-world solid tumor research. Panel-based genomic profiling enables the identification of clinically significant alterations that inform personalized diagnostic and treatment decisions. This study aimed to characterize the genomic landscape of solid tumors collected through the TMDU Biobank by summarizing mutational, copy number, and gene fusion alterations in a large and diverse patient cohort. This dataset contains summary-level genomic information from 1,996 tumor samples analyzed by ACT Genomics and ACT med using the ACTOnco Comprehensive 440-Gene Cancer Panel. Data provided includes (1) gene-level mutation counts, (2) a summary of copy number alterations, and (3) a summary of detected fusion events. Clinical attributes such as ICD-10 codes, age group, sex, tumor mutational burden (TMB), and microsatellite instability (MSI) status were included at the aggregate level. Summary statistics were generated from three source tables (gene, sample, and copy number data) using standardized processing rules. Due to contractual and ethical considerations, individual-level mutation locations and raw sequence data are not included; only summary-level information is provided. This dataset will support future research in tumor-type-specific genomic patterns, molecular characterization of solid malignancies, and precision oncology.

  Study JGAS000863

• Custom long non-coding RNA capture enhances detection sensitivity in different human sample types.

  Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

  Study EGAS00001005418

• Cell-free DNA sequencing data of healthy control, atrophic gastritis, and gastric cancer patients’ blood

  Sample cohort (n=48) is consisted of healthy, atrophic gastritis and gastric cancer patients. Some of the gastric cancer patients samples are collected at the separate time points: -1 before the operation; -2 after the operation; -3 during the control visit. For the hybridisation capture of the genes unique 15 gastric cancer-related gene panel was developed and very deep sequencing using TruSight Oncology Unique Molecular Identifier (UMI) Reagents (Illumina) was used.

  Dataset EGAD00001011153

• Dataset for "HPV integration induces gene fusions" (ONT)

  ONT whole-genome sequencing data for "HPV integration induces gene fusions" . We sequenced five HPV-positive head and neck cancer samples using Oxford Nanopore platform. The sequences was submitted in fastq format.

  Dataset EGAD50000001302

• FFPE CPA Accreditation Study

  FFPE CPA accreditation of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using targeted pulldown capture prior to Illumina sequencing.

  Dataset EGAD00001000678

• Single-cell RNA sequencing data of peripheral blood mononuclear cells obtained from 30 ATL patients, 11 HTLV-1-infected asymptomatic carriers, and 4 healthy donors.

  Single-cell RNA sequencing (scRNA-seq) data of peripheral blood mononuclear cells (PBMCs) obtained from 30 ATL patients (34 samples including 4 sequential ones), 11 HTLV-1-infected asymptomatic carriers, and 4 healthy donors.

  Dataset EGAD00001007015

• Drug-induced epigenomic plasticity reprograms circadian rhythm regulation to drive prostate cancer towards androgen-independence (ChIP-seq)

  Understanding how prostate cancer cells adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenetic plasticity towards pro-survival signaling, and uncovered circadian regulator ARNTL as an acquired vulnerability after AR inhibition, presenting a novel clinical lead for therapeutic development.

  Study EGAS00001006017

• Data Use Ontology (DUO)

