15041 results for "cancer studies using rna-seq"

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• Integrative single-cell and cell-free plasma RNA transcriptomics elucidates placental cellular dynamics

  10 single-cell placental RNA libraries were generated using the Chromium Single Cell 3′ Reagent Kit (10X Genomics). All single-cell libraries were sequenced with a customized paired end with dual indexing (98/14/8/10-bp) format according to the recommendation by 10X Genomics. The data were aligned using the Cell Ranger Single-Cell Software Suite (version 1.0). Moreover, plasma RNA from 22 samples were extracted using the RNeasy Mini Kit (Qiagen). cDNA reverse transcription, second-strand synthesis, and RNA-sequencing (RNA-seq) library construction were performed using the Ovation RNA-seq System V2 (NuGEN) kit according to the manufacturer’s protocol. For alignment of the plasma RNA library, adaptor sequences and low-quality bases on the fragment ends (i.e., quality score < 5) were trimmed, and reads were aligned to the human reference genome (hg19) using the TopHat (v2.0.4) software. All aligned reads were deposited in bam file format.

  Dataset EGAD00001003705

• 10x Genomics VDJ single cell sequencing and 10x Genomics sc RNA-Seq (5 individuals/17 VDJ runs,19 scRNA runs))

  single cell RNAseq and TCR sequencing data of 5 individuals. 10x Genomics VDJ single cell sequencing (17 runs) and 10x Genomics sc RNA-Seq (19 runs). The VDJ sequencing was done on a Nextseq 550 using the Chromium Single Cell VDJ Reagent Kit. The RNA-Seq was done either on HiSeq4000 or NovaSeq 6000 using the Chromium Single Cell 5 Reagent Kit.

  Dataset EGAD00001009986

• The Adhesion GPCR ADGRL2 Engages Galpha13 to Enable Epidermal Differentiation

  We sought to determine the role of ADGRL2 in epidermal homeostasis. Primary neonatal foreskin keratinocytes were obtained from discarded surgical samples at a California hospital. Samples were anonymous and not selected. ADGRL2, working through GNA13, was found to promote epidermal differentiation through the ADGRL2 intracellular loop 3 (ICL3) loop, using the methods of RNA-seq and Perturb-seq.

  Study phs004223

• Differential Presence of Exons in Cell-Free DNA Reveals Different Patterns in Colorectal Cancer Between Metastatic and Non-Metastatic Patients

  Tumor genotyping during disease follow-up is gaining an increasing interest for monitoring and clinical management in colorectal cancer (CRC). Growing evidences suggest that single biopsies are inefficient for molecular profiling, due to clonal evolution and intratumoral heterogeneity of primary tumors and/or metastasis. Circulating free DNA (cfDNA) has emerged as an alternative source of genetic material that seems to be representative of the continuously changing tumor molecular features. Thus, the so-called 'liquid biopsy' also offers the additional advantages of accessibility and non-invasiveness for the patient. Novel strategies based on next-generation sequencing (NGS) of cfDNA are being currently developed, with most studies focusing on targeted deep sequencing panels of potential clinically actionable genes. Here we describe an alternative approach, consisting of exome-sequencing in cfDNA at a relatively shallower depth to provide a more general overview of the circulating genome. In this study, two groups of colorectal cancer patients, with either disseminated (M) or localized disease (N), were compared using whole-exome sequencing and a RNA-seq bioinformatics analysis pipeline. Thus, a set of 379 exons present at different levels in cfDNA from both groups were identified, giving rise to a completely new concept in this field, termed as 'Differential Presence of Exons'. Differentially present exons (DPEs) were subsequently used for M and N patients clustering and classification, allowing for the design of a predictive algorithm with encouraging preliminary results in a small subset of patients that could not be initially classified attending to the selection criteria (U group, unassignable).

  Study EGAS00001002687

• Prostate Cancer - RNA-Seq unmapped reads

  Prostate Cancer - RNA-Seq unmapped reads

  Dataset EGAD00001003756

• RNA-Seq of serially passaged TIC-enriched spheres of colorectal cancer

  18 samples of RNA-Seq of serially passaged TIC-enriched spheres of colorectal cancer (CRC), sequenced on HiSeq2000 and HiSeq2500

  Dataset EGAD00001006264

• RNA-SEQ for the Caldas Lab breast cancer PDTX collection

  RNA-SEQ for the Caldas Lab breast cancer PDTX collection. This includes both single and paired end runs

