9296 results for "cancer rna-seq"

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• 10x Genomics Single Cell Gene Expression for SA1035X7XB03502

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 7

  Dataset EGAD00001009151

• 10x Genomics Single Cell Gene Expression for SA535X4XB02498

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009152

• 10x Genomics Single Cell Gene Expression for SA1035X6XB03216

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA1035 passage 6

  Dataset EGAD00001009154

• 10x Genomics Single Cell Gene Expression for SA535X5XB02891

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009155

• 10x Genomics Single Cell Gene Expression for SA535X5XB02895

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 5

  Dataset EGAD00001009156

• 10x Genomics Single Cell Gene Expression for SA535X9XB03617

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 9

  Dataset EGAD00001009157

• 10x Genomics Single Cell Gene Expression for SA604X9XB02425

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 9

  Dataset EGAD00001009159

• 10x Genomics Single Cell Gene Expression for SA610X3XB03802

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA610 passage 3

  Dataset EGAD00001009160

• Single Cell Genome Sequence for DLP+ library A95722A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A95722A

  Dataset EGAD00001009451

• Single Cell Genome Sequence for DLP+ library A95670B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6 on DLP+ library A95670B

  Dataset EGAD00001009449

• Single Cell Genome Sequence for DLP+ library A96154A

  Single Cell Genome Sequence for triple negative breast cancer patient SA1135, patient SA1162SA on DLP+ library A96154A

  Dataset EGAD00001009456

• Single Cell Genome Sequence for DLP+ library A96212B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 7 on DLP+ library A96212B

  Dataset EGAD00001009476

• Single Cell Genome Sequence for DLP+ library A98240A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA530 passage 3 on DLP+ library A98240A

  Dataset EGAD00001009480

• Single Cell Genome Sequence for DLP+ library A98247A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA530 passage 3 on DLP+ library A98247A

  Dataset EGAD00001009646

• Melanoma_C32_ENU_resistance_to_Combination_Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Study EGAS00001001614

• Melanoma_C32_ENU_Resistance_to_Single_Agent_Therapy

  This study involves mutagenizing C32, a melanoma cell line, with ENU to identify those mutations which engender resistance to a targeted treatment.

  Study EGAS00001001697

• Organoid Derivation Project - GRCh38 - RNAseq (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011092

• Organoid Derivation Project - GRCh38 - WGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011093

• Organoid Derivation Project - GRCh38 - TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011094

• Sequencing data for oesophageal samples - Killcoyne, Gregson et al (sWGS)

  Data supporting: "Genomic copy number predicts esophageal cancer years before transformation." Killcoyne, Gregson et al. sWGS data 1000 samples BAM files

  Dataset EGAD00001006033

• Genome and transcriptome sequence data from a pre-B all (2nd relapse in CNS) tumor patient

  Genome and transcriptome sequence data from a pre-B all (2nd relapse in CNS) patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015291

• The University of Hong Kong Gastric Cancer XClone Study Single Cell CNV Data

  Single cell encapsulation and DNA libraries were prepared by Chromium™ Single Cell DNA Reagent Kits and Chromium™ Single Cell C and D Chip Kits.

  Dataset EGAD00001015383

• TGS - Comprehensive Molecular Characterization of Colorectal Cancer Metastases (2015-07-02)[MOSAIC]

  Systematic next generation sequencing efforts are beginning to define the genomic landscape across a range of primary tumours, but we know very little of the mutational evolution that contributes to disease progression. We therefore propose to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in a cohort of matched primary and metastatic colorectal cancers, and additionally to explore the extent to which those mutations identified as recurrent in the metastatic setting are able to subvert normal biological processes using both genetically engineered mouse models and established cancer cell lines. This study will enable us to define to what extent primary tumour profiling can capture the biological processes operative in matched metastases as well as the significance of intratumoural heterogeneity. This dataset contains all the data available for this study on 2015-07-02.

  Dataset EGAD00001001426

• Lung cancer organoids

  Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here we describe a method to establish long-term culture conditions of human airway epithelial organoids that contain all major cell populations and allow personalized human disease modelling. We collected macroscopically inconspicuous lung tissue from non-small-cell lung cancer (NSCLC) patients undergoing medically indicated surgery and isolated epithelial cells to engineer 3D organoids. We exploit the potential to derive sub-clones from AOs to demonstrate the feasibility of CRISPR gene editing. Finally, we show that AOs readily allow modelling of viral infections such as RSV and for the first time demonstrate the possibility to study neutrophil-epithelium interaction in an organoid model. Taken together, we anticipate that human AOs will find broad applications in the study of adult human airway epithelium in health and disease.

  Dataset EGAD00001004013

• Direct transcriptional consequences of somatic mutation in breast cancer

  The disordered transcriptomes of cancer encompass direct effects of somatic mutation on transcription; co-ordinated secondary alterations in transcriptional pathways; and increased transcriptional noise. To catalogue the rules governing how somatic mutation Overall, 59% of 6980 exonic substitutions were expressed. Compared to other classes, nonsense mutations showed lower expression levels than expected with patterns characteristic of nonsense-mediated decay. 14% of 4234 genomic rearrangements caused transcriptional abnormalities, including exon skips, exon reusage, fusion transcripts and premature poly-adenylation. We found productive, stable transcription from sense-to-antisense gene fusions and gene-to-intergenic rearrangements, suggesting that these mutation classes may drive more transcriptional disruption than previously suspected. Systematic integration of transcriptome with genome data therefore reveals the rules by which transcriptional machinery interprets somatic mutation.

  Dataset EGAD00001002236