516 results for "cancer rna-seq"

in 7.15 milliseconds.

• RNA-seq from PDAC samples

  Bam and fastq files from RNA-seq of PDAC samples described in Transcription phenotypes of pancreatic cancer are driven by genomic events events during tumour evolution

  Dataset EGAD00001005799

• 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

• Spatial heterogeneity, stromal phenotypes, and therapeutic vulnerabilities in colorectal cancer peritoneal metastasis

  Peritoneal metastases (PM) in colorectal cancer (CRC) portend a poor prognosis. We sought to elucidate molecular features differentiating primary tumors (PTs) from PMs and actionable targets facilitating transcoelomic dissemination and progression. We performed multi-omic profiling of 217 samples from 136 patients, including 56 primary tumor (PT) and 120 PMs comprising 25 matched PT-PM pairs. Whole exome, and bulk RNA-seq analysis was conducted to identify underlying genomic aberrations and transcriptomic differences between primary and peritoneal lesions.

  Study EGAS50000000813

• Single-Cell ATAC and RNA Sequencing of Human Breast Cancer Reveals Salient Cancer-Specific Enhancers

  Matched single-cell chromatin accessibility sequencing (scATAC-seq) and single-cell transcriptomic sequencing (scRNA-seq) were performed for 12 human breast tumor samples, 4 normal human mammary reduction samples, and 4 human breast cancer cell lines (HCC1143, SUM149PT, MCF7, and T47D). Both scATAC-seq and scRNA-seq assays resolved the underlying cellular heterogeneity in chromatin accessibility and transcriptional output for each patient sample. In this work, we quantitatively linked variation in chromatin accessibility to gene expression across malignant and non-malignant cell types from patient samples. We then showed that our findings were recapitulated in the breast cancer cell line samples. These data will serve as an important resource for the single-cell genomics and breast cancer research communities.

  Study phs003253

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

  Genotyping by array and Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001000880

• LUD2015-005 Operable Cohorts Single Cell RNA-Seq

  Single cell RNA sequencing from primary tumour biopsies of 16 patients from the operable cohorts of the LUD2015-005 oesophageal cancer study (NCT02735239, EudraCT 2015-005298-19).

  Dataset EGAD00001016815

• Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing in HGSOC

  Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Long-read single-cell RNA sequencing (scRNA-seq), capturing full-length transcripts, lacked the depth to provide this information so far. Here, we increased the PacBio sequencing depth to 12,000 reads per cell, leveraging multiple strategies, including artifact removal and transcript concatenation, and applied the technology to samples from three human ovarian cancer patients. Our approach captured 152,000 isoforms, of which over 52,000 were novel, detected cell type- and cell-specific isoform usage, and revealed differential isoform expression in tumor and mesothelial cells. Furthermore, we identified gene fusions, including a novel scDNA sequencing-validated IGF2BP2::TESPA1 fusion, which was misclassified as high TESPA1 expression in matched short-read data, and called somatic and germline mutations, confirming targeted NGS cancer gene panel results. With multiple new opportunities, especially for cancer biology, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

  Study EGAS00001006807

• Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

  In this study, we performed systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets. We generated SMART-seq2 data for 70 CD45- single cells, which were derived from two colorectal cancer patients (P0411 and P0413). The average sequencing depths of these cells were 1.4 million reads per cell. We also generated tumor and adjacent normal bulk WES data, as well as tumor bulk RNA-seq data for these patients.

  Dataset EGAD00001005373

• Molecular Characterization of Prostate Cancer Specimens by Bulk and Single Cell Analysis

  Version 1: We performed single-cell RNA-sequencing (scRNA-seq) of fresh biopsies from metastatic castration resistant prostate cancer. Additionally, where available, we performed matched bulk whole exome sequencing (WXS; tumor and normal paired) and whole transcriptome sequencing for the same individuals. We also performed bulk whole exome (WXS) and RNA-sequencing (RNA-Seq) from FFPE pre-treatment biopsies from patients with clinically localized high-risk prostate cancer.Version 2: We added data from a randomized phase 2 trial of radium-223 with or without pembrolizumab in patients with Metastatic Castration-Resistant Prostate Cancer (mCRPC) to bone. After enrollment, study participants underwent bone biopsy and were randomized 2:1 to receive radium-223 at 55 kBq/kg every 4 weeks + pembrolizumab at 200 mg every 3 weeks (Arm A, R223+P) or radium-223 at 55 kBq/kg every 4 weeks alone (Arm B, R223). Stratification factors were alkaline phosphatase ≥220 vs. Data generated includes bulk whole exome sequencing (WXS), ultra-low pass whole genome sequencing (WGS) at 0.1X coverage, and targeted-capture sequencing. The ultra-low pass sequencing was used as part of the selection criteria for the target capture sequencing. Note: Two subjects in this study are linked to phs001577 and phs000915 respectively.

  Study phs001988

• Metabolic Biomarkers in Thoracic Cancers

  Here we present RNA sequencing (RNA-Seq) of non-small cell lung cancer (NSCLC) and adjacent lung tissue. These samples were acquired from primarily untreated, surgically resected primary tumors. The first analysis published with this data was to determine the metabolic signatures between primary tumors and adjacent lung tissue.The associated clinical trial (NCT02095808) includes patients with many kinds of thoracic cancers, including NSCLC and other diseases, such as thymoma and esophageal adenocarcinoma. However, the current dbGaP data is focused on the NSCLCs described in the associated paper.

  Study phs003880