  Data Use Ontology Data Use Ontology at EGA The EGA is committed to its involvement in the work of GA4GH. In an effort to enhance data discoverability and streamline data access, EGA have implemented the use of the Data Use Ontology (DUO), based on consent codes as described in Dyke et al. 2017. The Data Use Ontology codes will be displayed on the live dataset page of your submission to advise any would be requestor on how the data can be used and also to enhance data discoverability as users will be able to search on these codes to find applicable datasets. DUO can be browsed online via the Ontology Lookup Service Learn more reading the Data Use Ontology publication and the GA4GH Machine-Readable Consent Guidance! Check our DAC Portal Take The Tour and learn how to add DUO codes to your policy! Term Shorthand Label Description DUO:0000004 NRES no restriction This data use permission indicates there is no restriction on use. DUO:0000042 GRU general research use This data use permission indicates that use is allowed for general research use for any research purpose. DUO:0000006 HMB health or medical or biomedical research This data use permission indicates that use is allowed for health/medical/biomedical purposes; does not include the study of population origins or ancestry. DUO:0000007 DS disease specific research This data use permission indicates that use is allowed provided it is related to the specified disease. DUO:0000011 POA population origins or ancestry research only This data use permission indicates that use of the data is limited to the study of population origins or ancestry. DUO:0000012 RS research specific restrictions This data use modifier indicates that use is limited to studies of a certain research type. DUO:0000015 NMDS no general methods research This data use modifier indicates that use does not allow methods development research (e.g., development of software or algorithms). DUO:0000016 GSO genetic studies only This data use modifier indicates that use is limited to genetic studies only (i.e., studies that include genotype research alone or both genotype and phenotype research, but not phenotype research exclusively) DUO:0000018 NPUNCU not for profit, non commercial use only This data use modifier indicates that use of the data is limited to not-for-profit organizations and not-for-profit use, non-commercial use. DUO:0000019 PUB publication required This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community. DUO:0000020 COL collaboration required This data use modifier indicates that the requestor must agree to collaboration with the primary study investigator(s). DUO:0000021 IRB ethics approval required This data use modifier indicates that the requestor must provide documentation of local IRB/ERB approval. DUO:0000022 GS geographical restriction This data use modifier indicates that use is limited to within a specific geographic region. DUO:0000024 MOR publication moratorium This data use modifier indicates that requestor agrees not to publish results of studies until a specific date. DUO:0000025 TS time limit on use This data use modifier indicates that use is approved for a specific number of months. DUO:0000026 US user specific restriction This data use modifier indicates that use is limited to use by approved users. DUO:0000027 PS project specific restriction This data use modifier indicates that use is limited to use within an approved project. DUO:0000028 IS institution specific restriction This data use modifier indicates that use is limited to use within an approved institution. DUO:0000029 RTN return to database or resource This data use modifier indicates that the requestor must return derived/enriched data to the database/resource. DUO:0000043 CC clinical care use This data use modifier indicates that use is allowed for clinical use and care. DUO:0000044 NPOA population origins or ancestry research prohibited This data use modifier indicates use for purposes of population, origin, or ancestry research is prohibited. DUO:0000045 NPU not for profit organisation use only This data use modifier indicates that use of the data is limited to not-for-profit organisations. DUO:0000046 NCU non-commercial use only This data use modifier indicates that use of the data is limited to not-for-profit use. Point to Notice: For the consent code DUO:0000007 where data is restricted to use on a specific disease, please accompany it with an appropriate ontology from MONDO e.g., If the data is restricted to the use of research into juvenile idiopathic arthritis the code should be displayed as DUO:0000007; MONDO:0011429.

  Documentation access/data-access-committee/data-use-ontology

• CSER: Evaluating Utility and Improving Implementation of Genomic Sequencing for Pediatric Cancer Patients in the Diverse Population and Healthcare Settings of Texas: The KidsCanSeq Study

  Through this Clinical Sequencing Evidence-Generating Research (CSER) with Enhanced Diversity project we are completing a clinical study (The Texas KidsCanSeq Study) comparing the results of targeted cancer panel sequencing versus genome-scale testing in pediatric cancer patients across diverse clinical settings. We will compare the targeted pediatric cancer panel to germline whole exome sequencing (WES) of unselected childhood cancer patients. We will also compare an RNA/DNA targeted pediatric cancer panel versus WES, transcriptome sequencing and copy number array of FFPE tumor samples for the subset of patients with high-risk tumors. Exome sequencing is performed from a blood or saliva sample of the pediatric age (0-18) patient. We are building on our success completing the CSER program BASIC3 exome sequencing trial (which included 60% Hispanic and African-American patients from a single large academic center) in this large multi-institutional study of an even more diverse patient population from six pediatric oncology healthcare settings across Texas. Data from the Texas KidsCanSeq study will be submitted through the CSER Data Coordinating Center for access in NIH-supported databases.

