14970 results for "cancer studies using rna-seq"

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• Resistance studies in Lung Cancer

  The purpose of the study was to compare gene expression profiles from a cohort of crizotinib-resistant ALK-rearranged lung tumors and a cohort of treatment-naive ALK-rearranged lung tumors. Expression profiles were generated by RNA-seq. In parallel, gene expression profiles were obtained from ALK-rearranged lung cancer cell lines in the presence or absence of the ALK inhibitor TAE684. Gene expression profiles were also obtained from ALK-rearranged cells ectopically expressing genes associated with ALK inhibitor resistance that were identified from a functional genetic study.

  Study phs000855

• RNA sequencing data from 10 patient derived colorectal cancer organoids

  This dataset comprises RNA-sequencing data from ten colorectal cancer (CRC) organoids, derived from histologically verified primary tumor tissues of patients undergoing surgical resection. Paired-end sequencing produced 20 FASTQ.gz files. The organoids were cultured in Geltrex-based droplets using PDO culture medium and harvested for RNA extraction. Total RNA was isolated and subjected to full-length mRNA sequencing, producing high-quality RNA-seq data. The dataset includes transcriptomic profiles of seven microsatellite stable (MSS) and three microsatellite instable (MSI) tumors, providing a valuable resource for the analysis of non-canonical tumor-specific antigens (ncTSAs) using the NovumRNA pipeline. This dataset aids in uncovering potential immunotherapy targets in CRC, particularly in hard-to-treat low-TMB MSS cases.

  Dataset EGAD50000000962

• Mechanisms of Chemotherapy Resistance in T-ALL

  The goals of this study are to unravel the molecular mechanisms underlying leukemic transformation and chemotherapy resistance in T-cell acute lymphoblastic leukemia (T-ALL). Towards this end, a cohort of T-ALL lymphoblast specimens was collected at the time of initial T-ALL diagnosis (prior to the start of therapy) from children enrolled on contemporary clinical trials, Children's Oncology Group study AALL0434 and Dana-Farber Cancer Institute 05-001. Samples were analyzed using targeted exon sequencing and RNA sequencing analysis. Results of these analyses form the basis of studies that have been submitted for publication, and will be linked to this entry once published.

  Study phs001513

• Single-cell RNA-seq profiling of patient derived organoids

  The aim of this study was to profile developement of resistance to cancer treatments using organoids. Two patient derived colorectal cancer organoids were profiled using single cell RNAseq after treatment with a combination of different anticancer drugs. The organoids were profiled both after 4 weeks of treatment and after a drug holiday of 2 weeks.

  Study EGAS50000001025

• Whole Exome Sequencing of 60 tumor/normal matched liver Cancers (HCC)

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome (WGS) and whole exome sequencing (WGS), integrated with DNA methylation and RNA sequencing (RNA-seq) data. The present series corresponds to 60 whole exome tumor/normal pairs with matched RNA-seq.

  Dataset EGAD00001004180

• Identification of novel fusion genes in lung cancer using breakpoint assembly of transcriptome sequencing data

  Genomic translocation events frequently underlie cancer development through generation of gene fusions with oncogenic properties. Identification of such fusion transcripts by transcriptome sequencing might help to discover new potential therapeutic targets. We developed TRUP (Tumor-specimen suited RNA-seq Unified Pipeline (https://github.com/ruping/TRUP), a computational approach that combines split-read and read-pair analysis with de-novo assembly for the identification of chimeric transcripts in cancer specimens. We apply TRUP to RNA-seq data of different tumor types, and find it to be more sensitive than alternative tools in detecting chimeric transcripts, such as secondaryrearrangements in EML4-ALK-positive lung tumors, or recurrent inactivating rearrangements affecting RASSF8.

