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• Genome-Wide Association Study of Relapse of Childhood Acute Lymphoblastic Leukemia

  Using risk-directed therapy for childhood acute lymphoblastic leukemia (ALL), outcome has improved dramatically in the last 40 years. However, a substantial portion of patients experience relapse, many of whom have no known risk factors. Taking a genome-wide approach, we sought to evaluate the relationships between germline SNP genotypes and the risk of relapse in 2,535 children with newly diagnosed ALL after adjusting for genetic ancestry and treatment regimen. We examine prognostic value of selected SNPs in the context of known relapse risk factors (molecular subtypes, minimal residual disease, age and leukocyte count at diagnosis). Associations of relapse-related SNPs with pharmacokinetic and pharmacodynamics of antileukemic drugs offer plausible mechanism by which they are linked to treatment outcome. Finally, we aim to identify SNPs that are related to both genetic ancestry and relapse which are likely to contribute to racial disparities in ALL survival.

  Study phs000638

• Single-cell analysis reveals different age-related somatic mutation profiles between stem and differentiated cells in human liver

  Highly metabolic liver is exposed to many sources of spontaneous mutagenesis resulting in increased genome instability that, potentially, causes age-related functional liver failure. Here we comparatively characterize the somatic mutational landscape in single human liver cells from individuals of various ages. Mutation frequency is very high in normal human hepatocytes, increasing dramatically with age as compared to other tissues and cell types, e.g., B-cells, neurons, and clonal surrogates of various adult stem cells. Moreover, differentiated hepatocytes tend to accumulate more mutations compared to age-matched proliferating liver stem cells (LSCs). Mutation spectra also differ dramatically between different cell types. Elevated oxidative hallmarks are observed in mouse liver and ex vivo cultured LSCs, but not during liver aging. Overall, human liver is prone to high age-dependent genome instability, compared to other tissues, and reveals functional difference between LSCs and hepatocytes.

  Study phs001956

• PDAC Prognosis Biomarkers in Genomic and Transcriptomic Molecular Data

  Our study was designed to identify biomarkers related to the prognosis of Pancreatic Ductal AdenoCarcinoma (PDAC) patients using genomic variants and gene expression in coding regions of the human genome (exome). PDAC is the 7th cause of cancer deaths and is among types of cancer having the poorest prognosis. To improve treatment outcomes of PDAC, biomarkers related to PDAC prognosis are needed. The goals of our study are to find genomic and transcriptomic biomarkers, which can classify prognosis subtype of PDAC patients, and eventually to predict subtypes or prognosis for each patient. In this study, we generated Whole Exome Sequencing (WES) and exome capture RNA sequencing data of 450 tumor and matched normal samples from 150 PDAC patients. Our data may be useful for understanding genomic and transcriptomic features, prognosis prediction, and precision medicine for PDAC.

  Study phs002347

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Genetic Epidemiology of COPD Study (COPDGene)

  Cohort Description The Genetic Epidemiology of COPD (COPDGene) study is a non-interventional, multicenter, longitudinal, case-control study at 21 US sites of smokers with a ≥10 pack-year history of smoking, with and without COPD, and healthy never smokers. The initial goal was to characterize disease-related phenotypes and explore associations with susceptibility genes. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 3683 COPDGene participants in C4R. Wave 2 questionnaire data includes 447 variables for up to 2191 COPDGene participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 1692 COPDGene participants in C4R. Derived data includes 43 variables for up to 4082 COPDGene participants in C4R. Phenotype data includes 113 variables for up to 4082 COPDGene participants in C4R.

  Study phs002910

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Mediators of Atherosclerosis in South Asians Living in America Study (MASALA)

  Cohort Description The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a prospective cohort of South Asians that aims to identify risk factors for heart disease in a large, growing Asian American subgroup. MASALA enrolled 906 South Asians in 2010-2013 and then added a new wave of 258 South Asian participants from 2017-2018, for a full cohort size of 1,164. Data Being Submitted Wave 1 questionnaire data includes 3967 variables for up to 431 MASALA participants in C4R. Wave 2 questionnaire data includes 448 variables for up to 313 MASALA participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 248 MASALA participants in C4R. Derived data includes 43 variables for up to 528 MASALA participants in C4R. Phenotype data includes 113 variables for up to 429 MASALA participants in C4R.

