4002 results for "*geneti*"

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• Single cell transcriptional evolution of myeloid leukaemia of Down syndrome – scRNA

  Abstract: Children with Down syndrome have a 150-fold increased risk of developing myeloid leukaemia (ML-DS). Unusually for a childhood leukaemia, ML-DS arises from a preleukaemic state, termed transient abnormal myelopoiesis (TAM), and via a conserved sequence of mutations. Here, we examined the relationship between the genetic and transcriptional evolution of ML-DS from a rich collection of primary patient samples through single cell mRNA sequencing, complemented by phylogenetic analyses in progressive disease. We distilled the transcriptional consequence of each genetic step in the evolution of ML-DS, showing that TAM-defining GATA1 mutations account for most of the ML-DS transcriptome, including those of progressive disease. This transcriptional backbone may thus represent a common vulnerability in ML-DS. We extracted the transcriptional difference between TAM and ML-DS which, unexpectedly, reflected features shared across childhood leukaemia. Our approach delineates the transcriptional evolution of ML-DS and provides an analytical blueprint for distilling consequences of mutations directly from patient samples.

  Dataset EGAD00001015452

• GLASS-NL DNA-Methylation

  Study EGAS00001007546

• NOTCH1 fusion genes in pediatric T-cell lymphoblastic lymphoma hallmark a common high-risk subgroup with blood TARC levels as possible biomarker

  T-cell lymphoblastic lymphoma (T-LBL) is a common pediatric malignancy accounting for approximately 20% of the non-Hodgkin lymphomas during childhood. Survival rates of T-LBL are ~80%, but outcome after relapse is dismal, with salvage rates reaching only ~15. Considering the extremely poor prognosis after relapse and absence of clinically relevant high-risk genetics, there is an urgent need for the identification of molecular risk factors and new prognostic biomarkers in T-LBL, as well as identification of new therapeutic strategies. In this study we present a novel entity of high-risk pediatric T-LBL patients characterized by previously unknown NOTCH1 gene fusions and highly elevated blood TARC levels

  Study EGAS00001007703

• GCAT | Genomes for life: cohort study of the genomes of Catalonia

  The GCAT Study have recruited 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. A total of 5459 genomic profiles have been characterised by comprehensive genotyping. Genome-wide genotypes have been generated using Illumina Infinium SNP-bead array technology. We chose the Multi-Ethnic Global (MEGAEX, V.2) consortium array, a multipurpose, multiethnic genotyping array with two million selected markers (including previously described germline mutations, insertions-deletions (InDels) and SNPs).We have strictly followed the standard manufacturer recommended automated protocol for the Infinium HTS Assay scanned with a HiScan confocal scanner (Illumina, San Diego, California, USA). Genome Studio V.2011.1 has been used for raw data analysis. Genotyping was performed at the Genomics and Bioinformatics Unit of the PMPPC Institute for Health Science Research Germans Trias i Pujol, in Badalona, Spain. Future plans The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites. http://dx.doi.org/10.1136/bmjopen-2017-018324

  Study EGAS00001003018

• Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

  Background Despite advances in identification of genetic markers associated to severe COVID-19, the full genetic characterisation of the disease remains elusive. Imputation of low-coverage whole genome sequencing (lcWGS) has emerged as a competitive method to study such disease-related genetic markers as they enable genotyping of most common genetic variants used for genome wide association studies. This study aims at exploring the potential use of imputation in lcWGS for a highly selected severe COVID-19 patient cohort. Findings We generated an imputed dataset of 79 variant call format (VCF) patient files using the GLIMPSE1 tool, each containing, on average, 9.5 million single nucleotide variants. The validation assessment of imputation accuracy yielded a squared Pearson correlation of approximately 0.97 across sequencing platforms, showing that GLIMPSE1 can be used to confidently impute variants with minor allele frequency up to approximately 2% in Spanish ancestry individuals. We conducted a comprehensive analysis on the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms specifically developed to characterise severe COVID-19 symptoms for this cohort. Conclusion This dataset highlights the utility and accuracy of lcWGS imputation in the study of COVID-19 severity, setting a precedent for other applications in resource-constrained environments. The methods and findings presented here may be leveraged in future genomic projects, providing vital insights for health challenges like COVID-19.