  Study EGAS00001000659

• RNA-seq as a tool for evaluating human embryo competence

  The majority of embryos that are created through IVF do not implant. It seems plausible that rates of implantation would improve if we had a better understanding of molecular factors affecting embryo competence. Currently, the process of selecting an embryo for uterine transfer utilizes an ad-hoc combination of morphological criteria, the kinetics of development, and genetic testing for aneuploidy. However, no single criterion can ensure selection of a viable embryo. In contrast, RNA-sequencing of embryos could yield highly dimensional data, which may provide additional insight and illuminate the discrepancies among current selection criteria. Indeed, recent advances enabling the production of RNA-sequencing (RNA-seq) libraries from single cells have facilitated the application of this technique to the study of some transcriptional events in early human development. However, these studies have not assessed the quality of their constituent embryos relative to commonly used embryological criteria. Here, we perform proof-of-principle advancement to clinical selection procedures by generating high quality RNA-seq libraries from a trophectoderm biopsy as well as the remaining whole embryo. We combine state-of-the-art embryological methods with low-input RNA-seq to develop the first transcriptome-wide approach for use in future predictive embryology studies. Specifically, we demonstrate the capacity of RNA-seq as a promising tool in preimplantation screening by showing that biopsies of an embryo can capture valuable information content available in the whole embryo from which they are derived. Furthermore, we show that this technique can be used to generate a RNA-based digital karyotype, and to identify candidate competence-associated genes. Together, these data establish the foundation for a future RNA-based diagnostic in IVF.

  Dataset EGAD00001005044

• Transcriptome Sequencing PPGL (2)

  RNA-Seq data (n=81) library preparation was performed as previously reported by Calsina et al 2023 using QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015). The prepared libraries were subsequently sequenced on NovaSeq™ system (Illumina). To combine the new CNIO samples (n=81) with the CNIO+TCGA dataset generated in Calsina et al., 2023, RNA data were processed using ComBat-seq as described in that same article.

  Study EGAS50000000013

• RNA sequencing data from glioblastoma primary cell lines treated with indisulam

  This study aimed to develop NovumRNA, a bioinformatics pipeline designed to predict non-canonical tumor-specific antigens (ncTSAs) from human tumor RNA-sequencing data, potentially broadening the target pool for cancer neo-epitope-based immunotherapies. The splicing-modulating drug indisulam was investigated for its potential to induce aberrant transcriptomes, potentially increasing ncTSAs, subsequently predicted by NovumRNA. Four glioblastoma primary cell lines were derived from patient Cavitron Ultrasonic Aspirator (CUSA) material, cultured as neurospheres, and treated with indisulam or vehicle control. RNA was extracted at 6 and 16 hours post-treatment and sequenced using the NovaSeq platform. The resulting RNA-seq data was analyzed with NovumRNA, using healthy brain GTEx samples as controls, to identify and validate ncTSAs induced by indisulam treatment. NovumRNA is available at: https://github.com/ComputationalBiomedicineGroup/NovumRNA.

  Study EGAS50000000680

• Multi-omics analysis of treated cancer samples

  We treated patient-derived cell lines with effective drug inhibitors that we discovered. To understand the mechanisms behind how the inhibitor can effectively inhibit tumor growth, we performed a series of RNA-Seq, Histone ChIP-Seq, TF ChIP-Seq and ATAC-Seq profiling for samples with and without treatment with the inhibitor. There are 18 RNA-Seq, 20 ATAC-Seq and 182 ChIP-Seq experiments performed. This includes 2 biological replicates. Novaseq 6000 was used to sequence the samples. Library preparation was performed in house for ChIP-Seq and ATAC-Seq, using NEB indexes, while libraries for RNA-Seq were constructed by the sequencing company hired. Total RNA was sequenced. Sequencing parameters used are PE150, bi-directional sequencing. For ATAC-Seq, sequencing was performed up to 40M reads, for ChIP-Seq, sequencing was performed up to 20M reads and for RNA-Seq, sequencing was performed up to 20M to 40M reads. Fastq files are uploaded here.

  Dataset EGAD50000000349

• Genomic Analysis of Head and Neck Cancers

  These aggregate studies focus on specimens from head and neck squamous cell carcinoma (HNSCC) patients who were treated with standard of care or window of opportunity clinical trial approaches. Specimens were interrogated with exome and RNA-Seq analysis. Patient derived xenografts (PDXs) represent a robust platform for performing co-clinical trial approaches. In the first study we generated a large panel of PDXs and compared genomic profiles between the primary tumor and matched PDX with the patients blood serving as a source of somatic variants. In the second study, we performed a window of opportunity clinical trial in oral cavity squamous cell carcinoma patients using the MEK inhibitor trametinib. In addition, PDX models were generated from these specimens also and analyzed as in the first study. Finally, the third approach used pembrolizumab, an anti-PD1 blocking antibody, in a window of opportunity setting in HNSCC patients. For the latter two studies genomic matched sample analysis was performed to define response and resistance mechanisms. Together, these genomic data will inform several therapeutic approaches for HNSCC patients.

