11935 results for "cancer rna-seq"

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• Patient-derived lung cancer organoids

  Patient-derived lung cancer organoids cram files : targeted seq 13 samples, whole exome seq 12 samples mutation profiles of PDO and matched tissue : aggregated vcf 1 file details : https://www.nature.com/articles/s41467-019-11867-6

  Dataset EGAD00001005317

• Transcriptomic analysis of membranes from proliferative vitreoretinal diseases and isolated human retinal pigment epithelium

  This study investigates the transcriptomic profiles of proliferative vitreoretinal diseases (PVD) membranes, including proliferative vitreoretinopathy and epiretinal membranes collected from patients undergoing surgery, as well as human retinal pigment epithelial cells isolated from post-mortem donor eyes. Total RNA was extracted using AllPrep DNA/RNA Micro Kit (Qiagen) and cDNA libraries were prepared using the Ovation Solo RNA-seq Kit (Tecan). Multiplexed libraries were loaded onto a NovaSeq 6000 platform (Illumina), and sequences were generated using a 200-cycle kit. This study includes over 80 pathological and healthy samples to allow comparison of gene expression profiles and gain insights into the molecular mechanisms of PVD.

  Study EGAS50000001253

• TenK10K Phase 1:  Single Cell

  Understanding the genetic basis of gene expression can shed light on the regulatory mechanisms underlying complex traits and diseases. Single-cell resolved measures of RNA levels and single-cell expression quantitative trait loci (sc-eQTLs) have revealed genetic regulation that drives sub-tissue cell states and types across diverse human tissues. Here, we describe the first phase of TenK10K, the largest- to-date dataset of matched whole-genome sequencing (WGS) and single-cell RNA-sequencing (scRNA-seq). We leverage scRNA-seq data from over 5 million cells across 28 immune cell types and matched WGS from 1,925 individuals. This provides power to detect associations between rare and low-frequency genetic variants that have largely been uncharacterised in their impact on cell-specific gene expression. We map the effects of both common and rare variants in a cell type specific manner using SAIGE-QTL.

  Study EGAS50000001653

• RNA-Sequencing data of patient derived normal fibroblasts (NFs), cancer associated fibroblasts (CAFs) and tumor spheroid samples

  RNA-Sequencing data of patient derived normal fibroblasts (NFs), cancer associated fibroblasts (CAFs) and tumor spheroid samples performed in the context of the characterization of an IL1R1 positive CAF population in CRC tumour samples.

  Study EGAS00001007205

• Childhood Cancer Data Initiative (CCDI): Comprehensive Genomic Sequencing of Pediatric Cancer Cases (CMRI/KUCC)

  This study provides paired tumor normal genomic sequencing data from ~ 200 children with cancer, including both solid tumors and leukemias, done by the Children's Mercy Research Institute (CMRI) and University of Kansas Cancer Center (KUCC). These data include whole genome sequencing (generally, ~20x), whole exome sequencing (generally, ~300x), bulk RNA sequencing (generally, ~80 million reads), and single-cell RNA and ATAC sequencing (>50,000 reads/cell). Additional phenotypic, pathologic, and genetic data, gathered clinically for these samples, are also provided.

  Study phs002529

• Idiosyncratic and generic single nuclei and spatial transcriptional patterns in papillary and anaplastic thyroid cancers

  Thyroid cancer is the most common endocrine malignancy. This dataset encompasses two types of thyroid cancer : anaplastic which is the most de-differentiated and aggressive one, and papillary which is the most common one. We profiled 14 patients, including 10 papillary and 4 anaplastic thyroid carcinomas, using both single nuclei RNA sequencing and spatial transcriptomics to link single cell resolution RNA sequencing with tissue morphology and better understand inter and intratumoral thyroid cancer heterogeneity.

