10417 results for "cancer rna-seq"

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• RNA-seq data from the tumor samples of a head and neck cancer patient

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006654

• RNA-SEQ data from 47 CAF (Cancer Associated Fibroblast) samples sorted from fresh breast cancer

  Dataset EGAD00001003808

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Dataset EGAD00001000817

• RNA-seq data from 121 tumor samples with muscle invasive bladder cancer.

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). RNA-seq was performed using the QuantSeq kit FWD HT kit (Lexogen) using 500 ng input RNA from 121 tumor samples. Data provided here consist of 780 fastq files for RNA-seq.

  Study EGAS00001004505

• Breast Cancer bulk RNA-Seq Dataset

  Bulk RNA-Sequencing of 18 primary breast cancers from Wu et al. (2021) study.

  Dataset EGAD50000000649

• PDiamond Korean Lung Cancer Data

  WES and RNA-seq data of PDiamond Korean lung cancer data

  Dataset EGAD50000000844

• Data access policy for PDTX Breast Cancer RNA-seq data from Ros et al (2020)

  Dac EGAC00001001620

• Longitudinal single-cell RNA-seq data of metastatic ovarian cancer

  Study EGAS00001005010

• Multi-omic analysis of SDHB-related PCPG

  Hereditary SDHB-mutant pheochromocytomas (PC) and paragangliomas (PG) are rare tumours with a high propensity to metastasize although their clinical behaviour is unpredictable. To characterize the genomic landscape of these tumours and identify metastasis biomarkers, we performed multi-omic analysis on 94 tumours from 79 patients using six molecular methods including: whole-genome sequencing (WGS) of tumour and matched blood (n=94), whole transcriptome sequencing (RNA-seq) (n=91), small-RNA seq (n=90), DNA methylation profiling (n=93). Droplet-based (10x) single nuclei (sn)RNA-seq and snATAC-seq was applied to 9 and 7 cases, respectively. Additionally snATAC-seq was applied to one sample of normal adrenal medulla.

  Study EGAS50000000346

• RNA-seq and WGS data of MB-0152

  Identification of fusion transcripts by RNA-sequencing and Whole genome sequencing of a breast cancer patient sample (METABRIC ID MB-0152)

  Dataset EGAD00001003342

• Ex vivo modeling of precision immuno-oncology responses in lung cancer

  Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.

  Study EGAS50000000593

• Integrative Molecular Characterization of Breast Cancer

  Version 1: We have performed integrative analyses of breast cancer tumors, including whole exome sequencing (WES) of tumor and normal pairs, whole transcriptome sequencing, and single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq), to determine molecular correlates of response to novel and immune therapies. We have focused on dissecting the role of anticancer immunity in response to these therapies and understanding biological mechanisms underlying therapeutic resistance. The goal of this work is to advance a precision medicine approach to breast cancer treatment by identifying molecular biomarkers to improve therapy selection and novel therapeutic targets to overcome resistance. Version 2: We performed bulk whole exome sequencing (WES) of fresh frozen tissue from primary hormone receptor-positive breast cancer. Additionally, where available, we performed bulk whole transcriptome sequencing for the same individuals (tumor only). Additionally, we performed single cell sequencing on fresh frozen tissue from primary hormone receptor-positive breast cancer.

  Study phs002419

• Single cell RNA-sequencing of tissues from NSCLC patients treated with chemoimmunotherapy

  The goal of this study is to characterize immune cell populations by single cell RNA-sequencing (scRNA-seq) in tumor and uninvolved normal tissues from non-small cell lung cancer (NSCLC) patients with resectable non-small cell lung cancer and who received neoadjuvant chemoimmunotherapy. scRNA-seq was performed on seven pairs of tumor and normal tissues as well as one lymph node (LN) sample. Data set includes pair-end fastq files for single cell RNA sequencing of 7 neo-immuno patients. (Total 15 samples and 50 runs).

  Dataset EGAD00001009778

• Analysis of chromosomal background of cancerous mutations using a long-read sequencer

  In our previous study detecting cancerous complex SVs, we performed whole-genome sequencing of lung cancer clinical samples using PromethION. In this study, its depth and quality was substantially enriched and we conducted the phasing analysis in human lung cancer genomes. In addition, long read and short read RNA-seq was performed for transcriptome analysis.