  Study phs001623

• Cancer RNA-seq consisting of FASTQ single-end reads from colon cancer sample

  Cancer RNA-seq consisting of FASTQ single-end reads from 1 colon-cancer individual RNA-seq was performed on illumina This dataset contains reads from a single region.

  Dataset EGAD00001007949

• Comprehensive genomic characterization of early stage bladder cancer - Total RNA-seq data

  This study looks at the non muscle invasive bladder cancer subtypes based on totRNAseq data of 414 bladder cancer samples collected in different European centers within the UROMOL project. All samples were sequenced using paired reads on Illumina platforms. The reads are mapped to hg38.

  Study EGAS50000000512

• HiDEF-seq single-molecule sequencing of single-strand mismatches and damage

  Our study sought to resolve, with single-molecule fidelity, the mismatches and damage events that precede DNA mutations. Using a novel single-molecule, long-read sequencing method (HiDEF-seq) we detect base substitutions when present in either one or both DNA strands. We also detect cytosine deamination, a common type of DNA damage, with single-molecule fidelity. This study profiled 134 samples from diverse tissues, including from individuals with cancer predisposition syndromes. These samples revealed single-strand mismatch and damage signatures. Since double-strand DNA mutations are only the endpoint of the mutation process, our approach enables new studies of how mutations arise in a variety of contexts, especially in cancer and aging.

  Dataset EGAD50000000460

• University of Michigan ALS Biorepository

  We are collaborators at the University of Michigan (Ann Arbor, MI) studying amyotrophic lateral sclerosis (ALS), employing sequencing-based approaches (e.g. bulk RNA-seq of whole blood) in case-control studies.

  Dac EGAC50000000598

• Small-RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  Single-end 75 basepair small-RNA sequencing of 90 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. Samples were prepared using the NEXTFLEX® Small RNA-Seq Kit v3 (Bioo Scientific ). Samples were sequenced at the molecular genomics core (Peter MacCallum Cancer Centre) using 50bp single end sequencing on the Illumina NextSeq 500 (Illumina, USA). This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000501

• Postmortem Single Nuclei and Bulk RNA-seq data of the Motor Cortex and Spinal Cord for Healthy, C9ALS and sALS Patients

  This dataset contains both snRNA-seq and bulk RNA-seq data from 19 different patients, comprising of 9 healthy controls (4 Spinal Cord, 5 Motor Cortex) as well as 5 C9ALS patients and 5 sporadic ALS patients, each with paired data from the spinal cord and motor cortex. For the snRNA-seq data, fastq files containing the raw reads are provided, many of these samples were pooled for sequencing and subsequently require demultiplexing using SNPs using a tool such as freemuxlet. The paired bulk RNA-seq data (raw fastq files) can be used to acquire the ground truth per patient - a list of pooled samples can be found in map.txt. snRNA-seq data was produced using the 10X Genomics 3' v3 kit and bulk RNA-seq was produced using the Illumina TruSeq v2 kit.

  Dataset EGAD00001009686

• Transcriptome profiling for Korean Early Onset Gastric Cancer

  To identify transcriptome profile in this unique subset of gastric cancers, RNA-seq analyses were performed using frozen cancer tissue. Adjacent normal tissue of the same patients were used in differently expressed gene selection and fusion gene prediction.

  Dataset EGAD00001002187

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an OR operator when a space is inserted between two words. Include AND (capitalised) between the keywords in your search in order to show only pages containing both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• DNA Methylation Markers and Pancreatic Cancer Risk in 3 Cohort Studies (NHS, PHS, HPFS)

  Pancreatic cancer is the 4th leading cause of cancer deaths in the US. Due to the lack of early symptoms, pancreatic cancer is difficult to identify at early stages and no screening is available for the general population. Identifying pancreatic cancer at earlier stages could significantly improve survival with increased opportunities for surgery. New high-dimensional arrays designed to measure DNA methylation levels at hundreds of thousands of CpG sites throughout the genome have opened opportunities to examine the role of DNA methylation in cancer risk using blood samples. Using this method, archived samples from prospective studies can be used to examine early changes in the DNA methylation levels in individuals who develop cancer months or years later, providing new opportunities to better understand biological mechanisms and, perhaps, identify biomarkers for early detection. Pancreatic cancer cases and matched controls were obtained from 3 large cohort studies, the Nurses' Health Study (NHS), the Physician's Health Study (PHS), and the Health Professionals Follow-up Study (HPFS), to measure DNA methylation in stored buffy coats using a nested case-control study design.

