EGAS00001003594-sc-2021-02-02T09:54:31Z - samples
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ABSTRACT:
PROVIDER: EGAD00001006934 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:We study lymphocyte somatic evolution through the sequencing of normal healthy lymphocytes. We perform whole-genome sequencing of single-cell derived T and B cell colonies to identify somatic mutations, and perform targeted deep-sequencing of these mutations. The lineages of T and B cells, and the frequencies of these mutations reveals the neutral and non-neutral evolutionary processes underlying lymphocyte growth and function. .
This dataset contains all the data available for this study on 2021-02-02.
| EGAD00001006935 | EGA
Project description:We study lymphocyte somatic evolution through the sequencing of normal healthy lymphocytes. We perform whole-genome sequencing of single-cell derived T and B cell colonies to identify somatic mutations, and perform targeted deep-sequencing of these mutations. The lineages of T and B cells, and the frequencies of these mutations reveals the neutral and non-neutral evolutionary processes underlying lymphocyte growth and function.
| EGAS00001003594 | EGA
Project description:We study lymphocyte somatic evolution through the sequencing of normal healthy lymphocytes. We perform whole-genome sequencing of single-cell derived T and B cell colonies to identify somatic mutations, and perform targeted deep-sequencing of these mutations. The lineages of T and B cells, and the frequencies of these mutations reveals the neutral and non-neutral evolutionary processes underlying lymphocyte growth and function.
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Project description:We study lymphocyte somatic evolution through the sequencing of normal healthy lymphocytes. We perform whole-genome sequencing of single-cell derived T and B cell colonies to identify somatic mutations, and perform targeted deep-sequencing of these mutations. The lineages of T and B cells, and the frequencies of these mutations reveals the neutral and non-neutral evolutionary processes underlying lymphocyte growth and function.
| EGAS00001003596 | EGA
Project description:De- and transdifferentiation of melanoma is a rare histopathological phenomenon that has not be characterised genetically. In this project we plan to sequence the genomes of de and transdifferentiated cases so as to define their genetic make-up. .
This dataset contains all the data available for this study on 2021-02-02.
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Project description:In this study we will perform whole genome sequencing on in vitro colonies. Colonies of various sizes (100-300 cells) will be collected for somatic mutation analysis.
| EGAS00001003112 | EGA
Project description: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. .
This dataset contains all the data available for this study on 2021-02-02.
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Project description:Somatic mutations in cancer are a potential source of cancer specific neoantigens. Acute myeloid leukemia (AML) has common recurrent mutations shared between patients in addition to private mutations specific to individuals. We hypothesized that neoantigens derived from recurrent shared mutations would be attractive targets for future immunotherapy and sought to study the Class I and II HLA ligandomes of thirteen primary AML tumor samples and two AML cell lines (OCI-AML3 and MV4-11) using mass spectrometry. We identified two endogenous, mutation-bearing HLA Class I ligands from NPM1, which are predicted to bind the common HLA haplotypes, HLA-A*03:01 and HLA-A*02:01 respectively. We further derived CD8+ T cells from healthy donor peripheral blood samples which bound mutant-peptide loaded A*03:01 and A*02:01 tetramers, suggesting a new source of NPM1 mutation-specific T cell receptors (TCRs) for future evaluation. Since NPM1 is mutated in approximately one-third of patients with AML, the finding of endogenous NPM1 neoantigens supports future studies evaluating immunotherapeutic approaches against this target, for this subset of patients with AML.
2019-07-17 | PXD012083 | Pride
Project description:Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. .
This dataset contains all the data available for this study on 2021-02-02.
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Project description:Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. .
This dataset contains all the data available for this study on 2021-02-02.
| EGAD00001006930 | EGA