  Study EGAS00001007573

• Single-cell RNA sequencing reveals cell-type specific eQTLs in peripheral blood mononuclear cells

  This proof-of-concept study shows that single-cell RNA sequencing in ~25,000 peripheral blood mononuclear cells (PBMCs) of 45 donors allows for the identification of cell type-specific cis expression quantitative trait loci effects (cis-eQTLs). Moreover, the significant top-eQTL effects show consistent allelic direction (94%) with previously published whole blood deepSAGE data. These cell type-specific eQTLs may give us additional insight into the downstream consequences of genetic risk factors for immune-mediated diseases.

  Study EGAS00001002560

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Oesophageal_adenocarcinoma

  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, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute (40X and 20X depth respectively). Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors 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 EGAS00001003702

• Chromosomal mutational signatures of DNA damaging agents at single cell resolution

  The chromosomal scale mutational spectrum of small molecules that interact with DNA has been hard to study at scale, as mutational events are distributed in location and occur in parallel in different cells. Here, we present a framework that pairs phylogenetic ancestry reconstruction with mutational signature decomposition to characterise recent, cell-private CNA mutational processes at single-cell resolution. We used this framework to characterize the cell-wise “foreground” of contemporaneous CNAs induced by strand-break inducing chemotherapeutic drugs of different mechanisms. We demonstrate that platinum salts, G-quadruplex stabilizers and topoisomerase II inhibitors, although mechanistically distinct, converge on a mutational signature dominated by telomere-bounded copy-number gain and losses. This signature is observed in different genetic backgrounds and in vivo, in drug treated patient derived-xenograft tumours. We also observe a high rate of endogenous telomere-bounded mutational foreground in BRCA1 deficient cells. We show that the single cell genome derived signature exposures are drug dose-dependent, and use this to identify the decay of mutational load after drug withdrawal. We observe that both cisplatin and a G4 binder molecule (CX5461) exhibit foreground mutational signature persistence up to 3 weeks after drug withdrawal, suggesting that residual effects of exposure may last longer than anticipated. Finally, extending the framework to serially drug-treated patient-derived xenograft (PDX) models, we show that telomere-bounded CNA signature exposure is associated with tumour response across and resistance states exhibit only background levels of mutational signatures. This is consistent with loss of mutational activity on the genome, as opposed to acquired tolerance to chromosomal mutations as a mechanism for some forms of chemotherapeutic drug resistance. Together our results show that scWGS can identify contemporaneous chromosomal mutation patterns induced by small molecules in human tissues.

  Study EGAS50000002035

• Integrated Genomic, Epigenetic, and Expression Analyses of Ovarian Cancer Cell Lines

  In this dataset there are 55 whole genome sequencing samples of epithelial ovarian carcinoma (bam files).

  Dataset EGAD00001004532

• Multi-faceted metastasis decoding: clonal fitness, site pressure, and transcriptional modulation

  The dynamics of unique pressures that evolve metastasis fitness due to genetic and non-genetic components in relationship to primary versus metastatic site has remained difficult to decipher due to the highly complex and intertwined relationship of these features to the heterogeneous clonal structures within tumors. To simplify this problem, we generated serially passaged patient-derived xenografts (PDX) that developed reproducible metastasis and examined them for robust clonal selection using DLP+ single cell genome sequencing. This clonal composition analysis identified a simpler PDX model of metastases that displayed very high intra-PDX clonal competitive fitness, marked with dominant and differential clonal sweeps at metastatic and primary sites by different clones. Further testing this fitness using the different combinations of clones’ transplantation, re-confirmed this high clonal selectivity of a metastatically fit clone at metastatic sites compared to the primary site, hence revealing copy number aberration (CNA) clonal state dependent fixed metastasis fitness. Overall, by testing 9 PDX lines we found two lines with reproducible metastasis which identified a model that simplified the complexities of tumor evolution during metastasis and revealed the dominant role of favorable CNA clonal structure in fixating fitness.

  Study EGAS50000001515