  Study phs002980

• Sequencing Lymphoma

  This repository contains sequencing data to describe the genomic profiles of several B-cell lymphomas including follicular lymphoma and Hodgkin lymphoma. We have used several different high throughput sequencing technologies to interrogate genomic DNA and RNA from tumor cells obtained from lymph node biopsies and normal cells obtained from skin biopsies and peripheral blood mononuclear cells. A number of sequencing platforms for DNA, RNA, and protein were used to generate the data. These platforms include exome sequencing and a custom capture panel (NimbleGen) that targets 7.05 MB corresponding to the exons and splice sites of 1716 genes related to lymphoma biology (WUSM-LP). Other platforms include single-cell RNA-seq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), which was used to describe transcriptional and proteomic changes in immune cell subsets within lymphoma patients pre- and post-therapy.

  Study phs001229

• Pediatric Preclinical Testing Consortium (PPTC)

  The Pediatric Preclinical Testing Consortium (PPTC) is addressing the unmet need of streamlining the development of new therapies for childhood cancers. The PPTC seeks to develop robust biomarkers of anticancer drug activity, and the majority of these are predicted to be genetic mutations that can be detected in tumor DNA and/or RNA. In order to design the most impactful experiments that can be rapidly translated to the clinic, PPTC investigators require a complete genomic characterization of the patient-derived xenograft tumor models that are utilized across the consortium. This will not only allow for the most robust experimental design, but also will increase the engagement of industry partners who seek collaborators poised to provide the proof-of-concept necessary for drugs in their development pipelines. All data and models will be made available to academically qualified investigators.

  Study phs001437

• Validation_of_a_Haloplex_platform_for_targeted_re_sequencing_of_the_exons_of_25_genes

  We recently used the Agilent SureSelect platform to re-sequence a set of genes known to bemutated in human AML. The results from 10 AML DNA samples were very satisfactory, butthe effort required was significant.Thus, we decided to re-sequence the same genes using the Haloplax system for targetenrichment in 48 AML samples. We planned to do this using MiSeq and have data from apilot of 3 samples. The data is promising but coverage appears pathcy so far.However, in order to get a better understanding of the data we will need deeper sequencing. Wewill need two lanes of HiSeq to get the same degree coverage as Sureselect.his data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001000285

• Patient-derived organoids model treatment response of metastatic gastrointestinal cancers (targeted and whole-genome sequencing)

  Patient-derived organoids (PDOs) have recently emerged as robust pre-clinical models, however, their potential to predict patient clinical outcomes remain unclear. We report a living biobank of PDOs from metastatic, heavily-pretreated colorectal and gastroesophageal cancer patients recruited in phase I/II clinical trials. Phenotypic and genotypic profiling of PDOs showed a high-degree of similarity to the original patient tumor. Molecular profiling of tumor organoids was matched to drug screening results, suggesting PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared ex vivo organoid responses to anticancer agents, and PDO-based orthotopic mouse tumor xenograft models to the response of the patient in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic, and have the potential to be implemented in personalized medicine programs.

  Study EGAS00001002784

• Genotyping_of_additional_Inflammatory_Bowel_Disease_cases___2014

  Both internal and external funding has enabled 5000 inflammatory bowel disease cases to be whole genome sequenced (CD @4X, UC @2X). The Anderson and Barrett groups are currently generating genotypes across these samples for comparison to 4000 population controls sequenced as part of UK10K. The UK10K project has shown that imputing sequenced genetic variation into previously GWASed samples greatly increases power to detect association. Given that our study has been designed to detect association to low frequency variation (0.5% and above) these gains in power are especially important. Overall, the UKIBDGC has GWAS data for around 1800 CD samples (Affy 500K) and 3000 UC samples (Affy6), though some of these samples have also been whole genome sequenced. Here, we apply to get all remaining non-GWAS and non-WGS IBD cases in the UKIBDGC (N=X) genotyped genotyped on the Illumina Core Exome Array

  Study EGAS00001000924