495 results for "cancer rna-seq"

in 6.84 milliseconds.

• RNA-seq analysis of TGF-β-induced transcriptional changes in 19TT cancer-associated fibroblasts

  The dataset contains 6 samples, three biological replicates for vehicle-treated 19TT cells, and three biological replicates for TGF-β-treated 19TT cells. The dataset was generated by the next generation high throughput sequencing. Strand specific transcriptome library construction is completed by enriching mRNA from total RNA, sequenced by DNBSEQ high throughput platform.

  Dataset EGAD50000001350

• CRUK Accelerator: oesophageal adenocarcinoma whole exome and RNA-seq raw sequencing data

  Whole exome and RNASeq raw sequencing data for a cohort of 7 male patients with oesophageal adenocarcinoma. Median age at diagnosis was 68. Tumour tissue and PBMCs were used for whole exome sequencing and RNA sequencing. This data was generated as part of a study funded by a Cancer Research UK Centres Network Accelerator Award Grant (A21998).

  Dataset EGAD00001008489

• Integrative Analysis for Multi-Omics Data in Non-Small-Cell Lung Cancer

  We conducted epigenetic and transcriptomic mapping of primary non-small cell lung tumors and non-neoplastic tissues. 20 non-neoplastic lung tissues and 18 non-small cell lung tumors were obtained from Biorepository and Tissue Technology Shared Resource at Moores Cancer Center, UC San Diego. All tumor samples were in pathological stages I and II. 8 pairs of samples were "matched", meaning that the tumor and normal tissues came from the same patient. Microfluidic oscillatory washing–based chromatin immunoprecipitation followed by sequencing (MOWChIP-seq) was used to profile the binding of H3K4me1, H3K4me3, H3K9me3, H3K27ac and H3K27me3. SmartSeq-2 was used to map mRNA expression. We identified a large number of differentially modified regions for both epigenetic and transcriptomic markers between tumors and normal tissues. Deep learning model showed extensive transcription factor network rewiring. Raw sequencing data of the ChIP-seq and RNA-seq libraries are available through this dbGaP submission.

  Study phs003113

• Pooled Mutant KRAS-Targeted Long Peptide Vaccine Combined with Nivolumab and Ipilimumab for Patients with Resected MMR-p Colorectal and Pancreatic Cancer

  We developed INtraCEllular Reverse Transcription with Sorting and sequencing (INCERTS), an approach to combine molecular indexing of receptor repertoires within intact cells and fluorescence-activated cell sorting (FACS). We demonstrate that INCERTS enables efficient processing of millions of cells from pooled human peripheral blood mononuclear cell (PBMC) samples while retaining robust association between T cell receptor (TCR) sequences and cellular phenotypes. We used INCERTS to discover antigen-specific TCRs from patients with cancer immunized with a novel mutant KRAS peptide vaccine. After ex vivo stimulation, 28 uniquely barcoded samples were pooled prior to FACS into peptide-reactive and non-reactive CD4+ and CD8+ populations. Combining complementary patient-matched single-cell RNA sequencing (scRNA-seq) data enabled retrieval of full-length, paired TCR alpha and beta chain sequences for future validation of therapeutic utility. Data available through dbGaP include scRNA-seq with matched TCR-seq.

  Study phs003425

• RNA-Seq dataset for Genomic rearrangements in Pediatric Cancer

  RNA-Seq data for systematic gene fusion detection in Pediatric Cancer

  Dataset EGAD00001008152

• Metastases of a cancer of unknown primary (CUP)

  RNA seq analysis of 6 CUP metastases (each in triplicate), analysed by paired sequencing with NextSeq 500. Whole exome sequencing of 15 CUP metastases, analysed by paired sequencing with NextSeq 500.

  Dataset EGAD00001005963

• WCDT deep RNA-seq

  This dataset includes fastq files for total RNAseq of 104 patient biopsies with metastatic castration resistant prostate cancer. The RNAseq libraries were rRNA depleted and sequenced at 150bp paired-end on Illumina Novaseq.

  Dataset EGAD00001008799

• Genomic profiling of subcutaneous patient derived xenograft models of solid childhood cancer

  The pediatric cancer cohort in this study included 70 PDX models from 65 different individuals. This cohort included a total of 16 different pediatric solid tumor subtypes, including fourteen Wilms tumors, thirteen hepatoblastomas, thirteen osteosarcomas, ten germ cell tumors, four neuroblastomas, three clear cell sarcomas, two adrenal cortical carcinomas, two leydig cell tumors, two medulloblastomas, one embryonal rhabdomyosarcoma (ERMS), one Ewing sarcoma, one pleomorphic sarcoma, one adenocarcinoma, one glioblastoma, one mesothelioma and one ovarian tumor. Notably, we have five samples with multiple PDX models from same patient, including two cases with duplicates (564 and 564-Dup, 1796 and 1796-Dup), one case with two different metastasis (560-SM, 560-LM), one case with two blocks from same tumor (1939 and 1939-Dup), and one case with different primary tumor from same patient (2264 and 1932). We have a total of 353 sequencing data, including 82 RNA sequencing data (RNA-seq), 138 whole-exome sequencing (WES) and 135 low-pass whole-genome sequencing (WGS). For RNA-seq data, we have 61 PDXs and 21 PTs; for WES, we have 67 PDXs, 30 PTs and 40 matched normal germlines; for WGS, we have 64 PDXs, 30 PTs and 40 matched normal germlines. Of which, 19 PT-PDX paired RNA-seq, 28 paired PT-PDX paired WES and WGS were included.

  Dataset EGAD00001009863

• 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

• Breast Cancer - immune clusters - RNA-seq

  In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

  Dataset EGAD00001004985