14977 results for "cancer studies using rna-seq"

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• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  RNA from snap-frozen breast tissue biopsies were purified after lysing by tissuelyser (Qiagen) and using RNA Purification Plus Kit (Norgen biotek CORP, 47700) with additional on-column DNase-I treatment (Qiagen, 79254) at 27 °C. RNA purity and integrity (RIN) were quantified using RNA 6000 Nano kit (Agilent Technologies, 5067-1511) on the 4200 TapeStation (Agilent, Santa Clara, USA). Library preparation and 2x75bp paired-end of 160 ng total RNA input was performed using Illumina Stranded Total RNA Prep Ligation kit and Illumina HiSeq4000 system (Illumina, Sand Diego, CA, USA). RNA sequencing data from HiSeq4000 were quality checked and aligned to GRCh38 (GCA_000001405.15) reference genome using HISAT2 2.0.5 and submitted to subread v.1.5.2 for feature counts calculation. Finally, 36 paired biopsies samples (metformin n=26 and placebo n=12) were sequenced and included in the final analyses.

  Dataset EGAD50000001274

• RNA-Seq profiles from the CheckMate-649 Clinical Trial

  This dataset contains RNA sequencing (RNAseq) data of 814 patients from the CheckMate 649 clinical trial whose ICF allows data deposition into a public repository. Gene expression profiling was performed retrospectively using RNAseq on a subset of baseline tumor samples. Paired-end FASTQ files were processed on Seven Bridges platform (Seven Bridges Genomics).

  Dataset EGAD50000001105

• Characterizing Immune Profiles in Extrapulmonary Tuberculosis via RNA Sequencing

  This dataset provides insight into the immune response in extrapulmonary tuberculosis (EPTB), using bulk RNA-seq data to analyze different disease severities. Hierarchical clustering identified three distinct levels of severity, revealing disease progression driven by interferon and IL-1β-mediated signaling. Additionally, the dataset helped develop a diagnostic gene expression signature for both EPTB and pulmonary TB.

  Dataset EGAD50000000943

• SCLC study MGH - RNAseq dataset

  RNA-seq analyses were performed on cDNA libraries prepared from PolyA+ RNA using the Illumina TruSeq protocol for mRNA. The final libraries were sequenced with a paired-end 2×75 bp protocol aiming at 8.5 Gb per sample for a 30x mean coverage of the annotated transcriptome. All sequencing reactions were conducted on an Illumina HiSeq instrument (Illumina, San Diego, CA, USA).

  Dataset EGAD00001003969

• LongRNA_Monocytes

  This dataset consists of bulk RNA sequencing profiles generated from monocytes isolated from peripheral blood mononuclear cells (PBMCs) collected from 315 patients enrolled in the ImmunAID consortium. Each sample corresponds to a unique patient, providing a resource to investigate transcriptional variation in circulating monocytes across the cohort. PBMCs were isolated from whole blood, and monocytes were subsequently enriched using standard immunological protocols. Total RNA was extracted and libraries were prepared using the SMART-Seq mRNA Library Preparation Kit with Unique Molecular Identifiers (UMIs). Sequencing was performed on an Illumina NovaSeq 6000 platform following manufacturer-recommended protocols.

  Dataset EGAD50000002341

• RNA-seq data from the MESOMICS project (French project of molecular characterization of malignant pleural mesothelioma)

  The dataset includes fastq files from 109 tumor samples as well as RNA-seq gene expression R data, RNA-seq transcript expression R data, RNA-seq gene counts matrix, RNA-seq transcript counts matrix, RNA-seq gene FPKM matrix RNA-seq transcript FPKM matrix for the 109 samples. The sequencing was done with HiSeq 2000 instrument.

  Dataset EGAD00001007024

• Raw RNA-seq data for clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial

  ABACUS is a single arm phase 2 study that investigated 2 cycles of atezolizumab (1200mg Q3) prior to cystectomy in 95 patients with muscle invasive transitional cell cancer (T2-4N0M0). Pathological complete response (pCR) occurring in ≥20% of patients was the primary endpoint. Biomarker analysis on sequential tissue was a co-primary endpoint. This dataset includes the raw RNA-seq data.