  Study phs001917

• Natural variation of circulating RNAs in human serum

  Non-coding RNA (ncRNA) molecules have fundamental roles in cells and many are also stable in body fluids as extracellular RNAs. In these studies, we used RNA sequencing (RNA-seq) to investigate the profile of small non-coding RNA (sncRNA) in human serum. We analyzed more than 10 billion Illumina reads from more than 500 serum samples, included in the Norwegian population-based Janus Serum Bank (JSB). Our results suggest that many circulating RNAs in serum can be potential biomarkers and they are associated with different traits including age, sex, smoking, body size etc.

  Study EGAS00001002814

• Mutational Landscape of Plasmablastic Lymphoma

  Plasmablastic lymphoma (PBL) represents a clinically heterogeneous subtype of aggressive B-cell non-Hodgkin lymphoma. Although targeted sequencing studies and a single center whole exome sequencing (WES) study in HIV+ patients recently revealed several genes, associated with PBL pathogenesis, the global mutational landscape and transcriptional profile of PBL remain elusive. To inform on disease-associated mutational drivers, mutational patterns and perturbed pathways in HIV+ and HIV- PBL we performed WES and RNA-sequencing (RNA-seq) of 34 PBL tumors.

  Study EGAS00001004906

• single cell RNA-seq of small cell lung cancer tumors

  This study investigated the transcriptomic profile of small cell lung cancer from 10 patients. Tumors were rapidly dissociated after the surgical procedure. Libraries were constructed using the VDJ NextGEM v1.1 10x Genomics Chromium kit. Samples were sequenced on a NextSeq 550 sequencer (Illumina).

  Study EGAS50000001400

• Integrating Genomic and Transcriptomic Data to Identify Breast Cancer Susceptibility Genes

  Genetic factors play an important role in the etiology of both sporadic and familial breast cancer. Since 2007, common genetic variants in ~200 loci have been identified in genome-wide association studies (GWAS) in relation to breast cancer risk. However, it is often difficult to translate GWAS findings to disease prevention and treatment since causal genes in the large majority of GWAS-identified loci are unknown. Furthermore, a large fraction of breast cancer heritability remains unexplained. Recent studies suggest that nearly 80% of disease heritability can be explained by genetic variants regulating gene expression. Herein, we propose three well-powered transcriptome-wide association studies (TWAS) to systematically investigate the association of breast cancer risk with gene expression across the transcriptome of African, Asian and European descendants. In Aim 1, we will perform RNA sequencing and high-density genotyping assays using normal breast tissue samples, and build race-specific gene expression prediction models using data from 1000 women of African, Asian and European descent. These models will be applied to the GWAS data generated from approximately 320,000 breast cancer patients and controls to impute gene expression for association analyses of predicted gene expression with risk of breast cancer overall, and by estrogen receptor and HER2 status. In Aim 2, we will select the top 50 genes identified in Aim 1 for in vitro functional assays to assess their influence on major cell functions related to cancer biology. In Aim 3, we will evaluate whether TWAS-identified genes may express differently in normal breast tissues and breast cancer tissues collected from African, Asian, and European descendants to assess whether these genes may contribute to racial differences in breast cancer risk by molecular subtypes. With strong methodology and a large sample size, we believe that this proposed study should be able to identify and characterize a large number of novel genes related to breast cancer risk. Uncovering breast cancer susceptibility genes will greatly improve the understanding of the genetic and biological basis for breast cancer and accelerate the translation of genetic findings to disease prevention and patient care.

  Study phs003535

• GELATO clinical trial RNA-Seq data (metastatic lesions)

  RNA-Seq data of 46 matched lobular breast cancer metastatic samples obtained from 21 unique patients from GELATO clinical trial assayed at three timepoints: at baseline (directly after patient randomization), pre-atezolizumab (after induction treatment with carboplatin for two weeks) and on atezolizumab (after two cycles of atezolizumab combined with carboplatin). The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000 from fresh frozen material.

  Dataset EGAD00001009834