  Study JGAS000349

• PanCurX Translational Research Initiative - RNA-Seq mapped and unmapped reads

  Integration of Genomic and Transcriptional Features in Pancreatic Cancer Reveals Increased Cell Cycle Progression in Metastases - RNA-Seq mapped and unmapped reads

  Dataset EGAD00001004548

• 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

• GELATO clinical trial RNA-Seq data (primary tumors and local recurrences)

  RNA-Seq data of 10 lobular breast cancer primary tumors and 3 local recurrences obtained from 11 unique patients from GELATO clinical trial. The included raw transcriptome sequencing data in fastq format was generated using Illumina NovaSeq 6000 from archived FFPE material.

  Dataset EGAD00001009835

• Phenotypic Signatures of Circulating Neoantigen-Reactive CD8+ T Cells in Patients with Metastatic Cancers

  Following enrichment of neoantigen-reactive T cells using pHLA tetramers or cell-surface markers, we performed single-cell sequencing (RNA-seq and TCR-seq) of circulating CD8+ T cells from metastatic cancer patient samples (Melanoma, Colorectal, and Breast cancer). In samples from previously studied patients, we identified cells bearing neoantigen-reactive T cell receptors and were able to study their phenotypic program. By studying the circulating neoantigen-reactive T cell program, a gene-signature was derived to identify neoantigen-reactive T cells from blood samples prospectively.

  Study phs003064

• RNA sequencing for gastric cancer ascites

  Whole transcriptome sequencing (RNA-seq) was performed on 39 PC specimens. Among them, 21 specimens also had WES data.

  Dataset EGAD00001004365

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993

• Combined PDCD1, BRAF and MAP2K7 Inhibition in BRAFV600E Colorectal Cancer: A Phase 2 Trial

  In this study, we evaluated the clinical efficacy of combined PDCD1, BRAF, and MAP2K7 inhibition in metastatic BRAFV600E Colorectal Cancer (CRC) patients (NCT03668431). We performed bulk whole exome sequencing (WXS) and bulk RNA sequencing (RNA-Seq) in 35 pre-treatment tumor biopsies and matching germline for tumor mutational burden (TMB), BM1/BM2 transcriptional subtypes, and consensus molecular subtypes (CMS) analysis, which were previously reported to affect prognosis and response to therapy (PMIDs: 32047001, 27354468, and 26457756). Analysis of bulk data showed that TMB, BM1/BM2 subtypes, and CMS did not correlate with clinical response in this study. Analysis of single cell RNA sequencing (scRNA-Seq) data of tumor epithelial cells from 23 paired day 0 and day 15 tumor biopsies revealed greater induction of tumor cell-intrinsic immune programs and more complete MAPK inhibition in patients with better clinical outcome.

  Study phs003178

• RNA-sequençing of 21 inflammatory hepatocellular adenomas

  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.The present series corresponds to 21 RNA-seq of IHCA samples. Aligned bam files correspond to hg38 human reference.

  Study EGAS00001003685

• Transcriptomic profiling of patient-derived xenografts and organoids in prostate cancer

  Dataset contains RNA-seq of 30 samples (normal prostate tissue, human prostate cancer, PDX and organoids).

  Dataset EGAD00001006404

• Very Low Tumor Mutation Burden Identifies Inflamed Recurrent Glioblastomas Responsive to Cancer Immunotherapy

  A subset of available tumors (with matched blood) from patients enrolled in a phase I clinical trial testing a recombinant poliovirus, published by Desjardins et al. NEJM 2018 (PMID:29943666), were analyzed by whole exome sequencing and RNA-seq. It was discovered that low tumor mutation burden correlated with survival outcome, and also discovered that low tumor mutation burden correlated with higher inflammatory gene signatures by RNA-seq.

  Study phs002270

• Whole exome and transcriptome sequencing of gastric cancer patients

  Whole exome and RNA-seq of matched normal gastric mucosa (n=34) and gastric cancer tissues (n=34) from gastric cancer patients (n=34)

  Dataset EGAD00001004164