  Dataset EGAD00001006205

• Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

  The role of non-coding variants in polygenic skin disease and their connection to homeostatic transcription factors (TFs) in epidermis was investigated. Homeostatic TFs were identified by perturb-seq in differentiating keratinocytes, using either 10x or split-pool approaches to sequencing, with follow-up validation by siRNA and RNA-seq. TF binding events were profiled by population sampling Cleavage Under Targets & Release Using Nuclease (CUT&RUN), in which a random population of neonatal foreskin keratinocytes were assayed by CUT&RUN for homeostatic TFs under progenitor growth and differentiation conditions. Heterozygous alleles were identified from the TF binding data and examined for allele specific binding events (ASBs). ASBs were then compared with non-coding variants linked to skin disease. Variants with potentially pathogenic effects were identified. Promoters and high TF occupancy genomic regions were found to be "buffered" against variants causing ASBs.

  Study phs003977

• SYSCOL Human colorectal cancer project pilot RNA-seq and germline genotyping

  Genotyping by array and Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001000880

• RNA-Seq of kidney cancer organoid biobank

  Dataset EGAD00001005318

• SU2C-MARK Lung Cancer Consortium - Checkpoint Blockade Response Project

  Immune checkpoint inhibitors have become a critical part of the armamentarium against advanced Non-Small Cell Lung Cancer (NSCLC). While early studies have identified Tumor Mutation Burden (TMB) and PD-L1 staining as important biomarkers of response, they remain imperfect predictors. Here we describe a multi-institutional effort as part of the SU2C-MARK Lung Cancer Consortium in which Whole Exome Sequencing (WES) - and in select cases, RNA sequencing – was performed on a large collection of NSCLC samples prior to immune checkpoint blockade therapy, along with matched clinical response data following therapy. (Profiling of a small number of on- or post-treatment samples was also performed.) We anticipate this dataset to be of high value for the development of a deep understanding of the molecular determinants of immune checkpoint blockade response as well as the discovery of novel biomarkers.

  Study phs002822

• Genomic and Transcriptomic Data from Advanced Rare Cancer Patients Treated with Pembrolizumab

  This dataset comprises whole exome sequencing data and RNA-seq data of advanced rare cancer patients.

  Dataset EGAD50000002182

• Proteogenomic Landscape of Curable Prostate Cancer

  Aligned RNA-seq sequences in this dataset are from the Proteogenomic Landscape of Curable Prostate Cancer study

  Dataset EGAD00001004875

• Hyperdiploid Acute Lymphoblastic Leukemia RNA-Seq

  Few genetic drivers of children with hyperdiploid precursor B-cell acute lymphoblastic leukemia (ALL) have been identified to date. In an effort to detect novel genomic rearrangements that could be promoting leukemogenesis in this subset of patients, we sequenced ribosomal RNA-depleted transcriptomes isolated from 5 hyperdiploid acute lymphoblastic leukemia samples. In addition to looking for novel genomic rearrangements, we also sought to identify transcripts that could be created as a result of a novel posttranscriptional process. Surprisingly, we identified transcripts created as the result of exon circularization. The results of our studies are present in the paper "Circular RNAs Are the Predominant Transcript Isoform from Hundreds of Human Genes in Diverse Cell Types" published in PLoS ONE (PMID: 22319583).

  Study phs000522

• Detection and characterization of lung cancer using cell-free DNA fragmentomes

  Lung cancer remains the leading cause of cancer death world-wide, largely due to its late diagnosis. Non-invasive approaches for assessment of cell-free DNA (cfDNA) provide an opportunity for detection and intervention that may have broader accessibility than current imaging approaches. Using a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation, we examined a prospective study of 365 individuals at risk for lung cancer (Lung Cancer Diagnostic Study, LUCAS), including 129 individuals ultimately diagnosed with lung cancer and 236 individuals determined to not have lung cancer. We externally validated the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 predominantly early stage lung cancer patients. Combining fragmentation features with clinical risk factors and CEA levels followed by CT imaging detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites could be used to distinguish individuals with small cell lung cancer (SCLC) from those with non-small cell lung cancer (NSCLC) with high accuracy (AUC=0.98). Among individuals with lung cancer, a higher cfDNA fragmentation score was associated with tumor size and invasion, and represented an independent prognostic indicator of survival. These studies provide a facile approach for non-invasive detection of lung cancer and clinical management of this disease.

  Study EGAS00001005340