  Study phs002378

• Transcriptome profiling of head and neck squamous cell carcinomas with paired patient-derived xenografts

  This dataset includes transcriptome profiling of 88 head and neck primary patient tumor samples and 45 paired patient-derived xenograft samples that successfully engrafted in mice. NGS was performed using the Illumina TruSeq stranded total RNA sample preparation kit on the Illumina NovaSeq X Plus platform at the Princess Margaret Genomics Centre. This dataset consists of paired-end fastq files.

  Dataset EGAD50000000995

• Single-cell RNA sequencing of cytokine-induced T cell responses

  Cytokines affect T cell responses by polarising them to different phenotypes. We isolated T cells from healthy platelet donors and cultured them in resting and stimulated condition, as well as in the presence of Th2, iTreg and Th17 polarizing cocktail. To characterize the efficacy of cytokine induced porization and subpopulation specific response, we profiled single cell transcriptome five days following polarization using 10x platform (3' v2).

  Dataset EGAD00001005290

• Diseased heart analysis: RNA Adult (2025-10-14)

  Samples for this project are taken from adult human hearts which have been preserved using 2 different methods (cold static storage on ice, and hypothermic perfusion). Samples have been acquired at various time points during post-preservation normothermic perfusion. The aim of the project is to assess the transcriptional changes caused by these differing methods of preservation. . This dataset contains all the data available for this study on 2025-10-14.

  Dataset EGAD00001015738

• Raw sequencing data for donor and patient fecal samples

  Microbiome analysis was performed on the patient samples collected pre-FMT and on days after FMT, and on samples collected from the FMT donor. Genomic bacterial DNA was extracted from fecal samples using the QIAamp DNA Stool kit (Qiagen, Hilden, Germany), with the addition of a bead-beating lysis step. Genomic 16S ribosomal-RNA V4 variable regions were amplified and sequenced on the Illumina MiSeq platform.

  Dataset EGAD00001004371

• Comparison of fresh and slow-frozen cancer samples for different applications

  Surplus tissues from multiple solid human cancers directly slow-frozen after resection can subsequently be used for different types of methods including the establishment of 2D, 3D, and ex vivo cultures as well as single cell RNA sequencing (scRNAseq) with similar results when compared to freshly analyzed material.

  Study EGAS00001005891

• Personalised Mapping of Tumour Development in Synchronous Colorectal Cancer Patients

  We performed an in-depth characterisation of 12 tumours from 3 syCRC patients by analysing histopathological, whole genome sequencing and RNA-sequencing data. We assessed the extent of genetic overlap between synchronous tumours and examined associations between clinicopathological information and the molecular, microbial and immune features of each tumour genome.

  Study EGAS00001004413

• CRISPR transduction of iPS cells

  Genome engineering using CRISPR/Cas9 technology enables simple, efficient and precise genomic modifications in human cells. Conventional immortalized cell lines can be easily edited or screened using genome-wide libraries with lentiviral transduction. However, cell types derived from the differentiation of induced Pluripotent Stem Cells (iPSC), which often represent more relevant, patient-derived models for human pathology, are much more difficult to engineer as CRISPR/Cas9 delivery to these differentiated cells can be inefficient and toxic. Here, we present an efficient, lentiviral transduction protocol for delivery of CRISPR/Cas9 to macrophages derived from human iPSC with efficiencies close to 100%. We demonstrate CRISPR/Cas9 knockouts for three non-essential proof-of-concept genes - HPRT1, PPIB and CDK4. We then scale the protocol and validate for a genome-wide pooled CRISPR/Cas9 loss-of-function screen. This methodology enables, for the first time, systematic exploration of macrophage involvement in immune responses, chronic inflammation, neurodegenerative diseases and cancer progression, using efficient genome editing techniques.

  Study EGAS00001005102

• The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk

  Deposited here are whole-genome sequencing bam or fastq files from the experimental isogenic cell lines used in the study "The chemotherapeutic CX-5461 is extremely mutagenic and may increase cancer risk, Koh, Gene (2023)". The bam files were aligned to GRCh38/hg38 using BWA-MEM. The corresponding variant call data have been deposited on Mendeley Data, V2, doi: 10.17632/d58cv549v6.2

  Dataset EGAD50000000036

• WES and RNAseq dataset

  This dataset includes WES and RNAseq for 7 patients with metastatic melanoma (4), non-small cell lung adenocarcnioma (1), cervix adenocarcinoma (1), and Epidermoid nasophaeyngeal carcinoma (1), enrolled in phase I clinical trials (NCT03475134m NCT04643574 and NCT05195619). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 4000/6000 and Illumina NextSeq 500/550 systems.