  Study EGAS00001007574

• Single-nuclei RNA sequencing of pheochromocytoma and paraganglioma arising from germline SDHB mutations

  10x genomics single-nuclei RNA sequencing of 9 SDHB-deficient non-metastatic and metastatic pheochromocytoma and paraganglioma. snRNA-seq was performed using the ‘Frankenstein’ protocol (dx.doi.org/10.17504/protocols.io.bqxymxpw). Briefly, nuclei were extracted from frozen tissues and subjected to fluorescence-activated nuclei sorting (FANS) using 4′,6-diamidino-2-phenylindole (DAPI). Sorting was performed using a BD FACSaria 2 instrument, sorting between 3000 and 10,000 nuclei per sample, capturing both diploid and tetraploid populations. FAN-sorted nuclei were immediately processed using either the 10x Chromium Single Cell 5’ (PN-1000006, 4 samples) or 3’ (PN-1000075, 4 samples) Library & Gel Bead Kit (10x Genomics, USA). Once processed, snRNA-seq libraries were sequenced on the Illumina Nova-Seq 6000 (Illumina, USA) using 150bp paired-end sequencing. This dataset contains raw sequencing reads in FASTQ format.

  Dataset EGAD50000000505

• SF4324 Primary Wildtype GBM Female, 57.

  Single Cell RNA-Seq of Primary GBM. Gender Female, Age, 57.

  Dataset EGAD00001006017

• GENCORD2_RNAseq

  GENCORD2 RNA-seq BAM files using BWA

  Dataset EGAD00001000425

• National Cancer Institute Cancer Genome Characterization Initiative (CGCI)

  The Office of Cancer Genomics at the National Cancer Institute sponsored a series of studies as part of the Cancer Genome Characterization Initiative (CGCI) to assess novel emerging sequencing technologies in cancer. The CGCI program included comprehensive characterization of the genetic aberrations found in different pediatric and/or adult tumors. CGCI characterized a number of B-cell non-Hodgkin lymphomas (including diffuse large B-cell lymphoma (DLBCL) from patients with and without HIV+ infection, follicular lymphoma (FL), as well as adult and pediatric Burkitt lymphomas), and additional HIV-associated tumors (including DLBCL, lung, and cervical cancers). All data from these projects is available at the NCI Genomic Data Commons. Individual project descriptions are available by disease on the substudy pages (links are included at the top of this page). Brief summaries are as follows: Non-Hodgkin Lymphoma (NHL) - CGCI investigators probed genomic alterations more deeply than had previously been possible by using state-of-the-art RNA sequencing (mRNA-seq) and whole genome shotgun sequencing (WGS) coupled with leading edge bioinformatics, data management and analysis approaches. The project sequenced tumor DNA and/or RNA from 117 NHL tumor samples and 10 cell lines. This included the genomes or exomes of 1 Follicular Lymphoma (FL) and 13 diffuse large B-cell lymphoma (DLBCL) cases, all with matched constitutional DNA sequenced to comparable depths, RNA-sequencing (mRNA-seq) of 92 DLBCL, 12 FL and 8 B-cell NHL cases with other histologies and 10 DLBCL-derived cell lines. The DLBCL cases and cell-lines are from the two major subtypes of DLBCL: germinal center B-cell (GCB) and activated B-cell (ABC). HIV+ Tumor Molecular Characterization Project (HTMCP) - This project was a joint effort of the Office of Cancer Genomics (OCG) and the Office of HIV and AIDS Malignancy (OHAM). Its goals were to characterize HIV-associated cancers (obtained from HIV-infected patients) and compare them to the same types of cancers from patients without HIV infection. Investigators performed 30X genome sequencing of 100 cases of paired tumor and germline DNA, along with transcriptome sequencing in each of 3 types of HIV+ tumors (DLBCL, lung and cervical cancers). These platforms allow discovery of mutations both in coding and non-coding genomic regions, gene expression and genomic alterations (including translocations, insertions and deletions). Comparing tumors of cancer patients both with and without HIV-infection provides insight into the potential function of this virus in certain cancers. Burkitt Lymphoma Genome Sequencing Project (BLGSP) - This project was a collaborative effort between the National Cancer Institute and the Foundation for Burkitt Lymphoma Research to develop a databank of the many alterations found in Burkitt lymphoma (BL), an uncommon type of Non-Hodgkin lymphoma that occurs most often in children and young adults. The goal of the BLGSP was to explore potential genetic changes in patients with BL that could lead to better prevention, detection and treatment of the cancer. The project characterized the alterations of the tumors' genomes (with matched normal as control) and transcriptomes by sequencing the DNA and RNA of each case. Using the data generated, the ultimate goals of the project was to discover the molecular changes that are present in BL patients and then determine how those changes correlate with treatment regimen and outcome. CGCI data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the CGCI Publication Pages at the GDC (Please see the "Supplemental Links" section of any CGCI publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original CGCI research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data), as well as higher level data generated by the GDC. Please see the CGCI Use and Publication Guidelines for updated details on the sharing of any CGCI substudy data, including how to cite CGCI.To learn more about the CGCI studies, please visit the CGCI Program website.