  Dataset EGAD00001006307

• Genomic Characterization CS-MATCH-0007 Arm Z1D

  The CS-MATCH-0007 protocol is a collaboration between the Center for Cancer Genomics (CCG) and the Division of Cancer Treatment and Diagnosis (DCTD) to perform whole-exome sequencing, RNA sequencing and if possible, whole-genome, methylation and miRNA sequencing using pre-and posttreatment tumor biopsy specimens from all patients enrolled on a treatment arm of the NCI-MATCH Clinical Trial (EAY131). The goal of this study is to identify the molecular basis for response and resistance to targeted therapies that are matched to their corresponding genomic alterations. Arm Z1D is one of the treatment sub-protocols within the NCI-MATCH Clinical Trial (EAY131) where patients with MMR deficiency are treated with nivolumab. This subprotocol is one of the treatment arms in the EAY-131 trial included in the CS-MATCH-0007 protocol and will provide specimens for the program including DNA from tumor tissue and whole blood and RNA from tumor tissue. A fuller genomic landscape of tumors from these patients will enable discovery of potential molecular features correlated to clinical outcomes or lay the foundation for future studies leading to new and more effective treatments.

  Study phs001859

• Genomic Characterization CS-MATCH-0007 Arm Y

  The CS-MATCH-0007 protocol is a collaboration between the Center for Cancer Genomics (CCG) and the Division of Cancer Treatment and Diagnosis (DCTD) to perform whole-exome sequencing, RNA sequencing and if possible, whole-genome, methylation and miRNA sequencing using pre-and post-treatment tumor biopsy specimens from all patients enrolled on a treatment arm of the NCI-MATCH Clinical Trial (EAY131). The goal of this study is to identify the molecular basis for response and resistance to targeted therapies that are matched to their corresponding genomic alterations. Arm Y is one of the treatment sub-protocols within the NCI-MATCH Clinical Trial (EAY131) where patients with AKT mutations are treated with AZD5363. This subprotocol is one of the treatment arms in the EAY-131 trial included in the CS-MATCH-0007 protocol and will provide specimens for the program including DNA from tumor tissue and whole blood and RNA from tumor tissue. A fuller genomic landscape of tumors from these patients will enable discovery of potential molecular features correlated to clinical outcomes or lay the foundation for future studies leading to new and more effective treatments.

  Study phs001904

• Genomic Characterization CS-MATCH-0007 Arm H

  The CS-MATCH-0007 protocol is a collaboration between the Center for Cancer Genomics (CCG) and the Division of Cancer Treatment and Diagnosis (DCTD) to perform whole-exome sequencing and RNA sequencing using pre-and post-treatment tumor biopsy specimens from all patients enrolled on a treatment arm of the NCI-MATCH Clinical Trial (EAY131). The goal of this study is to identify the molecular basis for response and resistance to targeted therapies that are matched to their corresponding genomic alterations. Arm H is one of the treatment sub-protocols within the NCI-MATCH Clinical Trial (EAY131) where patients with BRAF V600K/V600E mutations are treated with Dabrafenib and Trametinib. This subprotocol is one of the treatment arms in the EAY-131 trial included in the CS-MATCH-0007 protocol and will provide specimens for the program including DNA from tumor tissue and whole blood and RNA from tumor tissue. A fuller genomic landscape of tumors from these patients will enable discovery of potential molecular features correlated to clinical outcomes or lay the foundation for future studies leading to new and more effective treatments.

  Study phs001888

• RNA-Seq datasets in human islets cultured in low glucose conditions

  7 RNA-Seq datasets in human pancreatic islets from 7 donors, where islets were using low (4mM) glucose conditions.

  Dataset EGAD00001005208

• Leiomyosarcoma RNA-Seq

  RNA was extracted from fresh frozen LMS material for 29 untreated tumors (24 primary tumors, 5 metastatic12 relapses) and 13 tumors treated with radiation(7 primaries, 6 metastatic relapses). RNA-Seq sequencing was performed using established protocols on Illumina HiSeq 2500

  Dataset EGAD00001006628

• Single RNA-Seq of fCAB treated tRNAs from VDH01

  Single RNA-seq of fCAB treated tRNAs from vdh01 samples (5 replicates). tRNAs were extracted using Mirvana° Invitrogen kit. The samples were prepped with Il TruSeq Small RNA and sequenced on Illumina NextSeq550.

