12304 results for "cancer rna-seq"

in 35.78 milliseconds.

• WXS of 147 lung cancer patients treated with immunotherapy

  We first attempted to predict MHC-binding neoantigens at high accuracy with convolutional neural networks. This prediction model outperformed previous methods in > 70% of test cases. Importantly, our method remarkably increased the predictive value of neoantigen load especially in combination with known resistance parameters. We then developed a classifier that can predict resistance from point mutations that are deleterious to protein function. Notably, genes involved in the adaptive immune response, cytokine signaling,and EGFR signaling held high explanatory power. Furthermore, when integrated with our neoantigen profiling, these anti-immunogenic mutations revealed significantly higher predictive power than known resistance factors.

  Study EGAS00001003781

• NRG1 Fusions in KRAS Wild-type Pancreatic Cancer (H021)

  We used whole-genome and transcriptome sequencing to identify clinically actionable gene fusions in young adults with KRAS wild-type (KRASwt) pancreatic ductal adenocarcinoma (PDAC). These alterations included recurrent NRG1 rearrangements predicted to drive PDAC development through aberrant ERBB receptor-mediated signaling, and pharmacologic ERBB inhibition resulted in clinical improvement and remission of liver metastases in two patients with NRG1-rearranged tumors that had proved resistant to standard treatment. Our findings demonstrate that systematic screening of KRASwt tumors for oncogenic fusion genes will substantially improve the therapeutic prospects for a sizeable fraction of PDAC patients.

  Study EGAS00001002759

• NanoSring of PBMC from bladder cancer and RCC patients

  PBMCs from patients were isolated using 50 mL Leucosep™ tubes (Greiner Bio-One International, Germany) and Ficoll-Paque™ PLUS (GE Healthcare, Sweden). Whole blood drawn into sodium heparin blood collection tubes were diluted 3x with phosphate-buffered saline (PBS) without calcium or magnesium (Lonza, Walkersville, MD). Diluted cell suspensions were carefully layered on Leucosep tubes and centrifuged for 15 minutes at 800 x g at room temperature (RT). Interphase containing PBMCs were harvested and washed with PBS and subsequently centrifuged for 10 minutes at 250 x g at RT before further processing.

  Study EGAS00001004229

• Chromosome Segregation Errors Promote a Diverse Spectrum of Simple and Complex Genomic Rearrangements

  Cancer genomes are frequently characterized by numerical and structural karyotypic abnormalities. Here we combined an inducible centromere-specific inactivation approach with selection for a conditionally essential gene, a strategy we term ‘CEN-SELECT’, and show that single-chromosome missegregation during cell division can directly drive a broad spectrum of structural rearrangement types. Cytogenetic profiling revealed that missegregated chromosomes are 120-fold more susceptible to developing seven major categories of structural variants, including translocations, insertions, deletions, and reassembly into chromothriptically rearranged chromosomes. Whole-genome sequencing of clones with genetically propagatable derivative chromosomes identified complex rearrangements and copy-number alterations that can result in gene inactivation or extrachromosomal gene amplification. We conclude that chromosome segregation errors are sufficient to drive extensive structural variation that recapitulates those commonly associated with human cancers.

  Dataset EGAD00001004163

• Panbody_nanoseq

  Different cell types within the human body are subject to different endogenous and exogenous exposures which may result in distinct patterns of mutagenesis. In non-clonal tissues, it is difficult to assess these mutational patterns, including the rate at which they occur. This study will examine the mutational rates and signatures operating across a wide range of normal tissues using the nanoseq technology to reliably call variants, irrespective of their clonal composition. Each cell type will be enriched for using laser capture microdissection. This will serve as a reference against which future work on benign and malignant disease entities can be compared.

  Study EGAS00001005521

• The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET) - Intratumoural immune heterogeneity

  The clinical relevance of immune landscape intratumoural heterogeneity (immune-ITH) and its role in tumour evolution remain largely unexplored. Here, we uncover significant spatial and phenotypic immune–ITH from multiple tumour sectors and decipher its relationship with tumour evolution and disease progression in hepatocellular carcinomas (HCC). Immune–ITH is associated with tumour transcriptomic-ITH, mutational burden, and distinct immune microenvironments. Tumours with low immune–ITH experience higher immunoselective pressure and escape via loss of heterozygosity in human leukocyte antigens and immunoediting. Instead, the tumours with high immune-ITH evolve to a more immunosuppressive/exhausted microenvironment. This gradient of immune pressure along with immune-ITH represents a hallmark of tumour evolution, which is closely linked to the transcriptome-immune networks that contributes to disease progression and immune inactivation. Remarkably, high immune-ITH and its transcriptomic signature are predictive for worse clinical outcome in HCC patients. This in-depth investigation of ITH provides evidence on tumour-immune co-evolution along HCC progression.

  Dataset EGAD00001006570

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001001090

• Genome and transcriptome sequence data from a squamous cell carcinoma patient

  Genome and transcriptome sequence data from a squamous cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002040

• Genome and transcriptome sequence data from an adrenal cortical carcinoma patient

  Genome and transcriptome sequence data from an adrenal cortical carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002049

• Genome and transcriptome sequence data from a breast ductal carcinoma patient

  Genome and transcriptome sequence data from a breast ductal carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002047

• Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient

  Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002038

• Genome and transcriptome sequence data from an adenoid cystic carcinoma patient

  Genome and transcriptome sequence data from an adenoid cystic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002032

• Mutational Signatures of relapse in rectal cancer FFPE samples in the CR07 clinical trial

  The CRO7 clinical trial recruited patients with clinically operable rectal adenocarcinoma. Patients were randomized to either pre-operative short course surgery followed by chemo-radiotherapy only in those patients at high risk of local relapse. Patients in both arms the received standard %-FU based adjuvant chemotherapy as per local policy. We intend to use FFPE derived DNA from the primary tumours to identify patterns of mutations or copy number alterations that are predictive of local or distant relapse.

  Dataset EGAD00001000875

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004708

• Genome and transcriptome sequence data from a adenocarcinoma of the liver patient

  Genome and transcriptome sequence data from a adenocarcinoma of the liver patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004706

• Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004705

• Genome and transcriptome sequence data from a metastatic lung adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic lung adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004700

• Genome and transcriptome sequence data from a lung adenocarcinoma patient

  Genome and transcriptome sequence data from a lung adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004699

• Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient

  Genome and transcriptome sequence data from a peripheral T-cell lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004696

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004695

• Genome and transcriptome sequence data from a metastatic non-small cell lung carcinoma patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004692

• Genome and transcriptome sequence data from a angiosarcoma patient

  Genome and transcriptome sequence data from a angiosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004690

• Genome and transcriptome sequence data from a metastatic breast carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004688

• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004716

• Genome and transcriptome sequence data from a metastatic non-small cell lung adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004714