  Study phs000235

• Combined Tumor and Immune Signals From Genomes or Transcriptomes Predict Outcomes of Checkpoint Inhibition in Melanoma

  Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, and integrative models using DNA and RNA have improved predictions of clinical benefit. However, identification of immune and tumor-intrinsic features in models using DNA or RNA alone have not been comprehensively explored in melanoma. In this study, we sequenced DNA and RNA of tumors from 67 melanoma patients receiving CPB, and aggregated this cohort with previously published data, yielding whole exome sequencing (WES) data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences, and found that melanoma patients with high TMB and either high TCB or high BCB survived longer and had higher response rates, as compared to patients with low TMB and either low TCB or low BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes which are associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs which are associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Gene ID: 11184) and the transcription factor TBX3 (Gene ID: 6926) which is overexpressed in poorly differentiated melanomas. We concluded that RNA-based (MAP4K1 and TBX3) or DNA-based (TCB and TMB) models combining with immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

  Study phs002683

• PBMC scRNA-seq data using 10X Genomics platform

  Matrices of counts from single-cell RNA-seq data and single-cell CITE-seq data collected from 16 patients enrolled in GO30103 trial, and the cell level annotation. Specifically, PBMCs at C1D1, C1D15 (2 weeks after treatment), C2D1 (3 weeks after treatment) and C4D1 (9 weeks after treatment) were collected and subject to 10x Genomics protocol. Associated metadata also included.

  Dataset EGAD50000000370

• scRNA-seq data set for 13 AML patients

  This dataset includes single cell (sc)RNA-seq of N=13 AML patients. The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the human genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001346

• Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer

  Inhibitors of the programmed cell death-1 (PD-1/PD-L1) signaling axis are approved to treat non-small cell lung cancer (NSCLC) patients, based on their significant overall survival (OS) benefit. Using transcriptomic analysis of 891 NSCLC tumors from patients treated with either the PD-L1 inhibitor atezolizumab or chemotherapy from two large randomized clinical trials, we found a significant B cell association with extended OS with PD-L1 blockade, independent of CD8+ T cell signals. We then derived gene signatures corresponding to the dominant B cell subsets present in NSCLC from single-cell RNA-seq data. Importantly, we found increased plasma cell signatures to be predictive of OS in patients treated with atezolizumab, but not chemotherapy. B cells were also associated with the presence of tertiary lymphoid structures and organized lymphoid aggregates. Our results suggest an important contribution of B and plasma cells to PD-L1 blockade efficacy in NSCLC.

  Study EGAS00001005013

• CIAO Clinical Trial

  Whole exome and RNA sequencing for samples from CIAO (Checkpoint Inhibitors Assessment in Oropharynx cancer) (NCT03144778) clinical trial testing neoadjuvant anti-PD-L1 durvalumab with or without the anti-CTLA-4 tremelimumab in oropharynx cancer

  Study EGAS50000001174