  Dataset EGAD00001008743

• BPH Tissues for Cell Culture and Analysis - A Patient-Derived Xenograft Model Using Benign Prostatic Tissues

  Our goal was to establish and characterize a patient-derived xenograft model for benign prostatic hyperplasia (BPH) using human benign prostatic tissues. Characterization of the model was done by histology, immunohistochemistry, proteomics, and transcriptome analysis (RNAseq). The RNAseq data will be deposited in dbGaP. Subjects were undergoing prostatectomy for prostate or bladder cancer, with concurrent BPH, and consented to the use of surplus tissue for research studies. The RNAseq studies included 6 patients. Phenotypic data include patient age, sex, prostate size, serum prostate-specific antigen (PSA) level, and international prostate symptom score (IPSS). Sequencing was done on an Illumina NovaSeq 6000, and data were analyzed using Kallisto. Data are submitted as raw sequencing (fastq) files.

  Study phs003692

• RNA-seq from Glioblastoma treated with Indisulam

  The dataset includes RNA-sequencing results from four patient-derived glioblastoma cell lines treated with DMSO (vehicle control) or the splicing modulating-drug indisulam (at 6 and 16 hours), resulting in 12 RNA-seq samples. Paired-end sequencing produced 24 FASTQ.gz files. The RNA libraries were prepared using PolyA selection and ERCC spike-ins and sequenced on a NovaSeq platform. This dataset supports the analysis of drug-induced transcriptomic changes and the prediction of subsequent non-canonical tumor-specific antigens (ncTSAs) using the NovumRNA pipeline.

  Dataset EGAD50000000957

• RNA-seq of CRC patient-derived xenograft tumors

  We developed a novel antibody-drug conjugate (ADC), 84-EBET. To analyze mechanism of action of 84-EBET, we obtained bulk RNA-seq data of the patient-derived xenograft (PDX) model derived from colorectal cancer (CRC) patient. The PDX model was treated with 5-fluorouracil + oxaliplatin, 84-EBET or vehicle, and then gene expression changes were analyzed with bulk RNA-seq.

  Study EGAS50000000598

• Single-Cell RNA-seq of Primary High Grade Serous Carcinoma

  Single-cell RNA Sequencing of CD45- cancer cells.

  Study EGAS50000000068

• Genentech Small Cell Lung Cancer (SCLC) Screen

  Exome capture, RNA-Seq, whole genome sequencing of set of Small Cell Lung Cancer samples.

  Study EGAS00001000334

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. FInally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Study EGAS00001004064

• RNA alignment

  RNAseq aligned to hg38.p14 using nfcore/RNA-seq

  Dataset EGAD50000000428

• RNA-Seq of response to Tozinameran vaccination in individuals with KTx/dialysis or healthy controls.

  Low-input RNA-Seq (~200 cells per sample) of CD4+ T cells in patients with kidney transplants, dialysis, or healthy controls after the second dose of Tozinameran COVID-19 vaccine. RNA-Seq was performed using the Smart-Seq v4 ultra-low input protocol. The dataset includes 4 healthy control samples, 3 kidney transplant recipients, and 4 patients undergoing dialysis.

  Dataset EGAD00001007689

• Transcriptomic Profiles of Neoantigen-Reactive T Cells in Human Gastrointestinal Cancers

  This study explores the transcriptomic profiles of neoantigen-reactive tumor-infiltrating lymphocytes (TILs) from human bile duct and pancreatic cancer. The submitted data are bulk tumor RNA-Seq, tumor and germline whole-exome sequencing from 10 patients, and single cell RNA-Seq data from TIL of 5 of these patients.

  Study phs002765

• Gastric cancer RNA-seq data

  strand-specific RNA-seq data from 19 gastric tumors and their adjacent normal tissues, plus 16 gastric cancer cell lines, one normal gastric cell line, and 3 normal stomach RNAs

  Dataset EGAD00001003448

• Prostate Cancer - RNA-Seq unmapped reads

  Prostate Cancer - RNA-Seq unmapped reads

  Dataset EGAD00001004424

• Multi-omics analysis of cocaine use disorder in postmortem brain tissue of the ventral striatum [Proteomics data to study EGAS50000000623]

  We performed a multi-omics analysis of cocaine use disorder in postmortem human brain tissue of the ventral striatum in N=41 individuals (N=20 CUD cases, N=21 non-affected individuals). From the postmortem brain tissue samples, we generated miRNA-seq, RNA-seq, proteomics, and snRNA-seq data. The miRNA-seq, RNA-seq and snRNA-seq datasets can be found in the EGA under study ID EGAS50000000623. Proteomics data is deposited under study ID EGAS00001007945 (this study). The proteomic dataset was obtained using TMT-labeling. Measurement was performed on an Orbitrap Fusion Lumos device.

  Study EGAS00001007945