11805 results for "cancer rna-seq"

in 27.11 milliseconds.

• Pilot data of PDiamond Lung Cancer Data

  RNAseq pilot data of PDiamond Korean lung cancer data

  Dataset EGAD50000000843

• Cancer genome scanning in plasma: detection of tumor-associated copy number aberrations, single nucleotide variants and tumoral heterogeneity by massively parallel sequencing

  Study EGAS00001000370

• Genomic analysis of Japanese gastric cancer

  fastq of 345 Japanese gastric cancer

  Dataset EGAD00001004051

• Dynamic Evolution of Fibroblasts Revealed by Single Cell RNA Sequencing of Human Pancreatic Cancer

  Understanding the constituents of the pancreatic ductal adenocarcinoma (PDAC) microenvironment may illuminate more efficacious targets for treating this aggressive malignancy. However, the compositional changes occurring in untreated and treated PDAC relative to both normal and inflamed pancreas have not been well characterized. To provide a more comprehensive understanding of the PDAC tumor microenvironment relative to the non-cancerous pancreas, we performed single-cell expression profiling (10X Genomics Chromium) on samples of upfront resectable PDAC, FOLFIRINOX-based treated PDAC, chronic pancreatitis, and normal pancreas specimens from 19 patients. Spatial gene expression profiling (10X Genomics Visium) was performed to gain additional insight into cell populations. Our analysis revealed that fibroblasts are central mediators of cell-cell communication in both inflamed and malignant settings and defined unique roles for these cell populations in different disease contexts.

  Study phs003751

• Dataset for neuroendocrine_adrenal_tumor-RNA

  Rare cancer sequencing data of 145 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008862

• NCI Expanded Genome-Wide Association Study of Renal Cell Carcinoma

  The National Cancer Institute (NCI) expanded genome-wide association study (GWAS) of renal cell carcinoma (RCC) was a 2nd GWAS of this cancer performed by NCI to further investigate common genetic variants associated with RCC risk. The GWAS includes 2,781 cases and 2,940 controls of European background from 13 studies conducted in the US and Finland (10 cohorts, 2 case series, 1 hospital biorepository), scanned using the Illumina OmniExpress and Omni2.5 SNP arrays. SNP-level results from this GWAS were combined by meta-analysis with results from the earlier NCI RCC GWAS (phs000351.v1.p1; 1,311 cases and 3,424 controls) and others conducted by the International Agency for Research on Cancer (scan 1: 2,438 cases and 5,071 controls; scan 2: 2,781 cases and 2,940 controls), The University of Texas MD Anderson Cancer Center (893 cases and 556 controls) and the Institute for Cancer Research, UK (944 cases and 4,024 controls). Only data from the second NCI scan are included in this dbGaP submission. Data from 239 cases and 229 controls from this scan will be deposited in dbGaP separately under the Women's Health Initiative (phs000200).

  Study phs001736

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Pancreatic_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003520

• PDAC organoid genomic heterogeneity

  The establishment of patient-derived pancreatic cancer organoid culture in recent years creates an exciting opportunity for researchers to perform a wide range of in vitro studies on a model that closely recapitulates the tumor. Among the outstanding questions in pancreatic cancer biology are the causes and consequences of genomic heterogeneity observed in the disease. However, to use pancreatic cancer organoids as a model to study genomic variations, we need to first understand the degree of genomic heterogeneity and its stability within organoids. Here, we used single-cell whole-genome sequencing to investigate the genomic heterogeneity of two independent pancreatic cancer organoids, as well as their genomic stability with extended culture. Clonal populations with similar copy number profiles were observed within the organoids, and the proportion of these clones was shifted with extended culture, suggesting the growth advantage of some clones. However, sub-clonal genomic heterogeneity was also observed within each clonal population, indicating the genomic instability of the pancreatic cancer cells themselves. Furthermore, our transcriptomic analysis also revealed a positive correlation between copy number alterations and gene expression regulation, suggesting the “gene dosage” effect of these copy number alterations that translates to gene expression regulation.

  Study EGAS00001006782

• Research on the identification of cancer stem cells for peidatric and adult malignancies.

  In recent years, "cancer stem cells" have been reported to exist in many cancers. Many reports have shown that they are the main cause of cancer recurrence. The aim of this study is to test whether cancer stem cells can be identified using patient samples. We will perform omics analysis of rare pediatric cancers by integrating genomics, transcriptomics, proteomics and metabolomics, including somatic and germline mutations, to deepen our understanding of the biological characteristics of the target disease. Based on the knowledge gained and clinical information from patients, we will conduct preclinical pharmacological studies using mouse models and in vitro models to investigate the therapeutic effects of novel therapeutic agents and novel therapeutic strategies such as cellular therapies.

  Study JGAS000623

• Deep sequencing of 151 cancer genes

  Deep sequencing of 151 cancer genes in 6 synchronous CRC of 3 patients

  Dataset EGAD00001001870