9296 results for "cancer rna-seq"

in 39.29 milliseconds.

• Exploring_the_heterogeneity_of_sarcoma_using_single_cell_sequencing_

  Multi region samples are collected from patients, with consent, immediately after resection of the tumour. Samples are digested and sorted using FACS as single cells into lysis buffer. Cells are then stored until further processing for G&T-seq. After sequencing, we will explore intra-tumour heterogeneity using computational approaches to integrate RNA and DNA data onto the tumour phylogeny This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002866

• RNA-Seq data for AEL paper

  Dataset EGAD00001003412

• HNSCC RNA-seq

  Study EGAS00001004090

• Development of the prevention and therapy of CRC using patient derived culture tissues.

  Tumor tissues consist of functionally heterogeneous cells including those with high clonogenic potential called cancer stem cells (CSCs) which produce differentiated non-tumorigenic progenies. Transplantation assays have been performed to identify cell surface markers in solid tumors including colorectal cancers, which identified several CSC markers such as CD133 and CD44. In addition to the transplantation, the lineage tracing of genetically labelling cells in animal models of cancer qhas been employed to explore the cellular heterogeneity of tumors by identifying the CSCs and its progenies. This approach is particularly useful to investigate solid tumors, where cellular proliferation and differentiation are controlled by niche factors and cell-cell contact with adjacent cells. Lineage tracing of mouse model of cancer unravel the mode of growth of autochthonous tumor in their native environment. In this study, we used patient derived organoids (PDO) established from surgically resected CRC patients to identify the CSC. Single cell RNA-seq analysis identified potential CRC markers, which may exhibit self-renewal activity and produce progenitors

  Study JGAS000139

• Dissecting intratumor heterogeneity of nodal B cell lymphoma on the transcriptional, genetic, and drug response level

  Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

  Study EGAS00001004335

• Transcriptional Profiling of Macrophages In Situ in Metastatic Melanoma Reveals Localization-Dependent Phenotypes and Function

  Therapeutic interventions modulating immune function at the tumor site could improve outcomes in cancer. Here we analyzed metastatic melanoma, a tumor type highly responsive to T-cell based therapies, and found that the tumor-infiltrating T cells are localized closely to CD14+ monocytes/macrophages rather than to melanoma cells. Using novel immunofluorescence-guided laser capture micro-dissection, we analyzed transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages and melanoma cells in non-dissociated tissue. CD14+ cells localized in tumors beds displayed specific transcriptional signatures distinct from CD14+ cells that were localized in tumor stroma.The 8 distinct metastatic melanoma samples are from the Cooperative Human Tissue Network (CHTN). 64 RNA-seq files are available from 16 intraCD14+, 16 stromaCD14+, 16 cancer cells, 8 intraCD3+ and 8 stromaCD3+ cells. 2 LCM harvests were completed for the CD14+ and cancer cells on two sections of the samples. One harvest was done for the CD3+ samples.The processed data (TPM/raw counts tables) are available on GEO under GSE180124. 

  Study phs002564

• Sequencing Lymphoma

  This repository contains sequencing data to describe the genomic profiles of several B-cell lymphomas including follicular lymphoma and Hodgkin lymphoma. We have used several different high throughput sequencing technologies to interrogate genomic DNA and RNA from tumor cells obtained from lymph node biopsies and normal cells obtained from skin biopsies and peripheral blood mononuclear cells. A number of sequencing platforms for DNA, RNA, and protein were used to generate the data. These platforms include exome sequencing and a custom capture panel (NimbleGen) that targets 7.05 MB corresponding to the exons and splice sites of 1716 genes related to lymphoma biology (WUSM-LP). Other platforms include single-cell RNA-seq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), which was used to describe transcriptional and proteomic changes in immune cell subsets within lymphoma patients pre- and post-therapy.

  Study phs001229

• Single-cell RNA-seq of immune cells from Melanoma tumors (Li et al, 2018) - DAC

  Dac EGAC00001001089

• Data access committee for RNA-seq as a tool for evaluating human embryo competence

  Dac EGAC00001001215

• 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