  Dataset EGAD50000000337

• Paired ONT and downsampled Illumina cfDNA dataset

  The dataset contains 12 lung cancer plasma cfDNA samples, 8 bladder cancer and 2 healthy control urine cfDNA samples collected in EDTA collection tubes. Shallow WGS was performed using both Oxford Nanopore Technologies' MinION platform with an R9.4.1 flow cell and the SQK-PBK004 kit (22 files) and Illumina Novaseq platform with an S4 flow cell in PE150bp configuration (2x22 files).

  Dataset EGAD00001009392

• Cancer initiation organoids BAM files

  This dataset contains whole-genome sequencing data files from colon organoid cultures, which were mutated using CRISPR-Cas9 for specific genes (APC, KRAS, TP53 and SMAD4) to generate in vitro transformed cancer cells. After introducing each mutation, the resulting cultures were subjected to whole-genome sequencing. In addition, some cultures were xenotransplanted in recipient mice. The resulting primary tumors and corresponding metastases were subjected to whole-genome sequencing.

  Dataset EGAD00001002719

• Genetic and immune landscape evolution defines subtypes of MMR deficient colorectal cancer

  Genomic data from a cohort of 19 MMR deficient colorectal cancers and 1 MMR proficient colorectal cancer. All cases were target gene DNA sequenced using multiple primary and where available metastatic tumour regions from surgical resection samples.

  Dataset EGAD00001008334

• Chondrosarcoma Validation Study

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from Chondrosarcoma cancer and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to re find and validate the findings of those exome libraries using bespoke pulldown methods and sequencing the products.

  Dataset EGAD00001000392

• RNA seq of intestinal biopsies and blood cells from patients with celiac disease and controls

  This study presents RNA sequenced from several intestinal phenotypes such as from patients with damaged intestinal mucosa as well as from those without a damaged intestine but still an autoantibody response to tissue transglutaminase (“potential celiac disease”) or those with treated celiac disease. By combining phenotypes in a much larger number of tissue samples from both peripheral blood and mucosal biopsies, we found highly differentially expressed genes, but also identify genes involved in controlling and triggering celiac disease associated changes. This study identifies molecular mechanisms involved in celiac disease and new possible targets for treatment and for identification of individuals at risk.

  Study EGAS50000001104

• WXS and RNA-seq raw sequence data for TG project

  WXS sequence data from 112 samples, RNA-seq sequence data from 117 samples, all sequence data are raw sequence data in fastq format, sequenced by Illumina platform.

  Dataset EGAD00001007989

• Arcagen - NET / NEC G3

  Arcagen is an EORTC/SPECTA pan-European project that aims to recruit 1000 rare cancer patients from different tumour domains of EURACAN. This study collected samples from advanced or metastatic rare cancer from patients older than 12, and analysed them using Foundation Medicine next-generation sequencing (NGS) panels (FoundationOne CDx for FFPE samples or FoundationOne Liquid CDx for blood samples). Here were are submitting the dataset that contains NGS files in .BAM format from 85 patients with extra-pulmonary neuroendocrine tumour or cancer grade 3 (NET / NEC G3).

  Dataset EGAD50000000913

• Bulk TCRseq data from 149 patients with bladder cancer

  For this dataset we performed bulk TCRseq on tumor and serial blood samples from patients with muscle invasive bladder cancer (n patients = 119, n blood = 209, n tumor = 28) or non-muscle invasive bladder cancer (n patients = 30, n blood = 56, n tumor = 19) for a total of 149 patients and 312 samples. Each sample consists of a paired fastq file. Sequenced on the Ilumina NovaSeq6000 platform using SP and S1 flow cells (v1.5, 2x101 cycles), with a mean yield of 42 million reads covering the CDR3β region.

  Dataset EGAD50000001382